Assignment 2

This assignment is the second in our Research Project series. It provides an opportunity for you to begin digging deeper into the topic you identified in the Research Project: Topic Development Worksheet Assignment by writing a brief review of each article. As you work through this process you will learn to read and critically examine scholarly literature, and you will formulate and express ideas in an efficient, scholarly manner. These are skills that will serve you well in this course and in the future.

Research Project: Literature Review Assignment

Instructions

Overview

Instructions

For this assignment, you will:

1. Critically examine your area of interest by digging into the research articles identified in the Research Project: Topic Development Worksheet Assignment.

2. Write a brief review (300 words) of each article approved by the instructor in the Research Project: Topic Development Worksheet Assignment. Writing in a scholarly “voice” (do not use first person perspective), use at least 1-2 sentences to identify/include each of the following:

a. Purpose statement

b. Hypothesis

c. Brief description of methods (participants and procedure)

d. Results

e. One strength

f. One weakness

g. An explanation of how the findings support the thesis statement you created in the Research Project: Topic Development Worksheet Assignment.

3. Your submission for this assignment will include the following:

a. Title page (formatted according to APA student standards).

b. An introduction that:

· identifies your topic,

· explains why it is important, and

· incorporates your thesis statement

c. Five reviews (300 words each) each introduced with an APA-formatted level-one heading.

d. Reference page with all five sources listed following current APA guidelines.

4. This assignment will implement current APA formatting throughout.

Note: Your assignment will be checked for originality via the Turnitin plagiarism tool.

Cypriot Journal of Educational
Sciences

Volume 15, Issue 3, (2020) 492-501
www.cjes.eu

Prediction of learners’ mathematics performance by their emotional

intelligence, self-esteem and self-efficacy

Christian S. Ugwuanyi a *: Postdoctoral Fellow, School of Education Studies, Faculty of Education University of
the Free State, Bloemfontein, 9300, South Africa. https://orcid.org/0000-0003-2174-3674
Chinedu I.O. Okeke b: Host, Professor, and Head, School of Education Studies, Faculty of Education, University of
the Free State, Bloemfontein, 9300, South Africa. https://orcid.org/0000-0003-3046-5266
Chinyere G. Asomugha c: Department of Science Education, University of Nigeria, Nsukka, Nigeria

Suggested Citation:
Ugwuanyi, C. S., Okeke, C.I.O. & Asomugha, C.G., (2020). Prediction of learners’ mathematics performance by
their emotional intelligence, self-esteem, and self-efficacy. Cypriot Journal of Educational Science. 15(3), 492-
501. DOI: 10.18844/cjes.v%vi%i.4916

Received from November 21, 2019; revised from February 20,2020; accepted from June 15, 2020 .
Selection and peer review under responsibility of Prof. Dr. Huseyin Uzunboylu, Higher Education Planning,
Supervision, Accreditation and Coordination Board, Cyprus.
©2020 Birlesik Dunya Yenilik Arastirma ve Yayincilik Merkezi. All rights reserved.

Abstract

In spite of the place of mathematics in the Nigerian education system, the performance of students in both external and
internal examinations is on the downward trend. Research on factors affecting students’ achievement in mathematics has
often neglected the impact of psychological variables, such as emotional intelligence, self-esteem, and self-efficacy. This study,
therefore, was designed to study how emotional intelligence, self-esteem and the self-efficacy of students predict their
academic achievement in mathematics. The correlational survey research design was employed with a population of 2,937
senior secondary 2 students and a sample of 400 students sampled from 16 secondary schools in the Nnewi Education zone of
Anambra State. Emotional intelligence, Self-esteem, Self-efficacy Questionnaires, and Students’ Academic Achievement Score
Form (SAASF) were used to collect data through the direct delivery method. Data were analyzed using simple linear regression
analysis. The results showed that emotional intelligence, self-esteem, and self-efficacy had significant predictive powers on
students’ academic achievement in mathematics. Thus, emotional intelligence, self-esteem, and the self-efficacy of students
are prime determinants of their achievement in mathematics. It was recommended that students should be taught using
methods that will enhance their emotional intelligence, self-esteem, and self-efficacy.

Keywords: Emotional intelligence, Mathematics Achievement, Secondary school, Self-efficacy, Self-esteem.

Address for Correspondence: Dr. Christian S. Ugwuanyi Postdoctoral Fellow, School of Education Studies, Faculty of
Education University of the Free State, Bloemfontein, 9300, South Africa.
Email: UgwuanyiCS@ufs.ac.za Tel: +27739212220.

http://www.cjes.eu/

https://orcid.org/0000-0003-2174-3674

https://orcid.org/0000-0003-3046-5266

mailto:UgwuanyiCS@ufs.ac.za

Ugwuanyi, C. S., Okeke, C.I.O. & Asomugha, C.G., (2020). Prediction of learners’ mathematics performance by their emotional i ntelligence,
self-esteem, and self-efficacy. Cypriot Journal of Educational Science. 15(3), 492-501. DOI: 10.18844/cjes.v%vi%i.4916

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1. Introduction

Students’ poor performance in mathematics examinations in Nigeria is a source of worry to
mathematics educators and parents in general. For instance, in the years 2009 to 2012 and 2015, the
percentage pass in mathematics with credit and above in Nigeria was 23.0% in 2009, 31.0% in 2010,
24.94% in 2011, and 38.98% in 2012, 38.68% in 2015 (Iyi, 2016). Efforts have been made by researchers
in mathematics education to find solutions to the problem of the poor achievement of students in
mathematics, but considerable success has not been achieved. Such efforts were mainly based on
cognitive factors with little or no attention to psychological factors. The development of the cognitive
domain of learning as a determinant of academic achievement and success in life has been the concern
of formal education in Nigeria over the years (Osenweugwor, 2018).

Most education institutes pay much attention to the intelligent quotient level of learners than
their Emotional Intelligence (Monica & Ramanaiah, 2019). In line with that, Azuka (2012) opined that
other than cognitive factors, students’ emotional intelligence also does influence their academic
achievement in mathematics. Osenweugwor (2018), however, reported that researchers have begun to
recognize that factors other than intellectual ability play important roles in the academic success of
learners. Such factors include emotional intelligence, self-esteem, self-efficacy, etc. (Azuka, 2012;
Osenweugwor, 2018; Koc, 2019; Monica & Ramanaiah, 2019).

1.1 Conceptual and theoretical Background

Emotional intelligence is a subcategory of social intelligence that enables a learner to control

his or her emotions (Ranjbar, Khademi, & Areshtana, 2017). Emotional Intelligence (EI) includes
important aspects of an individual’s internal and external relationships that determine their academic
achievement (Fallahzadeh, 2011). EI refers to the cognitive ability of the learners to consider and
manage their emotions (Pool & Qualter, 2012). Emotional intelligence is the ability to perceive
accurately, appraise, and express emotion to promote emotional and intellectual growth (Koc, 2019).
Emotional intelligence is based on Goleman’s (2006) theory of emotional intelligence.

Goleman stated that individuals are born with a general emotional intelligence that determines
their potential for learning emotional competencies. Emotional intelligence theory links human
academic achievement with the knowledge of emotional intelligence. According to Goleman (2006),
emotional competencies are learned capabilities that must be worked on to achieve outstanding
performance rather than being innate (Goleman, 2006). This theory suggests that a student with a high
EI has greater academic achievement job performance and leadership skills. Thus, the researchers
validated the theory by establishing the relationship between students’ EI and their achievement in
mathematics. Related to the students’ emotional intelligence is their self-efficacy

Self-efficacy reflects the ability to exert control over one’s motivation, behavior, and social
environment (Bahmanabadi & Baluchzade, 2013). Self-efficacy refers to the beliefs about one’s
capabilities to learn or perform at designated levels (Bandura, 1997). Individual’s behavior is determined
by their beliefs about their abilities and the result of their efforts (Adeoyo & Feyisetan, 2015). Self-
efficacy is crucial in the theoretical framework of Bandura’s Social Cognitive Theory. SCT states that
learning occurs in a social context and that much of what is learned is gained through observation
(Bandura, 1977). Bandura’s Social Learning Theory shows a direct correlation between a person’s
perceived self-efficacy and behavioral change which relates to self-esteem.

Self-esteem relates to someone’s belief in personal success, purpose to be achieved, and
personal performances based on his or her previous experience. Self-esteem is the individual’s attitudes
and views about the outside world (Carmen-Mihaela & Alina, 2013). In line with the above assertion, an

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individual’s self-esteem encompasses their personal success expectations and determination which
results from their mental disposition (Carmen-Mihaela & Alina, 2013). Carmen-Mihaela and Alina
believe that people with high self-esteem are able to reason properly and have better views about
themselves. Yaratan and Yucesoylu (2010) opined that one’s self-esteem is dependent on the
treatments received by the family members, teachers, coaches, religious authorities, and peers. People
of poor self-esteem believe that they are unsuccessful and unhelpful when they do not achieve their
goals (Daglas, 2006).

Self-esteem is within the theoretical framework of identity theory by Sheldon Stryker (1980).
According to Stryker (1980), identities are a major part of self and are seen as the internal
conceptualization of the individual’s position and designations in various social contexts. Self-esteem
according to Stryker (1980) influences certain dependent variables like academic achievement,
happiness, among others.

1.2 Review of Relevant Literature

Emotional intelligence is an important element of research and also the predictor of the well-

being, health, and also the academic outcomes of the learners (Mudiono, 2019). Osenweugwor (2018)
found that emotional intelligence relates positively to learners’ academic achievement. Adeyemo and
Adeleye (2008) found self-efficacy relates positively with emotional intelligence. Emotional intelligence
skills had a significant influence on the locus of control and self- efficacy of learners in Nigeria (Umaru
& Umma, 2015). Ranjbar, Khademi, and Areshtanab (2017) found a low relationship between emotional
intelligence and educational achievement of Iranian students. Aghazade, Sharmin and Moheb (2017)
found that self-efficacy of learners in school activities had a significant relationship with emotional
intelligence. Azuka (2012) found that the achievement of learners in mathematics had a significant
positive relationship with emotional intelligence. Monica and Ramanaiah (2019) found that there is a
significant positive relationship between emotional intelligence and self-efficacy.

Korkmaz, Ilhan, and Bardakci (2018) found that the relationship between academic
achievement and self-efficacy as well as the locus of control was insignificant. Oyelekan, Jolayemi, and
Upahi (2018) found that learners’ achievement in chemistry had a significant positive relationship with
their self-efficacy. Njega, Njoka and Ndung’u (2019) found that self-efficacy has a strong positive
relationship with learners’ performance. Self-efficacy according to El-Adl and Alkharusi (2020) had a
statistically positive relationship with learners’ academic achievement. Adeoye and Feyisetan (2015)
found that learners’ academic achievement in the English language was determined by their self-efficacy
Similar studies found that learners’ academic achievement had a significant positive relationship with
their self-efficacy (Bushra & Lubna 2014; Hammed & Toyin 2015; Osenweugwor 2018; Hüseyin, Yıldız &
Mehmet 2018; Oyuga, Raburu & Aloka 2019; Nwaukwa, Onyemechara & Ndubuisi 2019).

1.3 Problem Statement and Objectives

The studies reviewed thus far have shown that there are a lot of inconsistent findings on the

relationship between emotional intelligence, self-efficacy and self-esteem, and students’ achievement
in mathematics. Most of such studies were limited to the nature and magnitude of such a relationship,

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using Pearson’s product-moment correlation coefficient rather than regression analysis. Besides, most
of them are foreign literature. These gaps in literature within the Nigerian context necessitated the
current study. The study sought to determine the amount of variation in learners’ achievement in
mathematics that can be attributed to their emotional intelligence, self-esteem, and self-efficacy.

1.4 Hypotheses

The following null hypotheses were tested at 5% probability levels.
Ho1: The amount of variation in learners’ achievement in mathematics as result of their emotional

intelligence is

not significant.

Ho2: The amount of variation in learners’ achievement in mathematics as result of their self-efficacy is

not significant.
Ho3: The amount of variation in learners’ achievement in mathematics as result of their self-esteem is

not significant.

2. Methods

2.1 Research paradigm and approach

This research is based on the assumptions of emotional intelligence theory by Goleman, Bandura’s Social
cognitive theory, and identity theory by Sheldon Stryker. Goleman believed that individuals are born
with a general emotional intelligence that determines their potential for learning emotional
competencies. This research adopted a pure quantitative research methodology. Quantitative
methods deal with numerical analysis of data collected through polls, questionnaires, and surveys, or
by manipulating pre-existing statistical data using computational techniques (Creswell, 2014).

2.2 Research Design

This study adopted a correlational survey research design that indicates the direction, magnitude, and
strength of the relationship between the variables.

2.3 Population, sample size, and sampling

The population for this study was 2,937 senior secondary 2 leaners in all the government
secondary schools in the Nnewi education zones of Anambra State Nigeria. A sample of 400 senior
secondary 2 learners out of the population was used for the study. This sample size was determined by
the use of Taro Yamane’s (1973) statistical formula for the determination of sample size. The multi-
stage sampling procedure was adopted in composing the sample. In the first stage, 3 Local Government
Areas in the Nnewi Education zone were sampled out of the 4 Local Government Areas, using a simple
random sampling technique. This was used to give each Local government area an equal chance of being
sampled for the study.

In the second stage, 16 secondary schools were sampled from the 3 Local Government Areas
in the zone, using a purposive sampling technique. In the third stage, the proportionate stratified
random sampling method was used to sample the students from the sampled 16 schools, making a total
of 400 senior secondary 2 students.

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2.4 Instrumentation

Four instruments were employed for this study, namely; Emotional Intelligence Inventory (EII),
Self-Esteem Scale (SES), General Self-Efficacy Scale (GSES), and Students’ Academic Achievement Score
Form (SAASF). Farn-Shing, Ying-Ming, Ching-Yua, and Chia-An’s (2007) emotional intelligence Inventory
was adapted for the study. The Emotional intelligence inventory has the response patterns; Never true,
Seldom true, sometimes true, often true, always true. In this study, the researchers used response
patterns; strongly agreed, agreed, disagreed, strongly disagreed.

2.6 Ethical measures

To conduct this study, the researchers sought ethical clearance from the Research Ethical Committee of
the Faculty of Education, University of Nigeria. Thus, the study was granted ethical approval with ID No:
REC/FE/27/0024. The researchers strictly followed the ethical standard specifications of the American
Psychological Association (APA, 2017).

2.7 Data analyses

Coefficient of determination, which is an aspect of linear regression, was used to answer the
research questions, while analysis of variance (ANOVA) was used to test the null hypotheses at 5 percent
probability level. These parametric statistics were used due to the assumption that the students are of
populations of equal variance.

3. Results

Ho1: The amount of variation in learners’ achievement in mathematics as result of their emotional
intelligence is not significant.

Table 1: Regression and Analysis of variance of the relationship between learners’ achievement in

mathematics and their emotional intelligence

Model R R2 Adjusted R2 Std. Error of the Estimate

1 .503a .253 .251 10.03978

Model Sum of Squares df Mean Square F Sig.

Regression 13582.127 1 13582.127 134.747 .000b
1 Residual 40117.311 398 100.797
Total 53699.437 399

a. Dependent Variable: Student Academic Achievement in maths

b. Predictors: (Constant), STUDENT EMOTIONAL INTELLIGENCE

Table 1 showed that the coefficient of determination for the relationship between emotional
intelligence and learners’ achievement is 0.253 meaning that 25.3% variation in learners’ achievement
is due to their emotional intelligence. Table 1 also showed that the amount of variation in learners’
achievement in mathematics due to their emotional intelligence is significant, F (1, 398) = 134.747, p < .050. The null hypothesis was rejected at p < .05. The inference drawn was that the learners’ emotional intelligence significantly predicts their achievement in mathematics. Ho2: The amount of variation in learners’ achievement in mathematics as result of their self-esteem is not significant.

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Table 2: Regression and Analysis of variance of the relationship between learners’ achievement in
mathematics and their self-esteem

Model R R2 Adjusted R2 Std. Error of the Estimate

1 .525a .275 .273 9.88900

Model Sum of Squares df Mean Square F Sig.

Regression 14778.084 1 14778.084 151.117 .000b
1 Residual 38921.354 398 97.792
Total 53699.437 399

a. Dependent Variable: Student Academic Achievement in maths

b. Predictors: (Constant), STUDENT SELF ESTEEM

Table 2 showed that the coefficient of determination for the relationship between self-esteem and
learners’ achievement is 0.275 meaning that 27.5% variation in learners’ achievement is due to their
self-esteem. Table 2 also showed that the amount of variation in learners’ achievement in mathematics
as a result of their self-esteem is significant, F (1, 398) = 151.117, p < .050. The null hypothesis was rejected at p < .05. The inference drawn was that learners’ self-esteem significantly predicts their achievement in mathematics. Ho3: The amount of variation in learners’ academic achievement in mathematics as result of their self- efficacy is not significant.

Table 3: Regression and Analysis of variance of the relationship between learners’ achievement in

mathematics and their self-efficacy
Model R R2 Adjusted R2 Std. Error of the Estimate

1 .597a .356 .355 9.31948

Model Sum of Squares df Mean Square F Sig.

Regression 19132.045 1 19132.045 220.281 .000b
1 Residual 34567.392 398 86.853
Total 53699.437 399

a. Dependent Variable: Student Academic Achievement in maths
b. Predictors: (Constant), STUDENT SELF EFFICACY

Table 3 showed that the coefficient of determination for the relationship between self-efficacy and
learners’ achievement is 0.356 meaning that 35.6% variation in learners’ achievement is due to their
self-efficacy. Table 3 also showed that the amount of variation in learners’ achievement in mathematics
due to their self-efficacy is significant, F (1, 398) = 220.281, p < .050. The null hypothesis was rejected at p < .05. The inference drawn was that the learners’ self-efficacy significantly predicts their achievement in mathematics.

4. Discussion and Conclusion

The study sought to find out how learners’ emotional intelligence, self-efficacy, and self-esteem
relate to their achievement in mathematics. The findings of the study revealed that that three
psychological variables (emotional intelligence, self-efficacy, and self-esteem) relate positively to
learners’ achievement in mathematics. Thus, emotional intelligence, self-efficacy, and self-esteem
significantly predict learners’ academic achievement in mathematics. These findings are not far from
reality in that several studies have found that emotional intelligence, self-efficacy, and self-esteem are
prime determinants of students’ academic achievement in different subjects. These findings are in
agreement with the findings of the recent studies (Mudiono 2019; Osenweugwor 2018; Ranjbar,

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Khademi & Areshtanab 2017; Aghazade, Sharmin & Moheb 2017; Monica & Ramanaiah 2019; Korkmaz,
Ilhan & Bardakci 2018; Njega, Njoka & Ndung’u 2019; Oyelekan, Jolayemi, & Upahi 2018; Asakereh &
Yousofi 2018; Hadinezhad & Masoudzadeh 2018; El-Adl & Alkharusi 2020)

Emotional intelligence is an important element of research and also the predictor of the well-
being, health, and also the academic outcomes of the learners (Mudiono 2019). Osenweugwor (2018)
found that emotional intelligence relates positively to learners’ academic achievement. Adeyemo and
Adeleye (2008) found self-efficacy relates positively with emotional intelligence. Emotional intelligence
skills had a significant influence on the locus of control and self- efficacy of learners in Nigeria (Umaru
& Umma, 2015). Ranjbar, Khademi, and Areshtanab (2017) found a low relationship between emotional
intelligence and educational achievement of Iranian students. Aghazade, Sharmin and Moheb (2017)
found that self-efficacy of learners in school activities had a significant relationship with emotional
intelligence. Azuka (2012) found that the achievement of learners in mathematics had a significant
positive relationship with emotional intelligence. Monica and Ramanaiah (2019) found that there is a
significant positive relationship between emotional intelligence and self-efficacy.

Korkmaz, Ilhan and Bardakci (2018) found that the relationship between academic achievement
and self-efficacy as well as the locus of control was insignificant. Oyelekan, Jolayemi, and Upahi (2018)
found that learners’ achievement in chemistry had a significant positive relationship with their self-
efficacy. Njega, Njoka and Ndung’u (2019) found that self-efficacy has a strong positive relationship with
learners’ performance. Self-efficacy according to El-Adl and Alkharusi (2020) had a significant positive
relationship with learners’ academic achievement. Adeoye and Feyisetan (2015) found that learners’
academic achievement in the English language was determined by their self-efficacy Similar studies
found that learners’ academic achievement had a significant positive relationship with their self-efficacy
(Bushra & Lubna 2014; Hammed & Toyin 2015; Osenweugwor 2018; Hüseyin, Yıldız & Mehmet 2018;
Oyuga, Raburu, & Aloka 2019; Nwaukwa, Onyemechara & Ndubuisi 2019).

Adeoye and Feyisetan (2015) found that self-esteem significantly contributed to academic
achievement learners. Asakereh and Yousofi (2018) found that self-esteem had a significant relationship
with academic achievement of Iranian students. Self- esteem has a low relationship with academic
achievement (Hadinezhad & Masoudzadeh 2018). However, self-esteem does not significantly affect
academic performance (Sepahi, Niroumand, Keshavarzi & Ahmade 2015). Emotional intelligence, self-
esteem, and self-efficacy are parts of the psychological factors that determine students’ academic
achievement in mathematics. This implies that those factors are significant predictors of learners’
academic achievement in mathematics. In order words, learners’ successes in schools are dependent
on their level of emotional intelligence, self-esteem, and self-efficacy.

These findings have some educational implications. For instance, to improve the academic
achievement of learners in mathematics, measures that improve student emotional intelligence factors
should be put in place, both at home and in school. Also, using appropriate instructional strategies that
encourage self-esteem among the students by the teachers will translate to enhanced academic
achievement. Enhancing students’ self-esteem through the adoption of best practices in teaching will
result in the improved academic achievement of the students. Finally, making students believe in their
ability to succeed in a specific situation or accomplish a task will increase their self-efficacy, which, in
turn, will lead to enhanced academic achievement.

4.1 Limitations

As correlational survey research, the generalizability of the findings of this study may be limited by some
other factors not studied, such as gender, location, the cultural inclination of the participants, etc. In

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other words, the influence of those factors may have played into the findings of this study, thereby
limiting their generalizability to the entire population.

4.2 Recommendations

1. Parents and teachers should put measures in place at home and in school, respectively, that could
improve learners’ emotional intelligence, self-esteem, and self-efficacy.

2. Appropriate instructional strategies that encourage self-esteem among the learners should be used
by the teachers to enhance the academic achievement of students in mathematics.

3. Teachers and school authorities, in general, should make students believe in their ability to succeed
in a specific situation or in accomplishing a task.

4.3 Acknowledgment

The researchers acknowledged all the people who made this research a success especially the
participants.

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sustainability

Article

Physical Activity and Academic Performance:
The Mediating Effect of Self-Esteem and

Depression

Sumaira Kayani 1,2, Tayyaba Kiyani 1, Jin Wang 1,*, María Luisa Zagalaz Sánchez 3,* ,
Saima Kayani 4 and Haroona Qurban 1

1 Department of Physical Education, Zhejiang University, XiXi Campus, 148 TianMuShan Road,
Hangzhou 310028, China; 11603033@zju.edu.cn (S.K.); kiyani@zju.edu.cn (T.K.); 11403034@zju.edu.cn (H.Q.)

2 Division of Continuing Education, Pir Mehr Ali Shah Arid Agriculture University Rawalpindi,
Punjab 46300, Pakistan

3 Department of Didactics of Plastic and Body Musical Expression, University of Jaen, Las Lagunillas Campus,
Building Humanities and Education Sciences I (D2), Unit: D2-125, 23071 Jaén, Spain

4 Department of Education, Women University Azad Jammu and Kashmir, Bagh 12500, Pakistan;
saima_kayani@wuajk.edu.pk

* Correspondence: jinwang47@live.cn (J.W.); lzagalaz@ujaen.es (M.L.Z.S.)

