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Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines

• Published: 28 May 2021
• Volume 26 , pages 7321–7338, ( 2021 )

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• Jessie S. Barrot   ORCID: orcid.org/0000-0001-8517-4058 1 ,
• Ian I. Llenares 1 &
• Leo S. del Rosario 1  

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Recently, the education system has faced an unprecedented health crisis that has shaken up its foundation. Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. Although many studies have investigated this area, limited information is available regarding the challenges and the specific strategies that students employ to overcome them. Thus, this study attempts to fill in the void. Using a mixed-methods approach, the findings revealed that the online learning challenges of college students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. The findings further revealed that the COVID-19 pandemic had the greatest impact on the quality of the learning experience and students’ mental health. In terms of strategies employed by students, the most frequently used were resource management and utilization, help-seeking, technical aptitude enhancement, time management, and learning environment control. Implications for classroom practice, policy-making, and future research are discussed.

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1 Introduction

Since the 1990s, the world has seen significant changes in the landscape of education as a result of the ever-expanding influence of technology. One such development is the adoption of online learning across different learning contexts, whether formal or informal, academic and non-academic, and residential or remotely. We began to witness schools, teachers, and students increasingly adopt e-learning technologies that allow teachers to deliver instruction interactively, share resources seamlessly, and facilitate student collaboration and interaction (Elaish et al., 2019 ; Garcia et al., 2018 ). Although the efficacy of online learning has long been acknowledged by the education community (Barrot, 2020 , 2021 ; Cavanaugh et al., 2009 ; Kebritchi et al., 2017 ; Tallent-Runnels et al., 2006 ; Wallace, 2003 ), evidence on the challenges in its implementation continues to build up (e.g., Boelens et al., 2017 ; Rasheed et al., 2020 ).

Recently, the education system has faced an unprecedented health crisis (i.e., COVID-19 pandemic) that has shaken up its foundation. Thus, various governments across the globe have launched a crisis response to mitigate the adverse impact of the pandemic on education. This response includes, but is not limited to, curriculum revisions, provision for technological resources and infrastructure, shifts in the academic calendar, and policies on instructional delivery and assessment. Inevitably, these developments compelled educational institutions to migrate to full online learning until face-to-face instruction is allowed. The current circumstance is unique as it could aggravate the challenges experienced during online learning due to restrictions in movement and health protocols (Gonzales et al., 2020 ; Kapasia et al., 2020 ). Given today’s uncertainties, it is vital to gain a nuanced understanding of students’ online learning experience in times of the COVID-19 pandemic. To date, many studies have investigated this area with a focus on students’ mental health (Copeland et al., 2021 ; Fawaz et al., 2021 ), home learning (Suryaman et al., 2020 ), self-regulation (Carter et al., 2020 ), virtual learning environment (Almaiah et al., 2020 ; Hew et al., 2020 ; Tang et al., 2020 ), and students’ overall learning experience (e.g., Adarkwah, 2021 ; Day et al., 2021 ; Khalil et al., 2020 ; Singh et al., 2020 ). There are two key differences that set the current study apart from the previous studies. First, it sheds light on the direct impact of the pandemic on the challenges that students experience in an online learning space. Second, the current study explores students’ coping strategies in this new learning setup. Addressing these areas would shed light on the extent of challenges that students experience in a full online learning space, particularly within the context of the pandemic. Meanwhile, our nuanced understanding of the strategies that students use to overcome their challenges would provide relevant information to school administrators and teachers to better support the online learning needs of students. This information would also be critical in revisiting the typology of strategies in an online learning environment.

2 Literature review

2.1 education and the covid-19 pandemic.

In December 2019, an outbreak of a novel coronavirus, known as COVID-19, occurred in China and has spread rapidly across the globe within a few months. COVID-19 is an infectious disease caused by a new strain of coronavirus that attacks the respiratory system (World Health Organization, 2020 ). As of January 2021, COVID-19 has infected 94 million people and has caused 2 million deaths in 191 countries and territories (John Hopkins University, 2021 ). This pandemic has created a massive disruption of the educational systems, affecting over 1.5 billion students. It has forced the government to cancel national examinations and the schools to temporarily close, cease face-to-face instruction, and strictly observe physical distancing. These events have sparked the digital transformation of higher education and challenged its ability to respond promptly and effectively. Schools adopted relevant technologies, prepared learning and staff resources, set systems and infrastructure, established new teaching protocols, and adjusted their curricula. However, the transition was smooth for some schools but rough for others, particularly those from developing countries with limited infrastructure (Pham & Nguyen, 2020 ; Simbulan, 2020 ).

