Keeping Students Motivated in Online Learning: An Exploration of Value, Expectancy, And Social Support

Keeping Students Motivated in Online Learning: An Exploration of Value, Expectancy, and Social Support

Norhisyam Jenal¹, Siti Aishah Taib^2*, Sharifah Amani Syed Abdul Rahman³, Nadzrah Sa’adan⁴ Nursyuhada Zakaria⁵, Aini Ahmad⁶

¹Faculty of Mechanical Engineering, University Technology MARA Cawangan Johor, Kampus Pasir Gudang

^2,3,4,5Academy of Language Studies, University Technology MARA Cawangan Johor, Kampus Pasir Gudang

⁶School of Languages, Civilization and Philosophy, University Utara Malaysia

^*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000103

Received: 25 June 2025; Accepted: 30 June 2025; Published: 01 August 2025

ABSTRACT

The shift to online learning has challenged educators to understand what sustains students’ motivation in virtual environments. This quantitative study investigates the roles of value, expectancy, and social support in shaping undergraduate learners’ motivation in online learning settings. Grounded in the Expectancy-Value Theory and drawing on Fowler’s framework of learning motivation, the study involved 106 undergraduate students who responded to a structured online survey. The instrument assessed three key components: value (including intrinsic and extrinsic goal orientation and task value), expectancy (self-efficacy and control of learning beliefs), and social support (social engagement and instructor support). Descriptive and correlational analyses revealed that students were highly motivated by extrinsic goals and task relevance, demonstrated moderate self-efficacy, and valued instructor feedback and support. Significant positive correlations were found between all three motivational components, with the strongest association observed between value and social support. These findings highlight the importance of fostering meaningful course content, encouraging student self-belief, and enhancing instructor-student interaction to sustain motivation in online learning environments. Implications for instructional design and student support strategies are discussed.

Keywords: online learning motivation, value, expectancy, social support, expectancy-value theory

INTRODUCTION

Background of Study

As universities adopt more online learning platforms, understanding the factors that keep students motivated has become a critical area of research. Motivation is a key determinant of student success, especially in self-directed settings that require sustained effort, strategic engagement, and emotional regulation (Fowler, 2018; Michikyan et al., 2025). Unlike traditional classrooms, online learning often limits real-time interaction, placing greater emphasis on learners’ internal beliefs and perceived support systems to maintain focus and persistence (Karyadi & Paristiowati, 2024; Taib et al., 2024).

Expectancy-Value Theory (Eccles & Wigfield, 2002; 2024) provides a robust framework to understand this motivation, suggesting that learners’ effort is influenced by their belief in success (expectancy) and the value they place on the task. In online contexts, motivation is further shaped by social dimensions, such as support from instructors and peers (Michikyan et al., 2025; Rahmat & Thasrabiab, 2024). Previous research also shows that extrinsic value and emotional support are strong predictors of academic engagement in virtual learning (Menon, 2022; Lee & Song, 2022).

As online learning becomes an enduring component of higher education, it is essential to understand how cognitive, emotional, and social factors interact to influence student motivation. This study explores how value, expectancy, and social support contribute to sustaining undergraduate learners’ motivation in online learning environments.

Statement of Problem

While online learning offers flexibility and accessibility, it also presents motivational challenges that can affect learners’ engagement and performance. Research has shown that students’ motivation in online environments is shaped not only by their cognitive beliefs about success (expectancy), but also by the value they place on learning tasks and the level of social support they perceive (Eccles & Wigfield, 2002; 2024; Fowler, 2018). Studies such as those by Michikyan et al. (2025) and Ghanem et al. (2025) highlight the importance of instructor feedback, task relevance, and emotional support in sustaining motivation. However, learners may still struggle with self-efficacy when content is difficult or when peer and teacher interaction is limited (Taib et al., 2024; Karyadi & Paristiowati, 2024).

Despite existing frameworks like Expectancy-Value Theory, there remains a need to better understand how these motivational constructs interact in real online learning contexts. Specifically, recent studies call for further exploration into how cognitive beliefs (e.g., control of learning), emotional experiences (e.g., anxiety), and external support systems (e.g., instructor responsiveness) collectively influence motivation in virtual classrooms (Rahmat & Thasrabiab, 2024; Sudarnoto et al., 2025). In response, this study investigates the relationships between value, expectancy, and social support to offer a more integrated view of undergraduate learners’ perceptions of motivation in online learning.

Objective of the Study and Research Questions

This study aims to examine undergraduate learners’ perceptions of motivation in the context of online learning. The research is guided by the following questions:

• What are undergraduate learners’ perceptions regarding value in online learning?
• What are undergraduate learners’ perceptions regarding expectancy in online learning?
• What are undergraduate learners’ perceptions regarding social support in online learning?
• Is there any relationship between value, expectancy, and social support for undergraduate learners’ perceptions of motivation in online learning?