Received: 25 August 2018; Accepted: 19 September 2018; Published: 11 October 2018
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Abstract: An important step to enhance the academic efficiency of students is increasing their physical
activity. For this reason, it is necessary to see to what extent physical activity is related to the academic
performance of the students and what might mediate this. A major objective of the study is to explore
self-esteem and depression as mediators between physical activity and academic performance.
On the basis of informed consent to participate in the study, 358 participants have been selected
from Universities in Pakistan, and they were asked about their physical activity, depression during
their study and self-esteem through self-report. Participants self-reported their self-esteem, level of
depression and their physical activity through standardized measures; the Rosenberg Self-esteem
scale (1965), the University stress scale (2016), and the short form of the International Physical Activity
questionnaire (2003), respectively. Academic performance had been measured as the cumulative
grade point average (CGPA) of the last two consecutive semesters. Self-esteem and depression were
found to be significant mediators between physical activity and academic performance. The total
effect of physical activity on academic performance was significant but smaller than the total indirect
effect through mediators. Though total indirect effect is the combination of the effect of self-esteem
and depression, but the larger contribution is of self-esteem which has been found to be the strongest
mediator between physical activity and academic performance. The study has implications for future
research, both in terms of testing the model and testing psychological constructs. Also, the study
emphasizes that the importance of physical activity has to be kept in mind while designing a
curriculum of an educational institution in order to foster sustainable development.

Keywords: physical activity; academic performance; self-esteem; depression; mediation effect

1. Introduction

The practice of physical exercise is extremely beneficial to health [1]. It has several health-related
benefits for children and adolescents [2]. Growing literature has exposed a significant relationship
between academic performance and physical activity [3]. There has been much interest in studies on
the potential benefits of physical activity for the development of cognitive abilities over the last several
years, strongly recommending physical activity as an effective instrument for building psychological
well-being [4,5]. Physical activity makes people feel good about them through decreasing depression

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Sustainability 2018, 10, 3633 2 of 17

or sadness, and rectifying and improving mood. The literature also shows that physical activity is
linked with a subsequent decrease in mental problems, including depression and insanity [6].

Physical activity is now believed to be an established treatment against depression for adults [7].
Additionally, properly managed physical activities are important for processing information,
particularly in adults [8]. As a result, the idea that a high level of physical activity is effective
for increasing thoughtfulness, meditation, and, as a consequence, academic performance is attractive
to the learners.

Sibley and E tnier reviewed 44 articles to examine the effect of physical activity on cognition in
children [9]. These articles include 28 cross sectional studies and 16 intervention studies with age
group 4 to 18 years. Taras reviewed 14 studies determining the relation between physical activity
and academic performance [9]. These studies include studies from 1984 to 2004 from age group
5 to 18 years. The study summarized findings of these results and found no or a weak relation
between physical activity and academic performance. It recommends that further analysis is required
to study the relation between physical activity and performance. Trudeau and Shephard reviewed
nine cross-sectional and seven experimental studies during 1966–2007 linking physical activity and
academic performance among school children [10]. The study did not find any significant trend
in the findings, and found that academic performance is not determined by the time allocated for
physical activity.

Hillman et al., determined the narrative research to identify the impact of physical activity
on cognitive abilities [11]. The research found no evidence for any harmful effects of increasing
physical activity on academic performance. Tomporowski et al. provided narrative research on studies,
determining the effects of physical activity on academic and cognitive performance [12]. The study
concluded on the basis of research work that physical activity is important for increasing cognitive
functioning and thus academic performance.

The literature cited several studies that report a direct effect of physical activity on academic
performance. However, we reviewed literature and suggested that physical activity has a prime
effect on depression and self-esteem, and so indirectly affect academic performance. More specifically,
physical activity has a positive relation with self-esteem [13] that also has a positive impact on academic
performance [14]. Moreover, we also found in the literature that physical activity has a negative relation
with depression [15], which also has a negative relation with academic performance [16].

1.1. Theoretical Background

1.1.1. Physical Activity and Academic Performance

Over the last several years, society has witnessed serious consequences due to the lack of
physical activity among students. The lack of physical activity is an antecedent condition for several
illnesses, such as obesity and diabetes. The literature includes contemporary views on the impact
of physical activity on learning procedure among students and recent studies show that regular
exercise leads to better mental health [17]. Historically, it was believed that non-academic activities
have a negative effect on academic performance [18]. In recent years, the relation between physical
activity and academic performance have been analyzed from several viewpoints, such as evaluating
the students’ participation in physical activities with the view that these activities are related to
academic performance [19,20]. These studies have reached to different contradictory findings on
this issue. One group of researchers have found no relation between physical activity and academic
performance [20]. Others have found positive relations between physical activity and academic
performance [21] A comparison between students who are involved in physical activity and who
are not involved has been conducted by Trudeau and Shephard [10], and it resulted in positive
significant relationship between physical activity and academic performance indicating that academic
performance is improved with increasing physical activity. Symons et al. found physical exercise to
be effective in improving inter-neuronal connections and increasing attentiveness [22]. Strong et al.,

Sustainability 2018, 10, 3633 3 of 17

confirmed a positive impact of physical activities on health though it failed to find any relation with
cognitive performance [23]. Lindner performed a study in Hong Kong and found a significant, but
weak, correlation between academic result and physical activity participation [18]. Later, a similar
study was conducted by Dwyer et al., in the context of Australian students and it found a weak
correlation between academic results and physical activities [24].

1.1.2. Physical Activity and Depression

Depression is commonly described in terms of hopelessness, difficulty concentrating, lack of
energy, agitation, restlessness, feelings of worthlessness or pessimism, and suicidal ideation [25].
However, there are a few studies on the relation between depression and physical activity on university
students as most of the studies have been conducted on children and adolescents from different age
groups linking their academic improvements with increasing physical activity [4]. The literature
revealed that physical activity and exercise have constructive effects on depression [26]. It has been
observed that people who do exercise and physical activity have less chances of exhibiting signs of
anxiety and depression as compared to those who do not practice physical exercise. De Mello et al.,
and his colleagues and Paluska and Schwenk found that less active people tend to be more depressed
than more active people and anxiety symptoms are improved with regular exercise and physical
activity [27,28]. De Moor and others also found proper physical activity is effective for reducing
depression in Brazil [29]. Surprisingly, though the advantages of routine physical activity on mental
health are evident [30], it is not practiced by most of the people [27].

Various studies with cross-sectional design consistently reported a negative relation between
physical activity and depression [31]. Additionally, recent studies have found that physical activity
has an important influence on the mood for clinical, as well as non-clinical, samples [32]. Moreover,
there is negative association between depression and physical activity in the case of adolescents as
well as adults [33]. Importantly, a few time series studies have been conducted to determine the
relation between depression and physical activity [34]. Jerstad observed bi-directional relation between
physical activity and depression in that increase in physical activity reduces the risk of depression and
in turn depression decreases physical activity [34]. One of the limitations the literature is that most of
the studies have been performed on adolescents covering a short period of time [34]

1.1.3. Physical Activity and Self Esteem

The concept of self-esteem is associated with positive feelings about oneself [35]. Physical activity
is considered to be important for physical as well as mental health. High self-esteem is positively
associated with greater wellbeing [36]. Consistent exercise and activity leads to psychological
well-being [37]. Physical activity has a positive significant relation with self-esteem in children [38]
as well as in older adults [39]. Sonstreom and Morgan’s model shows that physical activity is linked
to self-esteem [13]. On the basis of this model, Sonstreom and Alfermann [13,40] found that physical
activity is associated with a greater level of self-esteem while using a sample of adults and middle
age people. Noordstar and his colleagues [41] found that variation in self-esteem is linked with
sportsman abilities and physical activities ranging from moderate to extreme using a sample of
children. Guinn et al. [42] found a significant positive change in self-esteem after doing exercise.
Guinn and Jorgensen [43] also found self-esteem and physical activity to be related directly among
children and adolescents. Gruber reviewed 27 studies and found that physical activity has a moderate
relation with self-esteem in the case of pre-adolescents [44]. However, Walters and Martins [45] found
an insignificant relation between self-esteem and physical activity.

1.1.4. Academic Performance and Self Esteem

Self-esteem is defined with reference to the self-image of an individual. The concept evolved from
the hierarchy of needs theory, as proposed by Maslow, which included needs of esteem as one of the
higher order need of individual. However, it is defined by several psychological studies that depend

Sustainability 2018, 10, 3633 4 of 17

on the dimensions that these studies considered and there are differences in the definition between one
study and another. Rosenberg defined self-esteem as sense of worth that may be positive or negative
based on what one’s values. The concept of self-esteem shows significance for students and their
parents. The activities that make students feel encouraged help them to build their capabilities and
skills [46]. Many researchers found that an individual with high self-esteem has high level of efficiency
and effectiveness. Rahmani implies that self-esteem serves as a driving force to address problems in
life [47].

Academic achievement is referred to as knowledge that is obtained by an individual during
the academic period for a subject or group of subjects that one learns in an academic year such as a
semester. Vialle et al. [48] claims that academic performance is not merely dependent upon the degree
of intellectual energy, but rather on many other constructs, such as motivation, self-esteem, and social
factors. This shows that academic achievement is a multi-faceted concept that is covered by several
social, emotional, and personality factors.

The relation between self-esteem and academic performance is highlighted by a few studies
that claim that self-esteem is positively related with academic performance i.e., high self-esteem
encourages high performance, as Bankston and Zhou [49] states that high self-concept leads to high
academic performance.

There are many studies that have found a positive relation between self-esteem and academic
performance [50]. These studies claim that a higher level of self-esteem is associated with higher
levels of academic performance as students are developed with optimistic feelings for themselves
on account of previous achievements. Aryana [51] determined the relation between self-esteem and
academic performance on a sample of 100 students taken from Azerbaijan and found that there is a
significant positive association between academic performance and self-esteem. One study found that
an individual with high self-esteem tends to have high academic performance. Doodman et al. [52]
determined the relation between self-esteem and academic performance in Lamerd, obtaining a sample
of 169 students out of population of 300. Out of 169, 73 were male and 96 were female. The study
found a significant relation between self-esteem and academic performance.

On the basis of previous research, it is reasonable to say that the relation between self-esteem and
academic performance is positive with a few studies differing from the majority. Thus, the present
study is different in a sense that it studies the relation between self-esteem and academic performance
while using a sample from university students.

1.1.5. Academic Performance and Depression

Depression is a feeling formulated by tension, anxiety, and worries that are associated with the
stimulation of the nervous system [53]. A high level of depression makes life difficult and problematic.
Depression is included in diverse forms of emotional and behavioral disorders. Students who are
victim of any kind of emotional and behavioral disorder demonstrate negative attitudes towards their
studies, such as low interest in learning and poor performance in examinations [54]. The psychological
symptoms for depression among students include feelings of nervousness, going blank during exams,
falling asunder while doing any task, and low interest in difficult subjects. Additionally, academic
performance increases or decreases inversely with depression [55]. The importance of investigating
depression among university students has been acknowledged by students as well as researchers.

McCarty [56] found depression as one of the major determinants of academic performance
that is found to have a harmful effect on educational performance. Surprisingly, we do not find
many studies that exhibit the relation between high levels of depression and low levels of academic
performance. Aronen et al. [57] found that depression has several detrimental effects on students,
such as diminishing memory and creating distraction among students. However, in a few studies,
although researchers have found significant effects of depression on academic performance, they do
not only show negative effects on students. These studies also claim that depression has a positive

Sustainability 2018, 10, 3633 5 of 17

effect on academic performance. Vitasari et al. [54] found that high level of depression is associated
with a low level of academic performance.

The studies have found that the students with high level of depression have low memory, lack
of concentration, low confidence and poor way of thinking. Whitakar Sena et al. [16] observed that a
high level of depression is associated with a low level of academic performance, particularly in the
case of weak students.

1.1.6. Physical Activity and Academic Performance with Mediation of Depression and Self Esteem

The literature that we investigated only shows direct impact of physical activity on performance.
We do not find any study that determines indirect impact of PA on academic performance with the
mediation of depression and self-esteem, except Dorfman [58], who explored the mediation effect of
psychological well-being between physical fitness and academic achievement. However, on the basis
of the literature, we propose that improved physical activity decreases depression [37] and indirectly
increases academic performance [56]. Moreover, we also propose that improved physical activity
increases self-esteem [4], which also increases academic performance [50].

The major objective of this research is to determine the impact of physical activity on academic
performance with the mediation of self-esteem and depression. After a careful review of the literature,
we have formulated following hypotheses:

Hypothesis 1 (H1). Physical activity (PA) is positively associated with academic performance (AP).

Hypothesis 2 (H2). Physical activity is negatively significantly associated with depression (DEP).

Hypothesis 3 (H3). There is positive significant association between physical activity and
self-esteem (SE).

Hypothesis 4 (H4). There is a positive significant relation between academic performance
and self-esteem.

Hypothesis 5 (H5). There is a significant negative relation between academic performance
and Depression.

Hypothesis 6 (H6). The relation between physical activity and academic performance is significantly
mediated by self-esteem and depression.

2. Materials and Methods

2.1. Participants

Participants have been selected on the basis of informed consent to participate in the study.
A sample of 358 students was studied in terms of their physical activity, depression during their
study and self-esteem through self-report. The survey was conducted from early October, 2017 to
late December, 2017. Seven experienced teachers (including the researcher herself) from the selected
universities administered the questionnaire survey. All of the respondents were told about the purpose
of the research, the variables involved, and items on each questionnaire before they took the survey.
On the average, the respondents completed the self-esteem scale in 3 min, the university stress
scale in 10 min and the international physical activity questionnaire in around 10 min. So, it took
hardly 25 min to finish survey. The analyzed sample was comprised of 358 undergraduate students
(M age = 20.30, SD = 1.149, 46.1% girls, 53.9% boys) from five different Universities in urban and rural
areas of Pakistan. Since there is evidence for differences between rural and urban settings, for example
in the physical activity or physical fitness level of people [59], the Universities included were chosen
so that approximately the same number of them were located in urban (n = 3) and rural areas (n = 2).

Sustainability 2018, 10, 3633 6 of 17

Analyses of the students’ geographical area (M = 1.65, SD = 0.479) and the family income (M = 2.29,
SD = 0.845) provide evidence that the present sample is representative for a large population of
same-aged students from different social classes. Originally, the participants were 390, out of which
32 cases had to be excluded because of missing data for different variables. The response rate was
91.7%. The high response rate reflected the use of on-site questionnaires, teachers’ proper guidance,
and questionnaire verification after its completion.

In order to detect outliers, Mahalnobis distance values were calculated as χ2 at p = 0.001, df = 4,
which is equal to the number of variables [60]. According to chi-square distribution table, 32 cases
having Mahalnobis distance values greater than 18.47 were identified as outliers, which had been
excluded from data.

2.2. Measures

Data has been calculated by using the standardized research tools:

2.2.1. Physical Activity

The short version of the International Physical Activity Questionnaire [61] consisted of seven
items containing three indicators, light, moderate, and vigorous activity, and has now been used
in many international studies to measure physical activity. An example item is “During the last
7 days, on how many days did you do moderate physical activities like bicycling at a regular pace;
carrying light loads, and doubles tennis? Do not include walking” (Appendix A). There was also a
question about time spent by students in sitting which is not included in analysis. Means of the weekly
minutes for all the three activities have been taken for the analysis in order to keep all variables’ mean
scores regular. In the present study, this measure has been adopted to assess self-reported physical
activity in the sample. It has been extensively tested for reliability and validity [62]. The test-retest
reliability of the tool has been calculated. The coefficient for that was 0.862.

2.2.2. Academic Performance

CGPA has been taken as a latent variable for measuring the academic performance of students.
GPA, for the last consecutive semesters, has been considered as observed variables for the study.
The test-retest reliability was 0.986.

2.2.3. Self-Esteem

English version [63] of uni-dimensional Rosenberg Self-esteem Scale [46] was used to measure
the self-esteem of students. It is a widely used instrument that has been tested for reliability and
validity in many settings. It was consisted of 10 items, an example item of which is “I feel that I’m a
person of worth, at least on an equal plane with others” (Appendix B). The response was generated
on a four-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). Cronbach alpha
was calculated for the measure, which was 0.825, which shows that the measure was reliable to an
acceptable extent.

2.2.4.

University Stress Scale

Depression had been measured in terms of stress. In order to measure depression of participants
during their study, a 21 items University stress scale was adopted, which provides an index to measure
both the categories of stress experienced by university students, as well as the overall intensity of the
stress experienced [64]. One example of items is “Academic/coursework demands” (Appendix C).
All of the items were evaluated on a four point Likert scale ranging from 0 (Not at All) to 4 (Constantly).
Cronbach’s α for the tool was 0.807.

All of the alpha values met the benchmark of 0.65 [65] (Table 1).

Sustainability 2018, 10, 3633 7 of 17

Table 1. Descriptive statistics, Mean differences, SD, Correlations, and regression coefficients.

Test Variables Means SD 1.AP 2.PA 3.SE 4.DEP
Cronbach’s

Alpha
Regression
Coefficients

Sig.

1. Academic
Performance (AP)

3.1258 0.64501 1 – – – 0.986 – –

2. Physical activity
(PA)

3.1530 1.2985 0.222 ** 1 – – 0.862 0.164 0.034

3. Self-esteem (SE) 2.7782 0.51745 0.408 ** 0.304 ** 1 – 0.825 0.671 0.000

4. Depression (DEP) 2.0608 0.52764 −0.269 ** −0.352 ** −0.146 * 1 0.807 −0.335 0.000
R-square 0.0786 – – – – – – –

Adjusted R Square 0.0779 – – – – – – –

F 46.174 *** – – – – – – –

– N = 358, * p < 0.05, ** p < 0.01, (2-tailed), *** p < 0.001.

2.3. Parallel Mediation Model

A parallel mediation model has been used in the study. It is a basic mediation model (4b)
from Hayes PROCESS templates [66]. We had hypothesized (Figure 1) that physical activity (X)
would indirectly affect academic performance (Y) through two mediators: self-esteem (M1) and
depression (M2).

Sustainability 2018, 10, x FOR PEER REVIEW 7 of 17

Table 1. Descriptive statistics, Mean differences, SD, Correlations, and regression coefficients.

Test Variables Means SD 1.AP 2.PA 3.SE 4.DEP
Cronbach’s

Alpha
Regression

Coefficients
Sig.

Academic

Performance
(AP)

3.1258 0.64501 1 – – – 0.986 – –

Physical

activity (PA)

3.1530 1.2985 0.222 ** 1 – – 0.862 0.164 0.034

3. Self-

esteem

(SE)

2.7782 0.51745 0.408 ** 0.304 ** 1 – 0.825 0.671 0.000

4. Depression
(DEP)

2.0608 0.52764 −0.269 ** −0.352 **
−0.146

*
1 0.807 −0.335 0.000

R-square 0.0786 – – – – – – –
Adjusted R
Square

0.0779 – – – – – – –

F 46.174 *** – – – – – – –

– N = 358, * p < 0.05, ** p < 0.01, (2-tailed), *** p < 0.001.

2.3. Parallel Mediation Model

A parallel mediation model has been used in the study. It is a basic mediation model (4b) from
Hayes PROCESS templates [66]. We had hypothesized (Figure 1) that physical activity (X) would
indirectly affect academic performance (Y) through two mediators: self-esteem (M1) and depression
(M2).

Figure 1. Hypothesized Model

Firstly, the individual direct effects were calculated through the following paths:

M1 on X (a1)
M2 on X (a2)
Y on M1 (b1)
Y on M2 (b2)
Y on X (C’) which is direct effect of X on Y

Secondly, the specific indirect paths and total indirect effect were calculated as:

Indirect effect of X on Y via M1 = a1 × b1= a1

Indirect effect of X on Y via M2 = a2 × b2= a2

Total indirect effect via m1& m2 = a1b1 + a2b2

Depression

(m2)

Physical

Activity

(X)

Academic

Performa

nce (Y)

Self-

esteem
b2

c’

Figure 1. Hypothesized Model.

Firstly, the individual direct effects were calculated through the following paths:

M1 on X (a1)
M2 on X (a2)
Y on M1 (b1)
Y on M2 (b2)
Y on X (C’) which is direct effect of X on Y

Secondly, the specific indirect paths and total indirect effect were calculated as:

Indirect effect of X on Y via M1 = a1 × b1= a1b1

Sustainability 2018, 10, 3633 8 of 17

Indirect effect of X on Y via M2 = a2 × b2= a2b2
Total indirect effect via m1& m2 = a1b1 + a2b2

Finally, the total effect (c) was found out as = a1b1 + a2b2 + c′

2.4. Procedure

The first step was to inform authorities in the Universities about our research plans and objectives
and obtain formal permission to approach students. After the permission, seven teachers from the
included universities were contacted, who agreed to commit themselves to get the survey filled by
the students. Then, self-report questionnaires were completed under the supervision of teachers
during a regular semester time. All of the participants had signed an informed consent form approved
by the Institutional Review Board before their participation in the study. Data were treated with
confidentiality. It was hypothesized that both of the mediators significantly mediate between the
independent and dependent variable. Pearson correlation coefficients were calculated for all the study
variables. Then, a multiple mediation model was estimated by using the Hayes Process model 4b.
To identify the direct and indirect effects of physical activity on academic performance, regression
coefficients and bootstrapping was used to generate a confidence interval for the mediation effects.
The analyses yielded that there was significant mediation effect of both mediators supporting the
major hypothesis.

2.5. Analyses

All of the analyses were conducted through SPSS version 20 (IBM: Chicago, USA). In preliminary
analysis, data were checked for accuracy. It was found that there were no missing values in data.
Correlation was used in order to see the relationship among all the included variables. Outliers were
tested as regression analyses (to test H1–H5) had been conducted because independent variable (x)
and mediators (M) were expected to predict depending or outcome variable (Y).

For testing the major hypothesis-6 of the study, Hayes Process was used, which is considered as
more powerful and effective method than its alternatives [67] 5000 bootstrapping-based resamples
have been selected. Boot strapping does not have any assumption of normal distribution.

3. Results

3.1. Preliminary Analyses

3.1.1. Exploratory Factor Analyses (EFA)

First, exploratory factor analyses (EFA) while using principal component analysis with Varimax
rotation were run with random half of the sample. It revealed that physical activity, self-esteem and
depression measures constituted three interpretable separate factors with an Eigen value greater than
one. These factors accounted for 61.180% of the total variance. The loading on self-esteem ranged from
0.782 to 0.888, the loading on depression ranged from 0.686 to 0.853, while the loading on physical
activity ranged from 0.738 to 0.879. It can be concluded that all of the tools used in the study under
consideration are all valid.

3.1.2. Confirmatory Factor Analysis (CFA)

Then, confirmatory factor analysis verified the final factor structure of the three constructs under
study. The three factor structure presented a good fit for the data having CFI = 0.0931, TLI = 0.954,
RMSEA (root mean square error approximation) = 0.047 (95% confidence interval), and χ2 = 191.073,
df = 46. All of the factor loadings were significant (p > 0.001) as it was above 0.738 for self-esteem and
physical activity and above 0.686 for depression.

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3.1.3. Descriptive Statistics

Table 1 shows descriptive statistics; mean values and standard deviations and a correlation matrix
for all of the study variables. Correlation matrix exhibits a significant association between physical
activity and academic performance and the mediators under study i.e., self-esteem and depression.
The correlation coefficient for PA and AP was 0.222 supporting hypothesis 1. Similarly, the correlation
between PA and DEP was significant with the coefficient −0.352. It supports hypothesis 2, while for PA
and SE, it was 0.304, supporting hypothesis 3. On the other hand, there was a positive relation between
AP and SE with r = 0.408, which supports hypothesis 4. On contrary, AP and DEP were negatively
associated with r = −0.269 which supports hypothesis 5. Baron and Kenny [68] recommended that
mediators must be significantly associated with both the independent and dependent variables.
In order to establish the significance of study variables, all were analyzed through regression to judge
whether to include them in the path model or not. After analyses, it was found that both mediators
could be included in path model. Independent variable explained 6.12% (t = 4.32, R-square = 0.0786,
p = 0.000) variance on its own. Hence, Hypothesis 1 to Hypothesis 5 was supported.

3.2. Major Analysis

It was hypothesized that the effect of physical activity on academic performance is mediated both
by self-esteem and depression (Figure 1). So, there are two parallel mediators in the model. The model
was estimated through Process model 4 [69]. Four models have been estimated. The outcome variable is
academic performance and the explaining variable is physical activity, while two are the mediators i.e.,
self-esteem and depression with sample size 358. Table 2 describes all of the direct and indirect effects.

