The Relationship Between Learning Flexibility, the Learning Environment, and Online Learning Experiences Among Undergraduate Students in Malaysia

The Relationship between Learning Flexibility, the Learning Environment, and Online Learning Experiences among Undergraduate Students in Malaysia

Nik Sarina Nik Md Salleh¹, Roseliza Hamid²*, Amirah Syarwarshah Zawawi³, Iffah Farzana Zainal⁴, Noorazzila Shamsuddin⁵, Hasrudy Tanjung⁶

^1,2,3,4,5Faculty of Business and Management, University technology MARA Cawangan Kelantan, Kelantan, MALAYSIA

⁶Department of Management, Universitas Muhammadiyah Sumatera Utara, INDONESIA

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.9010213

Received: 05 January 2025; Accepted: 10 January 2025; Published: 13 February 2025

ABSTRACT

This study aims to explore the relationships between learning flexibility, the learning environment, and online learning experiences among undergraduate students in Malaysia, particularly in light of the global health crisis. With the widespread shift to online learning, understanding the factors that affect its effectiveness is vital, especially as it influences the competitive performance of educational institutions. The study also investigates the potential role of gender differences in shaping students’ online learning experiences. To gather data, survey questions from previous research were adapted and distributed to a sample of 129 undergraduate students at a UiTM branch campus, all of whom participated in online learning during the previous semester. The results reveal significant relationships between learning flexibility (r = 0.395), the learning environment (r = 0.619), and online learning outcomes. Moreover, the findings show a moderate level of engagement and success in online learning among the students (p-value less than 0.05), with gender differences contributing to variations in the experiences and outcomes of online learning. Based on the results, the study emphasizes the importance of incorporating interactive tools, such as quizzes, to improve students’ comprehension and engagement in online courses. These findings suggest that online learning can be optimized by considering flexibility in learning schedules and environments, as well as by addressing gender-specific challenges. The study calls for further research into these factors to better understand and enhance the online learning experience in the evolving landscape of higher education.

Keywords: Online learning; learning flexibility; environment; gender; students

INTRODUCTION

The global outbreak of the coronavirus disease (COVID-19) in December 2019, originating in Wuhan, marked a significant turning point for humanity. This unprecedented event has brought about exclusive changes across all facets of human life. In Malaysia, the virus emerged in late February 2020, prompting the implementation of the Movement Control Order (MCO) on March 18, 2020, following World Health Organization (WHO) guidelines. The education sector experienced substantial disruption during the MCO, leading to the closure of schools and the cancellation of campus events by higher education institutions (HEIs) (Gewin, 2020). This strategic move aimed to mitigate the transmission of the virus among staff and students, flattening the curve of COVID-19. Hence, the Ministry of Higher Education (MOHE) directed all HEIs, under exceptions, to shift lectures fully online (MOHE, 2020). This online learning approach necessitates non-traditional face-to-face interactions, utilizing digital devices and internet-based communication (Martinez, 2020; Schinkten et al., 2016).

Problem Statement

Undoubtedly, online learning offers enhanced flexibility, virtual connectivity, and student-centered learning (Heap, 2017; Millier, 2020; Mukhtar et al., 2020), providing a remote learning environment accessible anytime and anywhere. Platforms like WhatsApp and Telegram facilitate communication between students and lecturers, fostering increased engagement and independent learning. However, challenges such as inefficiency (Mukhtar et al., 2020) and gadget shortages (Pitnichenko, 2020) impede the acquisition of hands-on skills and may contribute to limited attention spans. Additionally, the costliness of online learning arises from the necessity for students to possess gadgets like smartphones or computers.

Given these circumstances, the implementation of online learning in Malaysian HEIs, including Universiti Teknologi MARA (UiTM), became inevitable. UiTM had fully adopted Open and Distance Learning (ODL) since March 2020, presenting unprecedented challenges. Notably, UiTM students faced challenges encompassing personal, technical, and family issues (Aileen Farida et al., 2021). The transition to online learning led to low motivation, adaptation struggles, and connectivity problems, exacerbated by household responsibilities that diverted attention from studies. This paper aims to explore the significant relationships between learning flexibility, environment, and online learning activities among undergraduates at a UiTM branch campus. Additionally, the study seeks to confirm whether gender differences play a significant role in undergraduates’ online learning experiences.