LITERATURE REVIEW

Theoretical Framework

Expectancy-Value Theory for Online Learning

Expectancy-Value Theory (EVT) offers a well-established framework for understanding learners’ motivation, particularly in academic and online learning contexts. The theory by Eccles and Wigfield (2002) proposes that individuals’ motivation to engage in a task is shaped by their expectancy for success which is their belief in their ability to perform the task, and the subjective value they assign to the task. Task value, in turn, comprises four components: intrinsic value, utility value, attainment value, and cost. Intrinsic value reflects the enjoyment or interest derived from doing the task, while utility value refers to the perceived usefulness of the task in achieving future goals, such as career advancement. Attainment value captures the personal significance of performing well, often tied to a student’s identity or sense of self-worth. In contrast, cost represents the perceived effort, anxiety, or opportunity lost when engaging in the task, and may reduce motivation even when the task holds value in other ways. Research has shown that high utility and attainment values are associated with increased engagement and strategic learning, particularly in online and goal-oriented contexts (Menon, 2022; Lee & Song, 2022; Eccles & Wigfield, 2024)

In a more recent development, Eccles and Wigfield (2024) introduced the Situated Expectancy-Value Theory (SEVT), which expands EVT by incorporating sociocultural and contextual influences. SEVT posits that learners’ motivational beliefs are not formed in isolation but are continuously shaped by interactions with parents, teachers, peers, and broader cultural contexts. This perspective allows for a more holistic understanding of how motivation is situated in learners’ lived experiences and evolving identities.

One central theme emerging from the literature is the role of expectancy beliefs in academic engagement and persistence. Studies have consistently shown that when students believe they can succeed, they are more likely to invest effort and remain committed to learning activities. For instance, Mohammed Raffi et al. (2025) found that expectancy was significantly correlated with motivation among undergraduates in an online Arabic course. Similarly, Fowler (2018) reported that students with higher expectancy beliefs demonstrated greater motivation to learn online, particularly among female learners, who scored higher in expectancy and extrinsic goal orientation.

Another theme evident across the literature is the multifaceted nature of task value. While some students are intrinsically motivated by interest and enjoyment, others are driven by more pragmatic concerns such as achieving good grades or acquiring useful skills. In Menon’s (2022) study of students in an introductory programming course, utility and attainment value were strongly associated with the use of self-regulated learning strategies. Likewise, Lee and Song (2022) found that task value, along with expectancy and emotional cost, predicted engagement in MOOCs. Notably, extrinsic value emerged as the strongest motivator in Fowler’s (2018) and Mohammed Raffi et al.’s (2025) studies, suggesting that external rewards and recognition remain powerful motivators in digital academic environments.

A recurring concern within the expectancy-value framework is the role of cost, particularly emotional cost, as a barrier to engagement. Lee and Song (2022) highlighted that emotional costs, such as anxiety and fear of failure, negatively affected student engagement in online learning. This underscores the importance of addressing affective dimensions of learning environments, especially in contexts requiring self-regulation and autonomy.

Finally, an important contribution to the evolving conversation on EVT comes from Sun et al. (2023), who proposed the Perceived Value of Knowing Learning Goals (PVKLG) as a mediating factor between self-directed learning and online engagement. Their study revealed that students who clearly understood the value of their learning goals were more likely to engage behaviourally, cognitively, and emotionally. This finding aligns with SEVT’s emphasis on the importance of contextually meaningful and identity-relevant goals in sustaining motivation.

Motivation in Online Learning

Motivation in online learning has emerged as a complex, dynamic process shaped by learners’ internal dispositions, instructional design, emotional regulation, and contextual factors. Drawing from perspectives such as Self-Determination Theory (SDT) and Social Cognitive Theory (SCT), recent research highlights the relationship between students’ motivation and their capacity for self-regulated learning (SRL). For instance, Sudarnoto et al. (2025) demonstrated that students with strong intrinsic motivation and self-efficacy were more likely to engage in SRL strategies such as goal setting, time management, and self-monitoring which suggests that motivated learners are also strategic learners. Similarly, Omar et al. (2021) found that highly motivated university students in Malaysia and Indonesia exhibited strong perseverance and planning behaviours in online English classes, regardless of their linguistic context, reinforcing the idea that motivation translates into deliberate academic engagement.

Instructional practices also play a critical role in shaping student motivation. Ghanem et al. (2025) found that course assessment was the strongest predictor of motivation, followed closely by student attitudes and teaching styles, highlighting that clear expectations and effective pedagogy significantly enhance learners’ willingness to participate. Michikyan et al. (2025) extended this by showing that perceived instructional support (e.g. encouragement, guidance, and empathy from teachers) fosters not only students’ academic interest but also behavioural engagement. The quality of interaction between students and teachers, especially in virtual settings, emerged as a powerful motivational factor. Karyadi and Paristiowati (2024) added that flexibility, creative assignments, and autonomy within the learning environment further supported students’ drive to engage with content, although low enthusiasm pointed to emotional fatigue and disengagement.

