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Abstract

Online learning is on the rise, and it is becoming an important mode of learning in higher education. However, there are limited models that explain the link between learners’ characteristics, learning processes, and outcomes in an online learning environment. This study aims to examine the influence of online self-regulation on the experiences and engagement of students during online learning processes and their outcomes in terms of satisfaction and academic performance. A quantitative correlational design was used to achieve the objective of this study. Data collection was carried out using an online survey. A total of 609 undergraduates from four public and private universities in Malaysia were sampled. The partial least squares structural equation modeling (PLS-SEM) was run to analyze the obtained data. The results showed that online self-regulation is a presage factor affecting online learning processes. It significantly and positively affects students’ experiences and engagement in an online environment. These three factors explained more than two thirds of the variance in satisfaction, a key learning outcome of online learning. However, its predictive value for academic performance was weaker. The developed model can be used as a framework to promote positive online learning outcomes among undergraduate students. The implications of the study and recommendations for future research are discussed in this paper.

Plain Language Summary

Online learning among university students is on the rise. However, the relationships between learners’ characteristics, learning processes, and learning outcomes in online learning environment are still unclear. This study aims to examine the influence of online self-regulation on the experiences and engagement of students during online learning processes and their outcomes in terms of satisfaction and academic performance. A quantitative correlational design was used to achieve the objective of this study. Data collection was carried out using an online survey. A total of 609 undergraduates from four public and private universities in Malaysia were sampled. The results showed that online self-regulation has significant and positive influence on students’ learning experiences and engagement, which significantly predicts satisfaction but not achievement in online classes. The findings could help relevant stakeholders identify factors that are crucial in promoting positive outcomes in online learning among undergraduate students. The implications of this study and recommendations for future research are discussed in this paper.

Keywords

online self-regulation engagement online learning experiences performance satisfaction

Introduction

Online learning refers to learning experiences using different devices in synchronous or asynchronous environments (Dhawan, 2020). Online learning is not new in Malaysia. Higher education institutions in the country started to implement online learning in the late 1990s. The demand for online learning is increasing due to its capacity to reach global audiences, accessibility, flexibility, and potential for cost reduction in the long run (Azhari & Ming, 2015). Online learning is also becoming a more viable alternative to conventional learning in higher education for adult learners (Koksal, 2020; Organization for Economic Cooperation and Development [OECD], 2020). Despite this rising trend, there is still resistance to adopting online education due to the lack of teachers’ presence and limited engagement with peers compared to face-to-face learning (Sharma & Alvi, 2021). The adoption of online learning during the COVID-19 pandemic was abrupt since it was immediately after the imposition of unprecedented lockdown. According to UNESCO, 186 countries have implemented nationwide closures in 2020 to stop the spread of the virus (UNESCO, 2020). Teaching and learning moved to online platforms during the pandemic to ensure the continuation of education (Adedoyin & Soykan, 2023; Zalat et al., 2021). After the lockdown was revoked, Asia Pacific saw the biggest rise of online learners, with 28 million new online learners enrolling in 68 million courses (World Economic Forum, 2022). Gurban and Almogren (2022) found that the majority of the students had a good opinion about online courses and once the pandemic is over, there will be a continuation of learning that leverages online platforms as study materials, online learning is here to stay (Muthuprasad et al., 2021) and it seems probable that the long-term adoption rate will increase significantly (Dumont et al., 2021).

Recent studies found that online learning during the pandemic could achieve similar or better learning outcomes than face-to-face learning before the pandemic (e.g., Alzahrani, 2022; Stevens et al., 2021; Zheng et al., 2021). Learning outcomes are goals expected to be attained during the learning process (Kustono et al., 2021). The term “outcomes’ can be measured through cognitive and emotional dimensions. Academic performance is considered the most important cognitive outcome. On the other hand, emotional outcomes refer to students’ satisfaction with their online learning experiences. Students’ satisfaction influences their decision to continue with or drop out of a course (Doménech-Betoret et al., 2017). Underperformed and dissatisfied students are less likely to be enrolled in prospectus online learning classes (Tang My et al., 2022). Therefore, universities are keen to provide support (Kim & Kim, 2021). However, effective support could not be delivered without a clear understanding on factors that influence online learning outcomes. There is a need to fill in this literature gaps since the Asia Pacific saw the biggest rise in the number of online learners (World Economic Forum, 2022) and almost 70% of the administrators in higher education institutions believe in expanding online courses (Xu & Xu, 2019).

Online learning is expected to continue growing because of its many positive aspects, offering a wide range of advantages to university students. One of its most significant benefits is flexibility (Alzahrani, 2022), allowing students to manage their schedules and complete coursework at their own pace. Online learning also eliminates geographical barriers, enabling students to access course materials from anywhere (Zhang & Wu, 2022). In addition, online learning is more student-centered in nature (Wang et al. (2022) and often provides various multimedia resources such as videos, podcasts, and interactive activities, enhancing learning and engagement. It also facilitates communication between students and instructors through discussion forums, email, and video conferencing, providing opportunities for collaboration and feedback (Jalal et al., 2022). Online learning is also more affordable than traditional classroom learning, with lower tuition fees and no additional expenses for transportation or housing, saving both travel time and costs (Shahrul et al., 2023). In addition, online learning provides universities with a wider audience reach, allowing them to enroll students who may be unable to attend traditional classes due to distance or other obligations. This can result in increased revenue for the university. Furthermore, it can save costs on resources and infrastructure, providing a more efficient use of resources. From the lecturers’ perspective, online learning provides a range of multimedia options that can enhance the learning experience, resulting in greater student satisfaction and engagement. Overall, online learning provides numerous advantages to students, lectures, and higher education institutions, making it a popular and effective alternative to traditional classroom learning.

Literature Review

Factors Influencing Students’ Online Learning Outcomes

Students’ academic performance and satisfaction are the leading indicators of learning outcomes (Doménech-Betoret et al., 2017). A scant amount of literature is available on factors that affect online learning outcomes during the COVID-19 pandemic (Rajabalee & Santally, 2021). Academic performance is typically defined as an academic result in an assignment, quiz, exam, single subject or a full degree. On the other hand, satisfaction is defined as students’ feelings of the perceived value of the educational content and services they have obtained in return for their time and resources sacrificed (Shahsavar & Sudzina, 2017). Past studies (e.g., Ayang & Richard, 2022; Commissiong, 2020; Kashif & Shahid, 2021) found that online self-regulation skills, engagement, and online learning experiences are among the key factors influencing online learning outcomes. Nevertheless, there is still limited research that examines the direct and indirect influences of these factors on the learning outcomes of undergraduate students within a single study.

