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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Introduction
Low student engagement reduces students’ chances of acquiring the necessary talents and skills
(Kuh, 2009). Besides students’ ability to apply knowledge in more complex situations (Primana, 2015),
students’ memory of learning materials and nal grades will also be low (Staikopoulos et al., 2015).
Thus, student engagement has become a critical issue in higher education as it signicantly inuences
the quality of learning students acquire (Staikopoulos et al., 2015; Xia et al., 2022). Moreover, due to
COVID-19, Higher Education Institutions (HEIs) worldwide were forced to apply online learning methods.
One of the issues often highlighted in online learning methods is closely related to student engagement
(Czerkawski and Lyman, 2016; Xia et al., 2022). According to previous studies, online learning methods
could increase student engagement (Khusniyah and Hakim, 2019; Kuntarto, 2017). On the contrary,
studies in Indonesia found that online learning methods reduced student engagement (Fatoni et al., 2020;
Rusman and Nasution, 2020; Sa’diyah, 2021).
Student engagement only happens when students involve their feelings and active thinking
processes in learning (Harper and Quaye, 2009). Fatoni et al. (2020) found that 100 students from ve
universities in Indonesia experienced student engagement problems during online learning. This nding is
supported by Rusman and Nasution (2020) on UIN Sumatera Utara Medan college students, who found
that out of 191 students, only 4.71% had high student engagement during online learning. Furthermore,
Sa’diyah (2021) found that students only join online classes to fulll their attendance but ignore the lessons
and do other activities. Thus making them have low student engagement.
Maulana and Iswari (2020), who analyzed student engagement in calculation-based courses, found
that online learning in courses (such as Statistics) causes students to experience stress and difculty
Factors Affecting Student Engagement in Psychology Undergraduates
Studying Online Statistics Courses in Indonesia
Astri Setiamurti1* , Rose Mini Agoes Salim1 , Maridha Normawati1 , Atikah Ainun Mudah1 ,
Frieda Maryam Mangunsong1 , Shahnaz Satri1
1Faculty of Psychology, Universitas Indonesia, Depok, Indonesia
E-mail: setiamurti.astri@gmail.com, romy.prianto@gmail.com, maridha.normawati@gmail.com,
tikahainun@gmail.com, friemangun@gmail.com, shahnazsatri@ui.ac.id
Abstract: This study aimed to assess the inuence of students’ intrapersonal factors, namely Academic Intrinsic Motivation
(AIM), Perceived Creativity Fostering Teacher Behavior (P-CFTB), Academic Self-Efcacy (ASE), and Self-Regulated Learning
(SRL) on student engagement in undergraduate psychology students taking online Statistics courses. A cross-sectional and
quantitative design was used from October to December 2022. The data collection procedure used a convenience sampling
technique, with questionnaires distributed online (via social media) and ofine (via lecturers, the Student Executive Board, and
the Association of the Faculty of Psychology from various universities in Indonesia). The research participants were psychology
undergraduates who had studied and passed the Statistics courses online, with 671 lling out the questionnaire. The results
showed that all students’ intrapersonal factors, namely AIM, P-CFTB, ASE, and SRL, can determine student engagement by
66.9%, with ASE having the highest inuence (23.99%) and P-CFTB having the lowest impact (9.78%). Moreover, the correlation
value between SRL and SE was r = 0.700, p < 0.001, signifying a robust positive relationship between both variables.
Keywords: student engagement, perception of creativity fostering teacher behavior, academic self-efcacy, academic
intrinsic motivation, self-regulated learning, online learning.
Original scientic paper
Received: September 05, 2023.
Revised: November 15, 2023.
Accepted: November 18, 2023.
UDC:
159.947.5.072-057.875(594)”2022”
316.644-057.875:37.018.43(594)”2022”
10.23947/2334-8496-2023-11-3-359-373
© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*Corresponding author: setiamurti.astri@gmail.com
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
understanding the learning material. It made them have low student engagement scores. Moreover,
students often view Statistics courses as very mathematical, challenging, and frightening because they
use many formulas (Carpenter and McDonald, 2017; Waruwu, Hao and Hia, 2022; Zaimil, 2017).