Table 2. Path Coefficients for Parallel Mediation Model.

Path Effect Boot-LLCI Boot-ULCI SE T P-Value

Total Effect 1.826 0.687 1.987 0.218 4.613 0.000
Direct Effect (c′) 0.613 0.189 0.895 0.228 2.817 0.000

IV-M1(a1) 0.932 0.205 0.986 0.037 4.726 0.000
IV-M2(a2) −0.389 −0.404 0.213 0.134 2.321 0.000

M1-DV(b1) 1.13 0.447 1.632 0.203 2.830 0.000
M2-DV(b2) −0.412 0.182 0.437 0.095 3.439 0.000

Total indirect effect 1.213 0.598 1.863 0.232 4.325 0.000
IV-M1-DV(a1b1) 1.053 0.456 1.732 0.312 3.565 0.000
IV-M2-DV(a2b2) 0.160 −1.841 0.425 0.58 −1.410 0.000

Note = This is path coefficients for parallel mediation model of Hayes process model 4, Indirect
effects and 95% Confidence interval predicting academic performance (N = 358), SE is standard
error, IV = Independent variable (Physical activity), DV = dependent variable (academic performance),
M1&M2 = parallel mediators (self-esteem & depression); a1, a2, b1, b2 are regression coefficients for X1
& X2 respectively; while b1, b2 are the regression coefficients for M1 & M2 respectively. Boot-LLCI and
Boot-ULCI are the abbreviations for lower limit bootstrap confidence interval and upper limit bootstrap
confidence interval respectively.

3.2.1. Direct Effects

Separate regression models have been estimated for the entire paths. Firstly, self-esteem, the first
mediator, is regressed on physical activity (path a1) and the unstandardized coefficient reported here
is 0.932, which indicates that physical activity is strongly predicted by self-esteem and it is statistically
significant as p values is 0.000 and 95% confidence interval is between 0.205–0.986.

Secondly, depression is regressed on physical activity (path a2). Again we have an unstandardized
coefficient, which is negative and statistically significant. The coefficient of effect is −0.389 and the
confidence interval is between −0.404 to 0.213 (p < 0.0001).

Similarly, in the model academic performance is regressed on self-esteem (path b1) and it is found
that there is a positive effect (1.13). On the hand, a negatively significant coefficient (−0.412) was the
result when academic performance is regressed on depression (b2).

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The direct effect is explored by regressing academic performance on physical activity, which is
0.613 (CI = 0.189–0.895, p < 0.0001).

3.2.2. Indirect Effects

The regression model predicts academic performance from self-esteem, depression, and physical
activity. Through all of the mediators, we can see a strong positive effect for self-esteem (a1b1 = 1.053,
CI = 0.456–1.732) and a negative effect for depression (a2b2 = 0.160, CI = −1.841–0.425). This shows
that both mediators are significantly associated to physical activity and academic performance, because
bootstrap CI is above zero while controlling for demographic variables, but most of the indirect effect
is due to self-esteem as a1b1 is 1.053 while a2b2 is 0.160.

3.2.3. Total Effect

The direct effect of physical activity on academic performance (c’) is 0.613 (CI = 0.189–0.895,
p < 0.0001) (Figure 2). In contrast, the total indirect effects via both mediators (a1b1 + a2b2) is 1.213 (CI = 0.598–1.863). Consequently, the total effect (a1b1 + a2b2 + c′) of X on Y is 1.826. Therefore, the total effect (c = 1.826, CI = 0.687–1.987) of physical activity on academic performance is due to an indirect path, as the coefficient for direct effect (c’ = 0.613) is smaller than the total indirect effect (1.213). Hence, it supports hypothesis 6 that self-esteem and depression are significant mediators between physical activity and academic performance.

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3.2.2. Indirect Effects

The regression model predicts academic performance from self-esteem, depression, and physical
activity. Through all of the mediators, we can see a strong positive effect for self-esteem (a1b1 = 1.053,
CI = 0.456–1.732) and a negative effect for depression (a2b2 = 0.160, CI = −1.841–0.425). This shows
that both mediators are significantly associated to physical activity and academic performance,
because bootstrap CI is above zero while controlling for demographic variables, but most of the
indirect effect is due to self-esteem as a1b1 is 1.053 while a2b2 is 0.160.

3.2.2. Total Effect

The direct effect of physical activity on academic performance (c’) is 0.613 (CI = 0.189-.895, p < 0.0001) (Figure 2). In contrast, the total indirect effects via both mediators (a1b1 + a2b2) is 1.213 (CI = 0.598-1.863). Consequently, the total effect (a1b1 + a2b2 + c′) of X on Y is 1.826. Therefore, the total effect (c = 1.826, CI = 0.687–1.987) of physical activity on academic performance is due to an indirect path, as the coefficient for direct effect (c’ = 0.613) is smaller than the total indirect effect (1.213). Hence, it supports hypothesis 6 that self-esteem and depression are significant mediators between physical activity and academic performance.

Figure 2. Path diagram for parallel mediation model

Discussion

It was aimed in this study to find out the relationships between physical activity, academic
performance, self-esteem, and depression. It also examined whether the relationship between
physical activity and academic performance is mediated by self-esteem and depression.

The major findings of the present research are (1). Physical activity, academic performance, and
self-esteem are positively related; (2) Physical activity, academic performance and depression are
negatively associated; and, (3). Self-esteem and depression play a significant mediating role in the
relationship between physical activity and academic performance [58].

Figure 2. Path diagram for parallel mediation model.

4. Discussion

It was aimed in this study to find out the relationships between physical activity, academic
performance, self-esteem, and depression. It also examined whether the relationship between physical
activity and academic performance is mediated by self-esteem and depression.

The major findings of the present research are (1). Physical activity, academic performance,
and self-esteem are positively related; (2) Physical activity, academic performance and depression are

Sustainability 2018, 10, 3633 11 of 17

negatively associated; and, (3). Self-esteem and depression play a significant mediating role in the
relationship between physical activity and academic performance [58].

Most of the studies have used physical fitness as a predictor variable for academic performance
and success [58,70,71]. But, in this study, physical activity has been used. Many studies reported
correlational findings, while connecting physical activity and academic performance [72]. It is the
strength of the present study that it has examined the mediators between these two variables.

The pattern of the findings in the present research are in line with the results reported
in Dorfman’s [58], and other studies [73–75] exhibiting positive correlation among self-esteem,
physical activity and academic performance, and negative correlations among depression, physical
activity, and academic performance. But, correlations among all of the constructs that are involved in
the present study are much lower, except for self-esteem and academic performance. The authors in
the current study have discovered physical activity as a significant but weak explaining variable for
academic performance.

A systematic review explored evidence of association between cognition and physical activity [76].
Higher cognitive activity and understanding is linked with positive psychological concepts,
like self-esteem [73]. On the other hand, depression and anxiety disorders lead to decrease in
information processing [77].

In the case of direct effects, physical activity has been identified as significantly related to
self-esteem [4] and an important factor for enhancing it. The present study shows that students
who are engaged in physical activity have a greater level of self-esteem. Physical activity has also
proved to enhance self-esteem, which could reduce depression [78]. Self-esteem has also found to
have a significant positive impact on academic performance [14,50,73]. Further, in the case of using
self-esteem as a mediating variable, we found a strong and significant positive relation between
physical activity and academic performance. This demonstrates that physical activity has a strong
relationship with academic performance with self-esteem as a mediating variable i.e., it was found to
be a strong mediator of the relationship between physical activity and the academic performance of
university students. [58].

In addition, depression was found to be a significant predictor for academic performance, which is
in line with a number of studies [56,79]. Physical activity was also found here to have a significant
negative relation with depression. This is similar to other studies [37,80]. As far as the indirect relation
is concerned, we found that physical activity has a negative effect on academic performance when
depression is the mediator. This shows that, with an improved level of physical activity, depression is
reduced and that ultimately improves academic performance [58]. Physical activity is now believed to
be established treatment against depression for adults [7].

The literature contains several studies that report the direct effects of physical activity on academic
performance. However, we reviewed literature that suggested that physical activity has a primary
effect on depression and self-esteem and an indirect effect on academic performance. More specifically,
physical activity has a positive impact on self-esteem [13] that also has positive impact on academic
performance [14]. Moreover, we also found in the literature that physical activity has a negative impact
on depression [70] and also has a negative relation with academic performance [16].

There has been great interest in studies on the potential benefits of physical activity for the
development of cognitive abilities over the last several years, especially those that strongly recommend
physical activity as an effective instrument for building psychological well-being [5,81].

Finally, physical activity in students is a public health concern [23,82,83]. Although, too much
physical activity is often discouraged, because it drains energy and affects academic concentration [84],
yet it is generally recommended to provide and promote physical activity in school settings [85].
Physical activity has been recommended as a tool for developing students’ cognitive activity by
providing intervention programs that may include motor exercise and aerobics, which have positive
effects on the brain [86]. Therefore, the importance of physical activity has to be kept in mind while

Sustainability 2018, 10, 3633 12 of 17

designing the curriculum of an educational institution, as it aims at increasing academic performance
of students by decreasing depression, stress and anxiety; and by enhancing self-esteem.

5. Strengths and Limitations

While there is considerable work on the relation between physical activity, physical fitness,
and academic performance, relatively few studies have investigated both constructs through
psychological well-being. The present work focuses on the interference of self-esteem and depression
in relation to physical activity and academic performance. It has several strengths, like the application
of Hayes modeling and bootstrapping in analysis, gives insights into the mechanism mediating the
relation between physical activity and academic performance with Pakistani students who had never
been investigated about their physical activity and academic performance in relation to psychological
well-being, and also provides a deeper understanding of mental health, academic performance,
and physical exercise. Even so, it contains some limitations. The study is cross sectional in nature.
A longitudinal design with a large sample may generate different results. Next, the instruments for
assessment that were used in this study are all subjective measures, as they are self-report measures
of physical activity, self-esteem and depression of the participants. Researchers could measure the
underlying constructs with additional objective measures. Another limitation of the study is that
the academic performance has only been considered in terms of CGPA for the last two consecutive
semesters’ grade point averages (GPAs). The other aspects of measuring academic performance
could be used. The present study is also limited to the university students. So, the findings are only
represented to that particular age. Additional studies could also be undertaken with students of
varying age. The study is only mediation analysis. Age could be taken as a moderator in order to
present the study in different way i.e., the multiplicative effect could also be included. The study can
be conducted with a sample from other developing countries to increase generalizability. To sum up,
structural equation modeling could have been used instead of the Hayes Process to perform multiple
mediation modeling. All such limitations could be taken into account in future research.

Author Contributions: The first author Sumaira Kayani and the second author T.K. equally contributed to
writing the original draft, the conceptualization, data collection, formal analysis and methodology, Saima Kayani
contributed in data collection. H.Q. contributed towards writing method section. J.W. provided resources and
administered the project. M.L.Z.S. reviewed the article.

Funding: This work is supported by the authors themselves.

Conflicts of Interest: The authors declare no conflict of interest.

Appendix A

International Physical Activity Questionnaire Short form

1. During the last 7 days, on how many days did you do vigorous physical activities like heavy
lifting, digging, aerobics, running, fast bicycling, or fast swimming?

2. How much time did you usually spend doing vigorous physical activities on one of those days?
3. During the last 7 days, on how many days did you do moderate physical activities like bicycling

at a regular pace; carrying light loads, and doubles tennis? Do not include walking.
4. How much time did you usually spend doing moderate physical activities on one of those days?
5. During the last 7 days, on how many days did you walk for at least 10 min at a time?
6. How much time did you usually spend walking on one of those days?
7. During the last 7 days, how much time did you usually spend sitting on a weekday?

Sustainability 2018, 10, 3633 13 of 17

Appendix B

Rosenberg’s Self-Esteem Scale

1. On the whole, I am satisfied with myself.
2. At times I think I am no good at all.
3. I feel that I have a number of good qualities.
4. I am able to do things as well as most other people.
5. I feel I do not have much to be proud of.
6. I certainly feel useless at times.
7. I feel that I’m a person of worth, at least on an equal plane with others.
8. I wish I could have more respect for myself.
9. All in all, I am inclined to feel that I am a failure.
10. I take a positive attitude toward myself.

Appendix C

University Stress Scale

1. Academic/coursework demands
2. Procrastination
3. University/college environment
4. Finances and money problems
5. Housing/accommodation
6. Transport
7. Mental health problems
8. Physical health problems
9. Parenting issues
10. Childcare
11. Family relationships
12. Friendships
13. Romantic relationships
14. Relationship break-down
15. Work
16. Parental expectations
17. Study/life balance
18. Discrimination
19. Sexual orientation issues
20. Language/cultural issues
21. Other demands

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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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Homepage

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Theoretical Background
Physical Activity and Academic Performance
Physical Activity and Depression
Physical Activity and Self Esteem
Academic Performance and Self Esteem
Academic Performance and Depression
Physical Activity and Academic Performance with Mediation of Depression and Self Esteem

Materials and Methods

Participants
Measures
Physical Activity
Academic Performance
Self-Esteem
University Stress Scale
Parallel Mediation Model
Procedure
Analyses

Results

Preliminary Analyses
Exploratory Factor Analyses (EFA)
Confirmatory Factor Analysis (CFA)
Descriptive Statistics
Major Analysis
Direct Effects
Indirect Effects
Total Effect

Discussion

Strengths and Limitations

References

International Journal of

Environmental Research

and Public Health

Article

Associations between Profiles of Self-Esteem and
Achievement Goals and the Protection of Self-Worth
in University Students

María del Mar Ferradás 1,* , Carlos Freire 1 , José Carlos Núñez 2 and Bibiana Regueiro 1
1 Department of Psychology, University of A Coruña, 15071 A Coruña, Spain;

carlos.freire.rodriguez@udc.es (C.F.); bibiana.regueiro@udc.es (B.R.)
2 Faculty of Psychology, University of Oviedo, 33003 Oviedo, Asturias, Spain; jcarlosn@uniovi.es
* Correspondence: mar.ferradasc@udc.es; Tel.: +34-981-167-000-1865

Received: 3 June 2019; Accepted: 21 June 2019; Published: 23 June 2019
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Abstract: The high demands of academia and the fear of failure lead some university students
to prioritize defending their personal worth through the use of complex strategies such as
self-handicapping or defensive pessimism. Adopting a person-centered approach, this study
established two objectives: First, to analyze the conformation of different motivational profiles based
on the combination of self-esteem and achievement goals (learning, performance approach, and
performance avoidance); and second, to determine if the identified profiles differ from one another in
the use of self-handicapping and defensive pessimism. A total of 1028 university students participated
in the research. Four motivational profiles were obtained: (a) High self-esteem, low learning goals,
high performance approach goals, and high performance avoidance goals; (b) high self-esteem, high
learning goals, low performance approach goals, and low performance avoidance goals; (c) low
self-esteem, low learning goals, high performance approach goals, and high performance avoidance
goals; and (d) low self-esteem, high learning goals, high performance approach goals, and medium
performance avoidance goals. Profiles (c) and (d) were significantly related to self-handicapping
and defensive pessimism, respectively. These results suggest that students with low self-esteem are
more vulnerable to self-protection strategies. Additionally, under self-handicapping and defensive
pessimism, the achievement goals are slightly different.

Keywords: self-handicapping; defensive pessimism; self-esteem; achievement goals; motivational profiles

1. Introduction

In line with the challenges posed by the current social emphasis on self-directed and lifelong
learning [1,2], university students are subject to high academic requirements. Some students manage
this challenge with a clear orientation to success, indicating a high degree of motivation and enthusiasm
for learning [3]. For other students, this demanding context poses a significant threat because they
perceive academic failure as evidence of low self-worth [4]. Under these conditions, strategies
such as self-handicapping or defensive pessimism are powerful incentives to protect their feelings
of competence.

In this study, the role of self-esteem and achievement goals as motivational determinants of
self-handicapping and defensive pessimism strategies is analyzed. Researchers have analyzed the
role played by both motivational factors individually, but not in a combined manner. Adopting a
personal-centered approach, we sought to identify different students’ motivational profiles based
on their level of self-esteem and the use of three types of achievement goals (learning, performance
avoidance, and performance approach). Specifically, the method used is intended to determine which
student profiles are motivationally more vulnerable to self-worth protection strategies.

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Int. J. Environ. Res. Public Health 2019, 16, 2218 2 of 20

1.1. Self-Protection Strategies: Self-Handicapping and Defensive Pessimism

Self-handicapping constitutes an anticipatory mechanism through which the student sabotages
her or his own probabilities of success by creating an obstacle, real or fictitious, that serves as
an alibi against an expected failure [5]. This strategy, therefore, outsources the reasons for a
hypothetical low performance to focus attention on the handicap rather than on personal incompetence.
Self-handicapping can be effective in the short term because it serves to protect the student’s sense
of self-worth [6]; however, recurrent use often leads to academic damage (e.g., low performance,
dropout) [7], which result in fractured feelings of personal worth [8].

Similarly, through the strategy of defensive pessimism, the student, despite his or her previously
good academic record, establishes excessively low expectations of achievement that he or she perceives
as safe in response to the anxiety generated by a failure [9]. Paradoxically, the low expectations are
often the prelude to hard, generally successful work and aimed at avoiding hypothetical failure [10].
Therefore, in the short term, defensive pessimism usually contributes to academic success [11]; however,
in the long run, it entails significant detriments to emotional health [12].

One of the topics that has raised the most interest in the research on self-handicapping and
defensive pessimism is determining the motivational factors underlying these strategies. Among these
factors, according to the literature, are self-esteem and achievement goals.

1.2. Self-Esteem and Self-Protection Strategies

Self-esteem is a positive or negative attitude that reflects the degree to which the person
feels self-appreciation, -value, and -satisfaction [13,14], significantly affecting the involvement and
achievement of students [15,16]. The findings of the studies that have analyzed the relationship between
self-esteem and self-protection strategies have been inconsistent. Some works have demonstrated
a negative relationship between self-handicapping and self-esteem [17,18], whereas others have
asserted that self-handicapping is positively related to self-esteem [19,20]. Another suggestion was
that people with low self-esteem would use self-handicapping to protect their image and people with
high self-esteem to enhance their image [21]. Along the same lines, some studies have concluded that
defensive pessimism underlies negative self-reported thoughts [22,23]. However, Ferradás, Freire,
Valle, and Regueiro [24] identified profiles of defensive pessimistic university students with low
and high self-esteem. Consistent with this ambiguity, Yamawaki, Tschanz, and Feick [25] raised
the possibility that defensive pessimistic students show high self-esteem in some situations and low
self-esteem in other situations.

1.3. Achievement Goals and Self-Protection Strategies

Goals are generally conceptualized as cognitive representations of specific desired outcomes
or end states that the individual is committed to either approach or avoid; therefore, they guide
behaviors, emotions, and cognitions [26,27]. Accordingly, from the perspective of the achievement goal
theory [28,29], the achievement goals represent a future-focused cognitive representation of the results
or states that govern the behaviors of involvement and achievement of students in academic tasks [30].

Central to achievement goal theory is the definition of competence; that is, the standard or
referent used to determine if an individual is doing well or poorly [31]. Traditionally, researchers
have distinguished two major opposite views of competence in achievement situations, and such a
distinction has led to differentiation between learning goals and performance goals. Learning goals
characterize students who believe that competence is a malleable quality that can be developed and
cultivated. As a result, this type of student is fundamentally focused on their own competence [32];
thus, they are oriented toward achieving a broad range of intrapersonal standards [33], for example,
fulfilling their interest and curiosity, improving their competencies and doing better than one has done
in past situations, learning as much as possible, mastering the requirements of the task, and coping
with personal challenging activities.

Int. J. Environ. Res. Public Health 2019, 16, 2218 3 of 20

By contrast, students who adopt performance goals believe that competence is a fixed trait
(something that you have or you do not have); thus, their principal focus is on comparing their
competence with others [34]. Performance goals can be subdivided into performance avoidance goals
and performance approach goals. Students pursuing performance avoidance goals seek to avoid
doing worse than others (appearance criterion), avoid being negatively judged by others (normative
criterion), or both (evaluative criterion). Students with performance approach goals are oriented
toward outperforming other students (normative criterion), demonstrating competence to others
(appearance criterion), or both (evaluative criterion) [33].

In general, self-handicapping and defensive pessimism are associated with performance goals.
In this sense, some studies have affirmed that both strategies are fundamentally motivated by performance
avoidance goals [17,35,36]. However, other works have concluded that performance approach goals
also constitute a relevant antecedent of self-handicapping and defensive pessimism [37–41]. Regarding
learning goals, the adoption of this type of goal has been observed to reduce the tendency to
self-handicap [17,42]. Evidence has also been presented that learning goals are negatively related to
defensive pessimism [25,37], although some works have found some combined use of learning and
performance goals in defensive pessimistic students [38,43].

1.4. This Study

Based on our review of the literature, self-esteem and achievement goals emerge as powerful
motivational determinants of self-handicapping and defensive pessimism strategies in academic settings.
To date, the work carried out in this field has focused on analyzing the role played by self-esteem and
achievement goals individually. However, and notably, academic motivation, as suggested by Pintrich
and De Groot [44], is the result of combining three motivational components: A value component
that includes students’ goals in academic tasks (e.g., achievement goals); an expectancy component,
comprising students’ perceptions, judgements, and feelings about themselves (e.g., self-esteem,
self-efficacy); and an affective component, involving students’ emotional reactions to the academic
tasks (e.g., satisfaction, anxiety, fear of failure).

Accordingly, the approach adopted by researchers (a variable-centered approach, i.e., an approach
focused on the effect that each variable, or at least each motivational component, exercises independently
on other variables) had not accurately described this complex motivational reality. Therefore, studies
that adopt a person-centered approach are needed, because they would make it possible to know
how the student integrates different variables into an individual motivational profile [45]. Unlike
variable-centered approaches, which assume that the population is homogeneous with respect to
how the predictors operate on the outcomes, person-centered approaches assume that the population
is heterogeneous with respect to how the predictors operate on the outcomes; thus, they allow the
identification of subtypes of individuals who share particular attributes (having similar values in a
set of variables) that differ from other individuals [46]. In other words, person-centered approaches
identify “how individuals may be classified based on a set of variables, thus generating typologies
of people according to their shared responses across multiple markers” [47] (p. 289), and, as a result,
allow for the identification of specific combinations of variables that occur naturally in a sample.
In this study, profile typologies based on different functioning levels of achievement goals (value
component of academic motivation) and self-esteem (expectancy component of academic motivation)
markers might be useful for identifying subgroups of students that might be particularly susceptible to
self-handicapping and/or defensive pessimism strategies.

In the academic field, a relatively extensive body of research has found that students, rather than
adopting a single type of goals, tend to adopt several goals [48,49]. This person-centered approach has
revealed a wide range of possible combinations (i.e., profiles) of achievement goals in the university
context, based on the conjugation of learning goals and performance goals [50,51]. The identified
profiles of achievement goals have been associated with different cognitive, emotional, and achievement
outcomes. In particular, those groups of multiple goals that include high levels of learning goals

Int. J. Environ. Res. Public Health 2019, 16, 2218 4 of 20

(regardless of the level of each performance goals) present highly adaptive academic consequences,
such as high engagement and performance. By contrast, those profiles of multiple goals that combine
high levels of performance goals with low learning goals, are observed to be more vulnerable, and
the students with those profiles have displayed more anxiety and lower control beliefs about their
academic achievement [49,52–55].

However, in our review of the literature, no precedents have accounted for self-esteem in the study
of students’ multiple goal profiles. This approach seems pertinent because, in motivational terms,
defined achievement goals will be of little use if the student feels no self-worth. Accordingly, our study
attempts to identify motivational profiles of university students based on the combination of self-esteem
and the main achievement goals (i.e., learning, performance avoidance, and performance approach).

In general, the studies that have analyzed (from a variable-centered approach) the relationship
between self-esteem and achievement goals have linked self-esteem positively with learning goals
and negatively with performance avoidance goals [56–59]. The self-esteem–performance approach
goals relationship is even more controversial because positive [57,59] and negative [58,60] evidence
has been presented regarding relationship between these two elements. Furthermore, the few studies
that have considered the multiple goal perspective confirmed that self-esteem is higher in those
profiles that excel due to high orientation to learning goals, and those that combine learning goals and
performance approach goals. By contrast, the lowest self-esteem would be found in those profiles with
a predominance of performance avoidance goals [61,62].