Online Self-Regulation

The rise of online learning encourages students to take more responsibility for their learning. Digital technologies allow students to play a more active role in learning and lectures assume the role of a facilitator. However, the rapid transition to online learning is challenging for students. Those with poor self-regulation skills are at-risk of dropping out. Online courses demand students to be self-regulated learners (Boor & Cornelisse, 2021). Students need to apply self-regulated learning skills to monitor, control and regulate their learning processes. They cannot rely exclusively on lecturers and the assigned materials when learning (Aldosemani, 2022). Self-regulated learners will set clear goals, and effectively manage their learning environment, learning time and tasks. They will seek help when needed and constantly self-evaluate their own learning progress. Studies found that self-regulation skills have positive and significant effects on students’ academic performance (Kashif & Shahid, 2021) and it is also a vital predictor of learners’ satisfaction (Kara et al., 2021).

Engagement in Online Learning

Engagement refers to students’ level of attention, curiosity, interest and passion when they are learning (Anjarwati & Sa’adah, 2021). Engagement influences learning experiences and outcomes particularly academic performance and satisfaction (Filak & Sheldon, 2008). In fact, students’ engagement during online learning was found to be the most important factor influencing satisfaction with online learning (Gurban & Almogren, 2022). Contradictory findings, however, were found in some studies. Baber (2020) discovered that there were no positive relationships between learners’ interaction or engagement with learning outcomes. Other studies (e.g., Gray & DiLoreto, 2016; Kuo et al., 2013) supported this finding as student-interaction could not predict satisfaction in online learning. The mixed findings call for more studies on engagement in online learning. Engagement is divided into three dimensions, behavioral, cognitive and emotional engagement. Most past studies focused on behavioral engagement such as task persistence, participation and attendance, which are observable (Appleton et al., 2006). There were fewer studies on cognitive and emotional engagement and these engagements are subtler and difficult to observe (Pietarinen et al., 2014). Cognitive engagement refers to students’ attention, cognitive processes and absorption during the teaching and learning processes while emotional engagement refers to students’ emotional responses such as interest and joyful feelings during learning (Gao et al., 2020; Halverson, 2016). Literature reviews showed that cognitive and emotional engagement requires further research, and there is a need to move beyond behavioral engagement to fill in the literature gaps (Pietarinen et al., 2014).

Online Learning Experiences

Online learning experiences refer to any interaction, course, program, or other experience in which online learning takes place. A study by Mok et al. (2021) found that only 26.89% of university students felt satisfied with their online learning experiences during the pandemic. This implies that most students are not fully satisfied with online learning due to disruption of online classes, inadequate educational resources, and a lack of institutional support (Xie & Yang, 2020). University students still prefer physical classes because the social aspect and the learning benefits of face-to-face interaction with instructors and peers are not fully replicable in the online environment. The findings suggest that learning experiences are the foundation of engagement in online learning (Yang, 2021). Apart from satisfaction, academic performance is another learning outcome related to engagement and social interactions. Engagement with lectures and peers can influence students’ learning experiences, and it is a detrimental factor to satisfaction and academic performance in online learning (Alabbasi, 2022; Yang, 2021).

The COVID-19 pandemic has led to a significant shift toward online learning. As a result, researchers are renewing their interest in understanding students’ learning outcomes in online environments. With almost all courses, including non-traditional ones, having adopted online learning during the pandemic, it is possible that university students’ online learning skills have improved since the health crisis began. The unprecedented situation may lead to more positive online learning experiences and greater engagement, which could have different effects on students’ learning outcomes compared to the pre-pandemic phase. Understanding the students’ characteristics, their learning processes, and their online learning outcomes is crucial to developing more effective instructional design and strategies for online learning. This is especially important given the increasing adoption of online learning across various education sectors, and there are still limited studies that examine the influences of online self-regulation skills, learning experiences, engagement, and outcomes on students who have adopted online learning during the pandemic. A structural equation model can be used to analyze the influences of these variables simultaneously, providing novel insights into the structural relationships of these variables from presage, process, and learning outcomes.

Research Objectives

1. To determine the influences of online self-regulation, learning experiences and engagement on online learning outcomes (academic performance & satisfaction) of undergraduate students.

2. To determine the mediating effects of online learning experiences and engagement on the relationships between online self-regulation and online learning outcomes (academic performance & satisfaction) of undergraduate students.

Hypothesis

Based on the research objectives, eight main hypotheses were formulated to examine the direct relationships between the variables. Additional hypotheses were also formulated to test the mediating effects of online learning experiences and engagement.

H_o1 : There is a significant influence of online self-regulation on academic performance.

H_o2 : There is a significant influence of online self-regulation on online learning experience.

H_o3 : There is a significant influence of online self-regulation on satisfaction toward online learning.

H_o4 : There is a significant influence of online learning experience on satisfaction toward academic performance

H_o5 : There is a significant influence of online learning experiences on engagement in online learning

H_o6 : There is a significant influence of online learning experiences on satisfaction toward online learning

H_o7 : There is a significant influence of engagement in online learning on academic performance.

H_o8 : There is a significant influence of engagement in online learning on satisfaction toward online learning.