Affective experiences, particularly anxiety and emotional strain, were found to significantly influence motivation. Taib et al. (2024) reported that while students exhibited generally positive motivation, test-related anxiety and emotional discomfort were common, especially during assessments. Such affective barriers can suppress motivation even in otherwise capable students. Emotional support from teachers and peers becomes vital in such cases, as noted by Michikyan et al. (2025), who emphasized the mediating role of teacher care in mitigating emotional disruption. Overall, the literature emphasizes that motivation in online learning extends beyond cognitive beliefs as it is a fluid, socially situated construct that thrives when students feel competent, supported, and emotionally secure.

Past Studies 

Past Studies on Motivation in Online Learning

Research on motivation in online learning has grown significantly, especially in response to the increasing reliance on virtual instruction during and after the COVID-19 pandemic. Past studies have explored various factors that influence students’ motivation, including internal beliefs, instructional practices, emotional experiences, and self-regulation strategies.

Ghanem et al. (2025) investigated key factors that influenced university students’ motivation in online learning through a quantitative survey involving 265 students from Irbid National University, Jordan. Using a validated questionnaire, the study examined variables such as student attitudes, teaching styles, learning preferences, and assessment practices. Regression analysis revealed that course assessment had the most significant effect on student motivation, followed by learning styles and teaching approaches. The authors emphasized the need for educators to prioritize fair, transparent assessments and responsive teaching strategies to maintain motivation in online settings.

In a different context, Karyadi and Paristiowati (2024) conducted a qualitative descriptive study to understand the motivational experiences of 53 high school students engaged in online learning in Indonesia. From the data collected through observations, interviews, journals, and questionnaires, the study found that students exhibited strong motivation in terms of independence, encouragement, and consistency. However, enthusiasm was notably low, and students reported reduced peer interaction and emotional connection. The findings suggest that while online learning can support autonomy and comfort, it often lacks the social and emotional richness of face-to-face classrooms, calling for more engaging and interactive virtual environments.

Omar et al. (2021) focused on comparing academic motivation, specifically self-regulated learning (SRL) and self-efficacy, among Malaysian and Indonesian undergraduates taking online English classes. Utilizing a modified version of the Motivated Strategies for Learning Questionnaire (MSLQ), the researchers surveyed 206 students from Malaysia and 174 from Indonesia. The study found high levels of motivation in both groups, particularly in perseverance and goal setting, despite minor differences in linguistic context (ESL vs. EFL). Moderate scores in time management and help-seeking behaviours were noted, underscoring the importance of explicit support in developing SRL skills. The study concluded that language background did not significantly affect online learning motivation, and that students benefit from structured guidance and autonomy-supportive teaching.

Michikyan et al. (2025) expanded the scope of inquiry by examining the roles of self-perception and instructional support in shaping motivation and engagement among over 1,200 high school students in the United States. Using Structural Equation Modeling (SEM), the study found that academic self-concept and perceived teacher support were strong predictors of both interest and behavioural engagement. Self-efficacy was shown to influence motivation indirectly through self-concept. These results suggest that students are more motivated and engaged when they feel competent and supported by their teachers, highlighting the importance of both emotional and academic scaffolding in virtual learning spaces.

In a subject-specific context, Sudarnoto et al. (2025) explored the relationship between student motivation and self-regulated learning in online chemistry classes. Using a correlational design and questionnaire-based data collection, the study found that students with high intrinsic motivation and self-efficacy demonstrated greater use of SRL strategies such as planning, monitoring, and goal setting. These findings reinforce the idea that motivation and self-regulation are mutually reinforcing and should be addressed simultaneously in instructional design. The authors recommend that teachers explicitly teach SRL techniques while also fostering motivational factors like curiosity and confidence.

Lastly, Taib et al. (2024) investigated how value, affective, and expectancy-related components influenced online learning motivation among 151 Malaysian undergraduates during emergency remote teaching. Through a structured questionnaire and correlational analysis, the study found that students exhibited high extrinsic motivation and strong control beliefs. However, test anxiety emerged as a prevalent emotional barrier. While value and expectancy were closely linked, the affective component showed a weaker yet significant relationship with overall motivation. The study highlights the need for educators to address both the cognitive and emotional dimensions of learning by reducing anxiety and promoting a supportive learning climate.

These studies suggest that motivation in online learning is not a fixed trait, but a dynamic process influenced by teaching practices, emotional experiences, peer interaction, and learners’ beliefs about themselves. Effective online instruction must therefore be holistic by addressing not only content delivery but also fostering self-efficacy, engagement, emotional security, and strategic learning behaviours.