1.5. Aims of the Study

The present study had two main objectives: (1) To analyze the degree to which different
motivational profiles are conformed to, based on the combination of self-esteem and achievement
goals; and (2) to determine if the identified profiles differ in the use of self-handicapping and
defensive pessimism.

Regarding the first objective, the literature offers no precedent about the existence of combined
profiles of self-esteem and achievement goals. However, we have expectations regarding some results
according to the revised works on students’ multiple goal profiles and those studies that have analyzed
the relationship between self-esteem and achievement goals. As aforementioned, the research on
achievement goals has identified a broad spectrum of multiple goal profiles. In this sense, notable
differences have been observed across the studies regarding how and how many profiles endorse,
with findings ranging from four to seven profiles [49,53–55,63]. Based on the most common profiles
identified by the multiple goal literature, we expected to identify four profiles in our study: (a) High
in all achievement goals (learning, performance avoidance, and performance approach); (b) high
learning goals and low performance goals (both avoidance and approach); (c) high learning goals,
low performance avoidance goals, and high performance approach goals; and (d) low learning
goals, high performance avoidance goals, and high performance approach goals. Other students’
multiple goal profiles (e.g., a profile of high learning goals, high performance avoidance goals, and low
performance approach goals; a profile of low learning goals, low performance avoidance goals, and
high performance approach goals; a profile of low learning goals, high performance avoidance goals,
and low performance approach goals) are not expected, given their infrequent occurrence across the
studies that have adopted a person-centered approach [64].

Likewise, based on the revised studies that have analyzed the relationship between self-esteem
and multiple goal profiles by using a variable-centered approach, we plausibly hypothesized that
the profiles of students salient in performance avoidance goals would show low levels of self-esteem.
In effect, the students who adopt performance avoidance goals typically feel that they are unworthy
people [4,65]. By contrast, the students who exhibit a predominance of learning goals and performance
approach goals have been observed to feel more self-confident, either by attempting to improve her or
his ability or by demonstrating their competence to others [31,34]. Therefore, we expected that the
profile that combines high learning goals with low levels of both performance goals, and the profile

Int. J. Environ. Res. Public Health 2019, 16, 2218 5 of 20

that comprises high learning goals, low performance avoidance goals, and high performance approach
goals would show high self-esteem. The graphic representation of the hypothesized motivational
profiles is shown in Figure 1.

Int. J. Environ. Res. Public Health 2019, 16, x 5 of 21

performance approach goals would show high self-esteem. The graphic representation of the
hypothesized motivational profiles is shown in Figure 1.

Figure 1. Hypothesized motivational profiles and expected associations to self-protection strategies.

As a second objective, we aimed to determine which motivational profiles relate to a greater
extent to self-handicapping and defensive pessimism. Regarding self-handicapping, some of the
reviewed studies have found that these strategies are more prevalent in students with profiles that
combine a high use of the performance avoidance and performance approach goals [38,40,41].
Additionally, learning goals were observed to buffer the positive relationship between performance
goals and self-handicapping [41,66].

In this study, the distinction between behavioral and claimed self-handicapping was considered
because this typological taxonomy is well established in the field [67]. Behavioral self-handicapping
refers to any direct action (e.g., procrastinating, over involvement in multiple tasks simultaneously)
undertaken to serve the purpose of obstruction. Claimed self-handicapping entails the verbalization
of an impediment (e.g., fatigue) without performing a self-limiting external behavior. That distinction
is important because behavioral self-handicapping was observed to be more maladaptive than
claimed self-handicapping in academic settings [68] because the former always compromises
achievement, whereas claimed self-handicapping does not necessarily compromise achievement [69].
According to our review of the literature, the only precedent has studied the relationship between
multiple goal profiles and both types of self-handicapping [38] and found that the students who
combine low learning goals with high performance avoidance goals and high performance approach
goals tend to use claimed and behavioral self-handicapping to a high degree. Based on this finding,
we expected that the hypothesized profile of low learning goals, high performance avoidance goals,
and high performance approach goals would use both types of self-handicapping (behavioral and
claimed) to a greater degree.

Figure 1. Hypothesized motivational profiles and expected associations to self-protection strategies.

As a second objective, we aimed to determine which motivational profiles relate to a greater extent
to self-handicapping and defensive pessimism. Regarding self-handicapping, some of the reviewed
studies have found that these strategies are more prevalent in students with profiles that combine
a high use of the performance avoidance and performance approach goals [38,40,41]. Additionally,
learning goals were observed to buffer the positive relationship between performance goals and
self-handicapping [41,66].

In this study, the distinction between behavioral and claimed self-handicapping was considered
because this typological taxonomy is well established in the field [67]. Behavioral self-handicapping
refers to any direct action (e.g., procrastinating, over involvement in multiple tasks simultaneously)
undertaken to serve the purpose of obstruction. Claimed self-handicapping entails the verbalization of
an impediment (e.g., fatigue) without performing a self-limiting external behavior. That distinction is
important because behavioral self-handicapping was observed to be more maladaptive than claimed
self-handicapping in academic settings [68] because the former always compromises achievement,
whereas claimed self-handicapping does not necessarily compromise achievement [69]. According
to our review of the literature, the only precedent has studied the relationship between multiple
goal profiles and both types of self-handicapping [38] and found that the students who combine low
learning goals with high performance avoidance goals and high performance approach goals tend to
use claimed and behavioral self-handicapping to a high degree. Based on this finding, we expected
that the hypothesized profile of low learning goals, high performance avoidance goals, and high
performance approach goals would use both types of self-handicapping (behavioral and claimed) to a
greater degree.

Notably, although the relationship between self-esteem and self-handicapping has remained
controversial [17,20], a plausible consideration is that self-handicapping would be less likely in people
with high self-esteem. As Berglas and Jones [70] stated, no one would expect that “people who know
they have the talent and resources to master life’s challenges (…) to hide behind the attributional
shield of self-handicapping” (p. 406). Accordingly, we hypothesized that the motivational profile

Int. J. Environ. Res. Public Health 2019, 16, 2218 6 of 20

of low self-esteem, low learning goals, high performance avoidance goals, and high performance
approach goals would be the group that would more frequently resort to behavioral and claimed
self-handicapping (Figure 1).

Regarding defensive pessimism, this self-protective strategy is based on a double intention
to succeed and to avoid failure [9,37]. Accordingly, some of the revised works have found that
defensive pessimism is positively related to both performance avoidance and performance approach
goals [25,37]. Similarly, other studies have found a greater use of defensive pessimism in students who
adopt both performance goals and learning goals [38,43]. Likewise, a negative relationship between
defensive pessimism and self-esteem has been demonstrated [22,23]. Based on these considerations,
we hypothesized that the motivational profile of low self-esteem and high use of the three achievement
goals (learning, performance avoidance, and performance approach) would use defensive pessimism
to a greater degree than the other profiles (Figure 1).

2. Materials and Methods

2.1. Participants and Procedure

A total of 1087 university students from University of A Coruña (Spain) were selected by
convenience sampling to participate in the study. Of these students, 56 cases were excluded because
they presented a high rate of missing data (higher than 20%). Another 27 cases showed a lower rate
of missing data, which were treated using the full information maximum likelihood (FIML) method
through the MPlus 7.11 program [71]. In addition, three other cases presented as outliers (Mahalanobis
distance method) when exceeding the critical value χ

2
= 5.5 (gl = 7, p < 0.001); thus, they were

also discarded.
The final composition of the participating sample was 1028 students aged 18 to 25 years

(Mage = 21.36, SDage = 3.81): 887 (86.3%) were women and 141 (13.7%) were men. Of the participants,
69.9% were enrolled in health sciences degrees (nursing, physiotherapy, podiatry) and 31.1% in
education sciences (early childhood education, primary education, social education, and speech
therapy). Regarding grade levels, 37.2%, 32.5%, and 30.3% of the students were in their first, second,
and third years, respectively.

The study was conducted according to the guidelines of the Ethics Committee at the University
of A Coruña (ethical code: 03/04/2018) [72], with written informed consent from all participants,
as established in the Helsinki Declaration. The data for the research were collected in the classrooms
where the participants were taught, within the class schedule, and in a single session without a time
limit. The participants were asked for their impartial collaboration, and the researchers indicated the
objectives of the research and guaranteed the anonymity and confidentiality of participants’ data.

2.2. Instruments

Self-esteem. The validated version of the Rosenberg self-esteem scale in Spanish was used [73].
The instrument comprises 10 items that measure feelings of self-appreciation and self-acceptance.
Five items are positively worded (e.g., “In general, I am satisfied with myself”) and the other five are
negatively worded (e.g., “All in all, I am inclined to feel that I am a failure”). Participants’ responses
were evaluated using a Likert scale (1 = in total disagreement to 5 = in total agreement). The reliability
obtained by the scale in this study was α = 0.88.

Achievement goals. The Spanish adaptation of the Skaalvik goal orientation scale [74] was used to
analyze the following goals: Learning goals (six items; e.g., “It’s important for me to learn new things
in class;” α = 0.79) are conceptualized in terms of task orientation, that is, the students’ desire to learn
and gain new knowledge [33]; performance approach goals (five items, e.g., “I try to get better grades
than my classmates;” α = 0.85) represent the normative criterion (i.e., demonstrate higher abilities
than others) of these goals [33]; and performance avoidance goals (six items; e.g., “When I answer
incorrectly in class, I worry about what my classmates think of me,” α = 0.80) represent the appearance

Int. J. Environ. Res. Public Health 2019, 16, 2218 7 of 20

criterion (i.e., avoid being negatively judged by others) of these goals [33]. Participants’ responses
were recorded on a Likert scale (1 = never to 5 = always).

Self-handicapping. To evaluate self-handicapping, the Spanish adaptation [38] of the self-handicapping
scale [75] was used. The instrument provides two types of self-handicapping: Behavioral
self-handicapping, which evaluates active handicaps (nine items; e.g., “I tend not to attempt tasks;
that way I have an excuse if I don’t do as well as expected;” α = 0.84), and claimed self-handicapping,
which measures verbal handicaps (16 items; e.g., “I tell others that I am more exhausted than I really
am when I have to do homework or exams, so if I don’t do as well as expected, I can say that that is the
reason;” α = 0.90).

Defensive-pessimism. Defensive pessimism was evaluated using the defensive pessimism
questionnaire [76], which comprises 12 items (e.g., “Thinking about what can go wrong helps me
prepare”), whose reliability in our study was α = 0.89. For both instruments, participants’ responses
were recorded on a Likert scale (1 = never to 5 = always).

2.3. Data Analysis

To determine the latent categorical variables that allow for grouping the participants in classes
(profiles) according to their self-esteem characteristics and the three types of achievement goals
(learning, performance approach, and performance avoidance), a latent profile analysis (LPA) was
performed [77]. Using the MPlus program, version 7.11 [71], we determined, from among a finite set
of models, which model best fit the data, adding successive latent classes to the target model.

The optimal number of classes was determined by considering the Akaike information criterion
(AIC), the Schwarz Bayesian information criterion (BIC), the BIC adjusted for the sample size (SSA-BIC),
the adjusted Lo–Mendell–Rubin [78] maximum likelihood ratio test (LMRT), the parametric bootstrap
likelihood ratio test (PBLRT), the size of the sample for each subgroup, and the multivariate adjustment
tests for asymmetry and kurtosis. The p value associated with the LMRT and the PBLRT indicates
whether the solution with more (p < 0.05) or fewer classes (p > 0.05) is the solution that best fits the data.
The AIC, BIC, and SSA-BIC indices have a descriptive character, with the lowest values indicating a
better model fit. These criteria should complement the information provided by the LMRT and the
PBLRT, but in no case should it be replaced, and the latter are the criteria that allow a final decision
to be made. Furthermore, classes that contain less than 5% of the sample are considered spurious,
a condition indicative of the excessive extraction of profiles [79]. Likewise, we calculated a posteriori
probabilities and the entropy statistic to determine the classifying accuracy of the selected model.
The value of the entropy oscillates between zero and one, and the values closest to one represent the
greatest classifying accuracy.

Finally, a MANOVA was performed to determine the differences between the profiles of self-esteem
and achievement goals (criterion variable) in the use of self-handicapping and defensive pessimism
(dependent variables). Therefore, the probabilities of the participants belonging to the different classes
of the selected model were saved and then used in the MANOVA analysis. The effect size was
determined by the partial eta squared and d statistics [80]: Null effect: ηp2 < 0.01 (d < 0.09); small: ηp

2 = 0.01 to ηp2 = 0.058 (d = 0.10–d = 0.49); medium: ηp2 = 0.059 to ηp2 = 0.137 (d = 0.50–d = 0.79);
and large: ηp2 ≥ 0.138 (d ≥ 0.80).

3. Results

Table 1 shows the descriptive statistics and the Pearson correlations between the variables.
The correlation matrix shows that, with the exception of the correlation between claimed
self-handicapping and defensive pessimism, all correlations are statistically significant (p < 0.001). From a statistical perspective, the results provided by Bartlett’s sphericity test show that the variables are sufficiently intercorrelated (χ2(6) = 820.44; p < 0.001), a critical condition for subsequent multivariate analyses. Likewise, the asymmetry and kurtosis data indicate that the variables have a normal distribution.

Int. J. Environ. Res. Public Health 2019, 16, 2218 8 of 20

Table 1. Descriptions and correlations between self-esteem, achievement goals, and self-protection
strategies (N = 1028).

1 2 3 4 5 6 7

1. SE −
2. LG −0.12 * −

3. PApG −0.46 * −0.37 * −
4. PAvG −0.22 * −0.18 * 0.56 * −
5. BSH −0.31 * −0.31 * 0.21 * 0.09 * −
6. CSH −0.16 * −0.31 * 0.22 * 0.09 * 0.64 * −
7. DP −0.59 * 0.28 * 0.11 * 0.43 * 0.09 * −0.01 −

M 3.41 3.24 3.24 3.30 2.04 1.94 2.35
SD 0.52 1.00 0.87 0.93 0.77 0.76 0.87

Asymmetry −0.39 −0.45 −0.60 −0.51 0.96 0.88 0.83
Kurtosis −1.41 −0.65 0.05 −0.61 −0.05 −0.27 −0.49

Note: SE = self-esteem; LG = learning goals; PAvG = performance avoidance goals; PApG = performance approach
goals; BSH = behavioral self-handicapping; CSH = claimed self-handicapping; DP = defensive pessimism. All
measurement scales range from 1 to 5, where the highest scores reflect a higher level of self-esteem, achievement
goals and self-protection strategies; * p < 0.001.

3.1. Identification of Profiles of Self-Esteem and Achievement Goals

The adjustment of several models of latent profiles (models from two to six classes) has been
analyzed. The models were adjusted assuming that the variances could differ between the indicators
within each group, but it was specified as a restriction that they were equal between the groups.
Likewise, the independence between the indicators was imposed as a restriction, that is, within each
group and between groups.

Table 2 shows the results of the model adjustment. The adjustment of the six-class models was
stopped for several reasons: (a) The values of the AIC, BIC, and SSA-BIC statistics were higher in the
model of six classes than in the model of five; (b) the LRT and the PBLRT of the six-class model were
not statistically significant (p > 0.05, in both cases), which indicates that the six-class model does not
have a better fit than the five-class model; and (c) in the six-class model, a group of participants with a
representation of less than 5% of the total sample was obtained, and in the five-class model, all groups
exceeded that percentage. Only the entropy value and the multivariate asymmetry and kurtosis tests
suggested that both models presented a similar model fit.

Table 2. Statistics for identifying model adjustment of latent classes and classifying accuracy.

Models of Profiles of Self-Esteem and Achievement Goals

Two Classes Three Classes Four Classes Five Classes Six Classes

AIC 8395.682 7625.779 6953.791 6668.335 6678.335
BIC 8459.842 7714616 7067.304 6806.526 6841.202

SSA-BIC 8418.552 7657.446 6994254 6717.595 6736.391
Entropy 0.999 0.949 0.973 0.928 0.935

Number of groups with n ≤ 5% 0 0 0 0 1
LMRT 1471.051 ** 758.042 ** 1069.317 ** 287.174 * 0.000
PBLRT 1513.473 ** 779.902 ** 1100.153 ** 295.455 ** 0.000

Multivariate asymmetric adjustment test 0.000 0.000 0.020 0.310 0.320
Multivariate kurtosis adjustment test 0.160 0.650 0.030 0.320 0.250

Note: The models were adjusted assuming that the variances could differ between the indicators within each
group, but it was specified as a restriction that they were equal between the groups. Likewise, the independence
between the indicators was imposed as a restriction, both within each group and between groups. AIC = Akaike
information criterion; BIC = Schwarz Bayesian information criterion; SSA-BIC = BIC adjusted for the sample size;
LMRT = adjusted Lo–Mendell–Rubin maximum likelihood ratio test; PBLRT = parametric bootstrap likelihood
ratio test; * p < 0.01; ** p < 0.001

Although, as a whole, the statistical data supported a better fit of the five-class model compared
with that of the six-class model, when compared with the four-class model, we observed that the
five-class model had a similar but slightly lower AIC, BIC, and SSA-BIC values than the four-class

Int. J. Environ. Res. Public Health 2019, 16, 2218 9 of 20

model. The LRT and the PBLRT showed statistically significant values in both models. However, the
entropy value was higher in the four-class model (0.973) than in the five-class model (0.928), which
indicates a lower classifying accuracy of the latter. Additionally, when comparing in both models the a
posteriori coefficients of probabilities of each subject belonging to a given class, we observed that the
coefficients of the four-class model were closer to 100%, indicative of a very high classifying accuracy.
Table 3 shows the classifying accuracy of the four-class model and the number of subjects (total sample
and by sex) that comprise each class, that is, in absolute (n) and relative (%) terms. Each row of Table 3
considers the coefficients of the a posteriori probabilities of each subject belonging to a given class.
The coefficients associated with the groups to which the participants have been assigned are shown
along the principal diagonal of the table.

Table 3. Characterization of the latent profiles and classifying accuracy of the individuals in each profile.

Latent Profiles
n (%)

ngender (%)

1 2 3 4 Female Male

1. HSE/LLG/HPAvG/HPApG 0.967 0.033 0.000 0.000 153 (14.88) 136 (88.9) 17 (11.1)
2. HSE/HLG/LPAvG/LPApG 0.015 0.985 0.000 0.000 520 (50.58) 449 (86.4) 71 (13.6)
3. LSE/LLG/HPAvG/HPApG 0.000 0.000 0.991 0.009 124 (12.07) 95 (76.6) 29 (23.4)
4. LSE/HLG/MPAvG/HPApG 0.000 0.000 0.003 0.997 251 (22.47) 207 (89.6) 24 (10.4)

Note: HSE/LLG/HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance avoidance
goals, and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high self-esteem, high learning
goals, low performance avoidance goals, and low performance approach goals; LSE/LLG/HPAvG/HPApG: Profile
of low self-esteem, low learning goals, high performance avoidance goals, and high performance approach goals;
LSE/HLG/MPAvG/HPApG: Profile of low self-esteem, high learning goals, medium performance avoidance goals,
and high performance approach goals. The coefficients associated with the groups to which the participants have
been assigned are shown in bold.

Likewise, the MANOVA results showed statistically significant differences among the four classes
in the criterion variables: Self-esteem (F(3,1024) = 4370.71; p < 0.001; ηp2 = 0.928), learning goals (F(3,1024) = 835.31; p < 0.001; ηp2 = 0.710), performance avoidance goals (F(3,1024) = 219.02; p < 0.001; ηp

2 = 0.391), and performance approach goals (F(3,1023) = 534.88; p < 0.001; ηp2 = 0.610). The effect size was large in all cases. Additionally, in the four-class model and the five-class model, the composition of each of the resulting profiles was analyzed based on conceptual criteria, demonstrating that in the five-class model, two practically identical motivational profiles were obtained. Therefore, the four-class model was better suited to the principle of parsimony that should govern the choice of conglomerates [81].

In summary, based on the statistical data related to the adjustment of models, according to the
results of the MANOVA conducted to analyze the contribution of each of the variables making up the
profiles to the ability of differentiating between classes, and also according to the conceptual criteria,
the four-class model was the most appropriate.

3.2. Description of Profiles of Self-Esteem and Achievement Goals

The average scores of the subjects belonging to the latent classes of the chosen model are presented
in Table 4.

Int. J. Environ. Res. Public Health 2019, 16, 2218 10 of 20

Table 4. Description of latent profiles (means, standard errors, and confidence intervals).

M SE
Confidence Intervals

Lower 5% Upper 5%

HSE/LLG/HPAvG/HPApG (n = 153)
Self-esteem 3.99 0.02 3.96 4.01

Learning goals 2.21 0.06 2.11 2.28
Performance avoidance goals 3.94 0.04 3.87 4.08
Performance approach goals 3.98 0.03 3.92 4.10

HSE/HLG/LPAvG/LPApG (n = 520)
Self-esteem 3.71 0.01 3.69 3.72

Learning goals 3.54 0.03 3.49 3.58
Performance avoidance goals 2.78 0.04 2.71 2.83
Performance approach goals 2.59 0.03 2.53 2.63

LSE/LLG/HPAvG/HPApG (n = 124)
Self-esteem 2.75 0.01 2.72 2.77

Learning goals 1.62 0.03 1.52 1.72
Performance avoidance goals 4.17 0.03 4.05 4.29
Performance approach goals 3.96 0.03 3.86 4.07

LSE/HLG/MPAvG/HPApG (n = 231)
Self-esteem 2.74 0.01 2.72 2.76

Learning goals 4.15 0.03 4.09 4.23
Performance avoidance goals 3.33 0.03 3.24 3.42
Performance approach goals 4.10 0.04 4.02 4.17

Note: HSE/LLG/HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance avoidance
goals, and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high self-esteem, high learning
goals, low performance avoidance goals, and low performance approach goals; LSE/LLG/HPAvG/HPApG: Profile
of low self-esteem, low learning goals, high performance avoidance goals, and high performance approach goals;
LSE/HLG/MPAvG/HPApG: Profile of low self-esteem, high learning goals, medium performance avoidance goals,
and high performance approach goals. All measurement scales range from 1 to 5, where the highest scores reflect a
higher level of self-esteem and achievement goals.

Two profiles with high self-esteem and two profiles with low self-esteem were obtained, each of
these profiles differed in the level of use of achievement goals. Thus, one group (n = 153; 14.88%) could
be characterized by presenting high self-esteem combined with a low orientation to learning goals
and a high orientation to performance goals (avoidance and approach) . The second group (n = 520;
50.58%) also presents high self-esteem, although it is combined with a high orientation to learning
goals and a low orientation to performance avoidance and performance approach goals. The third
group (n = 124; 12.07%) presents low self-esteem combined with a low use of learning goals and a high
orientation to performance avoidance and performance approach goals . The fourth group (n = 231;
22.47%) comprises participants with low self-esteem and a high orientation to learning goals, moderate
performance avoidance goals, and high performance approach goals. The graphic representation of
these profiles is shown in Figure 2.

Int. J. Environ. Res. Public Health 2019, 16, 2218 11 of 20

Figure 2. Graphical representation of profiles of self-esteem and achievement goals. Note.
HSE/LLG/HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance
avoidance goals, and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high
self-esteem, high learning goals, low performance avoidance goals, and low performance approach
goals; LSE/LLG/HPAvG/HPApG: Profile of low self-esteem, low learning goals, high performance
avoidance goals, and high performance approach goals; LSE/HLG/MPAvG/HPApG: Profile of low
self-esteem, high learning goals, medium performance avoidance goals, and high performance
approach goals.

3.3. Relationship between Profiles of Self-Esteem/Achievement Goals and Self-Protection
Strategies

Table 5. Descriptive statistics (means and standard deviations) corresponding to profiles of self-
esteem and achievement goals in self-handicapping and defensive pessimism.

