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Abstract

The objective of this study is to determine the relationship between learning engagement, academic motivation, and academic performance in undergraduate students and the importance of religion in determining the academic motivation and academic performance. A sample of 840 university students from different regions (provinces) of Pakistan is pooled through a convenient sampling technique. Engagement Versus Disaffection (EVD) and the Academic Motivation Scale (AMS) are administered for learning engagement and academic motivation, respectively. After applying analysis of variance (ANOVA), Pearson product-moment correlation analysis, and hierarchical regression, the findings of this research reveal that learning engagement and academic motivation have significant relationships with academic performance. Furthermore, it is also found that, in religiosity, academic motivation for both Muslims and non-Muslims do not induce learning engagement, but Muslim students have shown better academic performance than non-Muslims. This study can be beneficial for policymakers and practitioners to analyze the determinants of learning engagements and improve the academic performance of university students.

Keywords

learning engagement academic motivation academic performance religion ANOVA AMS EVD

Introduction

In this modern age, millions of students get their undergraduate degrees. Researchers have shown that the predictors of higher grades of students are their IQ level and intelligence. Duckworth and Seligman (2005) examine that the IQ is the predictor of success and failure, as well as higher and lower academic performance. However, researchers have also provided evidence which shows that, except for the IQ level and intelligence, there are certainly other variables that can predict the academic performance, that is, learning engagement and academic motivation (Lee, 2014; Skinner et al., 2008).

Students’ learning engagement is a widely recognized and researched topic by the researchers. It has been tried to explore various dimensions and subdimensions ranging from culture, society, academic motivation, religiosity, institutional belongingness and socialization, and academic performance. These topics vary from old traditional methods to technologically advanced methods by considering intercultural aspects.

Learning engagement is a nebulous concept, and it has been defined in different ways. One view holds that engagement is the “time and effort students devote to activities that are empirically linked to desired outcomes and what institutions do to induce students to participate in those activities” (Kolb & Kolb, 2012). Learning engagement of students refers to the participation in educational and noneducational pursuits, and dedication toward academic objectives and education (Shernoff, 2013). Truss et al. (2013) have defined learning engagement of the students as the capacity of behavioral, emotional, and cognitive involvement in scholastic pursuits. Pickett (2007) has argued that students’ learning engagement is a function of psychological contribution toward learning, knowledge appreciation, skills, and talent that promote voluntary academic work. Another view of engagement refers to the extent of a student’s active participation in learning actions (Pekrun & Linnenbrink-Garcia, 2012). Student engagement involves determining strategies and practices that encourage and provide students with opportunities to be inquisitive and participate in discourse within the culture of the classroom as well as society, especially religion, in general, so that they can be active members both in and outside of the educational setting (Quaye & Harper, 2014). Engagement is merely the depth of the student’s physical and cognitive interaction with the content (Butt, 2014).

Most of the above arguments defining the learning engagement of the students involve academic motivation and academic performance of the student. Researchers argue that motivation is a predicational process which is involved in the goal selection and the persistence and intensity of pursuing a selected goal (Axler, 2008; Chirkov & Ryan, 2001). Academic motivation has been characterized as a general desire or aura to succeed in academic work and the more particular tasks of school (Pekrun & Linnenbrink-Garcia, 2012). However, engagement has been related to positive student results, including higher evaluations and diminished dropouts (Connell et al., 1994; Schlechty, 2001). The most engaging work allows for creativity, sparks curiosity, provides an opportunity to work with others, and produces a feeling of success (Bowen, 2003). Therefore, academic performance is the degree to which students attain the concept of mastery (Reyes et al., 2012). The academic performance consists of grade point average (GPA) and academic standing (Zientek, 2008). Academic performance is a comprehensive measure that can incorporate evaluation point (GPA), standardized test scores, graduation rates, and honor or ranks (Reyes et al., 2012). Patrick et al. (2007) link student engagement with academic motivation, which is affected by various factors like a person’s objectives and cultural background, prior life experiences, and the teacher’s and peers’ opinions about the person. Wen and Lin (2012) argued that academic motivation is a psychological factor that brings either positive or negative behaviors in a student’s academic performance.