Conceptual Framework

Figure 1 illustrates the conceptual framework underpinning this study, which investigates the relationships among value, expectancy, and social support in the context of online learning. The framework is grounded in Fowler’s (2018) constructs of online learning motivation, which comprise value, expectancy, and social support. Additionally, it draws upon findings by Rahmat and Thasrabiab (2024), who reported a strong positive association between learners’ actions and their motivation to learn.

In this study, value is defined as the perceived personal benefit or positive outcome that learners expect to gain from acquiring knowledge. Specifically, it encompasses intrinsic and extrinsic goal orientations, as well as task value. Expectancy refers to learners’ beliefs in their ability to successfully complete academic tasks; in this context, it is operationalised through measures of self-efficacy and control of learning beliefs. Social support is conceptualised as the perceived availability of positive interpersonal relationships and institutional resources. Within this framework, it includes dimensions such as social engagement with peers and perceived support from instructors.

Figure 1- Conceptual Framework illustrating the relationship between Value, Expectancy, and Social Support in Online Learning Motivation

METHODOLOGY

This quantitative study was conducted to examine the motivational factors influencing learning among undergraduate students. A purposive sampling method was employed, resulting in a total of 106 participants who responded to the survey. Data were collected through an online questionnaire administered via Google Form. The instrument utilised was a structured survey based on a 5-point Likert scale. As shown in Table 1, the response options included: Never, Seldom, Sometimes, Often, and Almost Always.

Table 1- Likert Scale

The instrument employed in this study was a 5-point Likert-scale survey adapted from Fowler (2018) to examine the key variables outlined in Table 2. The survey consisted of four sections. Section A gathered information related to the participants’ demographic profiles. Section B contained 12 items designed to measure expectancy. Section C comprised 14 items assessing value. Section D included 12 items measuring social support.

Table 2- Distribution of Items in the Survey

Table 2 presents the distribution of survey items according to three key motivational constructs: Expectancy, Value, and Social Support, along with their respective sub-scales and item counts. The Expectancy section includes 13 items split between Self-Efficacy (8 items) and Control of Learning Beliefs (5 items), yielding a Cronbach’s alpha of .895, indicating high internal consistency. The Value construct comprises 16 items distributed across Intrinsic Goal Orientation (5 items), Extrinsic Goal Orientation (5 items), and Task Value (6 items), with a very high reliability score of .920. Meanwhile, Social Support consists of 12 items divided into Social Engagement (5 items) and Instructor Support (7 items), with a Cronbach’s alpha of .863.

Overall, the reliability of the entire 41-item instrument was confirmed with a Cronbach’s alpha of .94, suggesting that the questionnaire has excellent internal consistency and is suitable for addressing the study’s research questions. The data were further analysed using SPSS for descriptive and inferential insights.

FINDINGS

Findings for Demographic Profile

Table 3- Percentage for Demographic Profile

Table 3 presents the demographic breakdown of the respondents in terms of gender and academic discipline. A significant majority of the participants were male (80%), while only 20% were female. In terms of academic discipline, most respondents were from the Science and Technology field, making up 76% of the sample. This was followed by students from the Social Sciences (13%) and Business (10%) streams. These figures suggest a strong representation from male students and those enrolled in Science and Technology programs.

Findings for Value

This section presents data to answer research question 1- What are undergraduate learners’ perceptions regarding value in online learning? In the context of this study, value in learning is operationalised through three components: (i) intrinsic goal orientation, (ii) extrinsic goal orientation, and (iii) task value.

Table 4- Percentage for  (i) Intrinsic Goal Orientation (VI)

Table 4 presents data on students’ intrinsic motivation in online learning environments. The highest level of agreement was seen in learners’ willingness to stay motivated even when working independently on assignments (M = 3.8, SD = .74056). Participants also showed strong interest in deeply understanding online learning content and choosing assignment topics based on personal learning value rather than simply aiming for high grades (both M = 3.6). Additionally, they expressed a fair level of interest in exploring content that sparks curiosity (M = 3.5) and in engaging with challenging materials to expand their knowledge (M = 3.3). Overall, the responses suggest that students generally possess a positive level of self-driven motivation and a desire to grow intellectually through their online learning experiences.

Table 5- Percentage for (ii) Extrinsic Goal Orientation (VE)

 Table 5 shows that participants were highly driven by external rewards and outcomes in their online learning. The strongest agreement was observed in the desire to improve one’s cumulative academic performance, as reflected by the highest mean score (M = 4.5, SD = .98360). Learners also showed strong motivation to achieve high grades, outperform their peers, and demonstrate their academic success to others, with consistent mean scores of 4.2 across multiple related items. Additionally, many participants linked their academic efforts to future job prospects, indicating that career advancement and financial security were key motivational factors (M = 4.3). These findings highlight that students’ engagement in online learning is significantly influenced by goals related to achievement, recognition, and tangible benefits.