Profiles of Self-Esteem and Achievement Goals
BSH CSH DP

M (SD) M (SD) M (SD)

1. HSE/LLG/HPAvG/HPApG
Females 1.89 (0.59) 1.84 (0.66) 1.94 (0.50)
Males 1.95 (0.47) 1.94 (0.71) 1.91 (0.44)
Total 1.90 (0.57) 1.85 (0.66) 1.94 (0.50)

2. HSE/HLG/LPAvG/LPApG
Females 1.96 (0.65) 1.85 (0.62) 1.97 (0.50)
Males 1.82 (0.57) 1.77 (0.62) 1.94 (0.51)
Total 1.94 (0.64) 1.84 (0.62) 1.96 (0.50)

3. LSE/LLG/HPAvG/HPApG
Females 2.97 (1.05) 3.12 (0.93) 2.47 (1.11)
Males 3.36 (0.69) 2.11 (1.03) 2.11 (0.67)
Total 3.06 (0.99) 2.88 (1.05) 2.39 (1.04)

4. LSE/HLG/MPAvG/HPApG
Females 1.81 (0.55) 1.68 (0.53) 3.46 (0.62)
Males 1.91 (0.63) 1.87 (0.80) 3.44 (0.66)
Total 1.82 (0.56) 1.70 (0.56) 3.46 (0.62)

Note. HSE/LLG/HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance
avoidance goals, and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high
self-esteem, high learning goals, low performance avoidance goals, and low performance approach
goals; LSE/LLG/HPAvG/HPApG: Profile of low self-esteem, low learning goals, high performance
avoidance goals, and high performance approach goals; LSE/HLG/MPAvG/HPApG: profile of low
self-esteem, high learning goals, medium performance avoidance goals, and high performance
approach goals; BSH = behavioral self-handicapping; CSH = claimed self-handicapping; DP =
defensive pessimism; All measurement scales range from 1 to 5, where the highest scores reflect the
highest level of self-esteem, achievement goals and self-protection strategies.

Figure 2. Graphical representation of profiles of self-esteem and achievement goals. Note. HSE/LLG/
HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance avoidance goals,
and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high self-esteem, high
learning goals, low performance avoidance goals, and low performance approach goals; LSE/LLG/
HPAvG/HPApG: Profile of low self-esteem, low learning goals, high performance avoidance goals, and
high performance approach goals; LSE/HLG/MPAvG/HPApG: Profile of low self-esteem, high learning
goals, medium performance avoidance goals, and high performance approach goals.

3.3. Relationship between Profiles of Self-Esteem/Achievement Goals and Self-Protection Strategies

At the multivariate level, the profiles of self-esteem and achievement goals and the three
self-protection strategies are significantly related (λWilks = 0.348, F(9,2487) = 149.91, p < 0.001, ηp

2 = 0.296). At the univariate level, the profiles are significantly associated with the three external
variables as follows: Behavioral self-handicapping (F(3,1024) = 111.18, p < 0.001, ηp2 = 0.246), claimed self-handicapping (F(3,1024) = 93.87, p < 0.001, ηp2 = 0.216), and defensive pessimism (F(3,1024) = 341.52, p < 0.001, ηp2 = 0.500). The effect size is large in all three cases.

Table 5 shows the descriptive statistics obtained from the MANOVA. As shown, in the cases of
behavioral self-handicapping and claimed self-handicapping, the profile tending to significantly use
both strategies is that which combines low self-esteem with low learning goals, high performance
avoidance goals, and high performance approach goals. The post hoc contrasts (Games–Howell) reveal
that the differences with respect to the other profiles are, in addition to being statistically significant,
large (between d = 1.51 and d = 1.86) in all cases. The profile characterized by low self-esteem,
low learning goals, high performance avoidance goals, and high performance approach goals also
shows a significantly higher use of defensive pessimism than the two profiles with high self-esteem
(i.e., the profile that shows high self-esteem, low self-esteem, high performance avoidance goals, and
high performance approach goals; and the profile with high self-esteem, high learning goals, low
performance avoidance goals, and low performance approach goals), with medium effect sizes (d = 0.73
and d = 0.69, respectively). By contrast, the profile characterized by low self-esteem, high learning
goals, medium levels of performance avoidance goals, and high performance approach goals was
significantly more related to defensive pessimism, demonstrating large differences with respect to the
three remaining profiles (between d = 1.75 and d = 2.47).

Int. J. Environ. Res. Public Health 2019, 16, 2218 12 of 20

Table 5. Descriptive statistics (means and standard deviations) corresponding to profiles of self-esteem
and achievement goals in self-handicapping and defensive pessimism.

Profiles of Self-Esteem and Achievement Goals
BSH CSH DP

M (SD) M (SD) M (SD)

1. HSE/LLG/HPAvG/HPApG
Females 1.89 (0.59) 1.84 (0.66) 1.94 (0.50)
Males 1.95 (0.47) 1.94 (0.71) 1.91 (0.44)
Total 1.90 (0.57) 1.85 (0.66) 1.94 (0.50)

2. HSE/HLG/LPAvG/LPApG
Females 1.96 (0.65) 1.85 (0.62) 1.97 (0.50)
Males 1.82 (0.57) 1.77 (0.62) 1.94 (0.51)
Total 1.94 (0.64) 1.84 (0.62) 1.96 (0.50)

3. LSE/LLG/HPAvG/HPApG
Females 2.97 (1.05) 3.12 (0.93) 2.47 (1.11)
Males 3.36 (0.69) 2.11 (1.03) 2.11 (0.67)
Total 3.06 (0.99) 2.88 (1.05) 2.39 (1.04)

4. LSE/HLG/MPAvG/HPApG
Females 1.81 (0.55) 1.68 (0.53) 3.46 (0.62)
Males 1.91 (0.63) 1.87 (0.80) 3.44 (0.66)
Total 1.82 (0.56) 1.70 (0.56) 3.46 (0.62)

Note. HSE/LLG/HPAvG/HPApG: Profile of high self-esteem, low learning goals, high performance avoidance
goals, and high performance approach goals; HSE/HLG/LPAvG/LPApG: Profile of high self-esteem, high learning
goals, low performance avoidance goals, and low performance approach goals; LSE/LLG/HPAvG/HPApG: Profile
of low self-esteem, low learning goals, high performance avoidance goals, and high performance approach goals;
LSE/HLG/MPAvG/HPApG: profile of low self-esteem, high learning goals, medium performance avoidance goals,
and high performance approach goals; BSH = behavioral self-handicapping; CSH = claimed self-handicapping; DP
= defensive pessimism; All measurement scales range from 1 to 5, where the highest scores reflect the highest level
of self-esteem, achievement goals and self-protection strategies.

Considering the scores obtained by men and women of each motivational profile in the three
self-protection strategies, it was observed that in the profile of low self-esteem, low learning goals,
high performance avoidance goals, and high performance approach goals, men significantly used
behavioral self-handicapping to a greater extent (χ2(23) = 36.98, p < 0.05), whereas women significantly used claimed self-handicapping to a greater extent (χ2(29) = 50.82, p < 0.05). On the contrary, in the profile of low self-esteem, high learning goals, medium performance avoidance goals, and high performance approach goals, claimed self-handicapping was significantly more used by men (χ2(32) = 56.83, p < 0.05).

4. Discussion

Researchers of education are increasingly aware that students diverge not only in cognitive aspects,
but also in motivational aspects. Adopting a person-centered approach, this study aimed to analyze
the formation of different motivational profiles from the combination of self-esteem and achievement
goals. Likewise, we intended to identify profiles that underlie the use of self-protection strategies of
personal worth.

Regarding the first objective, four motivational profiles have been differentiated: Two with
high self-esteem and two with low self-esteem. The four profiles differ from one another in the
achievement goals they adopt. On the one hand, as hypothesized, our data suggest the existence
of a large group of students characterized by high self-esteem and high learning goals but with
little concern for performance goals (i.e., avoidance and approach). These students, therefore, show
feelings of appreciation and value to themselves, and they are academically oriented to improve their
knowledge without caring about the social comparison. Consequently, they are students neither
motivated by a desire to show their superiority to classmates, teachers, or parents, nor worried about
receiving negative criticism for their academic competence. This profile (i.e., high self-esteem, high
learning goals, low performance avoidance goals, low performance approach goals) seems to fit
with the characteristics of the “success-oriented” profile [82]. Success-oriented students are typically
self-confident students who focus their efforts on learning and perfecting themselves as students,

Int. J. Environ. Res. Public Health 2019, 16, 2218 13 of 20

without fearing poor performance or questioning their personal worth. In effect, the multiple goal
literature has endorsed the adaptiveness of this profile because it has been positively related to high
levels of engagement, performance, and emotional wellbeing [64].

Additionally, although not exactly as expected, we observed evidence of a second profile of
students with high self-esteem. Contrary to the profile of high learning goals and low performance
goals (avoidance and approach), this second profile of students with high self-esteem has eminently
focused on performance goals (in their tendencies towards avoidance and approach), but not on
learning goals. Therefore, they are oriented toward demonstrating their competence, but not to
increase it [34]. In other words, the students who are salient in performance goals judge their academic
competence based on interpersonal standards, such that they move between the desire to be praised for
their abilities and the fear of acquiring a negative social image. That finding may indicate, as Dweck
and Leggett [83] suggested, that when students pursuing performance approach goals experience
some fails, they could display doubts about their ability to be evaluated as better than others and
redirect their priorities to focus on performance avoidance goals.

Regarding the profiles with low self-esteem, as we hypothesized, there exists a profile of students
with low learning goals, high performance avoidance, and high performance approach goals. Thus, this
students show the same pattern of achievement goals that the students characterized in the previous
paragraph, differing only in their level of self-esteem. A priori, the identification of a profile of students
with low learning goals and a high adoption of performance goals (i.e., avoidance and approach)
associated with low self-esteem is not surprising, given that the latter usually implies a high emotional
vulnerability to criticism and an excessive desire to gain social approval [84], aspects that also define
performance goals. However, our data do not allow us to offer an accurate answer to why some
students with low learning goals and high performance goals (both avoidance and approach) show
low self-esteem and other students with the same achievement goals profile show high self-esteem.
A plausible assertion is that differences in self-esteem are associated with the level of performance
achieved. Previous research has shown that among students who adopt high performance goals,
there is a high fear of failure, associated with the connection established by these students between
self-worth and their need to demonstrate their competence [3]. Thus, attaining high performance
would lead to a competitive social image and, with it, high self-esteem. By contrast, low performance
would lead to a less competitive social image and, therefore, low self-esteem. In any case, this is merely
a tentative explanation that should be analyzed more rigorously by future research.

Finally, and also as expected, we observed a second profile of students with low self-esteem.
In terms of achievement goals, this student profile combines a high interest in learning (i.e., learning
goals) with a moderate concern over presenting a social image of incompetence (performance avoidance
goals) and a strong desire to stand out and be considered a high performer (performance approach
goals). In line with this finding, some works on multiple goals in university students [49,61] have
observed a profile that combines the adoption of learning goals with both types of performance goals in
a high degree. As stated by Wormington and Linnenbrick-García [64], this student profile has generally
been considered equally as adaptive as the profile of high learning goals and low performance goals
(i.e., avoidance and approach) in terms of engagement and performance. However, less beneficial
outcomes have been observed regarding control beliefs and emotional wellbeing. Accordingly,
the students who conjugate high learning goals with moderate performance avoidance goals and
high performance approach goals usually exhibit high academic engagement and performance but
vulnerability, for example, anxiety, stress, fear of failure [85], linked to social comparison. The results
of our study expand the characterization of this motivational profile by specifying that these students
have low self-esteem.

Likewise, our findings suggest that the high use of learning goals is not always associated with
high self-esteem. In this sense, Niiya and Crocker [86] asserted that when self-esteem is threatened
(i.e., low self-esteem), students may be interested in learning as a means to demonstrate their ability,
either in an effort to excel in front of their peers or to avoid appearing incompetent. Therefore,

Int. J. Environ. Res. Public Health 2019, 16, 2218 14 of 20

this scenario could be the case of the students that, in our study, embody the profile that combines
low self-esteem with high learning goals, medium levels of performance avoidance goals, and high
performance approach goals.

In relation to the second objective, the results of this work suggest that two of the identified
motivational profiles (the profile characterized by low self-esteem, low learning goals, high performance
avoidance goals, and high performance approach goals; and the profile of students who exhibit low
self-esteem combined with high learning goals, medium levels of performance avoidance goals, and
high performance approach goals) are especially vulnerable to the use of self-handicapping and
defensive pessimism, respectively.

This finding may be interpreted in two ways. On the one hand, it seems that, independent
of the achievement goals of students, low self-esteem is a risk factor for becoming involved in the
self-protection mechanisms of self-worth, with high self-esteem being a protective factor. Thus, our
results would be aligned with the findings of other studies that, from a variable-centered approach,
link low self-esteem to self-handicapping and defensive pessimism [17,18,22]. On the other hand, our
results suggest that beneath the self-handicapping and defensive pessimism are distinct academic
motivations. Thus, regarding self-handicapping, the use of this strategy, in its behavioral and claimed
forms, is greater in students who, having a negative self-assessment, show a high orientation to both
performance goals. This finding would support the consideration of performance avoidance goals
as an important determinant of self-handicapping, as suggested by other research [35,40]. However,
similar to other studies [37,38], a high use of performance approach goals is also observed in this profile
linked to self-handicapping. In this regard, it is possible that some students who seek to excel over
other students are also imbued with the need to protect their personal worth when faced with the fear
of failing to achieve their goal [4]. For these students, as our results seem to indicate, self-handicapping
would become an attractive mechanism of self-protection.

Regarding defensive pessimism, our findings suggest that this strategy is especially recurrent in
students with low self-esteem eager to learn and achieve high performance. However, these students
also show a moderate motivation to avoid low performance that compromises their personal worth
through negative social judgments. This finding is in line with other research [82] that has characterized
the defensive pessimist as a student with a high behavioral commitment to success but whose behavior
is cognitively linked to the fear of failure.

Additionally, the identification of motivational characteristics that underlie self-protection
strategies allows us to corroborate, in accordance with other studies [17,66], that even in students with
low self-esteem, the high use of learning goals reduces the likelihood of resorting to self-handicapping.
However, our data suggest that this buffering effect would not be achieved with defensive pessimism.

4.1. Educational and Health Implications

The findings in this study entail some educational and health implications of scope. Students
in the university stage are especially vulnerable to the adoption of self-handicapping and defensive
pessimism strategies [36] because of the many academic, social, emotional, and economic challenges
they must manage during this period, which may damage their psychological well-being [87,88]
in case of failure. In effect, the recurrent use of self-handicapping is associated with important
damages, not only for academic performance [7], but also for the student’s psychological health
(e.g., decreased self-esteem, social rejection, increased depressive symptoms, reduced satisfaction
with life) [5,89,90]. Similarly, the repeated tendency to imagine the worst scenarios—so common
among defensive pessimists—may involve long-term costs in the form of emotional and physical
problems [12,91]. Other studies have associated defensive pessimism with burnout and, ultimately,
with a decrease in academic performance [92].

Considering the dysfunctional nature of self-handicapping and defensive pessimism [8,12],
it is, therefore, necessary to eliminate or reduce those factors in the university context that may be
especially threatening for profiles of students identified by our study as motivationally more prone

Int. J. Environ. Res. Public Health 2019, 16, 2218 15 of 20

to both self-protection strategies. To this end, motivation research [82,93–95] has emphasized the
importance of educators adopting some guidelines aimed at preventing the use of self-handicapping
and defensive pessimism in academic settings: (a) Agree with the students’ learning objectives,
activities, or projects to be developed, evaluation criteria, and deadlines; (b) divide the academic tasks
and the study into smaller steps and encourage the students to elaborate action plans to carry them
out; (c) encourage students to analyze the causes of their mistakes and provide them with additional
opportunities to pass exams or improve academic work; (d) emphasize the importance of improving
one’s performance and not competing with others, as well as making explicit the positive qualities of
each student; (e) provide students with evaluative feedback based on the degree of effort expended and
the appropriate/inadequate use of work strategies (i.e., controllable factors); (f) promote cooperative
learning structures.

Complementary to these classroom initiatives, there are other therapeutic interventions that
have shown to be effective in improving the psychological health of self-handicappers and defensive
pessimists, reducing the need to use these strategies. Among these interventions, one could cite
the cognitive behavioral coaching (CBC) [96], the motivation and engagement wheel [97], and the
self-compassion therapies [98].

4.2. Study Limitations

The contributions of this work should be taken with caution, considering the limitations of the
study. First, the cross-sectional design adopted did not allow causal relationships between the variables
to be extracted. Future works could analyze these types of relations by using longitudinal study
designs. Second, the sample used includes only students assigned to two branches of knowledge
(educational and health), which restricts the possible generalization of the results to the whole university
population. Third, more than 86% of the participants in the sample were women, which could have
influenced the findings. In this regard, it has been observed that in the present study, women got
involved in the four motivational profiles by a wide margin. However, the percentage of men is
almost double in the profile of low self-esteem, low learning goals, high performance avoidance
goals, and high performance approach goals than in the three remaining profiles. Therefore, future
studies that have more symmetrical samples regarding gender should be conducted to observe the
extent to which these differences between women and men are replicable in terms of the motivational
profiles they adopt. Likewise, other studies should contemplate the gender perspective to analyze
the relationship between motivational profiles and strategies of self-protection. In a tentative way,
our data suggest the possible existence of statistically significant differences between women and
men in the use of claimed self-handicapping (both in the profile of low self-esteem, low learning
goals, high performance avoidance goals, and high performance approach goals, and in the profile of
low self-esteem, high learning goals, medium performance avoidance goals, and high performance
approach goals) and behavioral self-handicapping (profile of low self-esteem, low learning goals, high
performance avoidance goals, and high performance approach goals). Considering the important
imbalance in the representation of both sexes in our sample, future research should analyze with
greater rigor the differences observed in the present study. Fourth, the data for this study have been
gathered from self-report tests. Future work could benefit from the wealth of information provided by
a combination of methods that include classroom observations, questionnaires, and interviews with
students. Finally, the goals and self-protection strategies used in this study are not the only ones that
students can adopt, and this factor should be considered. In further research, therefore, the existence of
motivational profiles should be analyzed from the inclusion of other goals, namely, academic or social.
In this regard, the inclusion of other goals, such as work avoidance goals, might allow differences in
the use of claimed and behavioral self-handicapping to be observed, as stated by other studies [38].
Likewise, other self-protection strategies, such as self-affirmation, could be considered.

Int. J. Environ. Res. Public Health 2019, 16, 2218 16 of 20

5. Conclusions

The results of this work contribute to broadening existing knowledge about the complex
motivational reality of university classrooms [99] by determining the existence of unprecedented
profiles of students according to how they integrate two basic elements of academic motivation, namely,
achievement goals and self-esteem. Specifically, our results allow us to improve the characterization of
the profiles of the multiple goals identified in the literature. Thus, those students with high learning
goals and low performance goals (i.e., avoidance and approach) are also characterized by their high
self-esteem, and students who adopt the three goals to a high degree (or moderately high, in the case
of performance avoidance goals) exhibit low self-esteem. Our data also allow us to determine the
existence of two differentiated profiles of students with low learning goals and high-performance goals
(i.e., avoidance and approach): High self-esteem and low self-esteem. Likewise, the identification of
motivational profiles particularly linked to self-handicapping (profile of low self-esteem, low learning
goals, high performance avoidance goals, and high performance approach goals) and defensive
pessimism (profile of low self-esteem, high learning goals, medium performance avoidance goals, and
high performance approach goals) allows for an effective response not only to the question of what
factors determine the adoption of these strategies but also to the question of who is more vulnerable to
these factors.

Author Contributions: Conceptualization, M.d.M.F. and C.F.; methodology, M.d.M.F., C.F., and J.C.N.; formal
analysis, M.d.M.F., C.F., and J.C.N.; investigation, M.d.M.F., C.F., and J.C.N.; writing—original draft preparation,
M.d.M.F., C.F., and B.R.; writing—review and editing, M.d.M.F., C.F., and B.R.; supervision, M.d.M.F., C.F., and B.R.

Funding: This research received no external funding.

Acknowledgments: The authors thanks to the students who participated in the study.

Conflicts of Interest: The authors declare no conflict of interest.

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Introduction

Self-Protection Strategies: Self-Handicapping and Defensive Pessimism
Self-Esteem and Self-Protection Strategies
Achievement Goals and Self-Protection Strategies
This Study
Aims of the Study

Materials and Methods

Participants and Procedure
Instruments
Data Analysis

Results

Identification of Profiles of Self-Esteem and Achievement Goals
Description of Profiles of Self-Esteem and Achievement Goals
Relationship between Profiles of Self-Esteem/Achievement Goals and Self-Protection Strategies

Discussion

Educational and Health Implications
Study Limitations

Conclusions

References

Alberta Journal of Educational Research, Vol. 65.4, Winter 2019, 305-319

Critical Thinking as a Predictor of Self-
Esteem of University Students

Seyithan Demirdag

Zonguldak Bulent Ecevit University,

Turkey

This study aimed to examine the predictive role of critical thinking on university students’ self-

esteem. The participants of the study included 433 undergraduate students in a variety of

programs. Of the 433 students, 294 were female, 139 were male, and the mean age was 20.2

years. Data collection tools included the California Critical Thinking Scale and Coopersmith

Self-Esteem Inventory. The relationship between the critical thinking and self-esteem of

university students were determined using correlation analysis and multiple regression

analysis. The findings of the correlation analysis showed that analyticity, open-mindedness,

inquisitiveness, self-confidence, truth-seeking, and systematicity were significantly correlated

with self-esteem. In addition, a regression analysis showed that self-esteem was predicted by

open-mindedness, inquisitiveness, and self-confidence.

Cette étude avait comme objectif d’examiner le rôle prédictif de la pensée critique sur l’estime de

soi des étudiants à l’université. Les participants à cette étude comprenaient 433 étudiants

provenant de différents programmes du premier cycle, dont 294 femmes et 139 hommes d’un

âge moyen de 20,2 ans. Parmi les outils de collecte de données, notons le California Critical

Thinking Scale et le Coopersmith Self-Esteem Inventory. Le rapport entre la pensée critique et

l’estime de soi des étudiants à l’université a été établi par une analyse de corrélation et une

analyse de régression multiple. Les résultats de l’analyse de corrélation indiquent que

l’analyticité, l’ouverture d’esprit, la curiosité, la confiance en soi, la recherche de la vérité et la

systématicité présentent une corrélation significative avec l’estime de soi. De plus, une analyse

de régression a indiqué que l’ouverture d’esprit, la curiosité et la confiance en soi étaient des

facteurs prédictifs de l’estime de soi.

Education is a process that occurs between individuals and their social environment (Lindeman,

2015). It is through education that people are encouraged to acquire the desired and validated

behaviors that their community deems necessary (Hawkins & Weis, 2017). Moreover, each

society aims to produce individuals who reflect the characteristics of the society itself. Passing

on such characteristics for people living in the society may be possible through education. For a

long time, the acquisition of knowledge was seen as the main purpose of school and life;

however, this vision has changed: the acquisition of knowledge is no longer seen by researchers

as memorizing concepts, principles and processes (Bloch, 2018; Harlen, 2018). In contrast, the

way one uses knowledge is emphasized more than how they acquire it: this approach aims to

educate individuals who may be able to use information, think logically, conduct research, and

gain critical thinking abilities (Harlen, 2018). By doing so, these individuals would learn how to

S. Demirdag

306

question, investigate, make connections between concepts, obtain new knowledge, and

contribute to society, thus feeling good about their self-worth (Fahim & Masouleh, 2012).

Because today’s societies are considered to be information societies, in order to support the

intellectual development of students, new regulations need to be made within educational

systems (Drucker, 2017). Improving student academic potential depends on their effective

critical thinking, creative thinking, scientific thinking, relational thinking, and reasoning skills

through a qualified educational system (Fahim & Masouleh, 2012; Özden, 2005). All of these

thinking approaches may be possible through a quality education. Quality education is a

student-centered education system that allows students to focus on a given topic, think about a

concept, increase their imagination, and be able to make constructive criticism while being eager

to seek knowledge (Hopkins, 2015). In that sense, the main aim of education is helping students

to make meaningful connections between series of phenomena (Kafai & Resnick, 2012).

Education should aid students in determining ways of learning, thinking critically, assessing

their own learning, and being aware of their potential. In other words, educational systems

should help students learn “how to think” rather than “what to think” (Meichenbaum, 2017).

