The Influence of Social Support on Expectancy and Value in Online Learning Motivation

The Influence of Social Support on Expectancy and Value in Online Learning Motivation

Faizah Eliza Abdul Talib, Ezathul Zerafena Mohd Ris, Juritah Misman, Yasmin Hanafi Zaid, Idafiaton binti Jamaluddin

Centre of Foundation Studies, Universiti Teknologi MARA Cawangan Selangor, Kampus Dengkil, Selangor, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2024.803384S

Received: 18 October 2024; Accepted: 23 October 2024; Published: 27 November 2024

ABSTRACT

The outbreak of COVID-19 has brought significant changes to the learning process, with online learning becoming increasingly prominent in education. This shift towards digital education presents new challenges and opportunities to better understand the factors that drive motivation and engagement in online learning environments. This study aims to explore learners’ perceptions of social support, expectancy, and value with online learning. A quantitative survey was conducted to investigate the relationship between social support, expectancy, and value in online learning motivation. The instrument for this study was adapted from Fowler (2018). The survey comprises four sections: Demographic Profiles, Expectancy, Value, and Social Support. The Demographic Profiles section includes four items, while the Expectancy and Social Support sections each contain 12 items, and the Value section has 14 items, totalling 38 items across Value, Expectancy, and Social Support. A purposive sampling method was employed, selecting 143 undergraduate students from the Faculty of Computer and School of Education at a public university in Malaysia to complete the survey, which was administered online via Google Forms. Data were analysed using descriptive statistics via IBM SPSS version 28. The findings revealed a strong relationship between social support, expectancy and value in influencing online learning motivation. A positive correlation was observed among these factors. expectancy, value and social support was also observed. This paper also discusses educational implications and potential areas for future research based on the findings.

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

INTRODUCTION

Background of Study

Motivation can mean different things to different people depending on the situation or feelings that drive individuals to pursue what they want to achieve or accomplish. In the education context, it has become crucial to define what learning motivation entails. Lin, Hu, Chen and Zhu (2023) define motivation as a driving factor that encourages an individual to initiate and maintain a specific level of activity in pursuit of a goal. Astuty (2019) reinforces the idea that motivation in learning catalyses students to actively participate in learning tasks to gain something valuable from the educational experience (as cited in Che Soh, Puteh, Mahmud, Abdul Rahim, Soegiono & Rahmat, 2022). According to Tezci, Aktan & Sezer (2015), motivation can include two factors – internal and external- that drive individuals to initiate, sustain, and direct their behaviour towards achieving specific goals or fulfilling certain needs in the context of online learning. Motevalli (2020) further asserts that in the realm of learning, motivation, alongside behaviour, is recognised as a crucial factor that drives students to acquire knowledge and develop new skills.

Examining motivation to learn online among students in Malaysia requires consideration of various factors influencing their engagement in digital education. Sani (2020) identifies key issues in online learning among Malaysian students including a lack of motivation or self-confidence, leading to feelings of loneliness and isolation; the crucial role of lecturers or teachers in guiding and assisting students during the rapid transition; and the significance of a conducive learning environment, that supports educational engagement (as cited in Jenal, Taib, Mohamad Iliyas, Sa’adan, Saleh & Noorezam, 2022).

Fowler (2018) suggests that motivation for online learning may stem from elements such as social support, expectancy, and value, which contribute to the desire to engage in online learning (as cited in Yusop et.al, 2022). Social support in the context of online learning refers to the assistance, encouragement, and resources provided by peers, instructors, family members, and other members of a learner’s social network.

This study will therefore investigate how social support influences students’ expectancy and the value of their motivation to engage in online learning.

Statement of Problem

In the realm of online education, where virtual platforms serve as essential mediums for learning, understanding the factors that contribute to students’ motivation is paramount. One significant area that warrants exploration is the influence of social support on expectancy and value in the context of online motivation.

Studies consistently emphasise the importance of social support in online learning environments. Edwards (2020) highlights how social support mechanisms shape students’ perceptions of expectancy and value in online learning. This notion is echoed by Yildirim and Gurleroglu (2022), who explore the complex relationship between social support, expectancy, and value, suggesting that supportive online environments enhance students’ motivation and engagement. Chiu, Lin, and Lonka (2021) and Meşe and Sevilen (2021) contribute longitudinal perspectives to the discourse, illustrating how social support influences expectancy and value over time in online education contexts. Their findings suggest that sustained social support fosters positive motivational outcomes, indicating the enduring importance of supportive networks in online learning environments. Overall, these studies underscore the pivotal role of social support in shaping students’ expectancy and value perceptions, ultimately influencing their motivation and engagement in online education. They highlight the importance of fostering supportive online learning communities to enhance students’ learning experiences and outcomes.