Theoretical Framework

Teaching and learning in online environments involve using technology to enable remote education. The 3P Model (Presage-Process-Product) (Biggs, 2003; Li et al., 2023) provides a comprehensive model of the influencing factors of students’ online learning outcomes. According to this model, teaching and learning are relational phenomena that concurrently intertwine with the presage, process, and product factors in the learning environment (Han, 2014). This model is a key framework in student learning theory (Ginns et al., 2014). It provides a powerful means of understanding the relationships between students’ learning skills, learning processes, and learning outcomes in online learning. Presage factors include learners’ characteristics that exist before the teaching and learning processes. Past studies suggest that students’ online self-regulation skill is a presage factor that influences their experiences and engagement in learning processes (He et al., 2022), subsequently affecting their learning outcomes such as academic performance. The complex interactions between learners, instructors, and the learning experience during the teaching and learning process and how these factors affect students’ learning outcomes are supported by the Community of Inquiry (CoI) model (Garrison et al., 1999; Xue et al., 2023). Learners must possess effective self-regulation skills, in addition to receiving facilitation from instructors and being engaged in the learning experience, to achieve positive outcomes (Wandler & Imbriale, 2017; Wang et al., 2022). This CoI model emphasizes the importance of social, cognitive, and teaching presence in creating meaningful and effective online learning. Social presence refers to the ability of learners to establish a sense of community and connection with each other and the instructor. Cognitive presence focuses on the critical thinking and reflective processes that are central to deep learning. Teaching presence encompasses the design, facilitation, and direction provided by the instructor to support student learning. By understanding and cultivating these three domains, instructors can create engaging and effective online learning experiences for students. Overall, the 3P model (Biggs, 2003) and the CoI model (Garrison et al., 1999) complement each other (Figure 1) by providing a comprehensive understanding of the factors that influence students’ online learning outcomes. Figure 1 depicts the influence of the presage factor (online self-regulation skills) on process factors (online learning experiences, engagement) and subsequently on outcome factors (academic performance, satisfaction) in online learning.

Figure 1.

Factors influencing students’ learning outcomes (adapted from Presage-ProcessProduct Model, Biggs, 2003; Community of Inquiry, Garrison et al., 2000).

Method

A quantitative correlational design was employed to achieve the research objectives. Data collection was carried out using an online questionnaire survey, which has greater strengths than the traditional survey modes (Park et al., 2019). This was an efficient option for collecting data from universities in various locations in Malaysia. An online survey is also more cost-effective and in line with the context of the study, which focuses on online learning environments. The population of this study was undergraduate students enrolling in degree courses at universities in Malaysia. A total of 609 undergraduate students from four public and private universities were sampled: three from Peninsular Malaysia and one from the east coast. This sample size was greater than 500 and fulfilled the rule of thumb on sampling size (Krejcie & Morgan, 1970; Long, 1997). The samples were adequate to generalize the findings to the research population optimally. A multistage cluster sampling method was utilized to select undergraduate students from four universities. Cluster sampling involves dividing a population into clusters and then selecting a sample from these clusters. For this study, the sampling process was divided into two stages. Firstly, five major programs in the universities, namely pure arts, applied arts, engineering, computing, sciences, and other programs were selected as clusters. These selected programs met the criterion of having at least 80% of online teaching and learning activities, whether synchronous or asynchronous, which fulfilled the criteria for online courses (Müller & Mildenberger, 2021). Next, a list of undergraduate students who were active in the semester was obtained from the registry for each of the five programs. The relevant faculty or school distributed the survey link through the lecturers in charge of the program, and the students were informed that participation was voluntary. The average response across the universities was 41%, which is within the acceptable range for online surveys (Wu et al., 2022). The data collection commenced in early to mid-2022 during the pandemic phase. The online survey consists of five sections: (a) student profile, (b) online self-regulation, (c) online learning experiences, (d) engagement in online learning, and (f) learning outcomes (academic performance & satisfaction). Data on academic performance were gathered based on self-reported cumulative grade point average (CGPA). Responses were gathered on a four-point Likert scale, ranging from strongly agree (4) to strongly disagree (1).

The Online Self-regulated Learning Questionnaire (24 items), developed by Barnard et al. (2009), was used to measure students’ online self-regulation in terms of goal setting (five items), environment structuring (four items), task strategies (four items), time management (three items), help-seeking (four items), and self-evaluation (four items).The instrument has high internal consistency, α = .94 (Barnard et al., 2009). The Community of Inquiry Survey (34 items), adapted by Zhang (2020) from Arbaugh et al. (2008), was used to measure students’ experiences in online learning. Learning experiences were measured from the perspectives of social presence (nine items), cognitive presence (12 items) and teaching presence (13 items) during online learning. The instrument has a Cronbach’s alpha value of α = .97. Responses for all the quantitative instruments were collected using a four-point Likert scale, which ranged from strongly agree (1) to strongly disagree (4). The Engagement in Online Learning Subscale was adapted from the Engagement Scale (Gao et al., 2020). The 15-item instrument was used to measure two dimensions of engagement; cognitive engagement (eight items) and emotional engagement (seven items). This instrument was valid and reliable (Gao et al., 2020), with a Cronbach alpha’s value of α = .94.

Data analysis was carried out through partial least square structural equation modeling (PLS-SEM). The Smart PLS version 3.0 a second-generation multivariate technique, was employed to simultaneously evaluate the measurement and structural models to minimize error in variance. The analysis used a recommended 5,000 bootstrap sample to determine the significance level for loadings, weights, and path coefficients (Hair et al., 2017).

Data Analysis and Results

Assessment of the Reflective Measurement Model

In PLS-SEM model assessment, scholars suggest that the assessment of a reflective measurement model should be initiated by evaluating its reliability and validity (Hair et al., 2017; Henseler et al., 2009). The following sections present the results of the assessment of measurement and structural model.

Construct Reliability

The researchers began with evaluating internal consistency reliability. It is the first criterion for assessing construct validity of the latent variables. Reliability is a quality criterion of a construct; it requires a high level of correlation among the indicators of a particular construct (Kline, 2011). According to Hair et al. (2010) reliability extends to which a variable or set of variables is consistent in what it is intended to measure. Table 1 displayed both Cronbach’s alpha and composite reliability of each latent variable. The results indicated that Cronbach’s alpha of the latent variables ranged from .86 to .91. In addition, the composite reliability of each latent variable (0.90–0.93) fulfilled the minimum value of 0.6 (Garson, 2016). The high internal consistency was confirmed in the latent variables.

Table 1.

Results of Measurement Model.

Construct	Item	Outer loading	α	rho_A	CR	AVE
Online self-regulation	OSL1	0.761	.86	0.86	0.90	0.59
	OSL2	0.790
	OSL3	0.747
	OSL4	0.727
	OSL5	0.789
	OSL6	0.787
Engagement in online learning	ENG1	0.812	.88	0.88	0.91	0.68
	ENG2	0.751
	ENG3	0.859
	ENG4	0.856
	ENG5	0.835
Online learning experience	OLE1	0.862	.91	0.91	0.93	0.73
	OLE2	0.842
	OLE3	0.754
	OLE4	0.895
	OLE5	0.914
Academic performance	CGPA	1.000	1.00	1.00	1.00	1.00
Satisfaction toward online learning	SOL1	0.862	.87	0.87	0.91	0.72
	SOL2	0.850
	SOL3	0.856
	SOL4	0.832

Note. α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted.