Table 6-Percentage for (iii) Task Value (VT)

Table 6 illustrates students’ perceptions of the value and relevance of their online course content. Most respondents believed that the knowledge gained from the course could be applied to other subjects (M = 3.7, SD = .77216), and that the materials were useful and worth learning (M = 3.7, SD = .78987). The highest mean scores were recorded for the belief that understanding the subject matter is important (M = 3.9, SD = .84036) and for recognizing the significance of learning the course content (M = 3.9, SD = .73600). However, slightly lower agreement was observed for personal interest in the course topics (M = 3.6). On the whole, these findings indicate that learners generally see their online coursework as valuable and relevant, both for academic progression and personal development.

Findings for Expectancy

This section presents data to answer research question 2- What are undergraduate learners’ perceptions regarding expectancy in online learning? In the context of this study, expectancy in online learning is operationalised through two components: (i) self-efficacy, and (ii) control of learning beliefs.

Table 7- Percentage for (i) Self-Efficacy (ESE)

Table 7 reflects learners’ self-belief in their ability to succeed in online classes. The highest confidence was expressed in performing well in online tests (M = 3.7, SD = .79333) and in grasping foundational concepts taught in class (M = 3.6, SD = .82028). Students also felt reasonably assured about doing well despite the challenges of online learning (M = 3.6). Confidence in completing online assignments successfully was fairly positive (M = 3.5), though learners were less certain about understanding difficult materials and mastering specific online skills, with mean scores ranging from 3.1 to 3.2. The belief in achieving high grades also received a moderate mean score of 3.3. Overall, the findings indicate that while students have moderate confidence in their academic capabilities, they may require more support when dealing with complex content or acquiring new online skills.

Table 8- Percentage for (ii) Control of Learning Beliefs (ECB)

Table 8 reveals students’ beliefs about their control over learning outcomes in online settings. The strongest agreement was with the belief that understanding course material depends on personal effort (M = 4.0, SD = .77454), suggesting high learner accountability. Many respondents also felt responsible if they failed to learn, whether due to lack of effort (M = 3.7) or ineffective study methods (M = 3.7). Additionally, students showed strong agreement that it would be their fault if they failed to learn the material (M = 3.9), and most accepted full responsibility when they didn’t understand content (M = 3.8). These results indicate that learners generally believe that success in online learning largely depends on their own actions and strategies, reflecting a strong belief that they are in control of their success.

Findings for Social Support

This section presents data to answer research question 3- What are undergraduate learners’ perceptions regarding social support in online learning? In the context of this study, social support in online learning is operationalised through two components: (i) social engagement and (ii) instructor support.

Table 9- Percentage for (i) Social Engagement (SSE)

The findings on social engagement in online classes reveal moderate levels of interaction and connection among learners. The highest mean score (M = 3.5, SD = 0.86460) was recorded for students’ ability to pay attention in online classes (SSEQ2), suggesting that attentiveness is relatively well-maintained in virtual settings. However, feelings of disconnection from teachers and peers (ISSEQ1) and the lack of close relationships with fellow students (SSEQ5) both scored a lower mean of 3.2, indicating a sense of social distance. Similarly, enjoyment of class discussions (SSEQ3) received a mean of 3.3, while free communication (SSEQ4) was rated slightly lower at 3.0, highlighting potential barriers to open interaction. These results suggest that although students are somewhat attentive and engaged, there remains a need to enhance social connectedness and communication in online learning environments.

Table 10-Percentage for (ii) Instructor Support (SIS)

The results indicate that learners generally perceived strong instructor support in their online classes. The highest mean scores (M = 3.8) were found in two areas: the instructor’s ability to present materials in a relevant way (SISQ5), and the provision of regular feedback that helps students gauge their performance (SISQ7). These suggest that instructors were effective in content delivery and assessment communication. Other areas also scored relatively high, such as timely and clear responses to questions (SISQ2), freedom to guide one’s own learning (SISQ6), and receiving necessary guidance for success (SISQ4), all with means of 3.7. Students also felt moderately clear about instructor expectations (SISQ3, M = 3.5). However, the lowest score was for students’ perceived ability to communicate freely with instructors (SISQ1, M = 3.1), pointing to a possible gap in instructor-student interaction. Overall, the data suggests that while instructional support is generally strong, more effort may be needed to enhance open communication in online settings.

Findings for Significant Relationship between Value, Expectancy, and Social Support for Undergraduate Learners’ Perceptions in Online Learning

This section presents data to answer research question 4- Is there any relationship between value, expectancy, and social support for undergraduate learners’ perceptions in online learning?

To examine whether there is a statistically significant association among the mean scores of value, expectancy, and social support in the context of undergraduate online learning, correlation analysis was conducted using SPSS. The results of the analysis are presented separately in Tables 11, 12, and 13.