Stress regarding statistics courses also occurs in psychology students. Suminta (2016) found
psychology students are prone to experience anxiety when studying Statistics. For psychology students,
besides being difcult, the Statistics courses are also felt to be unrelated to their future career choices
(Lloyd and Robertson, 2012). In fact, those courses are one of the fundamental courses in the psychology
program. From developing new therapy techniques to evaluating the effectiveness of strategies, it is a
statistical analysis that plays a role in providing an overview and drawing conclusions. Psychologists use
statistical analysis to nd ways to interpret and draw conclusions from their data (Watts and Thomas,
2022). Given the importance of Statistics for Psychology students, it is urgent to explore the factors that
can affect student learning engagement (Firmansyah, 2017; Ulpah, 2009).
Several studies exploring factors affecting student engagement in higher education have been
conducted. Almarghani and Mijatovic’s (2017) study showed that the lecturers’ role and teaching skills
are the most inuential factors for student engagement. Elshami et al. (2022) explored that factors such
as techno-pedagogical skills, self-directed learning, peer-assisted learning, and collaborative learning are
required to support medical and health students’ engagement in online learning. Calabrese et al. (2022)
found that frequency and regular meetings, demographic factors such as course of study, expectation,
and perception of students, can affect student engagement in personal tutoring schemes.
Student engagement is an interaction process between contextual or learning environment and
intrapersonal factors (Christenson, Reschly and Wylie, 2012). Contextual factors include the context of
teaching and social relationships (support from teachers, friends, and parents) (Christenson, Reschly and
Wylie, 2012; Skinner, Kindermann and Furrer, 2009). However, several studies have found that contextual
factors are not always able to predict student engagement. In fact, prior research had shown that students’
perceptions of understanding contextual factors were predictive for student engagement (Christenson,
Reschly and Wylie, 2012; van Petegem et al., 2007). The students’ perceptions of these contextual factors
belong to intrapersonal factors, such as how they perceive their learning experience and the source of
knowledge (Christenson, Reschly and Wylie, 2012; Raviv et al., 2003).
Various intrapersonal factors have been reported to inuence student engagement, such as
academic self-efcacy (ASE) (Helsa and Lidiawati, 2021; Pramisjayanti and Khoirunnisa, 2022; Zhen et
al., 2017), academic intrinsic motivation (AIM) (Dierendonck et al., 2023; Myint and Khaing, 2020), and
self-regulated learning (SRL) (Lidiawati and Helsa, 2021; Setiani and Wijaya, 2020). ASE is a student’s
determination and behavior toward assignments and the educational process (Chang and Chien, 2015;
Zhen et al., 2017). Students with low ASE showed more indifference and low engagement in learning
(Bassi et al., 2007). In contrast, according to meta-analysis studies (Chang and Chien, 2015), students
with high ASE scores had higher student engagement.
AIM is also strongly related to engagement. This is proven by Dierendonck et al. (2023), who found
that students who study for intrinsic reasons tend to be more focused and actively engaged because
they enjoy learning. The next intrapersonal factor that is positively related to student engagement is self-
regulated learning (SRL) (Lidiawati and Helsa, 2021; Setiani and Wijaya, 2020). To improve academic
achievement, students must have self-regulated learning skills to stay engaged in lectures, especially
online learning.
Additionally, Primana (2015) found that college students in Indonesia perceive their lecturers as
the primary source of knowledge. Students’ perceptions of their lecturers also signicantly impact student
engagement (Pachler, Kuonath and Frey, 2019; Primana, 2015). Furthermore, Lawton and Taylor (2020)
investigated college student perceptions of engagement and teaching strategies in the Introduction to
Statistics course. They discovered activities that increased student engagement in online learning, such
as when lecturers gave simulation-based instructions and asked students to carry out group discussions
and independent study. On the other hand, students identied low engagement when there were no
hands-on activities and students were only taking notes and listening during online classes.