Despite the valuable insights provided by these studies, a notable gap persists in the literature (Gao, Wei, Li, Wang & Fang, 2023; Walter, 2019). Past researchers have highlighted this gap and called for further research advocating for more experimental or intervention-based studies to better understand the causal mechanisms underlying the relationship between social support, expectancy, and value in online learning. Additionally, there is a call for research examining the role of different forms of social support and how they interact to influence students’ perceptions in online learning environments. Addressing these research gaps will contribute to a more comprehensive understanding of the complex interplay between social support, expectancy, and value in online motivation, ultimately enhancing the design and implementation of effective online learning interventions.

Objective of the Study and Research Questions

This study aims to explore learners’ perceptions of their use of learning strategies. The objectives of this study are as follows:

• To examine the influence of social support on expectancy and value in online learning motivation.
• To investigate the relationship between social support, expectancy, and value

Specifically, this study seeks to answer the following questions;

• How do learners perceive social support in online learning?
• How do learners perceive expectancy in online learning?
• How do learners perceive value in online learning?
• Is there a relationship between social support, expectancy, and value in online learning?

LITERATURE REVIEW

Online Learning Motivation

Motivation is an essential factor during online learning. It affects how students approach their coursework, interact with the course materials, and engage with the lessons and their peers and teachers. When students are motivated, they are driven to overcome the challenges they face during online learning. Dörnyei (2020) stated that motivation and engagement go hand in hand. This means that to keep students engaged with online lessons, teachers, as social support, need to ensure that the students remain motivated to learn in online classrooms. Furthermore, Raime et al. (2020) explored factors that influenced UNITAR College students’ self-motivation and satisfaction with their online education. The survey, which used questions adapted from Eom et al. (2006) and Cobb (2009), was completed by 53 students. Students’ satisfaction was assessed using Cobb’s (2009) Social Presence Scale and Contentment Scale, while their self-motivation was measured through Eom et al. (2006)’s IDEA (Individual Development & Educational Assessment) student rating system. The results showed a significant correlation (53.8%) between students’ self-motivation and their level of satisfaction. This finding suggests that students who are self-motivated to learn in online classrooms are also more satisfied with their online classes. In brief, motivation is a crucial factor to consider in online learning.

Past Studies on Online Learning Motivation

Many studies have been conducted to investigate online learning motivation. As digital technology continues to evolve, online learning is becoming a common educational option. Essentially, educators and students need to understand the factors that influence motivation in the context of online learning.

A recent study by Mohd Zahid et al. (2024) provides an in-depth analysis of the influence of value, anticipation, and social support in online classroom settings. The respondents were 108 engineering students from various institutions in Malaysia. The study investigated how value, anticipation and social support contributed to learner motivation. Results highlighted the importance of intrinsic motivation, particularly driven by curiosity and interest, in addition to self-efficacy and perceived control. Furthermore, the study emphasized that social support from peers and lecturers promoted a sense of community that led to an increase in motivation.

Whereas, Yusop et al. (2022) highlight the importance of intrinsic motivation, perceived control, self-efficacy, and social support in influencing the motivation of Malaysian university students. The study employed a quantitative survey approach to investigate how social support, expectancy, and value affected students’ motivation to learn online. The questions on the instrument were taken from a prior study by Fowler (2018), and 102 answers from public university students were gathered. The study emphasized the need for engaging online learning environments, internal drive, self-confidence, and strong social support for improved student engagement and academic achievement.

Essentially, students’ internal motives to learn influenced their motivation to learn in an online learning classroom. Che Soh et al. (2022) conducted a quantitative study aimed at exploring how learners’ motives impacted their motivation in online learning, particularly in studying Social Marketing. They selected 89 participants who took the course on purpose. The survey used a 5-point Likert scale with 24 questions. Results indicated that students found understanding course content particularly satisfying. Getting good grades was a significant goal for learners, providing satisfaction. Expectancy components indicate students’ belief in their ability to achieve excellent results and their sense of control over their learning. Affective components revealed that students experience a fast heartbeat during exams. In brief, these studies highlight various factors influencing motivation in online learning and suggest that understanding these factors can impact student motivation.