Most of the existing literature exhibits studies in developed economies. Very less attention has been paid to developing countries, especially to those countries where religion is a dominant factor in their cultural and societal formations. This study is conducted on five full-scaled Pakistani university students, especially considering undergraduate students, to get a clear picture of student engagements in the learning process and the factors affecting their academic motivation and academic performance. Engagement Versus Disaffection (EVD; Skinner et al., 2008) and the Academic Motivation Scale (AMS; Vallerand et al., 1992) are administered for learning engagement and academic motivation, respectively. The student’s last semester cumulative grade point average (CGPA) measures academic performance. After applying analysis of variance (ANOVA), Pearson product-moment correlation analysis, and hierarchical regression on the sample of 840 university students from five full-scaled universities (main campuses and subcampuses) in different regions (provinces), the findings reveal that learning engagement and academic motivation have a significant relationship with academic performance, whereas learning disaffection has a negative relationship with academic performance. There is no significant impact of religion on the relationship of learning engagement and academic motivation, whereas it significantly affects the relationship of learning engagement with academic performance. Furthermore, it is also found that religion has no significant impact on academic motivation for both Muslims and non-Muslims and do not induce learning engagement, but Muslim students have shown better academic performance than non-Muslims.

The remainder of the study is organized as follows: Section “Theoretical Background” reviews the theoretical background and literature review. Section “Method” illustrates the sampling and methodology. Section “Results” explains the results and Section “Discussion and Conclusion” presents the main conclusions of this article with recommendations.

Theoretical Background

Many renowned scholars have proposed their theories to explain the learning process based on motivation and linking them with performance. Starting from Maslow’s theory of motivation (Maslow, 1943), who proposed that humans are motivated by a hierarchy of needs in which a person must meet one need to move to the next need. Kearsley and Shneiderman (1998) proposed that Engagement Theory specifically promotes students’ activities that involve cognitive processes such as creating, problem-solving, reasoning, decision-making, and evaluation in which students are motivated to learn due to the essential nature of the learning environment and activities, whereas technology is an essential factor in facilitating learning. They proposed that educational activities must be based on teamwork and project based, and these projects must be realistic and result oriented.

Goal orientations by Eison (1980) indicated that academic motivation is based on task-based achievements. He further divided academic motivation into two major classes. The first is the mastery goal, and the second is the performance goal. Besides, he explains that performance-oriented students are interested in competition, demonstrating their competence, and outperforming others. They tend to use other students as points of comparison, rather than themselves. Regarding performance goals, performance-approach-oriented students are interested in demonstrating that they are more competent than other students (i.e., have more ability than others). In contrast, performance-avoidance-oriented students are interested in avoiding appearing incompetent or stupid (Ames, 1992).

Self-determination theory (Deci & Vansteenkiste, 2004) is about using traditional empirical methods to explain human motivation and personality. It explains that self-motivation and personality growth are based on people’s inherent growth tendencies and innate psychological needs. Furthermore, they identified three basic needs “competence, relatedness, and autonomy” for facilitating optimal functioning of the natural tendencies for growth and integration and considered these needs essential for productive social development and personal well-being.

Both Sigmund Freud’s hypothesis and Martin Ford’s motivational frameworks hypothesis (MST), argue about human growth in natural, social, and ecological settings that are essential for development (Ford, 1992). Based on that, Campbell (2007) proposed an equation to determine achievement in a scientific way: Achievement = ([Motivation × Skill] / Biological Structure) × Responsive Environment. The recipe recommends that real “accomplishment and fitness are the consequences of a persuaded, capable, and organically proficient individual collaborating with a responsive domain.” This model attempts to provide a comprehensive theory of motivation and proposes that actual achievement and competence are the results of a motivated, skillful, and the biologically capable person interacting within a responsive environment.

Theory of Educational Productivity (Walberg, 1980) determines three groups of nine factors based on active, cognitive, and behavioral skills for optimization of learning that affect the quality of academic performance: aptitude (ability, development, and motivation), instruction (amount and quality), and environment (home, classroom, peers, and television).

Researchers have used multiple social factors to determine the motivation and performance in various fields, especially in education. They have shown that religion is one of the most important factors which affects motivation and performance in society (Kim & Wilcox, 2014; Nelson & Clews, 1973; Regnerus, 2003). Durkheim (2009), one of the leading sociologists, argues that religion has great importance in education development. He acclaims that religion influences personal as well as societal aims (education) and works as a functional element in society.

Pakistan is a Muslim-majority (>95%) country, which provides a good sample for determining the role of religion on the societal factors, especially on education. Hence, this study is going to add up in theory by considering the religious-based regional–cultural environment to determine the factors that affect student learning, academic motivation, and academic performance of Pakistani university students. This study is also going to determine which theory explains the most or best fit in the Pakistani context.

Literature Review and Hypothesis Development

Researchers have examined and used various approaches (i.e., cultural, religion, psychology, demographics) to explain the relationship between student engagement and academic performance. Some researchers have argued that learning engagement of university students is affected by behavioral engagement (defined as effort and perseverance in learning), and emotional engagement (defined as a sense of belonging) significantly predicts reading performance in developed countries (Lee, 2014). The effect of emotional engagement on reading performance was partially mediated through behavioral engagement. Amrai et al. (2011) study the correlation between academic motivation and academic achievement among Tehran University students. They also show a positive and significant correlation between academic motivation and academic achievement. Wonglorsaichon et al. (2014) examine the level of students’ school engagement on their learning achievement using structural equation modeling (SEM) analysis. They argue that school engagement has a direct and significant effect on achievement.