Inevitably, schools and other learning spaces were forced to migrate to full online learning as the world continues the battle to control the vicious spread of the virus. Online learning refers to a learning environment that uses the Internet and other technological devices and tools for synchronous and asynchronous instructional delivery and management of academic programs (Usher & Barak, 2020 ; Huang, 2019 ). Synchronous online learning involves real-time interactions between the teacher and the students, while asynchronous online learning occurs without a strict schedule for different students (Singh & Thurman, 2019 ). Within the context of the COVID-19 pandemic, online learning has taken the status of interim remote teaching that serves as a response to an exigency. However, the migration to a new learning space has faced several major concerns relating to policy, pedagogy, logistics, socioeconomic factors, technology, and psychosocial factors (Donitsa-Schmidt & Ramot, 2020 ; Khalil et al., 2020 ; Varea & González-Calvo, 2020 ). With reference to policies, government education agencies and schools scrambled to create fool-proof policies on governance structure, teacher management, and student management. Teachers, who were used to conventional teaching delivery, were also obliged to embrace technology despite their lack of technological literacy. To address this problem, online learning webinars and peer support systems were launched. On the part of the students, dropout rates increased due to economic, psychological, and academic reasons. Academically, although it is virtually possible for students to learn anything online, learning may perhaps be less than optimal, especially in courses that require face-to-face contact and direct interactions (Franchi, 2020 ).

2.2 Related studies

Recently, there has been an explosion of studies relating to the new normal in education. While many focused on national policies, professional development, and curriculum, others zeroed in on the specific learning experience of students during the pandemic. Among these are Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ) who examined the impact of COVID-19 on college students’ mental health and their coping mechanisms. Copeland et al. ( 2021 ) reported that the pandemic adversely affected students’ behavioral and emotional functioning, particularly attention and externalizing problems (i.e., mood and wellness behavior), which were caused by isolation, economic/health effects, and uncertainties. In Fawaz et al.’s ( 2021 ) study, students raised their concerns on learning and evaluation methods, overwhelming task load, technical difficulties, and confinement. To cope with these problems, students actively dealt with the situation by seeking help from their teachers and relatives and engaging in recreational activities. These active-oriented coping mechanisms of students were aligned with Carter et al.’s ( 2020 ), who explored students’ self-regulation strategies.

In another study, Tang et al. ( 2020 ) examined the efficacy of different online teaching modes among engineering students. Using a questionnaire, the results revealed that students were dissatisfied with online learning in general, particularly in the aspect of communication and question-and-answer modes. Nonetheless, the combined model of online teaching with flipped classrooms improved students’ attention, academic performance, and course evaluation. A parallel study was undertaken by Hew et al. ( 2020 ), who transformed conventional flipped classrooms into fully online flipped classes through a cloud-based video conferencing app. Their findings suggested that these two types of learning environments were equally effective. They also offered ways on how to effectively adopt videoconferencing-assisted online flipped classrooms. Unlike the two studies, Suryaman et al. ( 2020 ) looked into how learning occurred at home during the pandemic. Their findings showed that students faced many obstacles in a home learning environment, such as lack of mastery of technology, high Internet cost, and limited interaction/socialization between and among students. In a related study, Kapasia et al. ( 2020 ) investigated how lockdown impacts students’ learning performance. Their findings revealed that the lockdown made significant disruptions in students’ learning experience. The students also reported some challenges that they faced during their online classes. These include anxiety, depression, poor Internet service, and unfavorable home learning environment, which were aggravated when students are marginalized and from remote areas. Contrary to Kapasia et al.’s ( 2020 ) findings, Gonzales et al. ( 2020 ) found that confinement of students during the pandemic had significant positive effects on their performance. They attributed these results to students’ continuous use of learning strategies which, in turn, improved their learning efficiency.