Convergent Validity

The assessment continued with the convergent validity after confirming the internal consistency reliability. Convergent validity is the extent to which a measure correlates positively with an alternative measure of the same construct. To evaluate the convergent validity of reflective constructs, researchers consider the outer loadings of the indicators and the average variance extracted (AVE). Henseler et al. (2015) recommended that a reflective model should have outer loadings of 0.7 and above. The results showed that the outer loadings of all items in the present model were higher than 0.7. The results indicated that the construct explained more than half of the variance of its indicators because all constructs contained AVE values between 0.59 and 0.73, which were higher than the threshold of 0.5 (Garson, 2016). Overall, the assessment of outer loadings, AVE, Cronbach’s alpha, and composite reliability confirmed the convergent validity of the model.

Discriminant Validity

Next, the assessment continued with the discriminant validity by checking the Fornell-Larcker criterion and heterotrait-monotrait (HTMT) ratio of correlations (Henseler et al., 2015). Discriminant validity is concerned about the uniqueness of a construct, whether the phenomenon captured by a construct is unique and not represented by the other constructs in the model (Hair et al., 2013). Table 2 illustrates the Fornell-Larcker criterion (equivalent to the square root of AVE) for each construct. As shown, the bold values, 0.767, 0.824, 0.850 and 0.855, were greater than their correlation with any other latent variables. The HTMT values (0.673–0.889) were below the threshold of 0.90 for constructs that are conceptually very similar to reflective constructs (Henseler et al., 2015). Therefore, the results supported the establishment of discriminant validity of the model. In short, the results showed that all the constructs in the study contained excellent reliability and validity indices, evidently proving the fitness of the measurement model.

Table 2.

Discriminant Validity Results.

Latent constructs	ENG	OLE	OSL	SOL
Fornell-Larcker criterion
ENG	0.824
OLE	0.624	0.855
OSL	0.596	0.618	0.767
SOL	0.742	0.791	0.638	0.850
Heterotrait-monotrait ratio (HTMT)
ENG
OLE	0.693
OSL	0.673	0.690
SOL	0.844	0.889	0.730
Inner VIF values for collinearity assessment
ENG				1.854
OLE	1.000			1.934
OSL		1.000		1.833

Assessment of the Structural Model

Researchers suggest examining the structural model’s collinearity issue by checking the constructs’ values of variance inflation factors (VIF) (Sarstedt et al., 2021). The findings showed that VIF values of all combinations of endogenous and exogenous constructs were below the critical value of 5 (see Table 2), thus eliminating the problem of collinearity in the structural model.

The coefficient of determination (R²), which justifies the variance explained in each endogenous construct was examined. In the present model, online learning experiences (OLE) had an almost moderate R² value of .382, meaning that online self-regulation (OSL) explained 38.2% of the variance in online learning experiences (OLE). Second, engagement in online learning (EOL) had an R² value of .389, indicating OLE explained 38.9% of the variance in EOL. Third, satisfaction toward online learning (SOL) obtained an R² value of .734, above the high value of .70. It indicated that online self-regulation (OSL), engagement in online learning (EOL), and online learning experience (OLE) explained >two-thirds (73.4%) of the variance in satisfaction toward online learning (SOL). In contrast, academic performance (AP) only had an R² value of .057. This suggests that online self-regulation (OSL), online learning experience (OLE) and engagement in online learning (EOL) only recorded weak predictive accuracy on academic performance.

The analysis continued with assessing the inner model path coefficients for the relationship between the latent variables as shown in Figure 2. The results (Table 3) revealed that six direct path coefficients were positive and significant.

Figure 2.

Structural model.

Table 3.

Results of Path Coefficients.

jh	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	t statistics (\|O/STDEV\|)	p values
OSL > AP	0.318	0.315	0.062	5.129	.000
OSL > OLE	0.618	0.619	0.033	18.899	.000
OSL > SOL	0.116	0.116	0.036	3.255	.001
OLE > AP	−0.164	−0.162	0.057	2.857	.004
OLE > EOL	0.624	0.625	0.031	20.073	.000
OLE > SOL	0.491	0.492	0.045	10.912	.000
EOL > AP	−0.053	−0.053	0.056	0.940	.347
EOL > SOL	0.366	0.366	0.039	9.395	.000

Note. OSL = online self-regulation; OLE = online learning experience; EOL = engagement in online learning; AP = academic performance; SOL = satisfaction toward online learning

First, OSL exerted a significantly positive effect on OLE (β = .618, t = 18.899, p < .01), SOL (β = .116, t = 3.255, p < .01), and AP (β = .318, t = 5.129, p < .01). The construct OLE had the most significantly positive effect on EOL (β = .624, t = 20.073, p < .01), followed by its positive effects on SOL (β = .491, t = 10.912, p < .01). The construct EOL exerted a significantly positive effect on SOL (β = .366, t = 9.395, p < .01). In contrast, the model showed two negative path coefficients. OLE had a significantly negative on AP (β = −.164, t = 2.857, p < .01). There was only one insignificant path coefficient in the model. EOL did not exert a significant effect on AP (β = −.053, t = 0.940, p > .01).

Mediation Analysis

As for the mediation effects (Table 4), there were four specific indirect path coefficients in the model. The results showed that all four indirect path coefficients were significant; two were significantly positive, whereas another two were significantly negative. OLE had significantly positive effects on the relationship between (1) EOL and SOL (β = .194, t = 6.996, p < .01) and (2) OSL and SOL (β = .187, t = 6.996, p < .01). On the other hand, OLE had significantly negative effects on the relationship between (1) EOL and AP (β = −.065, t = 2.761, p < .01) and (2) OSL and AP (β = −.063, t = 2.588, p < .01).

Table 4.

Results of Indirect Path Coefficients.