In contrast, other studies reveal challenges related to motivation in online learning environments. Social support from teachers and classmates was important in motivating students in their online classrooms. The study by Messe and Servilen (2021) explored students’ perceptions of online teaching and how it affects their motivation throughout a seven-week course. In this qualitative study, students, through interviews and creative writing, agreed that online learning was not good for their motivation. Students felt this way because there was not enough social interaction, and their expectations did not match the content. In addition, there were organisational issues and problems with how learning environments were set up.

Several researchers studied organisational problems in online courses as a factor affecting student motivation. Meşe and Sevilen (2021) noted the lack of group cohesion as one issue. Mixing strong and weak students in online classes sometimes made tasks too easy, leading to demotivation among stronger students. Some students may struggle with technology proficiency. Next, Lin and Berge (2005) presented the findings of an exploratory factor analysis study conducted on a wide scale (n = 1,056) to identify the underlying variables that make up the barriers to online learning faced by students. Factors such as administrative concerns, social contact, academic skills, technical skills, learner motivation, time and support for studies, cost and internet connection, administrative issues, technical challenges, and learner motivation were the eight characteristics that were identified. Gender, age, ethnicity, type of learning institution, self-rating of online learning skills, effectiveness of online learning, enjoyment of online learning, discrimination in traditional classes, and the number of completed online courses were among the independent variables that significantly impacted the ratings of these barrier factors by students. Their findings stated that students who were most comfortable and confident using online learning technologies, or those with high technical proficiency, faced fewer obstacles in learning and boosted their motivation. It can be concluded that students who perceived that they felt comfortable, confident and were technically adept in using computers and phone devices were motivated to learn online.

Notably, these studies reveal the importance of fostering supportive variables so that students are motivated to learn in online settings. The research aims to explore various elements of motivation, focusing specifically on how social support has an impact on students’ expectancy and value in their online learning motivation and the relationship between social support, expectancy, and value. Earlier research has recognized the importance of these factors in affecting students’ motivation. This study will add to the literature on how these elements interact in the context of university students and online classrooms. This research is important as it offers a thorough examination on the influence of social support on the expectancy and value of online learning motivation. In addition, this study also studies the relationship between social support, expectancy, and value within the Malaysian higher learning institutions setting. Essentially, the insights garnered from this study will contribute to the advancement of online education and enhance learning experiences. This is important because there is a high demand for online courses in the future.

Conceptual Framework

Online learning can be more challenging than face-to-face learning, as learners need more motivation to stay engaged compared to traditional classrooms. According to Rahmat et al. (2021), satisfaction with online learning involves various motivational factors. Figure 1 below illustrates the conceptual framework of the study.  This research is rooted in Fowler’s (2018) model of motivation for online learning. In this context, the influence of social support is examined concerning the expectancy and value components of online learners.

Figure 1- Conceptual Framework of the Study- The Influence of Expectancy and Value in Online Motivation

METHODOLOGY

This quantitative study aims to explore motivation factors for online learning among undergraduates. A purposive sample of 143 participants responded to the survey. The instrument used is a 5-point Likert scale survey, rooted in Fowler (2018), to reveal the variables shown in Table 1 below. The survey has 4 sections. Section A has items on the Demographic Profile. Section B has 12 items on expectancy. Section C has 14 items on value and Section D has 12 items on social support.

This study aims to provide an in-depth exploratory examination of the relationship between motivational factors and social support among online learners. The sample size of 143 respondents serves as groundwork for future research involving larger and more diverse groups. The insights accumulated in this study shed light on trends and relationships that are likely to be relevant in similar learning environments, specifically within comparable educational settings. Emphasising depth rather than breadth, this study uses the small sample size to enable a more detailed analysis of individual learner experiences, providing rich and specific insights into motivational dynamics that might not be captured in larger-scale studies. In addition, it is believed that choosing a small sample size is a practical decision in balancing in-depth data collection with logistical considerations. This strategy ensures the collection of high-quality data within the study’s scope, granting valid and reliable insights that provide a valuable basis for broader applications in future research.

Table 1- Distribution of Items in the Survey

Table 1 also shows the reliability of the survey. The analysis reveals a Cronbach Alpha of .891 for Section B, Cronbach Alpha of .907 for Section C and Cronbach Alpha of .882 for Section D. This thus reveals a good reliability of the instrument chosen/used. Further analysis using SPSS is done to present findings to answer the research questions for this study.