Such approaches would support educators and students to understand the meaning and

significance of critical thinking (Flores, Matkin, Burbach, Quinn, & Harding, 2012). Quality

education would therefore help both educators and students feel confident about their

personalities as they start learning how to think and question certain situations (Crocker & Park,

2004).

Literature Review

Critical Thinking

In the field of education, critical thinking has been one of the most emphasized topics in recent

years. When the quality of the education system, teachers, and school is questioned by members

of society, it may be seen that this quality does not meet the desired standards outlined by the

Ministry of National Education in Turkey (Özden, 2005). Experts suggested that this problem is

mainly associated with teaching approaches based highly on memorization rather than daily

applications (e.g., Azar, 2011). Furthermore, as Azar (2011) indicated, the teaching methods,

which are employed by educators, do not allow students to develop their thinking skills (Azar,

2011). Critical thinking is thought to be a complex skill, which is difficult to explain with a

certain definition (Tsui, 2008). Even though there is no particular description of critical

thinking, there have been some agreements on the term. Critical thinking recognizes the

strengths and weaknesses of our own thinking in an improved form (Eales-Reynolds, Judge,

McCreery, & Jones 2013). According to Evancho (2000), critical thinking is defined as the

concepts that form an individual’s evaluative and conscious judgment in solving problems.

Bloom, Krathwohl, and Masia (1956) claimed that critical thinking is linked to higher order

thinking processes. Critical thinking involves a person’s ability to recognize relationships, make

inferences or deduce conclusions from data, make assumptions in an argument, and evaluate

evidence (Furedy & Furedy, 1985). Pascarella and Terenzini (2005) explained that a purposeful

instruction could enhance critical thinking. Tsui (2002) stressed that discussions involving

higher order thinking processes, integration of ideas, interactive exchanges, and the critique of

epistemological assumptions are associated with critical thinking, which may increase the

numbers of open-minded people for learning new information or concepts.

Critical Thinking as a Predictor of Self-Esteem of University Students

307

Facione (1990, 2013) identified critical thinking and its components: critical thinking is not

a recall of ideas but may be conceived as a process that includes a two-way learning process.

First, critical thinking evaluates the rational adequacy of an empirical statement, and second,

critical thinking tends to defend a logical statement meaning that it is rational and does not

depend on emotions, ideologies, or folk wisdom (Paul & Elder, 2006).

Facione (2013) defined critical thinking as self-regulatory judgment resulting in

interpretation, analysis, evaluation, and inference. Facione and Facione (1992) explained the

subscales of critical thinking as truth-seeking, open-mindedness, analyticity, systematicity, self-

confidence, and inquisitiveness. The authors also provided definitions for these subscales:

 Truth-seeking entailed pursuing information about a problem;

 Open-mindedness included tolerating while also considering new ideas and opinions;

 Analyticity used higher-order thinking approaches in order to provide solutions to

problems;

 Systematicity involved having effective plans and being focused while doing something;

 Self-confidence required believing in one’s own capabilities; and

 Inquisitiveness encompassed being eager in acquiring the best knowledge (Facione &

Facione, 1992).

These subscales are associated with obtaining or planning to acquire new information, meaning,

that people with these skills would eventually enhance their beliefs about their self-worth; thus,

they would be more satisfied with their life (Diener and Diener, 2009).

The theoretical framework of critical thinking has mainly been explained by Bloom’s

Taxonomy (Lipman, 1988), which is used to find differences in human cognition (Meyers, 1986).

Because critical thinking includes cognitive activities such as formulating hypotheses, solving

problems, and making plans, the cognitive model of Bloom’s Taxonomy may be used by

educators to help student improve their cognitive skills. According to Bloom, Engelhart, Furst,

Hill, and Krathwohl (1956), the model included six steps, including knowledge, comprehension,

application, analysis, synthesis, and evaluation. These steps are crucial for the higher-order

thinking skills of the students. This design is considered to be a logical framework in terms of

enhancing students’ learning, analyzing, and thinking skills. Using this model may allow the

educators to understand that a critical thinker has cognitive skills to approach issues in a critical

and thoughtful manner (Rickles, Schneider, Slusser, Williams, & Zipp, 2013). It is likely that

individuals with critical thinking dispositions tend to have social agency and self-confidence

since increased levels of critical thinking are related to one’s personal feelings and social actions

(Laird, 2005).

Self Esteem

It is crucial for students to understand the fact that having positive or negative attitudes towards

their own personality may affect their motivation, enthusiasm, and eagerness towards school,

classroom, friends, learning activities, and their higher-order thinking skills. One of the most

important things that may affect students is their level of self-esteem (Pyszczynski, Greenberg,

Solomon, Arndt, & Schimel, 2004).

Academicians have been divided with respect to self-esteem’s definition and function. Self-

S. Demirdag

308

esteem has been used as a subject in relation with violence, unemployment, social problems,

and teenage pregnancy. Some believe that self-esteem is essential for human life (e.g.,

Pyszczynski et al., 2004), while others argues that it does not include any value of life (e.g.,

Baumeister, Campbell, Krueger, & Vohs, 2003). Leary and MacDonald (2003) defined self-

esteem as the personal evaluation that may be positive or negative depending how a person feels

about their own self. Self-esteem involves psychological challenges, depression, loneliness, and

academic failure (Pyszczynski et al., 2004).

Although the definition and function of self-esteem have not been fully agreed upon by

researchers, there is an understanding that self-esteem includes three dimensions. The first is

called Global or Trait Self-Esteem, which is related to a personality variable that represents how

people feel about themselves (Crocker & Park, 2004). Researchers think that Global Self-Esteem

is a decision that individuals make about their worth (Crocker & Knight, 2005); it is something

that persists and is continuous. State Self-Esteem, on the other hand, refers to the emotions that

we call the feelings of self-worth (Heimpel, Wood, Marshall, & Brown, 2002). Different from

Global Self-Esteem, State Self-Esteem refers to feelings, which are considered to be temporary

(Pyszczynski et al., 2004). Finally, Domain Specific Self-Esteem occurs in situations when

individuals evaluate their various abilities (Marsh, 1993). According to this approach,

individuals tend to evaluate their certain attributes, abilities, and personality characteristics

(Marsh, 1993). People employing attributes that help them determine, analyze, and solve

problems may have positive effects on their personal self-worth (Hart Research Associates,

2010).

Some of the attributes of self-esteem include believing in your capacity to solve problems,

trusting your own judgments, making decisions and choices, and considering yourself self-

worthy (Scheirer & Kraut, 1979). Kafka et al. (2012) found that an individual’s the level of self-

esteem may elevate negative outcomes in their life. Negative outcomes mainly include increases

in aggression and prejudice. Haney and Durlak (1998) defined self-esteem as certain attitudes

that people have towards themselves. Rosenberg (1965) stressed that self-esteem involves

attitudes of approval or disapproval towards the concept of the self. Rosenberg, Schooler, and

Shoenback (1989) suggested the that three main elements of self-esteem are reflected appraisal,

social comparisons, and self-attribution. How other people view someone based on their own

assumptions, according to Rosenburg et al. (1989) is called reflected appraisal. The authors

defined making comparisons in terms of viewing between self and other people as social

comparisons. Lastly, self-attribution involved drawing a conclusion about ones-self from seeing

the successful and unsuccessful outcomes of one’s actions (Rosenburg et al., 1989).

Judge, Erez, Bono, and Thoresen (1998) emphasized that the concept of self-esteem has

become one of the most researched and important topics in the field of psychology (as cited in

Arum & Roksa, 2010). Some researchers strongly believe that self-esteem supports the academic

success of university students. Baumeister, Smart, and Boden (1996) suggested that

stakeholders and administrators in universities think that being persistent in the face of failure,

aspiring to be successful in life, and diminishing the feelings of incompetence are possible for

students if they have a higher level of self-esteem. As such, many universities have made

attempts to implement effective programs which may increase students’ critical thinking

disposition by increasing their level of self-esteem (Arum & Roska, 2010; Haney & Durlak, 1998;

Scheirer & Kraut, 1979).

Self-esteem and academic achievement. In their study, Pottebaum, Keith, and Ehly

(1986) found that most of the research conducted on self-esteem was about the investigation on

Critical Thinking as a Predictor of Self-Esteem of University Students

309

the relationship between high school students’ self-esteem and their academic achievement.

Findings from this study showed that the correlation between students’ self-esteem and their

academic achievement was quite little. Bachman and O’Malley (1986) found that the correlation

between two variables was weak as self-esteem was an insignificant predictor of students’

academic performance. Stupnisky et al. (2007) conducted a survey on 802 university students to

find out whether self-esteem was a predictor of students’ academic success. They found that self-

esteem was not a strong determinant of the academic success of these students. In terms of

finding a relationship between college students’ self-esteem and their academic achievement,

Crocker and Luhtanen (2003) found that self-esteem was not able to predict the students’

academic achievement. According to Marsh and Craven (2005), Peixoto (2003) as well as

Valentine and DuBois (2005), self-esteem has an incoherent association with performance

indicators such as academic achievement, motivation, and critical thinking. Although the

findings mentioned above suggest that the relationship between self-esteem and critical

thinking is inconsistent, the current study is aimed to outline whether a clear association occurs

between the effects of critical thinking in the context of self-esteem.

Woodard and Suddick (1992) investigated the relationships between students’ self-esteem

and their grades. Positive correlations were found between these variables. Rosenberg et al.

(1989) explained that academic achievement had an important and positive impact on students’

self-esteem (Ross and Broh, 2000). In addition, Demo and Parker (1987) conducted research on

the relationships between university students’ self-esteem and their academic achievement,

finding significant correlations between these variables. In their studies, Baumeister, Campbell,

Kruegger, and Vohs (2005) in addition to Diener and Diener (2009) revealed that there was

strong relationship between students’ self-esteem and their satisfaction in life. However, Liu,

Kaplan, and Risser (1992) found that due to students’ low self-esteem, they performed poorly in

their courses. Students with lower levels of self-esteem may have a negative outlook and lack of

confidence towards participating in problem solving activities requiring critical thinking skills

(Howard & Zoeller, 2007).

Self-esteem and critical thinking. Bachman and O’Malley (1986) conducted a study on

the relationship between self-esteem and critical thinking. They found that self-esteem was a

weak predictor of student critical thinking tendencies (Skaalvik and Hagtvet, 1990). Lyon

(1993), Marsh (1987), Muijs (1997), as well as Skaalvik and Hagtvet (1990) suggested that the

relationship between self-esteem and critical thinking tendencies of students is not very strong.

Based on the findings of Branscombe and Wann (1994) as well as Covington (1984), the weak

links between the self-esteem and critical thinking tendencies of students may be because of

their personal struggles. Similarly, Crocker and Luhtanen (2003), in addition to Rosenberg et al.

(1989), suggested that self-esteem was not a predictor of students’ critical thinking abilities.

Furthermore, Ewen (2001) found that there was no significant relationship between self-esteem

and critical thinking tendencies of nursing students.

In their study, Lui et al. (1992) expressed that self-esteem had an indirect influence on

students’ critical thinking and learning. On the other hand, Barkhordary, Jalalmanesh, and

Mahmodi (2009) discovered strong correlations between student critical thinking and self-

esteem. However, Crocker and Luhtanen (2003) found that self-esteem negatively predicted

student dissatisfaction with school and their critical thinking tendencies. It is likely that

students with low self-esteem may have difficulty expressing their needs and weaknesses, which

would make them reluctant to engage in critical thinking activities (Barkhordary et al., 2009;

Twenge & Campbell, 2001).

S. Demirdag

310

Facione (2013) concluded that people with critical thinking skills are more likely to initiate

logical thinking that aims to direct our attitudes. People with effective critical thinking abilities

are more likely to interpret, analyze, and evaluate surrounding conditions due to their self-

esteem (Scheirer & Kraut, 1979). Haney and Durlak (1998) emphasized that people who believe

in their capacities can make decisions, solve problems, and trust their own judgment.

This Study

Based on the review of the literature on critical thinking and self-esteem, and in particular self-

esteem and academic achievement and self-esteem and critical thinking, it seems as though (a)

critical thinking affects people’s beliefs, and (b) there may be a relationship between critical

thinking and self-esteem. From these factors I hypothesize that as the level of critical thinking

someone possesses increases, their self-esteem may also increase. In the same vein, as

someone’s self-esteem increases, so may their critical thinking abilities.

This research poses two hypotheses:

 Critical thinking is positively associated with self-esteem.

 Critical thinking is a predictor self-esteem.

Methods

Participants. A total of 433 undergraduate students participated in the study; 294 were female

and 139 were male. The students ranged in age from 18 to 27 and the mean age was 20.2 years.

The study took place at a university in the Western Black Sea Region of Turkey. Students were

enrolled in programs such as psychological counseling and guidance (n = 232), special

education (n = 102), and Turkish education (n = 99). There were 46 first-year, 116 second-year,

141 third-year, and 130 fourth-year students in this research. The GPA scores of the participants

ranged from 1.81 to 3.89 on a 4.0 scale.

Measures. Two instruments were used for data collection. The first one was called the

California Critical Thinking Scale (CCTS). This was used to measure students’ critical thinking

skills. The construct was developed by Facione, Facione, and Giancarlo (2001). Kökdemir

(2003) adapted this instrument to for use with the Turkish language. The CCTS is a 5-point

Likert scale (from 1 = absolutely disagree to 5 = absolutely agree) and includes 51 items. The

CCTS is consisted of six dimensions: Analyticity, open-mindedness, inquisitiveness, self-

confidence, truth-seeking, and systematicity. The reliability coefficients of the instrument were

first measured by Facione et al. (2001) and found to be .78. Later on, the instrument’s reliability

was measured by Kökdemir (2003) after its adaptation and was found to be .81. Additionally,

the reliability coefficient for each subscale was .63 for systematicity, .77 for self-confidence, .75

for analyticity, .61 for truth-seeking, .78 for inquisitiveness, and .75 for open-mindedness

(Kökdemir, 2003).

The Coopersmith Self Respect Inventory (CSRI), developed by Coopersmith (1967) was used

as the second instrument in this research. This construct was used to measure students’ self-

esteem. Piskin (1999) adapted the instrument for the Turkish language. According to Leary and

MacDonald (2003), the Coopersmith Self Respect Inventory was developed to measure people’s

beliefs in their worthiness and capability. This five-point scale Likert scale (from 1 = absolutely

disagree to 5 = absolutely agree) included 58 items. The original instrument’s Cronbach alpha

Critical Thinking as a Predictor of Self-Esteem of University Students

311

coefficient was .88. After adapting the original scale, the overall internal consistency coefficient

of the scale was determined to be .81.

Procedure and statistical analysis. As soon as participants’ permits were received from

the department of ethics, students started participated in the study on voluntary basis. The pre-

study folder of the students included information about their identification numbers, ages,

grade levels, and GPA scores. It is also important to note that all of the participants were

informed about the main goal of this research. For the confidentiality of research, the folders

were kept in a safe place throughout the study. Statistical analyses such as correlation analysis

and multiple regression analysis were employed using SPSS 20.0. In order to test both

Hypothesis 1 and Hypothesis 2, CCTS and CSRI were used to collect data from the university

students. In regard to testing Hypothesis 1, Pearson’s correlation analyses were used to find out

about the relationships between critical thinking and self-esteem of university students. For

testing Hypothesis 2, a multiple regression analysis was used to determine the predictive role of

critical thinking in determining students’ self-esteem.

Results

Descriptive data and inter-correlations. The study variables such as internal consistency

coefficients, standard deviations, means, and inter correlations are shown in Table 1. According

to the study findings, systematicity (r = .16, p < .01), analyticity (r = .14, p < .01), truth-seeking

(r = .17, p < .01), self-confidence (r = .03, p < .01), open-mindedness (r = .30, p < .01), and

inquisitiveness (r = .34, p < .01) had positive associations with self-esteem. Based on these

findings, the correlation between these variables and self-esteem was quite weak. The findings

also indicated that the relationships between analyticity, open-mindedness, inquisitiveness, self-

confidence, truth-seeking, and systematicity were positive and significant.

Multiple regression analysis. Before applying the regression, assumptions of multiple

regression needed to be verified. A test of normality was conducted using the Kolmogorov-

Table 1

Descriptive Statistics, Alphas, and Inter-Correlations of the Variables

Variables 1 2 3 4 5 6 7

1. Analyticity –

2.Open-mindedness .18
a
–

3. Inquisitiveness .24
a
.36

a
–

4. Self confidence .20

a
.25

a
–

5. Truth-seeking .23

a
.27

a
.18

a
.45

a
–

6. Systematicity .13
a
.22

a
.27

a
.29

a
.31

a
–

7. Self-esteem .14
a
.30

a
.34

a
.03

a
.17

a
.16

a
–

Mean 3.57 3.05 3.10 3.29 3.22 2.78 2.77

Standard deviation .40 .307 .343 .47 .41 .41 .22

Cronbach’s a .71 .75 .73 .78 .67 .70 .65

Note. a: p < .01.; b: p < .05.

S. Demirdag

312

Smirnov test. The test results (p > .05) indicated that a normality of distributions of the scores

for all instruments in the current research were ensured. Levene’s test was also completed to

ensure the assumption of homogeneity of variance. Since the participants were made up of

students from three programs and four levels, I conducted an ANCOVA test to make sure that

these groups were equivalent as well. After the test, the results showed that the groups were

equivalent. The findings from these tests suggested that all assumptions were met (p > .05).

Moreover, outliers were examined using the Mahalanobis distance. An outlier within this study

may be defined as an observed case that employs abnormal distance from the majority of values

in a sample from a population. Tabachnick and Fidell (2001) suggested that a case is an outlier

which involves a value of D2, which is .001 or less. Based on this approach, six of the cases were

labeled as outliers and then deleted. Variance inflation factors (VIF) were used to determine

multicollinearity (Tabachnick and Fidell, 2001). The findings of VIF showed values less than 10.

This value suggested that there was no severe multicollinearity. After these assumptions were

met, a multiple regression analysis was performed. In this case, the dependent variable was self-

esteem and the independent variables were the subscales of critical thinking.

Firstly, a regression analysis was performed to determine the significant variables predicting

self-esteem. The analysis showed that the variables such as open-mindedness (p < .05),

inquisitiveness (p < .05), and self-confidence (p < .05) were able to predict self-esteem.

However, analyticity (p > .05), truth-seeking (p > .05), and systematicity (p >. 05) did not

significantly predict self-esteem, as seen in Table 2. After obtaining the findings of this test, a

multiple regression analysis was performed using the forward model as a type of stepwise

regression. This model was employed in order to start adding from the most significant

predictor to the least significant predictor in the model.

According to the findings of multiple regression analysis, summarized in Table 2,

inquisitiveness was entered in the equation first, accounting for 12% of the variance in

predicting self-esteem (R2= .12, adjusted R2 = .12, F(1, 431) = 59,821, p < .01). Second, open-

mindedness was entered accounting for an additional 3.5% variance (R2 = .03, ΔR2 = .03

adjusted R2 = .03, F(2, 430) = 40,490, p < .01). Lastly, self-confidence was entered, accounting

for an additional .6% variance (R2= .008, ΔR2 = .008 adjusted R2 = .006, F(3, 429) = 28,560, p

< .01).

Table 2

Summary of Linear Regression Analysis for Variables Predicting Self-Esteem

Variables B Standard error of B Beta t p

(Constant) 1.82 .14 12.51 .00

Analyticity .01 .02 .01 .41 .68

Open-mindedness .14 .03 .18 3.79 .00

Inquisitiveness .20 .03 .29 5.85 .00

Self confidence -.08 .02 -.16 -3.27 .00

Truth-seeking .04 .03 .07 1.45 .14

Systematicity .00 .02 .00 .07 .93

Note. p < .05.

Critical Thinking as a Predictor of Self-Esteem of University Students

313

The initial regression model included factors such as self-confidence, systematicity,

inquisitiveness, analyticity, truth-seeking, and open-mindedness; however, the final regression

design included only open-mindedness, inquisitiveness, and self-confidence, because

analyticity, truth-seeking, and systematicity were not statistically significant, as seen in Table 3.

The factors such as open-mindedness, inquisitiveness, and self-confidence were able to predict

the variances of self-esteem by 16.1%. The value of the standardized beta coefficient was found

to be significant for inquisitiveness (β= .29, p < .01), open-mindedness (β= .20, p < .01), and

self-confidence (β= -.14, p < .01).

Discussion

The aim of this research was to determine the predictive role of critical thinking of university

students on their self-esteem. The findings of the study showed that there were significant

relationships between some of the sub-factors of critical thinking on self-esteem. As one of the

few studies examining the predictive role of critical thinking on self-esteem of students in

Turkish universities, there were some indications of predictions. Based on the findings, as

subscales of critical thinking, the subscales of inquisitiveness and open-mindedness positively

predicted self-esteem. Conversely, self-confidence negatively predicted self-esteem. Lastly,

variables such as analyticity, truth-seeking, and systematicity did not emerge as significant

predictors in the regression model.

The findings of this study showed that students’ inquisitiveness positively predicted self-

esteem and had the highest association with self-esteem compared to the rest of the subscales of

critical thinking. The findings of the current research emphasized that students’ eagerness

towards learning was provided by their inquisitiveness (Facione and Facione, 1992). This

finding is consistent with the literature that university students with higher levels of self-esteem

are more determined to be persistent towards learning (Baumeister et al., 1996; Sternberg,

1987). Research has shown that students who are willing to search, analyze, and learn tend to be

ambitious in overcoming their own prejudices and feeling better about themselves (Baumeister

et al., 1996; Eales-Reynolds et al., 2013).

Table 3

Summary of Multiple Regression Analysis for Variables Predicting Self-Esteem

Model Variables B Standard error of B Beta t*

Model 1 Constant 2.06 .09 22.34

Inquisitiveness .22 .03 .34 7.73

Model 2 Constant 1.75 .11 15.29

Inquisitiveness .18 .03 .27 5.77

Open-mindedness .15 .03 .20 4.32

Model 3 Constant 1.83 .12 15.27

Inquisitiveness .19 .03 .29 6.06

Open-mindedness .16 .03 .22 4.63

Self confidence -.04 .02 -.09 -2.02

*All p < .01.

S. Demirdag

314

The findings of the study also suggested that as another subscale of critical thinking, open-

mindedness positively predicted self-esteem. It is understandable that students who are open-

minded may be able to build higher self-esteem as they are eager to learn new things, obtain

experience, and strengthen their visions (Paul and Elder, 2006). Students with more

experiences are able to see and interpret their attitudes in vigorous and successful ways

compared to their classmates and may draw more effective and affirmative conclusions about

life; this may contribute to their well-being and self-esteem (Rickles et al., 2013). Open-minded

individuals also take crucial attempts to solve problems and make choices that increase their

self-sufficiency in life (Diener and Diener, 2009).

In the study, however, students’ self-confidence negatively predicted their self-esteem. This

finding indicated that students with essential urges for believing in their capabilities were

reluctant to engage in activities acquiring critical thinking skills. In their similar findings,

Crocker and Luhtanen (2003) found that there was negative relationship between students’ self-

esteem and critical thinking. This result may be obtained due to students’ dissatisfaction with

their university. Many items such as poor physical conditions, inadequate quality of the

programs and faculty may negatively affect students’ enthusiasm for learning, questioning,

analyzing, and critical thinking (Valentine and DuBois, 2005). Therefore, school administrators

need to take urgent actions to implement effective programs, which will increase students’

satisfaction with their university in a more holistic approach (Haney and Durlak, 1998).

The other findings of the study showed that three subscales of critical thinking including

analyticity, truth-seeking, and systematicity did not predict students’ self-esteem. Supporting

my argument is that some of the previous studies suggested there was no relationship between

critical thinking and self-esteem (e.g., Ewen, 2001). These findings suggest that indicators such

as student achievement or critical thinking may not be able to predict students’ self-esteem

(Marsh & Craven, 2005; Peixoto, 2003). It may be inferred from these findings that some of the

students who tend to plan, seek knowledge, or try to find solutions to problems do not need to

possess any feelings of self-worth (Ewen, 2001).