	Original sample (O)	Sample mean (M)	Standard deviation (STDEV)	t statistics (\|O/STDEV\|)	p values
OSL > OLE > EOL >SOL	0.141	0.142	0.018	7.774	.000
OSL > OLE > SOL	0.303	0.304	0.034	8.982	.000
OSL > OLE > EOL > AP	−0.020	−0.020	0.022	0.928	.354
OSL > OLE > AP	−0.101	−0.101	0.037	2.742	.006
OLE > EOL > SOL	0.228	0.229	0.025	9.047	.000
OLE > EOL > AP	−0.033	−0.033	0.035	0.932	.351

Note. OSL = online self-regulation; OLE = online learning experience; EOL = engagement in online learning; AP = academic performance; SOL = satisfaction toward online learning.

The boostrapping results indicate that EOL served as a complementary mediator for the direct effect between OLE and SOL. However, EOL did not mediate the relationship between OLE and AP. Next, OLE partially mediated the significant relationship between OSL and SOL. Lastly, for the relationship between OSL and AP, OLE served as a competitive mediator because both the indirect and direct effects are significant and point in opposite directions (Hair et al., 2017).

In addition to checking the R² values of the endogenous constructs, the study also reported the f² values. According to the guidelines by Cohen (1988), f² values of 0.02, 0.15, and 0.35 represent small, medium, and large effects respectively. The f² effect sizes of the constructs were tabulated in Table 5. The largest f² effect size was found in the relationships between OLE→EOL (0.673), followed by OSL→OLE (0.618) and OLE→SOL (0.465). This explains that the strongest path coefficient between OLE and EOL had the largest f² effect size. A medium f² effect size was seen between EOL→SOL (0.274) relationship. A Small f² effect size was seen in the relationship between (1) OSL→AP, which recorded 0.059 and (2) OSL→SOL, which recorded 0.028.

Table 5.

f² Effect Sizes.

	AP	EOL	OLE	SOL
OSL	0.059		0.618	0.028
OLE	0.015	0.673		0.465
EOL	0.002			0.274

Lastly, the predictive relevance of the PLS path model was examined through a blindfolding procedure. In the structural model, Q² values larger than zero for a specific reflective endogenous latent variable means there is evidence of predictive relevance for certain dependent constructs (Hair et al., 2017). The result from the cross-validated redundancy approach demonstrated that SOL had a Q² value of 0.499, followed by OLE (Q² = 0.257), EOL (Q² = 0.244), and AP (Q² = 0.048). In this study, the three endogenous constructs had Q² values above zero, indicating the existence of predictive accuracy for the endogenous constructs.

Discussions

This study aimed to determine the influence of online self-regulation, learning experiences and engagement on undergraduate students’ online learning outcomes (academic performance & satisfaction). The results showed that online self-regulation is an important presage factor that explains more than one-third (38.2%) of the variance in online learning experiences. Self-regulation skills help students monitor and regulate their cognition, motivation, and behavior in the online learning environment. Online self-regulation skills allow students to set their learning goals, structure their individual learning environment, manage their learning tasks and time more effectively, seek help when needed, and self-evaluate their learning progress (Barnard et al., 2009). To promote self-regulation among undergraduate students, lecturers can encourage students to self-monitor and self-reflect on their learning progress frequently by keeping a learning journal (Vishwakarma & Tyagi, 2022). The findings of this study showed that online self-regulation skills are an important presage factor that exerts a significant positive effect on students’ learning processes, particularly their learning experiences. Students need to be supported in online learning. Scaffolding can be provided to help them develop online self-regulation skills. For example, lectures can remind students to plan and prepare for online lessons, read the learning materials posted on the learning management system before joining the class and complete the online quizzes to test their existing knowledge of a topic. The findings of this study suggests that two-thirds of the variance in online learning experiences could be explained by other learners’ factors other than self-regulation skills. Based on the current literature reviews, these factors may include students’ level of digital literacy, which can impact their ability to navigate online learning platforms and effectively use digital tools to enhance their learning experiences (Reddy et al., 2023). Motivation is another important learner’s factor that may influence the learning process, including engagement in an online environment. According to Lopez and Tadros (2023), learners’ motivation can influence classroom climates virtually and improve academic performance. On the contrary, students who lack motivation may struggle to complete assignments, participate in discussions, and seek out additional resources. Hence, digital literacy and motivation can be explored in future studies to strengthen the model developed in this study.

During the learning process, instructors should engage students in more online activities and focus on the interactions with students (Zhou et al., 2022). Positive learning experiences had the most significant positive effect on engagement. It explained more than one-third (38.9%) of the variance in engagement, with the largest f² effect size recorded in the model. The results showed that learning experiences and engagement are positively related. Students learning experiences are shaped by their social presence, for example communication and collaboration with others, and sense of belonging to the learning community. It is crucial for lectures to provide students with an avenue to communicate and interact with others, such as asking questions, debating, discussing and sharing experiences during online learning (Alabbasi, 2022). In addition, learning experiences can also be enhanced through the use of effective learning materials, multimedia tools and teaching methods. Students need to be active cognitively to engage in online learning. The presence of the lecturers as facilitators in the learning process and the feedback they give can enhance students’ experiences and engagement in learning (Arbaugh et al., 2008). The findings suggest that students’ learning experiences are an important factor influencing online learning engagement (Yang, 2021). The results revealed that online self-regulation is a presage factor that positively influences students’ experiences and engagement in online environments. These factors as a whole explained >two-thirds of the variance in satisfaction. In terms of indirect effects, online learning experiences were found to mediate the relationships between engagement and satisfaction in online learning and between online self-regulation and satisfaction in online learning. The findings suggest that student’s online learning experience is an important process factor that directly and indirectly affects students’ satisfaction with online learning. However, online self-regulation, learning experiences, and engagement only explained 5.7% of the variance in academic performance. Recent studies suggest that performance in online learning can also be influenced by environmental and organizational factors (Marlina et al., 2020).

This study has developed a model on factors that influence outcomes in online learning among undergraduate students. The model was produced based on the results of structural equation modeling analysis and selected variables measured quantitatively. Hence, it has a few limitations. Firstly, the model was meant for online learning at the undergraduate level, hence, it may not be generalization to postgraduate level. Secondly, the variables analyze for presage, process and outcome were guided by the current literatures. Other factors were beyond the scope of this study. In view that academic performance is a complex outcome that requires further investigation, it is recommended that future studies include other learners’ characteristics and environmental presage factors when examining online learning outcomes. As for the process factors, the online learning experience was found to have a negative mediating effect on the relationships between engagement and academic performance and online self-regulation and academic performance. More studies are needed to examine further the negative mediating effects found. In addition, other process factors like communication self-efficacy and online learning readiness (Wang et al., 2022) may be included and tested in the model to strengthen the model predictive value on academic performance. In addition, this study was quantitative in nature, which did not explore how the factors influence students’ learning outcomes in an in-depth manner, future studies may adopt a qualitative or mixed method approaches. Qualitative research methods, which involve techniques such as interviews, focus groups, and observations can be carried out to provide rich and detailed insights into the subjective experiences of university students in online learning and explain how the different factors influence their experiences, engagement, and outcomes. Mixed methods research, on the other hand, combines quantitative and qualitative research methods. It can provide a more comprehensive understanding of the research question or problem.