FINDINGS

Findings for Demographic Profile

Table 2-Percentage for Demographic Profile

Table 2 demonstrates the percentages of the respondents’ demographic profiles.  Based on the findings, the respondents consist of 61% female and 39% male.  This is probably because most of the respondents are from social science disciplines.  Next, as indicated in the table, a slightly higher percentage (53%) of respondents are from MUET levels 1 to 3, while the remaining 47% are from MUET levels 4 to 6. Next, as indicated in the table, a slightly higher percentage (53%) of respondents are from MUET levels 1 to 3, while the remaining 47% are from MUET levels 4 to 6.  The majority of the respondents reside at the college (86%) while only 14% live at home.  Following that, more than half of the respondents (61%) are from social science disciplines, while only 14% are from Engineering and 25% from Science and Technology.  As was emphasised earlier, this explains the high percentage of female respondents in the survey.

Findings for Social Support

This section presents data to answer research question 1- How do learners perceive social support in online learning?

Table 3- Mean for SOCIAL ENGAGEMENT (SSE)

Table 3 displays findings on how learners perceive social support in online learning. The results indicate that learners believe that paying attention in class (mean 4.0) and enjoying class discussions (mean 4.0) help them engage in online learning. Similarly, learners feel that they can freely communicate with other students in classes (mean 3.9). Undoubtedly, this situation arises from the environment created by the instructors, who managed to capture the learners’ attention and thus prolong their motivation to pay attention in class and participate in class activities. This is supported by the mean score of 3.8, which indicates that learners feel they have strong relationships with other learners in the course. This aligns with the statement by Moisey & Hughes (2008), who claimed that a supportive learning environment is the main responsibility of teachers and instructors. As instructors, they should be aware that it is their role to create a positive learning environment that encourages the learning process and learner participation. As the evidence shows, the lowest mean (2.6) corresponds to the statement about feeling disconnected from teachers and other learners in the class. These results indicate that learners enjoyed their online learning sessions despite not meeting each other physically.

Table 4- Mean for INSTRUCTOR SUPPORT(SIS)

Table 4 shows the learners’ perceptions of the support from the instructors. The results indicate positive responses from the learners, with an average mean of 4.2. The learners stated that instructors responded well to their questions (mean 4.2) and answered them clearly and appropriately (mean 4.2). The instructors were also reported to provide the necessary guidance for any given tasks in class (mean 4.2). Furthermore, the instructors managed to present their teaching material in a way that is relevant to the learners, allowing them the freedom to guide their learning. In addition, instructors were said to provide regular feedback to the learners, which aided their performance in class activities (mean 4.1). However, learners claimed that the instructors’ expectations of them were clear (mean 4.1), even though they felt they could not communicate freely with the instructors (mean 3.8).

Findings for Expectancy

This section presents data to answer research question 2- How do learners perceive expectancy in online learning?

EXPECTANCY(E)

Table 5- Mean for SELF-EFFICACY (ESE)

Table 5 above presents mean scores for self-efficacy beliefs (ESE) based on eight statements related to academic confidence. The participants rated their agreement on a scale, with 5 indicating strong agreement. The highest mean scores were observed for statements ESEQ6 (“I expect to do well”) and ESEQ3 (“I’m confident I can learn the basic concepts that are being taught”), both scoring 4.2 and 4.1, respectively. These results suggest a high level of confidence and positive expectations among the respondents regarding their academic performance. Conversely, the lowest mean scores were found for ESEQ2 (“I’m certain I can understand the most difficult material presented in the readings”) and ESEQ4 (“I’m confident I can understand the most complex material presented by the instructor”), both scoring 3.4. This indicates a comparatively lower confidence level in dealing with more challenging academic content. Overall, the participants appear to have strong self-efficacy beliefs, particularly in their expectations for overall performance and understanding of basic concepts.

Table 6- Mean for CONTROL OF LEARNING BELIEFS (ECB)

Table 6 displays mean scores for beliefs related to the control of learning beliefs (ECB), as reflected in four statements. The participants rated their agreement on a scale, with 5 indicating strong agreement. The highest mean scores were observed for ECBQ1 (“If I study in appropriate ways, then I’ll be able to learn the material”) and ECBQ3 (“If I try hard enough, then I’ll understand the material presented”), both scoring 4.1. These results suggest a strong conviction among respondents that effective study methods and effort contribute significantly to their ability to comprehend and learn academic material. Conversely, the lowest mean score was noted for ECBQ4 (“If I don’t understand the material presented, it’s because I didn’t try hard enough”) with a score of 3.7. This indicates a relatively lower agreement with the notion that a lack of understanding solely results from insufficient effort. Overall, participants express a belief in the efficacy of appropriate study strategies and personal effort in facilitating learning, while demonstrating a more nuanced perspective regarding the direct correlation between effort and understanding.