Similarly, Gunuc (2014) conducts a study to examine the relationships between student engagement and academic achievement. Student Engagement Scale and Demographic Variables Form were used for data collection. The results obtained via the analyses reveal that there is a significant relationship not only between the students’ academic achievement and student engagement but also between their academic achievement and the dimensions of cognitive engagement, behavioral engagement, and sense of belonging. Besides, it was found out that class engagement regarding cognitive, behavioral, and emotional engagements predicts academic achievement.

Yin and Wang (2016) explore the relationship between motivation and engagement of college undergraduate students in China. They show that the Motivation and Engagement Scale for college students is a promising and legitimate instrument for evaluating students’ engagement in Chinese colleges. Chinese students perform well in both adaptive and maladaptive motivation and engagement simultaneously. They further argue that social settings impact motivation and learning engagement of Chinese college students.

Reyes et al. (2012) examine the link between classroom emotional climate and academic achievement, including the role of student engagement as a mediator. By taking 63 fifth and sixth grade classrooms (N = 1,399 students) as a sample and by including classroom observations, student reports, and report card grades, they show that the positive relationship between classroom emotional climate and grades is mediated by engagement. Similarly, Broussard and Garrison (2004) also report a positive relationship between classroom engagement and academic achievement in first and third grade students. They further conclude that intrinsic motivation is positively related to academic achievement. Besides, Parikh (2008) also shows a direct relationship between student engagement and academic performance in higher education. By gathering data from 300 international students based on the College Student Experiences Questionnaire and their GPA, the author finds that international students are more engaged in activities that are positively correlated to GPA.

Moreover, the aforementioned studies showed that learning engagement, academic motivation, and performance are positively correlated. Students who had a higher level of engagement have better performance. Behavioral engagement involves a student’s behavior to study. Behavioral engagement is better to be explained as it is a highly dominating factor for an excellent performance. Students who were highly engaged in learning are motivated toward success. It is also shown that there are gender differences in the type of academic motivation; boys have extrinsic motivation, whereas girls have mostly intrinsic motivation, and they perform according to their motivation. Hence, based on the discussion as mentioned above, we can hypothesize that the following:

Hypothesis 1 (H1): Learning engagement and academic motivation significantly predict academic performance.

Furthermore, researchers have also shown that not only cultural settings affect learning engagements, but religious belief systems also interact with academic learning, motivation, and performance mechanisms (Li & Murphy, 2018; Trockel et al., 2000). Recently, researchers have shown much interest in exploring the association of belief system or religious identity with academic performance in higher education. However, researchers have shown contradictory results. They have also shown that religious association affects behavioral outcomes, which are beneficial for mental and physical health (Hill & Pargament, 2008). Researchers have also shown that having religious belief system of heaven and hell, good and evil, and reward and punishment controls individual ethical behaviors which are connected to a greater sense of well-being and, ultimately, results in higher academic performance (Bowman & Small, 2012; Lenski, 1961; McCleary & Barro, 2003). Regnerus and Elder (2003) argue that religiously active students have a positive relationship with learning engagements. Researchers have shown in their studies that personal religious beliefs or commitments have a strong association with a higher level of academic performance (Jeynes, 2003). Hence, because of the above discussion, we can formulate our hypotheses as follows:

Hypothesis 2_A (H2_A): Religion complements the relationship between learning engagement and academic performance.

Hypothesis 2_B (H2_B): Religion complements the relationship between learning engagement and academic motivation.

However, in the meanwhile, studies have shown that religious involvement is not significantly associated with academic success in higher education (Bryant, 2007). Bringle et al. (2010) have shown less enrollment of religiously active students in the second year than the first year. Bowman et al. (2014) further provide evidence in their multi-institutional and multiyear study about religious involvement and academic performance, that it is not possible to determine the association between religiosity and academic performance. Hence, based on that, we can formulate our hypotheses as follows:

Hypothesis 3_A (H3_A): Religion does not complement the relationship between learning engagement and academic performance.

Hypothesis 3_B (H3_B): Religion does not complement the relationship between learning engagement and academic motivation.

Method

This study has used five universities from various regions (provinces) of Pakistan, to collect the sample. One thousand copies of questionnaires were distributed among undergraduate students of a different department in all five teaching-oriented universities. We received 919 responses, but some of the responses were incomplete. We only selected the fully completed forms and equally distributed the sample on a gender basis (i.e., 50% male, 50% female) to control biasness. Hence, we obtained 840 (420 males, 420 females) responses for the analysis, in which 537 (63.9%) majored in sciences and 303 (36.1%) majored in humanities and social sciences. Table 1 presents the descriptive statistics and demographic data. This research aimed to explore the relationship between the study variables of learning engagement, academic motivation, and academic performance in undergraduate students.