Table 11- Correlation between Value and Expectancy

Table 11 displays the results of a Pearson correlation analysis between the value and expectancy components. The analysis indicates a statistically significant and strong positive relationship between the two variables, with a correlation coefficient of r = .575 and a significance level of p = .000. Based on the interpretation guidelines by Jackson (2015), this value exceeds the threshold for strong positive correlation, which is defined as ranging from 0.5 to 1.0. In contrast, moderate correlations range from 0.3 to 0.5, and weak correlations fall between 0.1 and 0.3. Since the correlation here falls within the strong range and is significant at the 0.05 level, it suggests there is a strong positive relationship between value and expectancy components.

Table 12- Correlation between Expectancy and Social Support

Table 12 presents the results of a Pearson correlation analysis between expectancy and social support. The findings reveal a statistically significant and strong positive correlation between the two variables, with a coefficient of r = .553 and a p-value of .000. According to Jackson (2015), a correlation coefficient is considered significant at the 0.05 level, and values above 0.5 indicate a strong positive relationship. This suggests that there is a strong positive relationship between expectancy and social support components.

Table 13- Correlation between Social Support and Value

Table 13 presents the correlation results between social support and task value components. The Pearson correlation coefficient was r = .665 with a p-value of .000, indicating a statistically significant and strong positive relationship. According to Jackson (2015), correlation values above 0.5 represent a strong association. This suggests that there is a strong positive relationship between social support and value components.

CONCLUSION

Summary of Findings and Discussions

Research Question 1: What are undergraduate learners’ perceptions regarding value in online learning?

The findings show that students place considerable value on their online learning experiences, particularly in terms of extrinsic motivation and task value. Many learners were driven by the desire to achieve good grades, improve their cumulative GPA, and gain academic recognition. Additionally, they viewed the course content as useful and applicable to future academic or career-related contexts. While intrinsic goal orientation was present as reflected by an interest in personal learning and understanding, it was not as prominent as extrinsic concerns.

This outcome is consistent with Fowler’s (2018) and Mohammed Raffi et al.’s (2025) findings, where extrinsic goal orientation emerged as a stronger motivator than intrinsic interest. Menon (2022) and Lee & Song (2022) similarly found that utility and attainment value were closely linked to students’ use of strategic learning behaviours. The task value component in the present study confirms that students are more likely to stay motivated when they see clear benefits or rewards associated with learning, whether in the form of academic success or career advancement.

Research Question 2: What are undergraduate learners’ perceptions regarding expectancy in online learning?

Students showed moderate levels of expectancy, particularly in their self-efficacy and control of learning beliefs. They expressed confidence in mastering foundational content and succeeding in online tests, especially when they made an effort. However, their confidence declined when the material was perceived as difficult or when assignments required high levels of independent problem-solving. This suggests a divide between students’ beliefs in their general ability to succeed and their perceived ability to overcome specific academic challenges in online settings.

Fowler (2018) and Mohammed Raffi et al. (2025) similarly found that expectancy levels fluctuate depending on the perceived difficulty of tasks and the learners’ previous experiences with online learning. Taib et al. (2024) reported strong control of learning beliefs among students, but also noted emotional factors such as test anxiety, which can undermine confidence. These findings support the idea that while many students believe they are responsible for their own success, they may still benefit from additional scaffolding when facing complex content or high-stakes assessments.

Research Question 3: What are undergraduate learners’ perceptions regarding social support in online learning?

Perceptions of social support revealed a notable difference between two dimensions: instructor support was perceived more positively than peer engagement. Students appreciated instructors who provided timely responses, constructive feedback, clear expectations, and flexible opportunities for self-directed learning. However, their sense of social connectedness with peers was relatively weak. Many reported limited interaction, feelings of disconnection, and an overall lack of meaningful collaboration in the online environment.

These findings align with Michikyan et al. (2025), who demonstrated that instructional support has a direct impact on students’ engagement and academic self-concept. Ghanem et al. (2025) also emphasized the importance of responsive teaching and fair assessments in maintaining motivation. Conversely, Karyadi and Paristiowati (2024) observed that peer interaction was often lacking in online settings, leading to emotional distance and reduced enthusiasm. Rahmat and Thasrabiab (2024) further argued that motivation is socially situated, and that peer and instructor engagement must work in tandem to sustain learning behaviours. This study affirms that although instructor support remains strong, efforts to enhance peer-to-peer interaction are still needed.

Research Question 4: Is there any relationship between value, expectancy, and social support for undergraduate learners in online learning?

The study found strong, statistically significant positive correlations among all three motivational constructs: value, expectancy, and social support. This suggests that students who perceive high value in their learning are also more likely to believe in their ability to succeed (expectancy) and to feel supported by their instructors and peers. Among these relationships, the strongest was observed between value and social support, indicating that perceptions of meaningful content are reinforced when students feel socially connected and guided.