A strategy and consistent effort of lecturers to encourage students to use their knowledge to think
independently and exibly by using new approaches to solve problems is called Creativity Fostering
Teacher Behavior (CFTB) (Cropley, 1997). Based on Lawton and Taylor (2020) ndings, we concluded
that the concept of creativity fostering teacher behavior (CFTB) was strongly related to how students
perceived the teaching strategies used by lecturers in the study. CFTB is a teaching strategy that aims to
develop students’ creative thinking or behavior” (Jeffrey and Craft, 2004). The CFTB concept also aligns
with learning material in Statistics courses, which require exible and creative thinking to solve complex
calculation problems (Grégoire, 2016).
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Up until now, most studies involving the CFTB variable measured CFTB from teachers’ or lecturers’
perceptions (Huang, 2022; Karwowski, Gralewski and Szumski, 2015; Palaniappan, 2009; Varatharaj,
2018); pre-service teachers (Katz-Buonincontro, Perignat and Hass, 2020; Kim et al., 2019; Orr and
Kukner, 2015); and the effect of CFTB on students’ creativity (Bell et al., 2014; Hazi and Kamarudin,
2020; Mao et al., 2020; Zhang et al., 2022). Meanwhile, the research that examined the relationship
and role of students’ perceptions of CFTB on student engagement is still limited. Even though, since
2000, Soh has suggested that the research on CFTB should be measured based on student perceptions.
Students’ perceptions of CFTB in this study will be called perceived CFTB or P-CFTB.
As previous studies have shown, student engagement is more inuenced by intrapersonal factors.
In addition, as Rusman and Nasution (2020) stated, there is a need for research that explores the factors
affecting student engagement in an online learning context. Therefore, it is vital to conduct exploratory
research on students’ intrapersonal factors by assessing the role of these factors in learning engagement
in online Statistics courses. Rodgers (2008) also said that to increase teaching effectiveness and academic
achievement, HEIs should consider developing online teaching strategies that encourage greater student
engagement.
Although some studies have investigated the role of AIM (Cayubit, 2022; Giesbers et al., 2013;
Gettle, 2022), ASE (Chang and Chien, 2015; Helsa and Lidiawati, 2021; Pramisjayanti and Khoirunnisa,
2022; Zhen et al., 2017), SRL (Lidiawati and Helsa, 2021; Nurtri and Aslamawati, 2021; Setiani and
Wijaya, 2020; Utami and Aslamawati, 2021) that inuence student engagement, however their role in
undergraduate psychology students taking online Statistics courses are still limited. Furthermore, empirical
research on CFTB is limited to teacher/lecturer (Huang, 2022; Karwowski, Gralewski and Szumski, 2015;
Palaniappan, 2009; Varatharaj, 2018) and pre-service teacher/lecturer (Katz-Buonincontro, Perignat and
Hass, 2020; Kim et al., 2019; Orr and Kukner, 2015), so few studies examine student perceptions of
CFTB. Therefore, the study hypothesizes that academic intrinsic motivation, perceived creativity fostering
teacher behavior, academic self-efcacy, and self-regulated learning simultaneously can determine
student engagement in undergraduate psychology students taking online Statistics courses.
Materials and Methods
Research Design
This study used a cross-sectional design from October to December 2022. The following criteria
were emphasized for the selection of participants: (1) The psychology undergraduates need to have
studied and passed the Statistics course through the online learning method, (2) The students should
follow the appropriate time interval for studying the course, and (3) The period used by the undergraduates
to complete the research questionnaire should not be more than three semesters. It has been done
since the implementation of online learning methods by most HEIs in the last three semesters during
the COVID-19 pandemic. The data collection procedure used a convenience sampling technique, with
questionnaires distributed online (via social media) and ofine (via lecturers, the Student Executive Board,
and the Association of the Faculty of Psychology from various universities in Indonesia).
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Table 1
Demographic Data of Participants (N= 533)
Participants
The research participants were psychology undergraduates who had studied and passed the
Statistics courses via online learning, with 671 lling out the questionnaire. However, only 533 participants
met the selection criteria and were spread from 11 provinces in Indonesia, with the majority originating
from DKI Jakarta (40.68%). Most of the participants were 19 years old (43.55%), female (81.54%), and
3rd-semester students (59.86%). A detail of participants’ demographic data of participants can be seen
in Table 1.