Table 1.

Descriptive Statistics of Demographic Variables (N = 840).

Variables	F	Percentage	M	SD
Gender
Male	420	50
Female	420	50
Birth order
First	154	18.3
Middle	406	48.3
Last	280	33.3
Family system
Nuclear	665	79.2
Joint	175	20.8
Religion
Muslims	812	96.7
Non-Muslims	28	3.3
Belong to
City	574	68.3
Village	266	31.7
Residence
Own	364	43.3
Rented	119	14.2
Hostel	357	42.5
Qualification
Semester 2	147	17.5
Semester 4	399	47.5
Semester 6	245	29.2
Semester 8	49	5.8
Extra cocurricular activities
Yes	686	81.7
No	154	18.3
Part-time job
Yes	42	5.0
No	798	95
Do you like your subject teacher?
Yes	826	98.3
No	14	1.7
How many teachers?
One	70	8.3
Two	126	15.0
All	630	75.0
None	14	1.7
Marital status
Single	830	98
Married	10	2
Father’s education
Up to matric	280	33.3
Up to intermediate	147	17.5
Up to bachelor’s	98	11.7
Up to master’s	203	24.2
Uneducated	112	13.3
Mother’s education
Up to matric	245	29.2
Up to intermediate	217	25.8
Up to bachelor’s	154	18.3
Up to master’s	112	13.3
Uneducated	112	13.3
Interest in subject
Yes	763	90.8
No	77	9.2
Who has chosen the subject?
Myself	784	93.3
Father	35	4.2
Brother	7	0.8
Anyone else	14	1.7
Age			4.25^a	1.62
Siblings			3.95	1.02
CGPA			3.49	0.513
Highest percentage of the last semester’s attendance			84.63	4.84
Monthly income			833.33	3,907.18
No. of study hours			4.44	1.96

Note. CGPA = cumulative grade point average.

1 = 18, 2 = 19 till 8 = 25.

Research Design

A correlational research design is used to assess the relationship between learning engagement, academic motivation, and academic performance.

Sample

The sample is composed of 840 undergraduate students (420 females and 420 males) from five different full-scaled universities in different regions (provinces) of Pakistan. Data are obtained randomly using a convenience sampling technique.

Inclusion Criteria

Students with an age range of 18 to 25 years are included.

The education level is only undergraduate.

Exclusion Criteria

Students with different levels of education (other than undergraduate students) are excluded.

Disabled students are excluded.

The descriptive statistics of demographic variables are shown in Table 1.

Operational Definitions

Learning Engagement

Learning engagement is defined as a student’s connection to their college, both academic and extracurricular. It includes participation in clubs or organizations, attending campus activities, using student services, interactions with faculty, administrator, and peers, library and technology usage, working, reading, and other involvement with the campus (Parikh, 2008).

Academic Motivation

Academic motivation is defined regarding the number of minutes a student spends working on class-related material outside of class within 24 hr (a day) from Monday noon to Tuesday noon (Gravetter & Forzano, 2015).

Academic Performance

An operational definition of academic performance is “cumulative grade point average” (CGPA; Rubin et al., 2009).

Assessment Measures

The following measures are administered to the participants as research tools:

Demographic information sheet

A demographic information sheet was administered in addition to the research questionnaire. The demographic information sheet included personal information about participants such as age, gender, religiosity, education, family system, father’s education, mother’s education, monthly income, and questions about their academic performance.

EVD

The EVD scale, initially developed by Skinner et al. (2008), has four subscales, including behavioral engagement, behavioral disaffection, emotional engagement, and emotional disaffection. Behavioral and emotional engagements are positively worded items, whereas the behavioral and emotional disaffection subscales are negatively worded items and reverse coded. Composites of the scores are obtained by calculating the positive and negative averages of the items. The scores range from 1 to 4, with higher scores indicating more of the respective construct, where 1 = not at all true, 2 = not very true, 3 = sort of true, and 4 = very true. The results are shown in Table 2.

Table 2.

Reliability Coefficients of the Scales Used in This Study (N = 840).