Conclusion

The structural equation model developed in this study offers a valuable tool for understanding the complex factors that influence undergraduate students’ learning in an online environment. The 3P model and the CoI model, which are two widely recognized models in the field of online learning, provide a solid foundation for the study’s structural equation model. By drawing upon these models, the study identified presage and process factors that significantly impact students’ learning outcomes. Presage factors, such as online self-regulation, are critical for effective online learning. Students with strong online self-regulation skills are better equipped to manage their learning effectively, stay focused, and remain motivated. Process factors, such as online learning experiences and engagement, also play a crucial role in students’ learning outcomes. Positive online learning experiences and high levels of engagement are associated with better learning outcomes, including higher academic achievement and greater satisfaction with online learning. The study’s findings suggest that there is a need to improve students’ online self-regulation skills, learning experiences, and engagement to enhance their learning outcomes. Educators can use the structural equation model to develop targeted interventions that address these areas and promote better online learning experiences for undergraduate students.

To strengthen the model, it may be useful to provide workshops and training programs for lecturers to improve their pedagogical knowledge and skills for effective online teaching and learning. By enhancing the quality of online teaching, lecturers can help to foster positive online learning experiences and engagement among their students. Additionally, future studies could explore how different factors, such as technical support services and learner characteristics, impact students’ online learning experiences and engagement. These factors could be included in the model to create a more comprehensive and accurate understanding of the factors that affect students’ learning outcomes. In conclusion, the study’s structural equation model provides a valuable framework for understanding undergraduate students’ learning in an online environment. By using this model, educators can develop targeted interventions to improve students’ online self-regulation skills, learning experiences, and engagement, which could lead to better learning outcomes. Further research is needed to strengthen the model and explore the impact of additional factors on online learning experiences and engagement.

Footnotes

Acknowledgements

The authors would like to thank the Universiti Sains Malaysia, Wawasan Open University, Sunway University and Swinburne University of Technology Sarawak Campus for the support to undertake this study and to all the postgraduate students and lectures who have facilitated and supported the data collection processes.

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 work was supported by a Matching Grant Project between Universiti Sains Malaysia (304/PGURU/6501229/S162), Swinburne University of Technology Sarawak (20226020-9375), Sunway University (RCO-LOC-SSW-001-2022) and Wawasan Open University (WOU/CeRI/2021(0042).

Ethical Approval

This study has obtained ethical approval to carry out the research from the Human Research Ethics Committee with the approval number: JEPeM USM Code: USM/JEPeM/21120836

ORCID iD

Melissa Ng Lee Yen Abdullah

Data Availability Statement

Data is available on reasonable request from the corresponding author

References

Adedoyin

O. B.

Soykan

(2023). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31, 863–875. https://doi.org/10.1080/10494820.2020.1813180

Alabbasi

(2022). Factors influencing students’ engagement in virtual classrooms and their impact on satisfaction. International Sciences Letters, 11(4), 1203–1215. https://doi.org/10.18576/isl/110420

Aldosemani

T. I.

(2022). Investigating students’ perceptions of their learning experience and self-regulated learning skills during emergency remote teaching. Journal of Web-based Learning and Teaching Technologies, 17(1), 1–15. https://doi.org/10.4018/ijwltt.302905

Alzahrani

(2022). Traditional learning compared to online learning during the COVID-19 pandemic: Lessons learned from faculty’s perspectives. Sage Open, 12(2), 1–11. https://doi.org/10.1177/21582440221091720

Anjarwati

Sa’adah

(2021). Student learning engagement in the online class. EnjourMe: Culture, Language, and Teaching of English, 6(2), 104–114. https://doi.org/10.26905/enjourme.v6i2.6128

Appleton

J. J.

Christenson

S. L.

Kim

Reschly

A. L.

(2006). Measuring cognitive and psychological engagement: Validation of the student engagement instrument. Journal of School Psychology, 44, 427–445. https://doi.org/10.1016/j.jsp.2006.04.002

Arbaugh

J. B.

Cleveland-Innes

Diaz

S. R.

Garrison

D. R.

Ice

Richardson

J. C.

Swan

K. P.

(2008). Developing a community of inquiry instrument: Testing a measure of the community of inquiry framework using a multi-institutional sample. Internet and Higher Education, 11, 133–136.

Ayang

Richard

(2022). A preliminary study on the factors affecting academic performance of foundation students during online learning. Malaysian Journal of Social Sciences and Humanities, 7(4), e001409–9. https://doi.org/10.47405/mjssh.v7i4.1409

Azhari

F. A.

Ming

L. C.

(2015). Review of e-learning practice at the tertiary education level in Malaysia. Indian Journal of Pharmaceutical Education and Research, 49(4), 248–257. https://doi.org/10.5530/ijper.49.4.2

10.

Baber

(2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19. Journal of Education and e-Learning Research, 7(3), 285–292. https://doi.org/10.20448/journal.509.2020.73.285.292

11.

Barnard

Lan

W. Y.

Y. M.

Paton

V. O.

Lai

S.-L.

(2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12(1), 1–6. https://doi.org/10.1016/j.iheduc.2008.10.005

12.

Biggs

J. B.

(2003). Teaching for quality learning at university. Open University Press/Society for Research into Higher Education.

13.

Boor

Cornelisse

(2021). How to encourage online self-regulation of Students. Communications of the Association for Information Systems, 48, 211–217. https://doi.org/10.17705/1cais.04827

14.

Cohen

(1988). Statistical power analysis for the behavioral sciences. (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Pubishers.

15.

Commissiong

M. A.

(2020). Student engagement, self-regulation, satisfaction and success in online learning environment [Doctoral dissertation]. Walden University. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=9794&context=dissertations

16.