Research Instrument
This research used ve research instruments, namely (1) The University Students’ Engagement
Inventory (USEI) by Morocco et al. (2016), (2) The Creativity Fostering Teacher Index (CFTIndex) by
Soh (2000), (3) The Indonesian College Academic Self-Efcacy Scale (CASES) by Ifdil et al. (2019), (4)
The Online Self-regulated Learning Questionnaire (OSLQ) by Mutiara and Rifameutia (2021), and (5)
The Academic Motivation Scale (AMS) by Marvianto and Widhiarso (2019). Each instrument was tested
for reliability through the Cronbach Alpha and CRiT values, with validity analyzed by using Conrmatory
Factor Analysis (CFA). The t index used as a criterion for the cut-off value was also CFI > 0.90, RMSEA
< 0.08, and SRMR < 0.08 (Hu and Bentler, 1999; Schermelleh-Engel, Moosbrugger and Müller, 2003).
Students’ Engagement (SE)
The Indonesian version of the University Student Engagement Inventory (USEI) instrument by
Morocco et al. (2016) was adopted to measure students’ engagement. This instrument consisted of three
dimensions (cognitive, behavioral, and emotional engagement), each with ve items. These items were
then assessed using a 6-point Likert scale, with 1 to 6 emphasizing never to always, respectively. The
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
total value was counted by adding up the scores of each item. An example of a sample item is, “I usually
do my homework on time.” The Indonesian version of the USEI had a reliability value of Cronbach’s α =
0.862, signifying that the instrument was reliable (Kaplan and Saccuzzo, 2017). All items also showed
good internal validity, with CRiT values ranging from 0.261-0.647 (Nunnally and Bernstein, 1994). In
addition, CFA showed that the USEI was valid due to meeting the goodness of t criteria with chi-square
= 3.72 (X2 = 320.355; df = 86, p < 0.001), CFI = 0.905, RMSEA = 0.072, and SRMR = 0.067.
Academic Intrinsic Motivation (AIM)
AIM was measured through the Academic Motivation Scale (AMS) by taking three dimensions
of Intrinsic Motivation from Vallerand et al. (1992), which has been adapted into the Indonesian version
by Marvianto and Widhiarso (2019). It consisted of three factors, namely IM-to know, IM-toward
accomplishment, and IM-to experience stimulation, each having four items. Using a 6-point Likert scale,
namely 1 (do not correspond at all) to 6 (corresponds exactly), the total scores of AIM were obtained
by adding up the scores of all items. The sentences in several items were also adjusted, such as the
replacement of ‘school’ with ‘college’ to t the research context. For example, a sample item stated,
“Because I experience pleasure and satisfaction while learning new things.” From the results, AMS had a
reliability value of Cronbach’s α = 0.896 and CRIT = 0.526 - 0.685. CFA also showed that AMS was valid
due to meeting the goodness of t criteria with chi-square = 4.17 (X2 = 204.624; df = 49, p < 0.001), CFI =
0.945, RMSEA = 0.077, and SRMR = 0.040.
Perceived Creativity Fostering Teacher Behavior (P-CFTB)
Researchers adapted the Creativity Fostering Teacher Index (CFTIndex) instrument by Soh (2000)
into the Indonesian version to measure lecturer creativity fostering from a student perspective, named
Perceived CFTIndex (P-CFTIndex). This instrument initially contained 45 items, which was reduced to 27
after being adapted based on the CFTB-Index procedure by Lee and Kemple (2014). It still consisted of
nine dimensions, each containing three items in the Indonesian version. Each item was also assessed
using a 6-point Likert scale, namely 1 (never) to 6 (always). Moreover, the total score was obtained by
adding up the scores of each item, with a sample example indicating the following, “Lecturer encourages
me to try out what I have learned in different situations.” From the results, P-CFTIndex had a reliability
value of Cronbach’s α = 0.944, signifying that the instrument was reliable. All items also showed good
internal validity, with the CRiT values ranging from 0.357 to 0.713. In addition, CFA showed that P-CFTB
measuring instrument was valid because of meeting the goodness of t criteria with chi-square = 3.25 (X2
= 936,660; df = 288, p < 0.001), CFI = 0.904, RMSEA = 0.065, and SRMR = 0.047.