	Range
Variables	K	α	Potential	Actual
Behavioral engagement	5	.83	5–20	7–20
Emotional engagement	5	.77	5–20	6–20
Behavioral disaffection	5	.79	5–20	6–18
Emotional disaffection	12	.91	12–48	12–42
Intrinsic motivation to know	4	.80	4–28	9–28
Intrinsic motivation toward accomplishment	4	.86	4–28	9–28
Intrinsic motivation to experience stimulation	4	.82	4–28	12–28
Extrinsic motivation to identified regulation	4	.85	4–28	14–28
Extrinsic motivation to introjected regulation	4	.79	4–28	10–28
Extrinsic motivation to external regulation	4	.70	4–28	14–28
Amotivation	4	.93	4–28	4–27

Note. Behavioral engagement measures action initiation, effort, working hard attempts, persistence intensity, involvement, and attention concentration. Emotional engagement measures enthusiasm, interest, enjoyment, satisfaction, and pride. Behavioral disaffection measures passivity, giving up, withdrawal, restlessness, distraction, unpreparedness, and burnout. Emotional disaffection measures boredom, disinterest, frustration/anger, sadness, worry, self-blame, and shame. Extrinsic motivation to external regulation measures whether students participate in activities to avoid negative consequences or achieve reward. Extrinsic motivation to introjected regulation assesses the experience of pressure and guilt. Extrinsic motivation to identified regulation assesses the desire to perform activities to gain a sense of importance and personal values. Intrinsic motivation for stimulus measures the desire to perform an activity to experience stimulation. Intrinsic motivation toward accomplishment measures the desire to perform an activity for the pleasure and satisfaction experienced from accomplishment or creation. Intrinsic motivation for knowledge assesses the desire to perform an activity for the pleasure and satisfaction experienced while learning. Finally, amotivation assesses the experience of a lack of motivation. K = no. of items; α = Cronbach’s alpha.

AMS

AMS is originally developed by Vallerand et al. (1992) to assess academic motivation in students. The AMS is consisted of 28 items, having the seven subscales which assess three types of motivation: intrinsic motivation, extrinsic motivation, and amotivation. In intrinsic and extrinsic motivation, there are three subscales, whereas in academic amotivation there is one subscale. The statements are rated on a seven-point Likert-type scale ranging from 1 = does not correspond at all, 2 and 3 = correspond a little, 4 = correspond moderately, 5 and 6 = correspond a lot to 7 = correspond exactly. AMS has a satisfactory level of internal consistency mean alpha value of .81 and temporal stability over a 1-month period (mean test–retest correlation = .79). Cronbach’s alpha results for the subscales range from .70 to .93.

Academic Performance

Academic performance is assessed by students’ previous semester CGPA.

Statistical Tools

For this study, the SPSS (Statistical Package for Social Sciences) software is used for the data analysis. To find out the relationship among learning engagement academic motivation and academic performance in undergraduate students, Pearson product-moment correlation is employed. Hierarchical regression is employed to find out the best predictor of academic performance. Independent samples t test is employed to find out the gender differences and birth order in academic motivation. One-way ANOVA is also employed to find out the significant difference among the residence of students and respondents’ birth order.

Ethical Considerations

Ethical considerations are kept in mind while conducting this research. The procedure and purpose of the study were thoroughly explained to the participants, and written informed consent was obtained from them individually. Only those participants who were willing to participate were included, and if any participant was not ready to fill the questionnaire, he or she was not forced to do it. The anonymity of the participant was maintained. The scales were used after permission from the authors through email.

Results

This research is conducted to explore learning engagement, academic motivation, and academic performance in undergraduate students. The data are analyzed in four key steps. In the first step, reliability analysis is conducted for each scale and Cronbach’s alpha for the reported scales. In the second step, the Pearson product-moment correlation is employed to assess the relationships among the study variables. In the third step, independent samples t test and one-way ANOVA are conducted for the differences among variables. In the fourth step, the hierarchical regression analysis is used to assess the academic performance of undergraduate students. Reliability coefficients of the scale used in this study are shown in Table 2.

Table 2 shows the reliability of the corresponding scales. In this study, the coefficient of reliability alpha ranges from .77 to .91. The internal consistency of behavioral engagement is .71. Behavioral disaffection is assessed using five items, and its internal consistency is .79. Emotional engagement is measured using five items and has an internal consistency of .77. Emotional disaffection is measured using 12 items and has an internal consistency of .91. It shows that the reliability of all the factors is in a reasonable range.

Results in Table 3 show that behavioral and emotional engagements are significantly positively related to academic performance, which means that the higher level of engagement will be the result of excellent academic performance, as this relation supports the hypothesis of this research. Behavioral and emotional disaffection are negatively significantly related to academic performance, which means that having the learning disaffection will be the result of poor academic performance, and this also supports the hypothesis of this research. All three types of intrinsic and extrinsic motivation are positively significantly related to academic performance. Motivation is negatively significantly related to academic performance, which means that amotivation will be the result of poor academic performance. Overall results support H1 of this research. All the values of the variables are in a reasonable range and do not pertain to any serious correlation issue.

Table 3.