This finding is consistent with Taib et al. (2024), who found that value and expectancy are closely linked in shaping students’ motivation in online environments. It also supports Rahmat and Thasrabiab’s (2024) argument that learners’ motivation is rooted in both self-regulation and perceived support structures. Michikyan et al. (2025) emphasized the importance of teacher encouragement and feedback in fostering motivation and engagement. Sun et al. (2023) added that understanding the value of learning goals can mediate the relationship between self-direction and engagement. Collectively, the results indicate that value, expectancy, and social support operate in an interconnected manner and should be addressed together in online instructional design.

Implications and Suggestions for Future Research

Theoretical and Conceptual Implications

The findings of this study provide further validation for the Expectancy-Value Theory (EVT) as a robust framework for understanding motivation in online learning environments. The significant relationships observed between value, expectancy, and social support reinforce Eccles and Wigfield’s (2002; 2024) assertion that students’ motivation is influenced by both their beliefs about success and the perceived value of the task. This study extends EVT by showing that these motivational components are still relevant and interdependent within a fully digital learning context. Notably, the strong association between value and social support suggests that motivation is not only driven by internal cognitive processes but also by contextual and interpersonal factors, as emphasized in the Situated Expectancy-Value Theory (SEVT) (Eccles & Wigfield, 2024).

The results also support the conceptual model introduced by Fowler (2018), who proposed value, expectancy, and social support as core constructs of online learning motivation. This study empirically confirms Fowler’s model by demonstrating that all three variables are not only present but significantly interrelated. The integration of findings from studies by Michikyan et al. (2025), Taib et al. (2024), and Rahmat & Thasrabiab (2024) further affirms the relevance of considering both cognitive beliefs and perceived external support when examining student motivation in virtual learning settings.

Conceptually, the study highlights the need for motivation to be viewed as situated and relational rather than purely individualistic. Students’ motivation is shaped by the value they assign to learning, their confidence in success, and the quality of interaction and feedback they receive. As such, future theoretical models of online learning motivation should account for the dynamic interplay between internal beliefs and social-instructional environments.

Pedagogical Implications

The findings of this study have several implications for improving teaching practices in online learning environments, especially at the undergraduate level. First, the strong role of extrinsic goal orientation and task value suggests that educators should clearly communicate the real-world relevance and long-term benefits of learning tasks. As Menon (2022) and Lee and Song (2022) observed, students are more engaged when they perceive course content as useful for achieving academic and career goals. Instructors should therefore design activities that connect academic concepts to practical applications and explicitly state how assignments can contribute to future success.

Second, given that learners showed moderate self-efficacy and strong control of learning beliefs, teaching strategies should support student autonomy while also providing scaffolding for complex tasks. This supports the recommendation by Fowler (2018) and Mohammed Raffi et al. (2025) to balance independent learning with guided instruction. Teachers can promote self-regulated learning by incorporating goal-setting tasks, self-assessment tools, and opportunities for formative feedback.

Third, the findings highlight the critical importance of instructor support, especially in the form of clear expectations, timely responses, and personalized feedback. This confirms the work of Michikyan et al. (2025) and Ghanem et al. (2025), who identified teacher support as a major driver of student motivation. Instructors should prioritize consistent communication and empathy, creating a learning climate where students feel seen, heard, and supported.

Finally, the lower ratings in peer interaction and social engagement indicate a need to strengthen the collaborative dimensions of online learning. As suggested by Karyadi and Paristiowati (2024), promoting student-to-student interaction through structured group work, peer feedback, and discussion forums may help reduce feelings of isolation and boost social motivation. Rahmat and Thasrabiab (2024) also emphasize that motivation is socially constructed, making social presence a critical design element in online pedagogy.

Suggestions for Future Research

While this study provides valuable insights into undergraduate learners’ motivation in online learning, it also opens several avenues for further investigation. First, future research could explore the longitudinal development of value, expectancy, and social support across different stages of students’ academic journeys. Since motivation is dynamic and context-dependent (Eccles & Wigfield, 2024; Sun et al., 2023), a longitudinal design would provide better  understanding of how these constructs evolve over time and under different learning conditions.

Second, additional studies could include qualitative or mixed-methods approaches to capture students lived experiences, emotional responses, and perceptions in greater depth. While the current study focused on quantitative correlations, qualitative insights could uncover the emotional costs of online learning such as anxiety, isolation, or burnout, as noted by Lee and Song (2022) and Taib et al. (2024). Exploring these affective dimensions could enrich theoretical models like EVT and SEVT by including emotional variability in digital contexts.

Third, the role of peer support deserves closer examination. This study found that social support from peers was rated lower than instructor support, matching the findings from Karyadi and Paristiowati (2024). Future research could investigate which specific forms of peer interaction (e.g., group work, peer tutoring, discussion forums) are most effective in enhancing motivation and engagement.

Fourth, comparative studies across disciplinary backgrounds, institutions, or cultural contexts could help determine how value, expectancy, and social support manifest in different learning environments. As motivation can vary by field of study and cultural norms (Omar et al., 2021; Michikyan et al., 2025), comparative research could support more inclusive and adaptable online pedagogical models.