Academic Self-Efcacy (ASE)
ASE was measured using the Indonesian CASES version by Ifdil et al. (2019), adapted from Owen
and Froman (1988). This instrument initially contained 33 items, which were then reduced to 17 items
and categorized into three dimensions. These dimensions included technical skills (5 items), overt social
situation (6 items), and cognitive operation (6 items). The total score was calculated by adding up the
scores of all items. Each item was also assessed by using a 6-point Likert scale, namely 1 (strongly
disagree) to 6 (strongly agree), with a sample example presented as follows, “I master most of the
lecture materials having many elements of calculation.” From the results, CASES had a reliability value
of Cronbach’s α = 0.914, with CFA emphasizing its validity due to meeting the goodness of t criteria with
chi-square = 2.36 (X2 = 1092.270; df = 462, p < 0.001), CFI = 0.905, RMSEA = 0.079, and SRMR = 0.054.
Self-Regulated Learning (SRL)
Online Self-Regulated Learning Questionnaire (OSLQ) was used to measure SRL (Barnard-Brak,
Paton and Lan, 2010) and adapted to the Indonesian version by Mutiara and Rifameutia (2021). This
instrument initially contained 24 items, which were then reduced to 21 elements after translation and
categorized into six dimensions. These dimensions included environmental structuring, goal-setting, time
management, help-seeking, task strategies, and self-evaluation, which consisted of 4, 5, 3, 2, 4, and
4 items, respectively. The items were also assessed using a 6-point Likert scale, namely 1 (strongly
disagree) to 6 (strongly agree). Moreover, the total score was obtained by adding up the scores of each
item, with the example of a sample stating the following, “I prepare questions before joining an online
lecture-discussion session.” OSLQ had a reliability value of Cronbach’s α = 0.918 and CRIT = 0.377 -
0.673. CFA also showed that the instrument was valid due to meeting the goodness of t criteria with
chi-square = 3.63 (X2 = 626.008; df = 172, p < 0.001), CFI = 0.906, RMSEA = 0.070, and SRMR = 0.055.
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Research Procedure and Data Analysis
The procedures and instruments of this study were carefully reviewed by the Faculty of Psychology
ethics committee under number 136/FPsi.Komite Etik/PDP.04.00/2022. The adaptation of measuring
instruments into Indonesian versions (P-CFTIndex and USEI) was also carried out regarding the
procedure of Beaton et al. (2000), which contained ve stages, namely (1) translation, (2) synthesis, (3)
back translation, (4) expert assessment, and (5) data collection. In addition, the results were analyzed by
performing multiple regression through JASP software version 0.164.
Results
Statistic Descriptive Analysis
Pearson’s test was carried out to determine the correlation of each variable, as described in Table
2. The results showed a moderate correlation among the variables, with ASE and SE showing the most
vital relationship than other independent determinants (r = 0.714, p <.001). However, P-CFTB and SE
exhibited a moderate correlation between the independent and the dependent variables (r = 0.593, p
<.001), with P-CFTB and AIM portraying the weakest relationship (0.468).
Table 3 explains the descriptive analysis of each variable, where the range of values included
45-90, 37-72, 77-162, 90-193, and 45- 126 for SE, AIM, P-CFTB, ASE, and SRL, respectively. Based
on the results, the Mean/Standard Deviation values were high for SE, AIM, and P-CFTB at 69.46/9.17,
58.20/7.72, and 126.47/18.18, respectively. Meanwhile, the Mean/Standard Deviation values were in the
moderate category for ASE and SRL at 71.90/12.63 and 88.92/16.18, respectively.
Table 2.
Variables Intercorrelation
Note: * p < .05, ** p < .01, *** p < .001
SE = Students’ Engagement, AIM = Academic Intrinsic Motivation, P-CFTB = Perceived Creative Fostering Teacher
Behavior, ASE = Academic Self-Efcacy, SRL = Self-Regulated Learning
Table 3.
Descriptive Statistic
Note: SE = Students’ Engagement; AIM = Academic Intrinsic Motivation; P-CFTB = Perceived Creative Fostering
Teacher Behavior; ASE = Academic Self-Efcacy; SRL = Self-Regulated Learning.