When the results of this academic research are evaluated, various limitations should be

taken into consideration. First, perhaps the most important limitation of the study is that the

findings are obtained from one Turkish university, meaning, that these findings should not be

generalized to other student populations in other universities in Turkey. Due to this reason,

further studies must take place to assess the relationships between critical thinking and self-

esteem. By doing so, other student populations may be targeted to obtain more concrete

associations between the constructs of this study. Causality is the second limitation of this

scholarly research. Because of the use of correlational statistics, definitive statements on

causality may not be acceptable. The third limitation involves the method of data collection. The

data collected in this study highly depended on quantitative data and lacked the qualitative data.

Therefore, a mixed method approach may be employed in further studies.

As a result, the current research provides crucial information on self-esteem predictors and

provides a better understanding of the psychological process of self-esteem. This is achieved

because the findings seemed to suggest that some of the factors of critical thinking are

significantly related to self-esteem. The implications of findings insinuate that when university

students improve their critical thinking abilities through inquisitiveness and open-mindedness

they would have some feelings of assurance related to their self-worthiness. These feelings

would help university students to trust their judgments and be eager to learn in order to solve

problems and make effective choices. On the other hand, the findings of the current study

Critical Thinking as a Predictor of Self-Esteem of University Students

315

demonstrated that self-confidence as another subscale of critical thinking has negatively

predicted self-esteem. This means that even though some of students had essential urges for

believing in their capabilities, they were unwilling to participate in critical thinking activities

(Crocker & Luhtanen, 2003).

Students, educators, and other stakeholders need to be aware that the benefits of critical

thinking may not only be considered on a personal level but should also be contemplated on

interpersonal levels within educational settings (Diener & Diener, 2009). This fact is important

as educators, who provide guidance for their students, may effectively focus on students’

relationships with their peers, families, or instructors in order to help improve their critical

thinking skills; in turn, this may assist students in solving their problems in a psychologically

beneficial manner (Paul & Elder, 2006). This approach may be useful when students experience

personal devaluations in midst of a crisis. It would also allow students to have a positive

reflected appraisal and self-attribution when they would have to deal with their negative

feelings. From these conclusions, it may be suggested that more studies must be conducted to

investigate the relationship between critical thinking of students and their self-esteem in order

to find what actually steers the relationships between the two. That way, specific cognitive and

emotional variables that illuminate the link between the two may be explained in more details.

Acknowledgement

This study was supported by Zonguldak Bulent Ecevit University (Project Number: 2019-YKD-

19959079-01).

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The four pillars of tertiary student engagement
and success: a holistic measurement approach

Jana Lay-Hwa Bowden, Leonie Tickle & Kay Naumann

To cite this article: Jana Lay-Hwa Bowden, Leonie Tickle & Kay Naumann (2021) The four pillars
of tertiary student engagement and success: a holistic measurement approach, Studies in Higher
Education, 46:6, 1207-1224, DOI: 10.1080/03075079.2019.1672647

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The four pillars of tertiary student engagement and success: a
holistic measurement approach
Jana Lay-Hwa Bowden a, Leonie Tickleb and Kay Naumanna

aDepartment of Marketing, Macquarie Business School, Macquarie University, Sydney, Australia; bDepartment of
Actuarial Studies and Business Analytics, Macquarie Business School, Macquarie University, Sydney, Australia

ABSTRACT
Creating the conditions that foster student engagement, success and
retention remains a perennial issue within the higher education sector.
Traditionally satisfaction has been prioritised in assessing student
success. A more expansive, holistic and ontological perspective of the
student experience that takes into account who and what students are
becoming is required. This study develops a holistic approach to
measuring student engagement. It models and measures two
antecedents to engagement, namely involvement and expectations, four
dimensions of engagement, namely affective, social, cognitive and
behavioural engagement, and their relative and differential impact upon
five specific student and institutional success outcomes namely,
institutional reputation, student wellbeing, transformative learning, self-
efficacy and self-esteem. A survey with a sample of 952 tertiary students
enrolled at a major Australian tertiary institution was employed. A
structural model was then specified to assess the structural relationships
between the constructs. The results show that student expectations and
involvement have an important seeding role in student engagement.
Affective engagement was the most important determinant of
institutional reputation, wellbeing, and transformative learning.
Behavioural engagement determined self-efficacy and self-esteem.
Cognitive and social engagement were necessary but not sufficient
conditions for student success.

KEYWORDS
Student engagement;
success; retention;
measurement; experience

Introduction

The notion of the ‘student experience’ in higher education has a long and rich history. Systematic
measuring of the student experience has historically focused on pedagogical approaches, edu-
cational practices, and student evaluations of teaching practice (Grebennikov and Shah 2013).
Measuring attribute level evaluations of the student experience has offered institutions the ability
to quantify and monitor the extent to which student’s baseline expectations are being met by the
institution. Student satisfaction is a key benchmark metric of institutional performance and it con-
tinues to be prioritised in government policy;

… as they are the most important clients of higher education, students’ own assessments of the service they
receive at university should be central to our judgement of the success of our higher education system. Their
choices and expectations should play an important part in shaping the courses universities provide and in

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original work is properly cited, and is not altered, transformed, or built upon in any way.

CONTACT Jana Lay-Hwa Bowden Jana.Bowden@mq.edu.au; jana.bowden-everson@mq.edu.au
This article has been republished with minor changes. These changes do not impact the academic content of the article.

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encouraging universities to adapt and improve their service (Department for Business, Innovation and Skills UK
2009, 70)

Assessment of student satisfaction with the tertiary experience has been increasingly built into the
practices of tertiary institutions and a range of international indices and barometers have now
gained prominence e.g. National Survey of Student Engagement (NSSE); National Student Survey
(NSS); Student Experience Survey (SES). From an institutional perspective, systematic information
on the tertiary experience enables providers to identify key areas of the tertiary experience that posi-
tively impact the student journey; focus resources into areas requiring enhancement; and monitor
performance. There is however growing discontent around the use of such barometers which, in
some instances, have been used for the purposes of; staff reduction (Machell and Saunders 2007);
reductionist changes in teaching practice (Dill 2007); and the coaching of students in how to fill
out surveys (Strathdee 2009) at the expense of enhancing the student experience and student
engagement. The increased use of quantitative data in institutional rankings and league tables
also fosters a culture of competition amongst institutions, as opposed to focusing on the way in
which they can enhance overall tertiary outcomes (Grebennikov and Shah 2013).

In addition, satisfaction has been found to be an inadequate measure performance due to its nor-
mative nature (Brown and Mazzarol 2009; Giese and Cote 2000). Satisfaction assumes a ‘tabula rasa’
blank slate effect, that is, that all students enter their tertiary journey in equilibrium, with similar experi-
ences, contexts, tacit and implicit expectations, affective responses, and objectives. This approach can
lead to a hollowing out effect whereby ‘education’ itself is arbitrarily separated out from ‘holistic
experience’ leaving the notion of ‘the student experience’ in a social, cultural and political vacuum
which is ‘discontinuous with what has come before it and insulated from all that is around it’ (Sabri
2011, 664). These perspectives again reiterate the need for a more holistic approach to conceptualising
the tertiary journey, and the way in which it shapes student outcomes, and indeed identity.

The complexity of the tertiary experience as a transformative force was perhaps first alluded to by
Dewey (1938 [1997], 35) who pointed out that the tertiary experience ‘modifies the one who acts and
undergoes’ and ‘covers the formation of attitudes that are emotional and intellectual’. The higher
education literature has arguably recently returned to this conceptualisation that takes into
account ontological perspectives of the tertiary experience and its contribution to who and what stu-
dents are becoming (Barnacle and Dall’Alba 2017). This perspective has been discussed under the
nomological frameworks of ‘student involvement’, ‘academic integration’, the ‘student experience’,
‘research-led teaching’ and ‘academic engagement’ (Fredricks and McColskey 2012; Khademi Ashk-
zari, Piryaei, and Kamelifar 2018; Trowler 2010) and more recently, ‘student engagement’, ‘student
partnership’ and ‘student collaboration’ (Healey, Flint, and Harrington 2014; Kahu and Nelson 2018).

Student engagement has been linked to an array of traditional success factors such as increased
retention (Khademi Ashkzari, Piryaei, and Kamelifar 2018); high impact and lifelong learning (Artess,
Mellors-Bourne, and Hooley 2017); curricular relevance (Trowler 2010); enhanced institutional repu-
tation (Kuh et al. 2006); increased citizenship behaviours (Zepke, Leach, and Butler 2014); student per-
severance (Khademi Ashkzari, Piryaei, and Kamelifar 2018); and work-readiness (Krause and Coates
2008). It has also been linked to more subjective and holistic outcomes for students themselves includ-
ing; social and personal growth and development (Zwart 2009); transformative learning (Kahu 2013);
enhanced pride, inclusiveness and belonging (Wentzel 2012); student wellbeing (Field 2009).

This study responds to calls for research to investigate the dimensions that drive positive student
evaluations of the tertiary experience (e.g. Kuh et al. 2006). It develops a revised, approach to measur-
ing student engagement. In order to capture the holistic nature of engagement on transformative
tertiary experiences, this paper statistically examines the interrelationships between two antece-
dents, namely student expectations and involvement and their role in shaping the four dimensions
of engagement namely affective, social, behavioural and cognitive engagement. This paper also
examines the interrelationships between the engagement dimensions, and key student and insti-
tutional success factors including student wellbeing, transformative learning, self-efficacy, self-

1208 J. L.-H. BOWDEN ET AL.

esteem and institutional reputation. In doing so it identifies the varied impact that these dimensions
have on key institutional and student success factors.

Theoretical framework and model development

Student engagement has been referred to in the literature as: ‘student involvement’, ‘academic inte-
gration’, the ‘student experience’, ‘research-led teaching’ and ‘academic engagement’ (Fredricks and
McColskey 2012; Khademi Ashkzari, Piryaei, and Kamelifar 2018; Trowler 2010). Recent conceptualisations
have begun to more consistently adopt the terminology of ‘student engagement’, ‘student partnership’
and ‘student collaboration’ (Healey, Flint, and Harrington 2014; Kahu 2013). Student engagement is now
considered to be an overarching ‘meta-construct’ in which an eco-system of students, educators, service
staff and institutions interact to create enriching tertiary experiences (Junco 2012; Kahu 2013; Trowler
2010; Zepke 2014). Reschly and Christenson (2012, 3) note that ‘academic time is important but not
enough to accomplish the goals of education – development across academic, social-emotional, and
behavioral domains. Student engagement is the glue, or mediator, that links important contexts’ such
as student’s home lives, university, peers, and community to student success. As a meta-construct, Fre-
dricks, Blumenfeld, and Paris (2004) note that engagement includes effort, persistence, concentration,
attention; thoughtfulness and willingness to exert mental effort; and emotional responses such as inter-
est, happiness, sadness, boredom and anxiety. In this way Blumenfeld et al. conceptualise student
engagement as multidimensional and consisting of cognitive, emotional and behavioural dimensions.

Student engagement is also noted to manifest through both positive and negative valences
(D’Errico, Paciello, and Cerniglia 2016). Positive valences are typically compiled of positive states
such as enjoyment, pride, satisfaction and negative valences consisting of negative states such as
anger, anxiety, frustration. Student engagement valences also vary in level of activation, polarity
and intensity (Russell 1980). Research has demonstrated that positive engagement valence contrib-
utes to student success including attention, immersion and problem-solving (Pekrun and Linnen-
brink-Garcia 2012). Conversely negative valences precipitate disengagement, avoidance and
withdrawal and undermine students’ intrinsic motivation (Pekrun and Linnenbrink-Garcia 2012). Posi-
tive engagement is therefore central to academic success and achievement (De Carolis et al. 2019;
D’Errico, Paciello, and Cerniglia 2016). See Table 1 for additional conceptualisations of engagement.

The most commonly accepted definition of student engagement is:

A multi-aspect construct that includes effort, resiliency, and persistence while facing obstacles (vigor), passion,
inspiration, and pride in academic learning (dedication), and involvement in learning activities and tasks (absorp-
tion) as the main facets of this construct. (Schaufeli et al. 2002)

Drawing upon the literature within higher education, and psychology, we reposition student engage-
ment as consisting of four distinct yet interrelated dimensions, namely behavioural engagement,
affective engagement, cognitive engagement and social engagement (Bowden et al. 2017). We
propose that these dimensions jointly motivate a student’s striving, persistence and retention
within academic contexts (Klem and Connell 2004; Kuh 2001, 2003). We therefore re-define
student engagement as multi-dimensional and as:

A student’s positive social, cognitive, emotional, and behavioural investments made when interacting with their
tertiary institution and its focal agents (such as peers, employees and the institution itself).

The following section introduces the constructs examined in this study and develops the research
hypotheses and structural model presented in Figure 1 to be examined.

Antecedents to student engagement

The shaping role of expectations

Pre-tertiary expectations are an important determinant of students’ engagement with their tertiary
experience, as Bryson, Hardy and Hand (2009, 1) explain:

STUDIES IN HIGHER EDUCATION 1209

The conception of engagement encompasses the perceptions, expectations and experience of being a student
and the construction of being a student.

Expectations capture the perceived difference between what is anticipated (expectations), and what
is delivered (performance) (Bartikowski and Llosa 2004; Oliver 1980) leading to confirmation, positive
disconfirmation or negative disconfirmation (Berry 1995; Teas 1981). The relationship between expec-
tations and engagement is well established in the literature, as Jaakkola and Alexander (2014, 257)
note;

… motivation to engage relates to their expectation of value outcomes

Most students enter university with a wide range of expectations, given their lack of prior experience
with the higher education sector (Kuh et al. 2006) and these expectations impact their subsequent
experience. For example, students who enter university lacking expectations to participate with aca-
demic staff and peers are likely to experience reduced behavioural engagement (Kuh et al. 2006). Stu-
dents with positive academic expectations (e.g. expected grades, hours of study, motivation and
intellectual challenge) are more likely to demonstrate enhanced cognitive engagement through per-
severance when presented with challenging academic tasks (Crisp et al. 2009). Exceeding students’
expectations is likely to generate strong affective engagement, as positive disconfirmation creates feel-
ings of delight, joy and surprise that create stronger emotional connections to the tertiary provider
(Bowden 2013). Conversely, if students experience isolation and loneliness, it can deter their willing-
ness to be socially engaged, as they perceive future attempts at social integration as tokenistic and
‘time wasting’ (Crisp et al. 2009; Kuh et al. 2006). As such, it is important to ensure that expectations
are congruent with students’ experiences at university in order to generate high levels of behavioural,
social, cognitive and affective engagement (Chipchase et al. 2017; Kuh et al. 2006). We therefore
propose that;

Table 1. Selected definitions of student engagement.

Definition Source Orientation

The extent to which students are engaging in activities that higher
education research has shown to be linked with high-quality learning
outcomes

Krause and Coates (2008) Behavioural

The concept of student engagement is based on the constructivist
assumption that learning is influenced by how an individual participates
in educationally purposeful activities

Coates (2007) Behavioural

Student engagement is concerned with the interaction between the time,
effort and other relevant resources invested by both students and their
institutions

Trowler (2010) Behavioural

An observable, action-orientated subtype (behavioural) and two internal
ones (cognitive and affective engagement) but then is differentiated
from motivation as engagement being action (observable behaviour),
motivation as intent (internal)

Christenson, Reschly and
Wylie (2012)

Psychological,
tricomponent

A multi-aspect construct that include effort, resiliency, and persistence
while facing obstacles (vigour), passion, inspiration, and pride in
academic learning (dedication), and involvement in learning activities
and tasks (absorption) as the main facets of this construct

Schaufeli et al. (2002) Psychological,
tricomponent

The extent to which students feel welcomed by institutional environments
and climates

Johnson et al. (2007)

Socio-cultural

The extent to which students succeed in integrating and the amount of
social support received

Eggens, Van der Werf, and
Bosker (2008)

Socio-cultural

A metaconstruct that includes; Behavioural engagement includes
involvement in academic and social or extracurricular activities; positive
conduct, absence of disruptive behaviours; effort, persistence,
concentration, attention; participation in governance. Cognitive
engagement incorporates thoughtfulness and willingness to exert the
effort. Emotional engagement encompasses students’ affective reactions
in the classroom, including interest, boredom, happiness, sadness, and
anxiety

Fredricks, Blumenfeld, and
Paris (2004)

Holistic/
transformational

1210 J. L.-H. BOWDEN ET AL.

H1: Expectations are positively related to behavioural engagement

H2: Expectations are positively related to affective engagement

H3: Expectations are positively related to social engagement

H4: Expectations are positively related to cognitive engagement

The shaping role of involvement

Involvement is defined as the ‘perceived relevance … based on inherent needs, values, and interests’
(Zaichkowsky 1985, 342). Involvement motivates one’s attention and commitment to the relationship
(Brodie et al. 2011; Kinard and Capella 2006; Pansari and Kumar 2017) before, during and after
exchanges (Bowden 2009; Dessart, Veloutsou, and Morgan-Thomas 2015). Involvement is a strong
driver of student engagement (Mahatmya et al. 2012; Skinner and Pitzer 2012; Trowler 2010). Stu-
dents who are not merely involved, but actively and multidimensionally engaged, are more likely
to benefit from favourable academic, social and personal outcomes from their university experience
(Kuh et al. 2008). According to Hu, Ching, and Chao (2012, 74) students;

… learn more when they are intensely involved in their education.

Involvement has also been found to drive behavioural engagement, as students’ involvement in
‘enriching educational experiences’ motivates their citizenship behaviours, strengthening their
active participation at university (Skinner and Pitzer 2012). Involvement has strong links with the
affective dimension of engagement, as the more involved a student is, the higher the propensity
for the creation of intrinsic value (e.g. joy, elation, enjoyment, passion, pride) from their tertiary
experience (Kahu 2013; Pansari and Kumar 2017). Lastly, involvement can motivate the social dimen-
sion of engagement, as increased involvement in the tertiary experience on campus and off campus,
enhances feelings of connection, belonging, warmth, and relatedness between students and their
peers, staff and the institution (O’Keefe 2013).

We therefore propose that;

H5: Involvement is positively related to behavioural engagement

H6: Involvement is positively related to affective engagement

Figure 1. Conceptual model of the four pillars of student engagement.

STUDIES IN HIGHER EDUCATION 1211

H7: Involvement is positively related to social engagement

H8: Involvement is positively related to cognitive engagement

Conceptualising the four pillars of student engagement

Students may exhibit the four dimensions of engagement, namely behavioural, affective, social and
cognitive simultaneously or in isolation. We address each of the dimensions of engagement in turn,
next, before discussing their interrelationships with specific institutional and student success factors.

Behavioural engagement

The behavioural dimension of engagement is defined as the observable academic performance and
participatory actions and activities (Dessart, Veloutsou, and Morgan-Thomas 2015; Schaufeli et al.
2002). Positive behavioural engagement is measured through observable academic performance
including: student’s positive conduct; attendance; effort to stay on task; contribution; participation
in class discussions; involvement in academic and co-curricular activities; time spent on work; and
perseverance and resiliency when faced with challenging tasks (Kahu et al. 2015; Klem and
Connell 2004). Behaviourally engaged students therefore exhibit proactive participatory behaviours
through their involvement and participation in university life, and extracurricular citizenship activities
(Ashkzari, Piryaei, and Kamelifar 2018). The behavioural dimension is the most frequently measured
dimension within national barometers of the student experience (Kuh 2009; Zepke 2014).

Affective engagement

The affective dimension of engagement relates to the summative and enduring levels of emotions
experienced by students and captures the degree of passion students feel towards the tertiary experi-
ence (Schaufeli et al. 2002; Bowden 2013). Affective engagement manifests through heightened
levels of positive emotions during on campus and off campus activities, which may be demonstrated
through happiness, pride, delight, enthusiasm, openness, joy, elation and curiosity (Klem and Connell
2004; Pekrun et al. 2002). Emotionally engaged students are able to identify the purpose and
meaning behind their academic tasks, and social interactions (Schaufeli et al. 2002). D’Errico, Paciello,
and Cerniglia (2016) also found that within e-learning, achievement emotions vary by learning task.
These positive emotions were also found to correlate with behavioural engagement (D’Errico et al.
2018). Despite being central to engagement, the emotional component of students’ experiences
has largely been under researched in the literature (Askham 2008; Kahu 2013; Pekrun et al. 2002).
However, emotions are closely linked with students’ learning, achievement, life satisfaction, and
health (Pekrun and Linnenbrink-Garcia 2012). Feelings of optimism, pride, joy, and enthusiasm can
create a sustained psychological investment in the tertiary experience that extends beyond university
(Pekrun et al. 2002).

Social engagement

The social dimension of engagement considers the bonds of identification and belongingness
formed between students and their peers, academic staff, administrative staff and other pertinent
figures in their tertiary experience (Pekrun and Linnenbrink-Garcia 2012; Wentzel 2012). It generates
feelings of inclusivity, belonging, purpose, socialisation and connection to the tertiary provider
(Eldegwy, Elsharnouby, and Kortam 2018; Goodenow 1993; Krause and Coates 2008; Vivek et al.
2014). Within the classroom, social engagement is characterised by the ‘unwritten’ rules of the learn-
ing environment, such as cooperation, listening to others, attending class on time, and maintaining a
balanced teacher–student power structure (Coates 2007; Pekrun and Linnenbrink-Garcia 2012;
Wentzel 2012). Outside the classroom, social engagement is displayed through students’

1212 J. L.-H. BOWDEN ET AL.

participation in community groups, study groups and student societies, where bonds are formed with
others based on shared values, interests or purpose (Freeman, Anderman, and Jensen 2007; Wentzel
2012). Social engagement strengthens the sense of achievement students gain from their university
experience (Finn and Zimmer 2012). Students who lack social engagement are more likely to experi-
ence loneliness, isolation (McIntyre et al. 2018) leading to reduced wellbeing (Hoffman et al. 2002;
McIntyre et al. 2018).

Cognitive engagement

The cognitive dimension of engagement reflects the set of enduring and active mental states experi-
enced with respect to focal objects of engagement (Vivek et al. 2014). This can include the level of
positive attention and interest paid to tertiary communications, and time spent planning and organ-
ising academic pursuits (Zepke, Leach, and Butler 2010). Students that are cognitively engaged
demonstrate an increased understanding of the value and importance of academic work through
their perceptions, beliefs, thought processing and strategies employed during academic tasks (Ashk-
zari et al. 2018; Kahu 2013). As such, cognitively engaged students are more likely to demonstrate
higher order thinking given their ability to be cognisant of the content, meaning and application
of academic tasks (Christenson, Reschly, and Wylie 2012; Kuh et al. 2006). The following section
explores the consequences of student engagement for specific student success factors.

The outcomes of student engagement

Institutional reputation

Institutional reputation is defined as ‘the overall perception … what it stands for, what it is associated
with and what individuals may expect’ (Helgesen and Nesset 2007, 42). From a strategic perspective,
a positive reputation can: increase profitability, improve perceptions of trustworthiness, reputability,
honesty and quality (Alan, Kabadayi, and Cavdar 2018; Robinson et al. 2018; Selnes 1998; Sung and
Yang 2008). A high-quality reputation can also positively frame pre-tertiary experiences through stu-
dents’ feelings of belonging, pride, trust and interest towards the institution (Sung and Yang 2008).
Importantly creating a positive institutional image generates a ‘halo’ effect extending to prospective
students and employees, and future industry partners (Alan, Kabadayi, and Cavdar 2018). Engaged
students are more willing to advocate as unofficial ‘spokespersons’ (Bowden 2011; Kahu 2013;
Krause and Coates 2008) and this in turn creates more trustworthy perceptions of an institution
(Beerli Palacio, Díaz Meneses, and Pérez 2002; Harrison-Walker 2001). Therefore, we propose that;

H9: Behavioural engagement is positively related to reputation

H10: Affective engagement is positively related to reputation

H11: Social engagement is positively related to reputation

H12: Cognitive engagement is positively related to reputation

Student wellbeing

In addition to traditional success factors such as institutional reputation, the tertiary experience can
have an enhancing effect on students’ lives through enhanced wellbeing (Christenson, Reschly, and
Wylie 2012). Wellbeing is defined by Field (2009, 9) as:

A dynamic state, in which the individual is able to develop their potential, work productively and creatively, build
strong and positive relationships with others, and contribute to their community. It is enhanced when an individ-
ual is able to fulfil their personal and social goals and achieve a sense of purpose in society.