Findings for Value

This section presents data to answer research question 3- How do learners perceive value in online learning?

VALUE (V)

Table 7- Mean for INTRINSIC GOAL ORIENTATION(VI)

Table 7 shows the mean for intrinsic goal orientation. The highest value is 3.8, indicating that students agree that the most satisfying thing for them is when they try to understand the content as thoroughly as possible. Next, students prefer materials that arouse their curiosity, even if they are difficult to learn, scoring 3.7. Finally, the lowest value for intrinsic goal orientation, with a score of 3.6, shows that students prefer materials that challenge them so that they can learn new things, and they also choose assignments that they can learn from, even if these assignments do not guarantee a good grade. This aligns with the statement by Mohd Zahid et al. (2024), who stated that the respondents felt the most satisfaction when they fully grasped the content of the learning materials. Overall, the data suggest that respondents are more inclined towards intrinsic incentive variables, such as thoroughly understanding the content and materials that arouse curiosity, over external rewards like obtaining good grades. In brief, students have a strong inclination towards a deep understanding of the study materials.

Table 8- Mean for EXTRINSIC GOAL ORIENTATION (VE)

Table 8 shows the mean for extrinsic goal orientation. Firstly, students express a high level of agreement, with a score of 4.6, that getting a good grade is the most satisfying thing for them. Next, an average mean of 4.3 indicates that students want to succeed and obtain better grades in their classes as they want to showcase their abilities to various people, such as their families, friends, and employers. The lowest mean, at 4.1, illustrates that students acknowledge the importance of getting better grades than most of their classmates. Achieving a good grade and improving their overall grade point average are highly important extrinsic goals for the respondents. The means suggest that students strongly agree with the importance of academic success and obtaining good grades.

Table 9- Mean for TASK VALUE (VT)

Table 9 provides the mean for task value. The highest value is 4.2, indicating that the students perceive the importance of learning the course materials and the content area, as well as considering the course materials and subject matter important. The lowest value, with a score of 4.1, shows that they perceive the content of the course as applicable in other courses, and they express a favourable attitude towards the subject. This suggests that the students have a positive attitude towards the course material, perceiving it as interesting, valuable, and applicable. These tasks are deemed valuable for university graduates in their broader academic pursuits.

Findings for Relationship between Social Support on Expectancy and Value in Online Learning?

This section presents data to answer research question 4: Is there a relationship between social support, expectancy, and value in online learning? To determine if there is a significant association in the mean scores between metacognitive, effort regulation, cognitive, social, and affective strategies, data were analyzed using SPSS for correlations. The results are presented separately in Tables 10, 11, 12, and 13 below.

Table 10-Correlation between Social Support and Expectancy

Table 10 shows that there is an association between social support and expectancy. Correlation analysis reveals a highly significant association between social support and expectancy (r = .594**) and (p = .000). According to Jackson (2015), the coefficient is significant at the .05 level, and a positive correlation is measured on a 0.1 to 1.0 scale. A weak positive correlation would be in the range of 0.1 to 0.3, a moderate positive correlation from 0.3 to 0.5, and a strong positive correlation from 0.5 to 1.0. This indicates a strong positive relationship between social support and expectancy 0.5 to 1.0. This means that there is also a strong positive relationship between social support and expectancy.

Table 11-Correlation between Expectancy and Value

Table 11 shows that there is an association between expectancy and value. Correlation analysis indicates a highly significant association between expectancy and value (r = .658**) and (p = .000). According to Jackson (2015), the coefficient is significant at the .05 level, and a positive correlation is measured on a 0.1 to 1.0 scale. A weak positive correlation would be in the range of 0.1 to 0.3, a moderate positive correlation from 0.3 to 0.5, and a strong positive correlation from 0.5 to 1.0. This suggests a strong positive relationship between expectancy and value.s that there is also a strong positive relationship between expectancy and value.