Dhawan

(2020). Online learning: A panacea in the time of Covid-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018

17.

Doménech-Betoret

Abellán-Roselló

Gómez-Artiga

(2017). Self-efficacy, satisfaction and academic achievement: The mediator role of students’ expectancy-value beliefs. Frontiers in Psychology, 8, 1193. https://doi.org/10.3389/fpsyg.2017.01193

18.

Dumont

A. Y.

Van Wart

Beck

Pei

(2021). The effect of the COVID pandemic on faculty adoption of online teaching: Reduced resistance but strong persistent concerns. Cogent Education, 8(1), 1–24. https://doi.org/10.1080/2331186x.2021.1976928

19.

Filak

V. F.

Sheldon

K. M.

(2008). Teacher support, student motivation, student need satisfaction, and college teacher course evaluations: Testing a sequential path model. Educational Psychologist, 28(6), 711–724. https://doi.org/10.1080/01443410802337794

20.

Gao

B. W.

Jiang

Tang

(2020). The effect of blended learning platform and engagement on students’ satisfaction—— The case from the tourism management teaching. Journal of Hospitality Leisure Sport & Tourism Education, 27, 1–11. https://doi.org/10.1016/j.jhlste.2020.100272

21.

Garrison

D. R.

Anderson

Archer

(1999). Critical inquiry in a text-based environment: Computer conferencing in higher education. Internet and Higher Education, 2(2-3), 87–105. https://doi.org/10.1016/s1096-7516(00)00016-6

22.

Garson

G. D.

(2016). Partial least squares: Regression and structural equation models. Statistical Associates Publishing.

23.

Ginns

Martin

A. J.

Papworth

(2014). Student learning theory goes (back) to (high) school. Instructional Science, 42(4), 485–504. https://doi.org/10.1007/s11251-013-9291-4

24.

Gray

J. A.

DiL oreto

(2016). The effects of student engagement, student satisfaction and perceived learning in online environment. International Education Leadership, 11(1), 98–119.

25.

Gurban

M. A.

Almogren

A. S.

(2022). Students’ actual use of e-learning in higher education during the COVID-19 pandemic. Sage Open, 12(2), 1–16. https://doi.org/10.1177/21582440221091250

26.

Hair

J. F.

Jr. Black

W.C.

Babin

B. J.

Andersen

R. E.

(2010). Multivariate data analysis (7th Ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

27.

Hair

J. F.

Ringle

C. M.

Sarstedt

(2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46, 1–12. https://doi.org/10.1016/j.Irp.2023.01.001

28.

Hair

J. F.

Hult

G. T. M.

Ringle

C. M.

Sarstedt

(Eds.). (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.

29.

Halverson

L. R.

(2016). Conceptualizing blended engagement [Unpublished thesis]. Brigham Young University.

30.

Han

J. H.

(2014). Closing the missing links and opening the relationships among the factors: A literature review on the use of clicker technology using the 3P model. Educational Technology & Society, 17(4), 150–168.

31.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8

32.

Henseler

Ringle

C. M.

Sinkovic

R. R.

(2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014

33.

Zhao

Y. S.

(2022). Effects of online self-regulated learning on learning ineffectiveness in the context of COVID-19. International Review of Research in Open and Distributed Learning, 23(2), 25–43.

34.

Jalal

Khanam

Chaudhary

(2022). Role of multimedia technology in online learning during COVID-19. International Journal of Creative Research Thoughts, 10(9), 566–570.

35.

Kara

Kukul

Çakır

(2021). Self-regulation in three types of online interaction: How does it predict online pre-service teachers’ perceived learning and satisfaction? Asia-Pacific Education Researcher, 30(1), 1–10. https://doi.org/10.1007/s40299-020-00509-x

36.

Kashif

M. F.

Shahid

(2021). Students’ self-regulation in online learning and its effect on their academic achievement. Global Educational Studies Review, i(III), 11–20. https://doi.org/10.31703/gesr.2021(vi-iii).02

37.

Kim

D. J.

(2021). Structural relationship of key factors for student satisfaction and achievement in asynchronous online learning. Sustainability, 13(12), 6734. https://doi.org/10.3390/su13126734

38.

Koksal

(2020). May 2). The rise of online learning. Forbes. https://www.forbes.com/sites/ilkerkoksal/2020/05/02/the-rise-of-online-learning/?sh=736ec1b772f3

39.

Krejcie

R. V.

Morgan

D. W.

(1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.

40.

Kuo

Y. C.

Walker

A. E.

Belland

B. R.

Schroder

K. E. E.

(2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distance Learning, 14(1), 16–39. https://doi.org/10.19173/irrodl.v14i1.1338

41.

Kustono

A. S.

Winarno

W. A.

Nanggala

A. Y. A.

(2021). Effect of accounting lecturer behavior on the level of online learning outcomes achievement. International Journal of Learning, Teaching and Educational Research, 20(3), 169–187. https://doi.org/10.26803/ijlter.20.3.11

42.

Zhu

Yang

H. H.

Guo

(2023). Impact of information literacy, self-directed learning skills, and academic emotions on high school students’ online learning engagement: A structural equation modeling analysis. Education and Information Technologies, 28, 13485–13504. https://doi.org/10.1007/s10639-023-11760-2

43.

Long

J. S.

(1997). Regression models for categorical and limited dependent variables. Sage Publications, Inc.

44.

Marlina

Tjahjadi

Ningsih

(2020). Factors affecting student performance in e-learning: A case study of higher educational institutions in Indonesia. Journal of Asian Finance Economics and Business, 8(4), 993–1001. https://doi.org/10.13106/jafeb.2021.vol8.no4.0993

45.

Mok

K. H.

Xiong

Bin Aedy Rahman

H. N.

Hamzah

A. N

., (2021). COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong. International Journal of Chinese Education, 10(1), 1–20. https://doi.org/10.1177/22125868211007011

46.

Müller

Mildenberger

(2021). Facilitating flexible learning by replacing classroom time with an online learning environment: A systematic review of blended learning in higher education. Educational Research and Reviews, 34, 100394.

47.

Muthuprasad

Aiswarya

Aditya

K. S.

Jha

G. K.

(2021). Students’ perception and preference for online education in India during COVID -19 pandemic. Social Sciences and Humanities Open, 3(1), 100101.