Summary of Intercorrelation Among Subscales.

Variables	1	2	3	4	5	6	7	8	9	10	11	12
1. Behavioral engagement	1	.772**	−.654**	−.731**	.571**	.480**	.633**	.380**	.388**	.514**	−.706**	.799**
2. Emotional engagement		1	−.674**	−.743**	.438**	.354**	.519**	.368**	.400**	.544**	−.640**	.713**
3. Behavioral disaffection			1	.741**	−.402**	−.321**	−.487**	−.284**	−.341**	−.398**	.602**	−.647**
4. Emotional disaffection				1	−.485**	−.418**	−.663**	−.368**	−.441**	−.544**	.677**	−.735**
5. EM external regulation					1	.570**	.659**	.175**	.196**	.230**	−.527**	.579**
6. EM introjected						1	.527**	−.008	.029	.079*	−.383**	.499**
7. EM identified							1	.204**	.252**	.301**	−.572**	.632**
8. IM to experience								1	.842**	.810**	−.442*	.558**
9. IM to accomplishment									1	.856**	−.582**	.601**
10. IM to knowledge										1	−.576**	.669**
11. Amotivation											1	−.714**
12. CGPA												1

Note. EM = extrinsic motivation; IM = intrinsic motivation; CGPA = cumulative grade point average.

p < .05. **p < .01.

Additional Analysis

An independent samples t test (Table 4) is conducted to find the differences between males’ and females’ academic motivation. Cohen’s d in the above table of t test shows that there is a significant effect size among males’ and females’ academic motivation. Females have higher intrinsic motivation, whereas males have higher extrinsic motivation.

Table 4.

Independent Samples t Test for Gender and Academic Motivation (N = 840).

	Males (n = 420)		Females (n = 420)		t	p	95% CI		Cohen’s d
Variables	M	SD	M	SD			Minimum	Maximum	Cohen’s d
IM to knowledge	20.2	2.8	24.2	3.7	−17.2	.00	−4.3	−3.4	1.2
IM toward accomplishment	19.9	2.4	24.2	4.1	−17.7	.00	−4.6	−3.7	1.2
IM to experience stimulation	20.4	2.7	24.5	3.2	−19.8	.00	−4.5	−3.7	1.3
EM to identified	25.2	3.6	23.2	3.0	8.7	.00	1.5	2.5	0.5
EM toward introjected	24.9	3.3	21.5	4.0	13.1	.00	2.8	3.9	0.9
EM to external regulation	24.5	3.3	22.6	3.3	8.3	.00	1.4	2.3	0.5

Note. CI = confidence interval; IM = intrinsic motivation; EM = extrinsic motivation.

Independent samples t test between demographics and academic motivation (Table 5) shows that religion (Muslims and non-Muslims) has an insignificant relationship with academic motivation among students, which confirms H3_B. The family system and the part-time job also do not affect the student’s motivation toward their education, whereas gender differences, teachers, majors, and involvement in extracurricular studies have a significant relationship with the academic motivation of the students. However, Cohen’s d values explain that the effect size of these factors is minimal (ranging from 0.05 to 0.31) except for extracurricular activities (1.1). These results are entirely consistent with prior studies.

Table 5.

Independent Samples t Test for Demographic and Academic Motivation (N = 840).

	Muslims (n = 812)		Non-Muslims (n = 28)				95% CI
Religion	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	145.3	12.1	138.4	13.3	2.9	.50	2.33	11.5	0.5
	Males (n = 420)		Females (n = 420)				95% CI
Gender	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	143.2	10.8	147.0	13.3	−4.6	.000	−5.5	−2.2	0.31
	Nuclear (n = 665)		Joint (n = 175)				95% CI
Family system	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	145.1	12.5	145.6	11.4	−0.60	.211	−2.6	1.4	0.04
	Yes (n = 826)		No (n = 14)				95% CI
Do you like your supervisor/teacher?	M	SD	M	SD	t	p	M	SD	Cohen’s d
	145.1	12.3	144.0	1.03	0.35	.000	−5.3	7.6	0.12
	Yes (n = 42)		No (n = 798)				95% CI
Any part-time job?	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	148.3	10.3	144.9	12.3	1.7	.442	−.45	7.1	0.2
	Yes (n = 763)		No (n = 77)				95% CI
Do you like your majors?	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	146.6	10.4	130.3	17.8	11.9	.000	13.6	18.9	1.1
	Yes (n = 686)		No (n = 154)				95% CI
Cocurricular activities	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	145.0	12.7	145.6	10.0	−0.55	.02	−2.7	1.5	0.05

Note. CI = confidence interval.

The one-way ANOVA conducted for birth orders and residence choices (Table 6) show that although the birth order does not affect academic motivation significantly, residence choices have a substantial impact on academic motivation. Results show that students living in hostels are highly motivated toward their academics.