STUDIES IN HIGHER EDUCATION 1213

Wellbeing is a desirable outcome of student engagement, as it can alleviate stress, anxiety and
depression which are increasingly reported amongst tertiary students (Hoffman et al. 2002; McIntyre
et al. 2018); and, results in enhanced self-worth and efficacy, which are crucial for continued success-
ful learning (O’Keefe et al. 2012). Purposeful engagement’ through strong social relationships is a key
antecedent to psychological and subjective wellbeing. Thus, when students engage with others it
provides them with a sense of purpose, pride, self-efficacy and control which in turn enhances
their sense of wellbeing across a number of life domains including their health, happiness and
social and community experiences (Anderson et al. 2013). We propose that;

H13: Behavioural engagement is positively related to wellbeing

H14: Affective engagement is positively related to wellbeing

H15: Social engagement is positively related to wellbeing

H16: Cognitive engagement is positively related to wellbeing

Transformative learning

Transformative learning aggregates how a student’s social and academic experiences during univer-
sity impact their view of;

… themselves, the campus, the community, and the world as a whole. (Zwart 2009, 86)

Transformative learning is a highly reflective outcome of student engagement that considers the
‘humanisation’ or identity building that occurs through tertiary education (Taylor 2007; Bryson,
Hardy, and Hand 2009). It both requires and creates a sense of maturity, independence and accep-
tance of oneself and others (Merriam 2004) and arises from deep engagement with multiple
aspects of the tertiary experience (Taylor 2007; Zwart 2009). It is ‘one of the essential factors in a trans-
formative experience’ (Taylor 2007, 179) and it provides students with a wider perspective on life that
is more inclusive, permeable and integrated with others’ views (Merriam 2004). These benefits are
realised at both an individual and collective level as they have a ripple effect throughout the tertiary
‘ecosystem’ to benefit other students, the institution and the community at large (Anderson et al.
2013). We posit that;

H17: Behavioural engagement is positively related to transformative learning

H18: Affective engagement is positively related to transformative learning

H19: Social engagement is positively related to transformative learning

H20: Cognitive engagement is positively related to transformative learning

Self-efficacy

Self-efficacy is defined as ‘beliefs in one’s capabilities to mobilize the motivation, cognitive resources,
and courses of action needed to meet given situational demands’ (Wood and Bandura 1989, 408).
Engaged students are more likely to persevere through academic challenge, which results in
higher self-belief (Chipchase et al. 2017; Kuh 2001; Schaufeli et al. 2002). Students appraise their
level of self-efficacy based on; their personal beliefs (thoughts); actual performance; interactions
with others, including peers and teachers’ persuasions or discouragement; physiological reactions;
and environmental conditions (Chipchase et al. 2017; Gist and Mitchell 1992). Engaging and suppor-
tive tertiary environments that facilitate ‘mastery experiences’, opportunities for new ways of think-
ing, and opportunities for social connection and modelling enhance student self-efficacy and aid in
positive self-belief (Chemers, Hu, and Garcia 2001; Gist and Mitchell 1992). Therefore, we posit that;

1214 J. L.-H. BOWDEN ET AL.

H21: Behavioural engagement is positively related to self-efficacy

H22: Affective engagement is positively related to self-efficacy

H23: Social engagement is positively related to self-efficacy

H24: Cognitive engagement is positively related to self-efficacy

Self-esteem

Self-esteem is defined as ‘a global personal judgment of worthiness that appears to form relatively
early in the course of development, remains fairly constant over time, and is resistant to change’
(Campbell 1990, 539). It includes perceptions of; pride; control; success; efficacy; social respect and
acceptance; teacher approval; and confidence in their ability to meet clear goals during tertiary
study and beyond degree completion (Heatherton and Polivy 1991). Students’ who are intrinsically
motivated to complete academic activities report higher levels of self-esteem compared with extrin-
sically motivated students who are ‘going through the motions’ (Shernoff et al. 2016). In order to have
intrinsic motivation, students must be cognisant of the value, meaning and relevance of their tertiary
experience, which is generated through affective, social, cognitive and behavioural forms of engage-
ment (Khademi Ashkzari, Piryaei, and Kamelifar 2018; Kahu 2013). We propose that;

H25: Behavioural engagement is positively related to self-esteem

H26: Affective engagement is positively related to self-esteem

H27: Social engagement is positively related to self-esteem

H28: Cognitive engagement is positively related to self-esteem

Research methodology

A questionnaire was designed adapting well-accepted scales to measure the model constructs. Data
was collected from students enrolled in the Business Faculty of one top ten ranked major metropo-
litan University in Australia. The specific Faculty has an approximate enrolment of 17, 000 students,
and contains a broad range of disciplines. A sample of 952 respondents was collected from the
Business school. The sample drawn for this study was broadly representative of the profile of the insti-
tution from which it was drawn. The institution has five Faculties namely, Business, Arts, Human
Sciences, Medicine, and Science of which Business is proportionally the largest in terms of student
enrolments. In 2018 the institution had total student enrolments of 44, 558 students enrolled com-
prised of 32, 115 (full time) and 12, 443 (part time) in 2018. A total of 51% of students enrolled were
male, and 48% female. Domestic students accounted for 73% of the total enrolment. The Australian
Higher Education sector in the first half of 2018 had a total enrolment of 1, 332, 822 students; 44%
were male, 55% were female; 74% were enrolled full time and 25% part time; 71% were domestic
students and 28% international (Australian Bureau of Statistics, 2018). Table 2 provides further
details on the sampling frame. The questionnaire contained 11 constructs as identified in Table 3.

The hypotheses were tested using a two-step structural equation modelling method (Anderson
and Gerbing 1988). The psychometric properties of each measure were tested through CFA using
AMOS 25. Analysis of the measurement model showed a good fit to the data: χ2 (2481); df = 764;
p = .00; comparative fit index [CFI] = 0.938; Tucker–Lewis index [TLI] = 0.93; root mean square error
of approximation [RMSEA] = 0.049; standardised root square mean residual [SRMR] = 0.05. Conver-
gent validity was established for all scales used in this study. All standardised loadings on all con-
structs exceed the 0.50 criterion and were significantly different from zero at the 1% level. In
addition, the average variance extracted estimates demonstrated that the measurement scales
accounted for a greater proportion of explained variance than measurement error as the AVE

STUDIES IN HIGHER EDUCATION 1215

statistics were above the >0.50 criterion value. Discriminant validity was examined according to
Fornell and Larcker’s (1981) stringent test to establish separation between latent constructs and it
was established for all construct pairs.

Results

Hypotheses testing

A structural equation modelling approach was used to test research hypotheses. The model estab-
lished an acceptable fit, with χ2 = 3323.93, (df = 790; p = .00), CFI = 0.91, NFI = 0.90, TLI = 0.90,
RMSEA = 0.05. A total of 24 hypotheses were supported. Four specific hypotheses were not sup-
ported; cognitive engagement and student wellbeing (H16); self-efficacy (H24) and self-esteem
(H28) and behavioural engagement (H17) and transformative learning as shown in Table 4.

The effect of students’ expectations on the four dimensions of engagement was of a moderate
magnitude (β = 0.21–0.31, p = 000) suggesting that expectations are an important precursor to the
development of engagement. Similarly, the effect of student involvement on the dimensions of
engagement was moderate and even (β = 0.21–27, p = 000) across the elements of engagement
except for affective engagement, for which it was found to be a strong driver (β = 0.51, p = 000).
Thus, both expectations and involvement were found to both be important determinants of the
development of holistic engagement.

Affective engagement was identified as the primary determinant of institutional reputation includ-
ing students’ perceptions of its trustworthiness, and their willingness to recommend it to others (β =
0.46, p = 000). Affective commitment was also found to be the strongest determinant of student well-
being (β = 0.40, p = 000), and transformative learning (β = 0.30, p = 000). The relationship between
affective commitment and these success factors was stronger than the effect of social, cognitive
and behavioural engagement. Conversely, behavioural engagement was found to be the primary

Table 2. Demographic profile.

Attributes Category Frequency

Gender Male 41.9%
Female 58.1%

Age <25 86.3% 25+ 13.7%

Undergraduate Yes 81.6%
No 18.4%

Domestic Domestic 62.7%
International 37.3%

Indigenous Yes 2.1%
No 97.9%

Full time Full time 91.4%
Part time 8.6%

Table 3. Scales to measure constructs.

Construct Scale(s)

Expectations Gustafsson, Johnson, and Roos (2005)
Involvement Zaichkowsky (1985)
Affective engagement Schaufeli et al. (2002), Hollebeek, Glynn, and Brodie (2014)
Cognitive engagement Vivek et al. (2014)
Social engagement Vivek et al. (2014)
Behavioural engagement Schaufeli et al. (2002)
Self-efficacy Schwarzer et al. (1997)
Self-esteem Heatherton and Polivy (1991)
Transformative learning Brock (2010)
Student wellbeing Sirgy et al. (2008)
Institutional reputation Veloutsou and Moutinho (2009)

1216 J. L.-H. BOWDEN ET AL.

and strong determinant of students’ self-esteem (e.g. confidence) (β = 0.49, p = 000) and self-efficacy
(e.g. problem-solving ability and ability to manage challenges) (β = 0.58, p = 000). The results for
social engagement suggest that it has a significant but weak impact upon student success and insti-
tutional success (β = 0.14–0.29, p = 000). Cognitive engagement was found to be a necessary, but not
sufficient condition for institutional and student success. It had a significant but weak effect upon the
establishment of institutional reputation (β = 0.09, p = 000) and transformative learning (β = 0.10, p =
000).

A comparative rival model was also developed and tested to examine whether or not the multi-
dimensional holistic model of student engagement was a stronger conceptualisation and measure-
ment approach to capturing student engagement than existing barometers. The competing model
utilised the ‘learner engagement’ dimension and scales from the Australian Student Experience
Survey, a national barometer measuring the student experience and a part of the Quality Indicators
for Learning and Teaching (QILT) survey program. These items captured the extent to which students
felt a sense of belonging, prepared, and participated in tertiary life. The rival model results demon-
strated poor goodness of fit (RMSEA = 0.07, CFI = 0.847; TLI = 0.838, p = 000).

Theoretical implications

Our findings form the basis of a revised holistic measurement approach for student engagement
that accounts for the complexity of the dimensions of engagement; the multifaceted nature of
the elements that constitute its process; as well as the differential effects that the dimensions of
engagement have on key institutional and student success outcomes. It advances our current
understandings of student engagement by firstly, attempting to empirically measure the pillars
of engagement and by secondly, assessing their relative impact on key student and institutional
success factors.

Table 4. Results.

Hypotheses Relationship between constructs Path weight

H1 Expectations > behavioural engagement 0.28***
H2 Expectations > affective engagement 0.31***
H3 Expectations > social engagement 0.21***
H4 Expectations > cognitive engagement 0.27***
H5 Involvement > behavioural engagement 0.24***
H6 Involvement > affective engagement 0.51***
H7 Involvement > social engagement 0.21***
H8 Involvement > cognitive engagement 0.27***
H9 Behavioural engagement > reputation 0.05***
H10 Affective engagement > reputation 0.46***
H11 Social engagement > reputation 0.23***
H12 Cognitive engagement > reputation 0.09***
H13 Behavioural engagement > wellbeing 0.24***
H14 Affective engagement > wellbeing 0.40***
H15 Social engagement > wellbeing 0.29***
H16 not supported Cognitive engagement > wellbeing −0.04^
H17 not supported Behavioural engagement > transformative learning 0.05^
H18 Affective engagement > transformative learning 0.30***
H19 Social engagement > transformative learning 0.16***
H20 Cognitive engagement > transformative learning 0.10***
H21 Behavioural engagement > self-efficacy 0.58***
H22 Affective engagement > self-efficacy 0.14***
H23 Social engagement > self-efficacy 0.22***
H24 not supported Cognitive engagement > self-efficacy −0.06^
H25 Behavioural engagement > self-esteem 0.49***
H26 Affective engagement > self-esteem 0.33***
H27 Social engagement > self-esteem 0.14***
H28 not supported Cognitive engagement > self-esteem −0.01^
***Denotes significance at the 0.05 level; ^ not significant.

STUDIES IN HIGHER EDUCATION 1217

Firstly, the revised measurement approach to conceptualising and capturing student engage-
ment advances current conceptualisations of engagement by underlining the importance
higher education as a transformative experience (Rosenbaum et al. 2011, 5). Tertiary education
serves several fundamental roles in that it shapes not only academic and career outcomes for
students, but it also supports, fosters and develops students’ sense of self and identity (Anderson
et al. 2013). Our study also supports the importance of the tertiary experience in shaping
student wellbeing including their overall life satisfaction, and their emotional, social, financial,
psychological, physical wellbeing. This is an important finding since individual wellbeing impacts
upon collective societal wellbeing – in turn strengthening social networks, communities, neigh-
bourhoods, cities and nations (Anderson et al. 2013). Lizzio and Wilson (2009, 70) support this
broader societal level impact noting that student engagement: can also be understood as part
of the emerging and related discourses of education for democracy and ‘universities as sites of
citizenship.’

Secondly, this study has argued that student engagement needs to expand from conceptual-
isations that focus at the individual level on singular dimensions of student engagement in iso-
lation, to examine the way in which the dimensions of engagement operate interdependently to
shape student and institutional success outcomes (Reschly and Christenson 2012). This more hol-
istic conceptualisation of engagement captures the deeper and more systemic effects that tertiary
education has upon the lives of students and their experience of the tertiary environment (Kuh
et al. 2006). This measurement approach also emphasises that models of engagement should
incorporate the initiating and seeding role of pre-tertiary expectations and involvement, and
the ways in which these factors act to precondition students’ propensities for positive student
engagement. These antecedents act as continual reference points throughout the student experi-
ence; they directly impact upon the four dimensions of engagement shaping the extent to which
students experience emotional, behavioural, cognitive and social engagement; and through these
dimensions, they ultimately impact upon student and institutional success. Together we argue
that the affective, social, cognitive and behavioural dimensions of engagement identified in
this study, comprise the four pillars of tertiary student engagement. They are closely interrelated
and when integrated effectively, they constitute critical success factors for tertiary institutions
seeking to facilitate and enhance engagement opportunities. We propose that these four dimen-
sions, when measured together, comprise an invisible tapestry of student engagement. They are
closely interrelated and when stitched together and constitute critical factors for institutional and
student success.

Thirdly this study advances the student engagement literature by examining how the four pillars
of student engagement differentially impact upon student and institutional success. We find that stu-
dents can be variably engaged across one or more of the dimensions, and that these dimensions
have varying impacts upon the extent to which institutions can build a positive reputation, and
support students in their transformative learning, self-esteem, self-efficacy, and personal wellbeing.
We propose that each dimension contributes to enhanced engagement and a more profound and
transformative tertiary experience which fostered positive dynamic and value-creating interactions
and created rich opportunities for learning, sense-making, and proactive learning (Bensimon
2007). However, within our specific study context, we find that the most important dimensions for
institutional measurement and monitoring were those of affective and behavioural engagement.
Affective engagement through positive feelings and emotions, supported students’ sense of well-
being and transformative learning. Students’ feelings of happiness, pride, delight, enthusiasm, open-
ness, joy, elation and curiosity led to the establishment of a strong institutional reputation as
indicated through positive referral and retention. Behavioural engagement empowered students
to develop a strong sense of self-esteem and self-efficacy. This subsequently strengthened their
sense of competence and enhanced their perceived work-readiness. The next section discusses
the implications of these findings for tertiary practice.

1218 J. L.-H. BOWDEN ET AL.

Managerial implications

Involvement was identified as a positive antecedent of student engagement, reinforcing the impor-
tance of framing university as interesting, relevant, inspiring and meaningful. Involvement was the
strongest driver of affective engagement, suggesting that highly involved students are more likely
to feel happiness, pride and enthusiasm towards their institution. The development of collaborative
partnerships between students and the institution prior to enrolment, and during enrolment in par-
ticular can facilitate engagement and reinforce feelings of attachment and commitment which help
prevent students from becoming withdrawn and disengaged.

As an antecedent, expectations also positively shaped student engagement, highlighting the
importance of understanding pre-tertiary expectations, especially given the heighted perceptions
of risk and ambiguity that surround institutional selection for prospective students. Expectations
start to influence engagement before students enrol and remain an important anchor for ongoing
quality judgements of the tertiary experience. Expectations had the strongest impact on students’
affective engagement. This may be because when students’ expectations are exceeded, they are
likely to experience positive feelings of surprise, delight and happiness. Understanding, monitoring
and measuring the multi-focal sources, drivers and characteristics of these pre-tertiary expectations
in relationship to the dimensions of engagement is essential to proactively shaping the student-insti-
tution relationship.

With regard to the four dimensions of student engagement, affective engagement was the primary
determinant of institutional reputation, student well-being and transformative learning. The
emotional connections students form with and among staff, peers, and the campus and institution
at large establishes a superordinate goal, creates a positive perception of the institution, provides
a heighten sense of well-being and facilitates transformative learning experiences. Tertiary insti-
tutions that facilitate emotional connectivity are more likely to be rewarded via student advocacy
and recommendation enhancing institutional reputation. In addition, meaningful community partici-
pation enhances students’ sense of inclusion and ownership over their tertiary journey, and this gen-
erates positive feelings of pride which improves student wellbeing. Lastly, when students are
emotionally engaged, they are more likely to be receptive to others’ ideas, opinions and, are more
malleable in their own beliefs and perceptions enhancing transformative learning.

Emotional connection can be further enhanced at the strategic level through institutional ‘place
identification’ communication strategies which use highly emotive and personalised imagery
drawing upon students lived experiences, successes and achievements, memories and connec-
tions to the institution, their transformative growth, and careers. These approaches celebrate col-
lective success and can be used to demonstrate that every student can have a personal victory
during their tertiary pursuit. Emotional connection can also be facilitated through pre-tertiary con-
nection through ‘relationship seeding’ initiatives such as by giving students a voice and allowing
them to express their interests and strengths in personal statements prior to enrolment. Insti-
tutions can respond to these with personalised communications which provide future students
with increased awareness of ‘connection’ points through institutional activities and events; and
opportunities to emotionally connect to the institution and to others through membership.
These approaches knit individual experiences into the collective core of institutional belonging,
facilitate pride and passion, support ambitions and foster a collective, connected and unified
emotional identity.

Our findings concerning the pivotal role of behavioural engagement on the remaining two student
success dimensions of self-esteem and self-efficacy affirm the importance of the more functional
dimensions of engagement. The findings point to the need for institutions to continue to facilitate
opportunities for and monitor indicators of behavioural engagement such as participation in
campus life; attendance (or view rates in digital environs); effort to stay on task; contribution; partici-
pation in class discussions; involvement in academic and co-curricular activities; time spent on work;
and perseverance and resiliency when faced with challenging tasks.

STUDIES IN HIGHER EDUCATION 1219

Behavioural engagement was found to strongly drive students’ sense of self-efficacy and self-
esteem, reinforcing student’s belief in their ability to achieve goals, and create positive evaluations
of self-worth. As such, this dimension reflects the autonomous aspects of university in the achieve-
ment of personal goals. The strong relationship between behavioural engagement and self-
efficacy is not surprising, as students largely appraise self-efficacy based on their performance on aca-
demic tasks. Therefore, students who dedicate appropriate time to mastering skills and tackling ter-
tiary learning demands with a motivation to succeed are more likely to report higher levels of self-
efficacy, career-readiness and employability.

Behavioural engagement can be further enhanced through fostering an organisational culture
that aims to encourage students’ effort, persistence, reliance and achievement at all student touch-
points (i.e. in the classroom, on online platforms, on campus). It may also be enhanced through posi-
tioning academic tasks, coursework and involvement in extra-curricular activities as a series of
positive and worthwhile ‘challenges’ to be tackled that will provide life-long skills. An institutional
vision that focuses on positioning the tertiary experience as an opportunity for personal growth
and development, alongside other outcomes such as grades and GPA’s is essential.

Perhaps somewhat surprising in nature, cognitive engagement, that is students’ intensity of immer-
sion in their educational experience was found to be much weaker and more diffuse in its impact on
engagement. In this sense, it is a necessary but not sufficient condition for holistic engagement. Cog-
nitive engagement operates as a core requirement of the tertiary journey however, this study
suggests that it does not provide for the key point of differentiation within the student experience.
Cognitive engagement should of course continue to be prioritised given its core role in the tertiary
experience and given that graduates become the workforce of the future. Strategies to enhance this
dimension should focus on enhancing students’ ambitions through transformative learning experi-
ences, and developing their knowledge, enterprise, collaborative, and employability competencies
and skill sets. Institutions should focus on inspiring agency for all students and giving access to
knowledge advancement for all students.

Social engagement, which reflects the bonds of identification and belongingness formed between
students, staff and the broader tertiary experience, was found to be a weak determinant of insti-
tutional and student success. Nonetheless, social connection is a necessary element of holistic
student engagement. Social engagement can create more inclusive campus environments, as it
fosters equality between teachers and students, and helps relax the hierarchical relationships stem-
ming from the often perceived ‘ivory tower’ effect of academic culture. The very act of engaging stu-
dents from a relational perspective builds trust, confidence and empowerment and fosters positive
and proactive habits of mind and heart that facilitate continuous learning, personal growth and
development, and increased educational and emotional commitment. It is important to recognise
that social engagement does not merely begin at ‘enrolment on-boarding’. It commences with
pre-connection, buy in and empowerment of the student voice during the upper years of secondary
school, is enhanced through the tertiary experience, and continues post-tertiary as students aspire to
and achieve their career goals.

For the recommendations of this study to be implemented effectively they will need to be
adapted to fit with the institution’s strategic vision for student engagement and connected commu-
nities. They will also need to be tailored to fit with other potentially competing priorities. If, as is com-
monly the case, institutional research is identified as a top tier priority, then this may leave students
questioning their level of (de)prioritisation by the institution. In light of this potential perception it is
important to reinforce an institution-wide understanding that students inherently experience
research through teaching and engagement with the broader campus culture and that a student-
centric vision is required.

This study represents a preliminary attempt to develop a holistic metric approach to student
engagement. Certainly, further research addressing the complexities of student engagement and
its effects will be needed to explore more deeply the way in which engagement operates. Future
research should examine the extent to which the metric approach and results are generalisable

1220 J. L.-H. BOWDEN ET AL.

across different segments of students, from low to high SES, domestic and international students,
undergraduate and postgraduate students; different Faculties within institutions; as well as across
cross-cultural contexts. It should also examine expressions of both positive and negative valences
of engagement across various cohorts such as young learners, adult learners, and online and
offline modes of teaching delivery and their relationship to the specific student success and insti-
tutional success factors identified. This research should also attempt to examine the way in which
specific attributes of the tertiary experience contribute to the four global dimensions of engagement.

Importantly, any metric approaches and subsequent policies developed to support student
engagement and ultimately student success must necessarily incorporate a top-down supported sys-
tematic quality assurance programme with monitoring and measuring milestones to ensure that hol-
istic student engagement is realised. Put simply, institutions cannot simply expect students to
engage themselves. Rather, the onus is on institutions to understand the determinants of engage-
ment, and to then proactively translate this understanding into effective ‘experience design’ which
fosters the conditions that allow diverse student populations to mutually interact and engage;

Most institutions can do far more than they are doing at present to implement interventions that will change the
way students approach college and what they do after they arrive. The real question is whether we have the will
to more consistently use what we know to be promising policies and effective educational practices in order to
increase the odds that more students get ready, get in, and get through.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Jana Lay-Hwa Bowden http://orcid.org/0000-0001-5681-5709

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1224 J. L.-H. BOWDEN ET AL.

Abstract

Introduction
Theoretical framework and model development
Antecedents to student engagement
The shaping role of expectations
The shaping role of involvement
Conceptualising the four pillars of student engagement
Behavioural engagement
Affective engagement
Social engagement
Cognitive engagement
The outcomes of student engagement
Institutional reputation
Student wellbeing
Transformative learning
Self-efficacy
Self-esteem
Research methodology
Results
Hypotheses testing
Theoretical implications
Managerial implications
Disclosure statement
ORCID
References

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