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Table 4.
Participants’ Categorization of Each Variable (N= 533)
Note: SE = Students’ Engagement; AIM = Academic Intrinsic Motivation; P-CFTB = Perceived Creative Fostering
Teacher Behavior; ASE = Academic Self-Efcacy; SRL = Self-Regulated Learning.
Table 4 presents the participant categorization of each variable, where the majority of samples
were observed in the high category for SE (70.54%), AIM (81.05%), ASE (50.47%), and P-CFTB at
70.54%, 81.05%, 50.47%, and 69.61%, respectively. However, most participants on the SRL variable
were included in the medium category at 52.53%.
Multiple Linear Regression Prerequisite Test
Multiple regression analysis was employed to determine whether the research model’s four
independent variables (IVs) collectively possess predictive abilities for student engagement. When
assessing multiple linear regression models, it is crucial to meet at least four prerequisite tests:
multicollinearity, data linearity, homoscedasticity, and multivariate normality (Osborne and Waters, 2002).
Multicollinearity Test
The multicollinearity test was applied to gauge the degree of correlation among the independent
variables. Midi, Sarkar and Rana (2010) stipulated that there is no multicollinearity when the tolerance
value is >0.1 and the Variance Ination Factor (VIF) is <10. Table 5 demonstrates the tolerance values for
each variable are >0.1, and the VIF values for all variables are <10, signifying that the model meets the
multicollinearity requirement.
Table 5.
Multicollinearity Test Result
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Data Linearity Test
The data linearity test establishes a linear relationship among the independent variables (Hayes,
2015). It is tested by examining the scatterplot between the DV and each IV. The outcomes of the linearity
test, illustrated in Figure 1 below, indicate the presence of a linear relationship between the dependent
and independent variables, thereby fullling the linearity assumption.
Homoscedasticity Test
A scatterplot of residuals against predicted values is used to evaluate homoscedasticity (Hariyanto,
Triyono and Köhler, 2020). Figure 2, presented below, illustrates the data distribution pattern, signifying
that the assumption of homoscedasticity has been met.
Figure 1. Data Linearity Test Results
Figure 2. Homoscedasticity Test Result
Multivariate Normality Test
The multivariate normality test is used to verify the normal distribution of data. Figure 3 indicates that
the residuals conform to a normal distribution, afrming that the model meets the multivariate normality
assumption.
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Figure 3. Normal Distribution Curve
Multiple Linear Regression Results
Multiple linear regression was conducted after all the prerequisite tests showed that the results met
the requirement. Table 6 below shows that F = 267.358; p < 0.001, meaning that AIM, P-CFTB, ASE, and
SRL can signicantly determine student engagement. This means the research hypothesis is accepted.
Table 6 also shows that multiple linear regression’s coefcient of determination (R2) is 0.669. Based on
the guidelines for interpreting the coefcient of determination (R2) by Sarjana, Hayati and Wahidaturrahmi
(2020), 0.669 is included in the strong inuence category. This means that AIM, P-CFTB, ASE, and SRL
signicantly predict learning engagement with an inuence contribution of 66.90%, with the remaining
33.10% inuenced by variables not included in this study.
Table 6.
Multiple Linear Regression Model Summary
Table 7.
Coefcients
Note: SE = Students’ Engagement; AIM = Academic Intrinsic Motivation; P-CFTB = Perceived Creative Fostering
Teacher Behavior; ASE = Academic Self-Efcacy; SRL = Self-Regulated Learning.
From Table 7 above, the relationship between variables can be seen in the following equation:
Y = α + β1X1 + β2X2 + β3X3 + β4X4 + e
Y (SE) = 11.539 + 0.292*AIM + 0.083*PCFTB + 0.244*ASE + 0.145*SRL + e
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
From the multiple linear regression equation above, it can be explained as follows:
a) The constant (α) has a positive value of 11.539. This shows that if all independent variables are
worth 0, the base value of student engagement is 11.539.
b) For every percentage increase in AIM, student engagement increases by 0.292, assuming other
independent variables remain constant.
c) For every percentage increase in P-CFTB, student engagement increases by 0.083, assuming
other independent variables remain constant.
d) For every percentage increase in ASE, student engagement increases by 0.244, assuming other
independent variables remain constant.
e) For every percentage increase in SRL, the student engagement increases by 0.145, assuming
other independent variables remain constant.