Online Learning 

The COVID-19 pandemic’s sudden onset compelled educational institutions worldwide, encompassing schools and Higher Education Institutions (HEIs), to swiftly adapt. Traditional face-to-face lectures in classrooms became impractical, leading educators to shift entirely to online teaching and learning (Dhawan, 2020). Tareen and Haand (2020) emphasize the significance of incorporating cutting-edge technology in the learning process, asserting its potential to enhance efficient communication between lecturers and students.

The use of devices such as smartphones and computers facilitate face-to-face communication, overcoming physical distances. Currently, widely used online communication platforms, including WhatsApp, Telegram, Google Workspace, Zoom, WebEx, Microsoft 365, and Microsoft Teams, offer substantial benefits for teaching and learning, especially amid the COVID-19 pandemic. A survey by the Ministry of Higher Education (MOHE) revealed that 78.4% of HEI students did not experience stress during online learning, and 92.82% of HEIs in Malaysia utilize Learning Management Systems (LMS) (Astro Awani, September 20, 2021). This survey underscores the adaptability of a majority of students to online learning challenges, potentially attributed to their age, maturity, and access to necessary facilities. However, Minister of Education Datuk Dr. Mohd Radzi Md Jidin highlighted the limited access to personal computers for students during the pandemic crisis, with only 15% having the necessary devices. Additionally, 36.9% of students lacked any devices for online learning (Justin, 2020). This situation particularly poses challenges for primary and secondary school students. Moreover, as UiTM faces increasing complexity in governance due to organizational changes responding to internal and external forces, it demands resilience, mental fortitude, and high spirits from its organizational members, including students (Salleh et al., 2017). The abrupt alterations in daily routines have proven challenging, with staff juggling professional responsibilities alongside childcare, household chores, and family demands. This struggle has resulted in low productivity, stress, and mental health issues. Similarly, students face distractions, low motivation, and technical difficulties during online learning.

Learning Flexibility

Numerous scholars assert that online learning provides significant flexibility for both educators and students. According to Mukhtar et al. (2020), this flexibility can be categorized into three sub-themes: remote learning, easy administration, and accessibility and comfortability. In this setup, students have the freedom to study at their own pace, while educators maintain control over the learning process. This arrangement is deemed to enhance learning activities efficiently and effectively, allowing individuals to study in a location of their choice and at their own convenience, with minimal supervision from instructors.

Furthermore, online learning offers the advantage of time savings, particularly in terms of eliminating the need for students to travel from home to campus (Fidalgo et al., 2020). The preference for online learning is heightened as it enables students to cut down on travel costs while enjoying the comfort of studying in their own homes. Baghdadi (2011) suggests that educators are more accessible during Online Distance Learning (ODL) compared to traditional learning methods. In the online learning environment, students can pose inquiries at any time through platforms like WhatsApp and emails, allowing educators to respond at their own pace. Additionally, Samsuri et al. (2024) emphasize that the rapid advancements in large Artificial Intelligence (AI) language models have instilled anxiety in many individuals regarding their future employability. This concern is also relevant in the context of online learning. As AI technology becomes more integrated into educational platforms, it can enhance learning experiences by providing personalized feedback, automating administrative tasks, and offering intelligent tutoring systems. However, it also raises questions about the future roles of educators and the skills students need to develop to remain competitive in an AI-driven world.

Environment

In the realm of online learning, participants, including learners and instructors, find themselves physically separated due to temporal and spatial distinctions. Nonetheless, advancements in technology have bridged this gap, enabling interaction between learners and instructors. Within an online learning environment, the flexibility exists to accommodate the diverse needs of learners at various proficiency levels. The advent of online communication platforms offers the freedom for both lecturers and students to select their preferred devices, such as smartphones, computers, or tablets. Students also have the autonomy to determine when to engage in learning, how to structure their study plans, and which supplementary materials to incorporate into the educational process (Song & Hill, 2017).

This self-paced learning dynamic within the online setting fosters a sense of autonomy among students in their learning journey. However, the drawback lies in the diminished social aspects inherent in online learning, leading to restricted social interaction among peers (Anna, 2020). The absence of these social dimensions creates challenges for students in discussing problems with their classmates. While alternative digital platforms may facilitate communication, they are not as effective as in-person and face-to-face interactions. Furthermore, Samsuri et.al. (2024) believe that as academic staff could play a crucial role beyond just disseminating knowledge, they nurture intellectual growth, address diverse student needs, and foster collaboration with colleagues, but this will require high emotional intelligence (EI) not only by academic staff as well as the students. This is particularly relevant in online learning, where understanding and addressing students’ emotional and cognitive challenges can significantly enhance their learning experience.