Table 6.

One-Way ANOVA for Variables and Academic Motivation.

	First (n = 154)		Middle (n = 406)		Last (n = 280)
Birth order	M	SD	M	SD	M	SD	F	p	η²
	144.5	14.6	145.8	8.9	144.3	14.7	1.5	.223	0.2
	Own (n = 364)		Rented (n = 119)		Hostel resident (n = 357)
Residence	M	SD	M	SD	M	SD	F	p	η²
	142.0	12.5	139.6	12.7	150.1	9.7	61.5	.000	0.5

Note. ANOVA = analysis of variance.

Furthermore, we check the academic performance of the students using the t test (Table 7). It shows that religion has a powerful impact on the academic performance of the students (Cohen’s d = 0.6), which confirms H2_A. Religion shows that Muslims pertain to high academic performance than non-Muslims in five universities of Pakistan. The plausible reason could be that Muslims (religiosity) can handle their stress level and get self-motivation to achieve high results in academics (Jeynes, 2003; Li & Murphy, 2018). However, birth order differences have shown an insignificant relationship with academic performance.

Table 7.

Independent Samples t Test for Demographics and Academic Performance (N = 840).

	Muslims (n = 812)		Non-Muslims (n = 28)				95% CI
Religion	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.5	0.50	3.1	0.65	3.5	.002	0.155	0.54	0.6
	Males (n = 420)		Females (n = 420)				95% CI
Gender	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.4	0.50	3.5	0.51	−3.9	.455	−0.20	−0.06	0.1
	Nuclear (n = 665)		Joint (n = 175)				95% CI
Family system	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.4	0.49	3.4	0.58	0.004	.034	−0.08	0.08	0.4
	Yes (n = 826)		No (n = 14)				95% CI
Do you like your supervisor/teacher?	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.4	0.51	3.6	0.06	−0.14	.004	−0.46	0.07	0.5
	Yes (n = 42)		No (n = 798)				95% CI
Any part-time job?	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.6	0.23	3.4	0.52	1.8	.005	−0.00	0.30	0.4
	Yes (n = 763)		No (n = 77)				95% CI
Do you like your majors?	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.5	0.41	2.7	0.75	14.8	.000	0.705	0.920	1.3
	Yes (n = 686)		No (n = 154)				95% CI
Cocurricular activities	M	SD	M	SD	t	p	Minimum	Maximum	Cohen’s d
	3.4	0.53	3.5	0.40	−1.8	.002	−0.17	0.004	0.04

Note. CI = confidence interval.

The family system (living independently or jointly) has an almost similar significant effect on academic performance, whereas teacher likeness, part-time job, and involvement in extracurricular activities have a significant effect on academic performance. Results show that students who like their majors have a strong association with academic performance, and Cohen’s d (1.3) shows that this group size is considerable in Table 7.

One-way ANOVA was conducted to find out the difference between students’ residence and birth order with their academic performance. It can be seen from Table 8 that there are significant results among the residence of students on their academic performance. It is interpreted that the “hostel residence” has high academic performance, whereas the birth order differences show an insignificant relationship with academic performance.

Table 8.

One-Way ANOVA for Variables and Academic Performance.

	First (n = 154)		Middle (n = 406)		Last (n = 280)
Birth order	M	SD	M	SD	M	SD	F	p	η²
	3.5	0.52	3.5	0.46	3.4	0.57	1.7	.18	0.2
	Own (n = 364)		Rented (n = 119)		Hostel resident (n = 357)
Residence	M	SD	M	SD	M	SD	F	p	η²
	3.3	0.57	3.3	0.55	3.6	0.35	41.0	.000	0.5

Note. ANOVA = analysis of variance.

The results from Table 9 show that demographic variables such as birth order, family system, interest in the subject, and part-time job do not predict academic performance. Religion and qualification also show no relation to academic performance. These results are consistent with those of Park and Kerr (1990). Learning disaffection significantly predicts negatively academic performance, which means that learning disaffection will predict poor academic performance, whereas learning engagement and academic motivation are the predictors of academic performance, which means that learning engagement and academic motivation will predict the high and excellent academic performance, which also confirms H1.

Table 9.

Hierarchical Regression Analysis Predicting Academic Performance (N = 840).

	Academic performance
Predictors	R ²	β
Step 1	.310
Age		0.07
Gender		0.11
Religion		0.00
Birth order		−0.05
Qualification		0.00
Attendance in the last semester		0.05
Family system		−0.01
Residence		0.09
Are you interested in this subject?		−0.07
Part-time job		−0.04
Step 2	.677
Learning engagement		0.738***
Step 3	.712
Learning disaffection		−0.658***
Step 4	.794
Academic motivation		0.694***

p < .05. **p < .01. ***p < .0.