Finally, future research may consider examining the impact of technological tools (e.g., gamified platforms, AI-based feedback systems, learning analytics) on student motivation. As digital tools become more embedded in online education, it is important to understand how they might strengthen or weaken the motivational components identified in this study.

REFERENCES

1. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53(1), 109–132. https://doi.org/10.1146/annurev.psych.53.100901.135153
2. Eccles, J. S., & Wigfield, A. (2024). The development, testing, and refinement of Eccles, Wigfield, and colleagues’ situated expectancy-value model of achievement performance and choice. Educational Psychology Review, 36(2), 51. https://doi.org/10.1007/s10648-024-09888-9
3. Fowler,S. (2018). The Motivation to Learn Online Questionnaire. Doctor of Philosophy Dissertation. Graduate Faculty, The University of Georgia. Retrieved from https://getd.libs.uga.edu/pdfs/fowler_kevin_s_201805_phd.pdf
4. Ghanem, R., Abdelhameed, H., Abdulmonem, M., Alfrehat, R., Altaisan, R., Soliman, M., Hameed, H. A., Abduljawad, R., Alrashed, Y., & Nasr, N. (2025). Exploring university students’ engagement and motivation: Critical factors. International Journal of Evaluation and Research in Education (IJERE), 14(2), 1323–1330. https://doi.org/10.11591/ijere.v14i2.30300
5. Jackson, S. L. (2015). Research methods and Statistics-A Critical Thinking Approach (5^tH Edition). Boston, USA: Cengage Learning.
6. Karyadi, P. A., & Paristiowati, M. (2024). Evaluating Learning Motivation: An Analysis of Students’ Engagement in Online Learning Environments. Journal of Educational Management and Learning, 2(1), 44-49. https://doi.org/10.60084/jeml.v2i1.193
7. Lee, Y., & Song, H. D. (2022). Motivation for MOOC learning persistence: An expectancy–value theory perspective. Frontiers in Psychology, 13, 958945. https://doi.org/10.3389/fpsyg.2022.958945
8. Menon, P. (2022). The role of task value and online learning strategies in an introductory computer programming course. Information Systems Education Journal, 20(4), 19–31.
9. Michikyan, M., Subrahmanyam, K., Regan, P., Cano, M. Á., Castillo, L. G., Harkness, A., & Schwartz, S. J. (2025). Differences in and relationship between digital access, use, connectivity, skill, engagement, and motivation: implications for digital inequality in online learning among first-generation and continuing-generation undergraduate students. Discover Education, 4(1), 9. https://doi.org/10.1007/s44217-024-00371-9
10. Mohammed Raffi, M.S., Amat Suparia, F.A, Ammar, A., Md Sallehuddin, N.H., Abdul Kadir, S., Rahmat, N.H. (2025). The Interplay of Expectancy Beliefs and Motivational Components in Online Learning Success. Asian People Journal, 8(1), 213-230.
11. Omar, S., Shaharuddin, W. Y. W., Azim, N. A. F., Nawi, N. S. M., Zaini, N., & Syahfutra, W. (2021). Academic motivation in English online classes: A comparative study of universities in Malaysia and Indonesia. Indonesian Journal of Applied Linguistics, 11(2), 477-487. https://doi.org/10.17509/ijal.v11i2.34538
12. Rahmat,N.H. & Thasrabiab,T. (2024). Exploring Motivation and Self-Regulation from The Social Cognitive View. International Journal of Academic Research in Business & Social Sciences, 4(1), 3276-3290. 6007/IJARBSS/v14-i1/20476
13. Sudarnoto, L. F., Handoko, M. T., Riyanto, A., & Arini, D. P. (2025). The impact of online learning, learning motivation, and interpersonal relationships on students’ wellbeing. Social Sciences & Humanities Open, 11, 101485. https://doi.org/10.1016/j.ssaho.2025.101485
14. Sun, W., Hong, J. C., Dong, Y., Huang, Y., & Fu, Q. (2023). Self-directed learning predicts online learning engagement in higher education mediated by perceived value of knowing learning goals. The Asia-Pacific Education Researcher, 32(3), 307-316. https://doi.org/10.1007/s40299-022-00653-6
15. Taib, S. A., Jenal, N., & Rahmat, N. H. (2024). Exploring the relationship between variables for online learning motivation among undergraduates. International Journal of e-Learning and Higher Education (IJELHE), 19(2), 377-397. https://ir.uitm.edu.my/id/eprint/95115/
16. Wei, X., Saab, N., & Admiraal, W. (2024). What rationale would work? Unfolding the role of learners’ attitudes and motivation in predicting learning engagement and perceived learning outcomes in MOOCs. International Journal of Educational Technology in Higher Education, 21(1), 5. https://doi.org/10.1186/s41239-023-00433-2