To determine the amount of inuence of each independent variable on the dependent variable
partially, we used the Beta*Zero Order formula. Based on the formula, it is known that the most signicant
inuence comes from the ASE, with an inuence contribution of 23.99%. This is followed by the SRL,
which contributes an inuence of 17.85%, the AIM of 15.27%, and the P-CFTB of 9.78%.
Discussions
This study provides an overview of the interaction between intrapersonal factors, namely
Academic Intrinsic Motivation (AIM), Academic Self-Efcacy (ASE), Self-Regulated Learning (SRL),
and Perceived Creativity Fostering Teacher Behavior (P-CFTB), in predicting student engagement in
undergraduate psychology students taking online statistics courses. Hypotheses were examined to test
whether these intrapersonal factors simultaneously affect student engagement. The results found that
the four independent variables studied (AIM, ASE, SRL, and P-CFTB) signicantly determined student
engagement, with a contribution of 66.9%. Based on the research results above, it can be concluded
that the study’s hypothesis is conrmed. These results support ndings in previous studies that state that
intrapersonal factors are the main factor in predicting student engagement (Christenson, Reschly and
Wylie, 2012; van Petegem et al., 2007).
Furthermore, we examined the power contribution of each independent variable to student
engagement. The results showed that AIM signicantly predicted SE among psychology undergraduates
in online Statistics courses (R2=15.27%; p<0.001). The results supported Gettle (2022), where AIM
signicantly affected SE in psychology undergraduate students. The ndings of this study also support
the ndings reported by Giesbers et al. (2013) and Gettle (2022), who discovered that intrinsic motivation
is closely linked to student engagement in online learning by utilizing technologies in applications, such
as chat, webcams, and microphones. The academic achievement of students is also strongly related to
student engagement through the usage of these tools.
Furthermore, ASE had the most signicant inuence on SE at 23.99%, compared to other variables.
The ndings support Warwick’s statement (Warwick’s, 2008) that student self-efcacy predicts student
involvement. Students who have condence that they are capable will be more persistent in facing
difculties. On the other hand, students with low self-efcacy will feel helpless and less persistent in
completing complex tasks. In line with this, research by Helsa and Lidiawati (2021) found that self-efcacy
had an effect of 36.9% on student learning engagement. Pramisjayanti and Khoirunnisa’s (2022) study
also found that self-efcacy inuenced 64.9% of student engagement in online learning.
The results also revealed that SRL signicantly affected SE at an impact level of 17.85%. This was
not in line with Lidiawati and Helsa (2021), where a more signicant effect of SRL on SE was observed
at 55.9%. Nurtri and Aslamawati (2021) found that self-regulated learning has a 57% impact on student
engagement. A similar result was also found in Utami and Aslamawati (2021), who explained that the
effect of self-regulated learning was 52.8% on student engagement. These differences emphasized
specic research measuring students’ SRL abilities when learning Statistics courses.
Moreover, this study found that student perceptions of creativity fostering teacher behavior (P-CFTB)
can predict student engagement in online learning, with a 9.78% contribution. Students’ perceptions of
teaching strategies that encourage creativity, such as independent learning, opportunities to develop and
share ideas, divergent thinking, reection, learning opportunities with a variety of materials and conditions,
and support for overcoming failures, play a signicant role in predicting student engagement in students
who take online Statistics courses. In line with previous research has found that students’ attitudes and
beliefs about lecturers inuence student engagement in the classroom (Christenson, Reschly and Wylie,
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369
Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
2012; Pachler, Kuonath and Frey, 2019; Primana, 2015; Raviv et al., 2003).