Table 12-Correlation between Value and Social Support

Table 12 shows that there is an association between value and social support. Correlation analysis demonstrates a highly significant association between value and social support (r = .699**) and (p = .000). According to Jackson (2015), the coefficient is significant at the .05 level, and a positive correlation is measured on a 0.1 to 1.0 scale. A weak positive correlation would be in the range of 0.1 to 0.3, a moderate positive correlation from 0.3 to 0.5, and a strong positive correlation from 0.5 to 1.0. This indicates a strong positive relationship between value and social support.

CONCLUSION

Summary of Findings and Discussions

The findings from this study underscore that students’ motivation in online learning settings remains high when they perceive active engagement from their lecturers, especially through prompts and constructive feedback. This kind of engagement fosters a learning environment where students feel supported and connected, as they benefit from dynamic interactions with peers and lecturers. This aligns with Gomez et al. (2012), who emphasised the critical role of lecturer-student interaction in sustaining motivation and engagement within the learning process (as cited in Ijlal, Nurul Asma, Adhanawati, Nur Aqilah, Noor Hanim, & Wan Mohd Hamdi, 2023). Consequently, consistent lecturer engagement is a cornerstone for student motivation, underlining the need for interactive elements within online learning environments.

The concept of expectancy in this study was gauged by examining students’ perceptions of self-efficacy and control beliefs. Learners indicated a strong sense of confidence in their ability to grasp fundamental concepts, complete assignments effectively, and succeed in assessments. This self-belief in their capabilities was bolstered by a range of resources supporting self-regulated learning that encouraged them to excel. Lunenburg (2011) parallels this finding, illustrating that individuals link their efforts to performance outcomes, which, in turn, enhance motivation through positive reinforcement (as cited in Jenal et al., 2022). The relationship between students’ belief in their capabilities and their expectation of success plays a vital role in their sustained motivation and dedication to learning.

The factor of value, which was examined through intrinsic goal orientation, extrinsic goal orientation, and task value, highlighted the importance of academic achievement, particularly through grades. Students feel that achieving high grades is the most motivating factor for them, suggesting a strong extrinsic goal orientation. This finding is consistent with Gustiani, Ardiansyah, & Simanjuntak’s (2022) study, which identified extrinsic motivators, such as grades, as primary drivers of student engagement in online learning. This emphasis on extrinsic rewards highlights the need for the online learning process to balance intrinsic and extrinsic motivational elements to maintain and nurture students’ sustained engagement.

In conclusion, the study highlights that social support, expectancy, and value are integral and interdependent components that collectively bolster student motivation in online learning environments. These factors not only contribute independently to motivation but also reinforce each other, creating a cohesive motivational framework that enhances student engagement and resilience in online learning.

Pedagogical Implications and Suggestions for Future Research

As online learning becomes increasingly prevalent in higher education across Malaysia, it is vital to address the multifaceted challenges students face in online learning environments. Among these challenges, the absence of face-to-face interaction has been particularly impactful, as it can diminish students’ emotional connection to the learning material and their sense of support. The lack of direct contact with lecturers can create an isolating experience, resulting in difficulties in comprehending the content and reducing motivation. These challenges pose significant barriers to students’ success in online education if a conducive learning environment does not exist.

The practical implications of this study highlight actionable strategies for educators and instructional designers. Recognising the role of social support in fostering motivation, higher learning institutions can focus on building supportive online communities and implementing collaborative learning activities. The study suggests that creating opportunities for students to connect and engage, such as group discussions, interactive feedback sessions, and real-time question-and-answer sessions, could alleviate the lack of physical interaction and maintain engagement. In this aspect, lecturers play a critical role and must proactively upskill to adapt to the technological demands of online learning. This upskilling includes developing competencies in using digital tools, personalising feedback, and understanding motivational factors to support students effectively.

The study also underlines the importance of considering human-centred motivational elements—specifically, social support, expectancy, and value—in the design and implementation of online learning environments. While technological advancements facilitate accessibility, motivation often hinges on the more personal aspects of learning. Therefore, in addition to technical proficiency, lecturers and institutions should prioritise fostering an emotionally supportive and intrinsically motivating environment.

For future research, it is recommended to extend this research to the perspectives of lecturers or instructors to gain a balanced understanding of the factors influencing student motivation. Investigating lecturers’ insights would provide a comprehensive view of the challenges and motivational strategies from the teaching perspective, offering further guidance on optimising online learning environments. This expanded focus could inform interventions aimed at bridging the motivational gaps observed in online learning and contribute to a more holistic, sustainable approach to online education.

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