Gender

Numerous authors have investigated the impact of gender on the online learning experience, as reported by researchers such as Cuadrado et al. (2010) and Kayany & Yelsma (2000). Building upon the work of Cuadrado et al. (2010), the distinctions between men and women are delineated in terms of computer usage, technology evaluation, and utilization. Specifically, women tend to view computers as social media tools and engage more actively in communicative activities compared to men (Venkatesh & Morris, 2000).

Research conducted by Boyte-Eckis et al. (2018) further emphasizes gender differences in online learning. Their findings indicate that female online students exhibit higher levels of engagement and stronger self-regulation, while males demonstrate more stable and positive attitudes, along with superior technical skills in the context of online learning.

Hypotheses Statement

In summary, this study posits these hypotheses based on a thorough review of existing literature and observations within the field of online education. Recognizing the increasing prevalence of online learning environments and the diverse factors influencing student engagement within these contexts, the hypotheses were formulated to address key areas of inquiry as below;

H₁: Learning flexibility is positively correlated with undergraduates’ engagement in online learning.

H₂: The online learning environment is positively correlated with undergraduates’ engagement in online learning.

H₃: There exists a significant difference in undergraduates’ online learning activities based on gender.

METHODOLOGY

The participants in this study consisted of students from one of UiTM’s branch campuses who engaged in online learning for a semester, which implemented online classroom learning. In terms of respondents’ gender distribution, 77% were female, while 23% were male. Most respondents fell within the age group of 21 to 24 years. Regarding academic levels, 77% were pursuing bachelor’s degrees, 22% were diploma students, and 1% were pre-diploma students. Analyzing the distribution across faculties, 50% of the respondents belonged to the Faculty of Business and Management, followed by 37% from the Faculty of Information Management, 7% from the Faculty of Art and Design, and 6% from the Faculty of Accountancy. Concerning the devices used for online learning, the majority (47%) utilized smartphones, followed by laptops (37%), desktop computers (12%), and tablets (4%). Rahiem’s (2020) perspective is referenced, indicating that students choose online learning devices based on compatibility, often sharing devices with family members due to limitations in access to such resources.

This study focused on an individual level, aiming to assess the extent of online learning utilization among students and explore the relationships between learning flexibility and the learning environment. The targeted population was 6190 students, and according to Krejcie and Morgan (1970), the minimum required sample size was 362. However, this study obtained 129 respondents, representing 35.73% of the required sample size. Vanderleest (1996) suggests that a 35.3% response rate is considered adequate. To collect data, self-administered questionnaires were employed, utilizing convenience sampling to ensure participant confidentiality. The questionnaires were designed with no right or wrong answers, and 129 usable responses were received for analysis. Established measures from previous studies were used to assess constructs such as online learning (Tareen and Haand, 2020), environment (Fidalgo et al., 2020), and learning flexibility (Tareen et al., 2020). A 5-point response scale (1 = strongly disagree to 5 = strongly agree) was employed, taking into account Chomeya’s (2010) suggestion that this scale allows respondents to express neutrality without affecting data analysis.

Skewness values for all variables fell within the range of -0.182 to 0.186, considered acceptable (Sharma and Ojha, 2020). Kurtosis values also fell within the acceptable range of -2 to +2, ranging from -0.348 to -0.245. Both skewness and kurtosis values met appropriate cut-off values, indicating normal data distribution. Internal consistency, crucial for reliability, was examined using Cronbach’s alpha coefficient. All constructs demonstrated acceptable reliability levels. For the online learning questions, Cronbach’s alpha was 0.841, falling within the 0.8 < 0.9 range. Learning flexibility achieved a Cronbach’s alpha of 0.780 (0.7 < 0.8 range), and the environment construct reached a Cronbach’s alpha of 0.808 (0.8 < 0.9 range). All constructs surpassed the recommended alpha level of 0.7, confirming their reliability. To explore the impact of learning flexibility and environmental factors on the online learning journey of undergraduate students in Malaysia, Pearson’s correlation analysis was employed. Furthermore, Anova tests were conducted to scrutinize the potential significance of gender disparities in the undergraduate online learning realm.