Discussion and Conclusion

This research is conducted to explore the relationship between learning engagement, academic motivation, and academic performance in undergraduate students. Moreover, it is also intended to find out which learning engagement (behavioral and emotional) and academic motivation will be positively related to academic performance and the importance of religion in determining academic motivation and academic performance. For this research, the sample size is N = 840 (420 males and 420 females) undergraduate students from five different universities in various regions (provinces) of Pakistan. For this study, Pearson product-moment correlation and hierarchical regression analysis are used for the assessment.

It is hypothesized that there is a positive relationship between learning engagement, academic motivation, and academic performance. The findings of this research support the hypothesis that there is a positive relationship between learning engagement, academic motivation, and academic performance. The results of this study (β = 0.524*** for learning engagement and β = 0.450*** for academic motivation at p < .01) are highly significant and show that learning engagement and academic motivation positively predict the academic performance. These findings show that the students who are engaged in learning are motivated toward studies and show higher academic performance. The findings of Connell and Wellborn (1991) support the first part of our hypothesis (H1) that high-quality learning is the result of behaviors and emotions, such as exertion, persistence, interest, and enjoyment, that reflect a motivation to master the academic material. This conceptualization incorporates behavioral and emotional dimensions and shows a strong relationship between engagement and academic motivation. Moreover, the research also supports the second part of the hypothesis that behavioral and emotional engagements are positively related to high grades which shows that learning engagement is positively related to high grades (Connell et al., 1994; Non & Tempelaar, 2016; Parikh, 2008; Reyes et al., 2012).

The findings of this study indicate that there are gender differences in academic motivation. After performing the t test in this study for gender differences and academic motivation, the value of “t” is negative in all types of intrinsic motivation at p < .05 in females. It shows that females have higher intrinsic motivation as compared with males. Moreover, males have higher extrinsic motivation at p < .05, which confirms Ayub (2010).

The results of this research show that the students perform better if they are interested in their study subject. After the analysis of the t test in this research on residence and CGPA, the result (t = 5.6, p < .05) indicates that the students who are interested in the subject that they are studying achieve high grades or CGPA and their academic performance is better. These results are consistent with prior studies (Non & Tempelaar, 2016; Omotade et al., 2016; Stanca, 2006).

The findings of this research show that the hostel residence has a high prevalence of academic performance. After running the ANOVA in this study, the results indicated that the hostel residence has higher grades and excellent performance (F = 5.7, p < .05). This research supports the findings of our study, which was to explore the effects of students’ housing on academic performance. Also, if there is a difference in the academic performance of students that are staying in on-campus and off-campus student’s housing. Moreover, the result showed significant differences in the academic performance of those students who live on and off campus. Those who live on campus have better academic performance than those living off campus. It was also shown that student housing positively affects the academic performance of students staying in on-campus housing, due to the high scores they obtain in their academic work as a result of less cost of transportation and fewer number of buses. The minimum distance to the department, the higher the CGPA (Owolabi, 2015). Moreover, our results show that religion has an insignificant relationship with academic motivation but relates it strongly to academic performance, which also supports H2_A and H3_B.

Like other research studies, this study also has some limitations and shortcomings. The sample size can be increased by considering universities in non-Muslim-majority areas as this study is administered only in a Muslim-majority city, so the generalizability of the findings can be limited. More religious and social factors can be included to have a deep insight. Disabled students can also be considered in future studies.

This research can contribute to the knowledge of policymakers, practitioners, and students to help and increase learning engagements in class so that the academic performance of the students can be improved. This research can provide new ways of teaching to help improve students’ performance by enhancing their behavioral and emotional engagements.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This article is supported by Zhejiang Higher Education Reform Project (Grant No. 20180116).

ORCID iD

Sultan Sikandar Mirza

Author Biographies

Fatima Khalid is a research fellow at the Department of Psychology and Behavioral Sciences,Zhejiang University,Hangzhou,China. Her research interests include social psychology,clinical psychology and behavioral finance.

Sultan Sikandar Mirza is PhD in Financial management and working as a lecturer at Waikato Management School,University of Waikato,Zhejiang University city college campus,Hangzhou,China. His research interests include corporate finance,financial engineering,behavioral finance and corporate governance.

Chai Bin-Feng is a renowned scholar and working as an associate professor at the School of Accounting,Zhejiang Gongshang University,Hangzhou,China. He has published his research work in several renowned journals. His research interests include corporate finance,financial engineering,behavioral finance and corporate governance.

Nighat Saeed is a research fellow at the Department of Clinical Psychology,Government CZhejiang University,Hangzhou,China. Her research interests include social psychology,clinical psychology and behavioral finance.

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