48.

Organization for Economic Co-operation and Development (OECD). (2020, July 24). The potential of online learning for adults: Early lessons from the COVID-19 crisis. https://www.oecd.org/coronavirus/policy-responses/the-potential-of-online-learning-for-adults-early-lessons-from-the-covid-19-crisis-ee040002/

49.

Park

Heo

Gustafson

(2019). What prompts college students to participate in online surveys? International Education Studies, 12(1), 69–79.

50.

Pietarinen

Soini

Pyhältö

(2014). Students’ emotional and cognitive engagement as the determinants of well-being and achievement in school. International Journal of Educational Research, 67, 40–51. https://doi.org/10.1016/j.ijer.2014.05.001

51.

Rajabalee

Y. B.

Santally

M. I.

(2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26, 2623–2656.

52.

Reddy

Chaudhary

Hussein

(2023). A digital literacy model to narrow the digital literacy skills gap. Heliyon, 9(4), e14878. https://doi.org/10.1016/j.heliyon.2023.e14878

53.

Sarstedt

Ringle

C. M.

Hair

J. F.

(2021). Partial least squares structural equation modeling. In Homburg

Klarmann

Vomberg

A. E.

(Eds.), Handbook of market research (pp. 1–47). Springer.

54.

Shahrul

N. M., K. T. M. Y.

Muhammad

B. F. M. P.

Mohammad

H. J.

Lee

S. W.

(2023). Investigating students’ perception and preferences of online learning in UiTM Pasir gudang campus. Asian Journal of University Education, 19(1), 223–234. https://doi.org/10.24191/ajue.v19i1.21228

55.

Shahsavar

Sudzina

(2017). Student satisfaction and loyalty in Denmark: Application of EPSI methodology. Plos One, 12(12), e0189576–NaN18. https://doi.org/10.1371/journal.pone.0189576

56.

Sharma

Alvi

(2021). Evaluating pre and post COVID 19 learning: An empirical study of learners’ perception in higher education. Education and Information Technologies, 26(6), 7015–7032. https://doi.org/10.1007/s10639-021-10521-3

57.

Stevens

G. J.

Bienz

Wali

Condie

Schismenos

(2021). Online university education is the new normal: But is face-to-face better? Interactive Technology and Smart Education, 18(3), 278–297. https://doi.org/10.1108/itse-08-2020-0181

58.

Tang My

Nguyen Tien

Tang My

Le Quoc

(2022). E-learning outcomes during the COVID-19 pandemic. International Journal of Learning, Teaching and Educational Research, 21(6), 160–177.

59.

UNESCO. (2020, April 21). COVID-19 educational disruption and response. https://en.unesco.org/themes/educationemergencies/coronavirus-school-closures

60.

Vishwakarma

Tyagi

(2022). Strategies for promoting self-regulation in online learning environment: An analytical review. Journal of Positive School Psychology, 6(2), 4258–4271.

61.

Wandler

Imbriale

W. J.

(2017). Promoting college student self-regulation in online learning environments. Online Learning, 21(2), 1–16. https://doi.org/10.24059/olj.v21i2.881

62.

Wang

Cao

Gong

Wang

(2022). Interaction and learning engagement in online learning: The mediating roles of online learning self-efficacy and academic emotions. Learning and Individual Differences, 94, 102128. https://doi.org/10.1016/j.lindif.2022.102128

63.

World Economic Forum. (2022). January 27). These three charts show the global growth in online learning. https://www.weforum.org/agenda/2022/01/online-learning-courses-reskill-skills-gap/

64.

M. J.

Zhao

Fils-Aime

(2022). Response rates of online surveys in published research: A meta-analysis. Computers in Human Behavior, 7, 100206. https://doi.org/10.1016/j.chbr.2022.100206

65.

Xie

Yang

(2020). Autonomous learning of elementary students at home during the COVID-19 epidemic: A case study of the Second Elementary School in Daxie, Ningbo, Zhejiang Province, China. Best Evidence in China Education, 4(2), 535–541. https://doi.org/10.15354/bece.20.rp009

66.

(2019). The promises and limits of online higher education: Understanding how distance education affects access, cost and quality. The American Enterprise Institute. https://files.eric.ed.gov/fulltext/ED596296.pdf

67.

Xue

P. P

., (2023). Student perceptions of the community of inquiry framework and satisfaction: Examining the role of academic emotion and self-regulation in a structural model. Frontiers in Education, 8. 1–9. https://doi.org/10.3389/feduc.2023.1046737

68.

Yang

L. H.

(2021). Online learning experiences of Irish university students during the COVID-19 pandemic. All Ireland Journal of Teaching and Learning in Higher Education, 13(1), 1–22.

69.

Zalat

M. M.

Hamed

M. S.

Bolbol

S. A.

(2021). The experiences, challenges, and acceptance of e learning as a tool for teaching during the COVID-19 pandemic among university medical staff. Plos One, 16(3), e0248758–NaN12. https://doi.org/10.1371/journal.pone.0248758

70.

Zhang

(2020). Exploring blended learning experiences through the community of inquiry framework. Language Learning & Technology, 24(1). 38–53. https://doi.org/10125/44707

71.

Zhang

(2022). Synchronous online learning during COVID-19: Chinese University EFL students’ perspectives. Sage Open, 12(2), 1–10. https://doi.org/10.1177/21582440221094821

72.

Zheng

Bender

Lyon

(2021). Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: Empirical evidence from a school-wide comparative study. BMC Medical Education, 21, 495. https://doi.org/10.1186/s12909-021-02909-z

73.

Zhou

Xue

(2022). Extending the technology acceptance model to explore students’ intention to use an online education platform at a university in China. Sage Open, 12, 1–13. https://doi.org/10.1177/21582440221085259

Factors Influencing Undergraduate Students’ Online Learning Outcomes: A Structural Equation Modeling

Abstract

Plain Language Summary

Keywords

Introduction

Literature Review

Factors Influencing Students’ Online Learning Outcomes

Online Self-Regulation

Engagement in Online Learning

Online Learning Experiences

Research Objectives

Hypothesis

Theoretical Framework

Method

Data Analysis and Results

Assessment of the Reflective Measurement Model

Construct Reliability

Convergent Validity

Discriminant Validity

Assessment of the Structural Model

Mediation Analysis

Discussions

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

References