RESULTS AND DISCUSSION

Descriptive Analysis

To assess the level of online learning among respondents, they were required to respond to five Likert-scale questions, yielding a mean score of 2.95. According to Terano (2015), values falling within the range of 2.50 to 3.49 are considered moderate. Hence, the mean value for online learning falls within this category, indicating a moderately acceptable level. The two highest mean scores were associated with the statements “Online learning improves my academic performance” and “Online learning enables me to accomplish tasks more easily compared to traditional face-to-face learning,” with mean scores of 3.26 and 3.10, respectively. This suggests that undergraduate students perceive online learning as having no distinctive difference from other methods like blended learning and traditional classroom instruction. These findings align with Chung et al.’s (2020) conclusions, indicating that students generally feel prepared for online learning, are satisfied with distance education, and consider their experience positive. However, a significant portion of survey respondents expressed a preference not to pursue online learning if given the choice, irrespective of gender or education level. Chung et al. (2020) acknowledged that, despite positive perceptions, lack of Internet connection and limited broadband data pose significant challenges for online learning among higher education students in Malaysia, particularly when live broadcasts are involved.

Moving on to learning flexibility, the mean score was 3.76, with the highest mean score of 4.08 associated with the statement “Online learning caters to individual learning needs.” This indicates that respondents largely agree that online learning accommodates their individual learning requirements. The second-highest mean score of 4.04, linked to the statement “The video lecture has sufficient coverage about a particular topic,” suggests a preference for online learning due to its flexibility. These findings align with Panigrahi et al. (2018), who highlighted the comparative advantages of online learning, such as flexibility in schedules and lower costs compared to traditional learning.

For respondents’ perceptions of the environment’s contribution to online learning, they were required to answer five Likert-scale questions, resulting in a mean score of 3.52. The highest mean score of 3.79 was attributed to the statement “I would need better equipment for online classes,” indicating that most respondents agreed that any device at their disposal would be useful for their online learning. The satisfaction of online learners is intricately linked to their ability to learn from online content, interact with others, and comprehend the requirements for success. It is noteworthy that various factors, including the learning environment, significantly impact satisfaction in online learning (Palmer & Holt, 2009).

Pearson’s correlation Analysis

To ascertain the strength of the correlation and relationship between each independent variable (learning flexibility & environment) and the dependent variable (online learning), Pearson correlation analysis was employed. Firstly, the correlation between learning flexibility and undergraduate students’ online learning was determined to be 0.395, indicating a positive correlation. Falling within the 0.30-0.50 range (r=0.395, p=0.01), this value suggests a low correlation between learning flexibility and online learning, signifying that learning flexibility exerts a limited influence on the respondents’ online learning activities. Evan (1991) defines flexibility as the capacity to adapt and the ability to change across various dimensions, such as time, expected or unforeseen changes, offensive or defensive aspects, and internal organization. Reevaluating aspects of democratizing and desirability in online learning activities could enhance enjoyment in the learning process. Despite traditional teaching approaches in higher learning institutions, which were resistant to change, being compelled to shift entirely to online teaching-learning (Dhawan et al., 2020), this study’s findings indicate that respondents moderately accept online learning. Furthermore, flexible learning emerges as the most influential factor in online learning compared to the learning environment and demographic factors like gender. Blayone et al. (2017) suggest that online learning, introduced as a flexible educational approach, can be democratizing and desirable.

Examining the relationship between the environment and online learning, a Pearson correlation coefficient of 0.619 was observed, reflecting a positive and moderate correlation. This implies that a supportive learning environment is beneficial for undergraduate students engaged in online learning activities. The preference for a supportive learning environment is crucial for ensuring an uninterrupted learning process, especially during global health crises like COVID-19. Naji et al. (2020) support this interpretation, emphasizing the significant role of learning environment support in facilitating changes in the education system, catering to student needs and offering timely scaffolding, particularly for those facing challenges or feeling isolated in online learning activities during the pandemic. Thornes (2012) similarly asserts that the online learning environment can accommodate the diverse needs of students with varying skill levels. Consequently, online educators can leverage learning analytics to explore student behavior and enhance education design and feedback in online learning environments, thereby promoting meaningful learning experiences (Martin & Ndoye, 2016). The results of the Pearson correlation coefficient are presented in Table 1.

Table 1:  Result of Pearson correlation coefficient

ANOVA

The study aimed to distinguish between male and female UiTM undergraduate students regarding their engagement in online learning. To achieve this, ANOVA and Measures of Association were employed, and the results are presented in Table 2. Notably, the obtained p-value was less than 0.05, signifying a significant difference in online learning practices between male and female undergraduate students. Table 2 reflects this statistical significance, indicating that male students exhibit a higher interest in online learning compared to their female counterparts. The Eta squared value for gender and online learning was 0.12, suggesting a small but meaningful effect (McLeod, 2019).