Solving statistical problems requires the ability to think creatively. Therefore, creative teaching
strategies (teaching for creativity) will also inuence students’ attitudes toward the statistics learning
process, and attitudes toward learning signicantly positively affect learning achievement (Hu, Deng and
Guan, 2016). This nding also expands on research by Golder (2018) and Lawton and Taylor (2020)
regarding student perceptions of lecturer behavior. Golder (2018) found that students’ perceptions of their
lecturers signicantly related to their attitudes toward learning. Furthermore, Lawton and Taylor (2020)
found that students’ perceptions of independent teaching and learning strategies can increase student
engagement.
Additionally, this research found that ASE strongly and signicantly correlated with SE in statistics
courses (r = 0.714, p < 0.001). This was not in line with this previous research; Fan and Williams (2010)
showed that students’ ASE in mathematics and English signicantly correlated with engagement in these
subjects. The results conrmed that the correlation between these variables was more substantial in
English lessons (r = 0.54) than in mathematics (r = 0.49). It means that in this research, students will be
more engaged in the learning process if they believe they can learn.
Furthermore, the correlation value between SRL and SE was r = 0.700, p < 0.001, signifying a
robust positive relationship between both variables. The result differs from Setiani and Wijaya (2020),
who reported a weak positive correlation between both variables (r = 0.262). However, the effect aligned
with Lidiawati and Helsa (2021), who found a strong positive correlation between SRL and SE (r = 0.748).
The strong positive correlation can happen because, according to Bond and Bedenlier (2019), cognitive
engagement and the ability towards self-regulation are highly associated. Anjarwati and Sa’adah (2021)
also explained that student engagement in the period of online learning is known to increase student
participation from students’ cognitive and behavioral aspects.
Conclusions
This study found students’ intrapersonal factors, namely Academic Intrinsic Motivation (AIM),
Academic Self-Efcacy (ASE), Self-Regulated Learning (SRL), and Perceived Creativity Fostering
Teacher Behavior (P-CFTB), can determine student engagement by 66.9%, with ASE having the highest
inuence (23.99%) and P-CFTB having the lowest impact (9.78%). It also found that most participants
belong to the high SE, P-CFTB, AIM, ASE, and moderate SRL categories. There was also a moderate
correlation among the variables, with ASE and SE showing the most substantial relationship (r = 0.714,
p <.001). However, P-CFTB and SE exhibited a moderate correlation between the independent and
the dependent variables (r = 0.593, p <.001), with P-CFTB and AIM portraying the weakest relationship
(0.468). This indicated that the strongest and weakest correlation values were found between ASE-SE
and P-CFTB-SE, respectively. The results also showed that P-CFTB, AIM, ASE, and SRL increased SE
among Psychology undergraduates taking online Statistics courses.
The limitation of this study is the high-value questionnaire items, causing participants to experience
fatigue during the ll-out process. The variables also promoted a high social desirability tendency.
Subsequently, a delay was found between lling out the questionnaire and completing the Statistics
courses. For example, when lling out the data instrument, students were already in semester 5, despite
the last Statistics courses conducted in semester 3.
According to the research, students’ intrapersonal factors in online Statistics courses signicantly
impact their level of engagement. Therefore, statistics lecturers are expected to create a learning
atmosphere that enhances students’ intrapersonal factors, namely AIM, P-CFTB, ASE, and SRL. The
research implies that our study can pinpoint the contribution of intrapersonal factors that affect student
engagement, enabling statistics lecturers to give these internal factors more attention.
Acknowledgments
The authors are grateful to HIBAH PUTI PASCASARJANA 2022, Universitas Indonesia (NKB-297/
UN2.RST/HKP.05.00/2022), for nancially supporting this research.
Conict of interests
The authors declare no conict of interest.
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Setiamurti, A. et al. (2023). Factors affecting student engagement in psychology undergraduates studying online statistics
courses in Indonesia, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3),
359-373.
Author Contributions
Conceptualization, A.S., R.M.A.S, M.N., A.A.M., F.M.M., and S.S.; methodology, A.S., R.M.A.S,
M.N., A.A.M., F.M.M., and S.S.; formal analysis, A.S., R.M.A.S, M.N., A.A.M., F.M.M.; writing—original
draft preparation, A.S., M.N., and A.A.M.; writing—review and editing, A.S., M.N., and A.A.M. All authors
have read and agreed to the published version of the manuscript.
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