Specifically, the mean score for online learning among male students was 3.44, surpassing the mean score for female students at 2.79. This discrepancy underscores a notable gender-based difference in enthusiasm for online learning. Despite the small effect size, the findings emphasize that male students show a greater inclination towards online learning than their female peers. Interestingly, despite this disparity, the study reveals that there is almost no substantial difference in online learning activities between male and female undergraduates. This aligns with previous studies (e.g., Tang et al., 2021; Chung et al., 2020; Naji et al., 2020) asserting that gender does not significantly influence virtual learning activities. The emphasis on active, interactive, and collaborative learning is considered crucial in supporting students’ self-directed learning (Chu & Tsai, 2009; Stewart, 2007; Stewart & Lowenthal, 2021). However, it’s worth noting that the current study contradicts some past research (e.g., Ashong & Commander, 2012; Shen et al., 2013), which found that gender does impact online learning. Keri (2002) suggests that males tend to be independent learners with a preference for applied learning, while females lean towards dependent or conceptual learning, preferring more reading and relying on instructor knowledge. Additionally, the study echoes findings from Lee and Chong (2017), emphasizing a significant contribution from both male and female students in online educational pursuits. In conclusion, while the study identifies a gender-based difference in online learning interest, it highlights the complexity of this relationship and underscores the need for further exploration and understanding.

Table 2:  ANOVA table

Hypothesis Testing 

To assess the hypotheses, the study examined the influence of learning flexibility and environment on the online learning experiences of undergraduate students. The results, outlined in Table 3, revealed that the significant value for learning flexibility was 0.000, falling below the significance threshold of 0.05. This outcome supports H₁, leading to the rejection of the null hypothesis. Consequently, there exists a statistically significant relationship between learning flexibility and undergraduate students’ online learning. Similarly, the significant value associated with the environment variable was 0.000, also below the significance level of 0.05. This finding indicates a significant relationship between the learning environment and undergraduate students’ online learning. Thus, H₃ is accepted, and its null hypothesis is rejected. Furthermore, the study investigated the significant difference in online learning based on gender. The significant value pertaining to this analysis was less than the acceptable significance level, leading to the acceptance of H₂ and the rejection of its null hypothesis. Consequently, there is a significant difference in online learning experiences between male and female undergraduate students. In summary, the study’s findings provide support for all proposed hypotheses, affirming the significant influence of learning flexibility, environment, and gender on undergraduate students’ online learning.

Table 3:  Summary of significant values

CONCLUSION AND FUTURE DIRECTION 

The primary objective of this study is to elucidate the changes in learning and motivation among undergraduate students in the context of the transition to online learning during the COVID-19 outbreak in Malaysia. Pearson correlation was employed to examine the relationships between each independent variable and the dependent variable. The analysis of the first independent variable, learning flexibility, revealed a significant but low relationship with online learning, falling within the 0.30-0.50 range. In contrast, the second independent variable, the environment, showed a positive and moderate correlation of 0.619 with online learning. Additionally, gender identification was explored using ANOVA and Measures of Association, demonstrating significant effects between male and female students in terms of online learning. This finding indicates a difference in online learning practices, as the p-value was less than 0.05. The results of the hypotheses suggest a significant relationship between learning flexibility and undergraduate students’ online learning, as well as a significant relationship between the environment and online learning, with both values falling below 0.05. The significant difference in online learning based on gender was also below the acceptable significance level, leading to the acceptance of all proposed hypotheses.

The study highlights the significant contribution of remote learning during the COVID-19 outbreak, enabling convenient management of classes by lecturers and easy access to teaching materials for students. The effects of learning flexibility and environment on online learning promote a student-centered approach, encouraging self-directed asynchronous learning at any time, especially during the pandemic.

The study underscores the need for further investigation in this area, emphasizing the simultaneous examination of learning flexibility, environment, and online learning among undergraduates during the COVID-19 pandemic. Future research is encouraged to employ qualitative methods or triangulation to explore “what” and “how” questions regarding the effects of learning flexibility and environment on online learning. Longitudinal research can provide insights into the evolving causal path, and comparative studies across different campuses and types of universities can enhance the generalizability of findings.

Incorporating additional variables in future research could provide a more comprehensive understanding of online learning. Educational games such as Kahoot! can reduce boredom and improve cognitive, motivational, and social aspects. Additionally, considering factors such as psychopathology in chronic stress, fatigue, perfectionism, competitive anxiety, sleep deprivation, negative attributions after failure, negative coping strategies, and negative stress recovery strategies could offer deeper insights into student challenges. This approach could help develop more effective interventions and support systems to enhance the online learning experience.

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