Finally, there are those that focused on students’ overall online learning experience during the COVID-19 pandemic. One such study was that of Singh et al. ( 2020 ), who examined students’ experience during the COVID-19 pandemic using a quantitative descriptive approach. Their findings indicated that students appreciated the use of online learning during the pandemic. However, half of them believed that the traditional classroom setting was more effective than the online learning platform. Methodologically, the researchers acknowledge that the quantitative nature of their study restricts a deeper interpretation of the findings. Unlike the above study, Khalil et al. ( 2020 ) qualitatively explored the efficacy of synchronized online learning in a medical school in Saudi Arabia. The results indicated that students generally perceive synchronous online learning positively, particularly in terms of time management and efficacy. However, they also reported technical (internet connectivity and poor utility of tools), methodological (content delivery), and behavioral (individual personality) challenges. Their findings also highlighted the failure of the online learning environment to address the needs of courses that require hands-on practice despite efforts to adopt virtual laboratories. In a parallel study, Adarkwah ( 2021 ) examined students’ online learning experience during the pandemic using a narrative inquiry approach. The findings indicated that Ghanaian students considered online learning as ineffective due to several challenges that they encountered. Among these were lack of social interaction among students, poor communication, lack of ICT resources, and poor learning outcomes. More recently, Day et al. ( 2021 ) examined the immediate impact of COVID-19 on students’ learning experience. Evidence from six institutions across three countries revealed some positive experiences and pre-existing inequities. Among the reported challenges are lack of appropriate devices, poor learning space at home, stress among students, and lack of fieldwork and access to laboratories.

Although there are few studies that report the online learning challenges that higher education students experience during the pandemic, limited information is available regarding the specific strategies that they use to overcome them. It is in this context that the current study was undertaken. This mixed-methods study investigates students’ online learning experience in higher education. Specifically, the following research questions are addressed: (1) What is the extent of challenges that students experience in an online learning environment? (2) How did the COVID-19 pandemic impact the online learning challenges that students experience? (3) What strategies did students use to overcome the challenges?

2.3 Conceptual framework

The typology of challenges examined in this study is largely based on Rasheed et al.’s ( 2020 ) review of students’ experience in an online learning environment. These challenges are grouped into five general clusters, namely self-regulation (SRC), technological literacy and competency (TLCC), student isolation (SIC), technological sufficiency (TSC), and technological complexity (TCC) challenges (Rasheed et al., 2020 , p. 5). SRC refers to a set of behavior by which students exercise control over their emotions, actions, and thoughts to achieve learning objectives. TLCC relates to a set of challenges about students’ ability to effectively use technology for learning purposes. SIC relates to the emotional discomfort that students experience as a result of being lonely and secluded from their peers. TSC refers to a set of challenges that students experience when accessing available online technologies for learning. Finally, there is TCC which involves challenges that students experience when exposed to complex and over-sufficient technologies for online learning.

To extend Rasheed et al. ( 2020 ) categories and to cover other potential challenges during online classes, two more clusters were added, namely learning resource challenges (LRC) and learning environment challenges (LEC) (Buehler, 2004 ; Recker et al., 2004 ; Seplaki et al., 2014 ; Xue et al., 2020 ). LRC refers to a set of challenges that students face relating to their use of library resources and instructional materials, whereas LEC is a set of challenges that students experience related to the condition of their learning space that shapes their learning experiences, beliefs, and attitudes. Since learning environment at home and learning resources available to students has been reported to significantly impact the quality of learning and their achievement of learning outcomes (Drane et al., 2020 ; Suryaman et al., 2020 ), the inclusion of LRC and LEC would allow us to capture other important challenges that students experience during the pandemic, particularly those from developing regions. This comprehensive list would provide us a clearer and detailed picture of students’ experiences when engaged in online learning in an emergency. Given the restrictions in mobility at macro and micro levels during the pandemic, it is also expected that such conditions would aggravate these challenges. Therefore, this paper intends to understand these challenges from students’ perspectives since they are the ones that are ultimately impacted when the issue is about the learning experience. We also seek to explore areas that provide inconclusive findings, thereby setting the path for future research.

3 Material and methods

The present study adopted a descriptive, mixed-methods approach to address the research questions. This approach allowed the researchers to collect complex data about students’ experience in an online learning environment and to clearly understand the phenomena from their perspective.

3.1 Participants

This study involved 200 (66 male and 134 female) students from a private higher education institution in the Philippines. These participants were Psychology, Physical Education, and Sports Management majors whose ages ranged from 17 to 25 ( x̅  = 19.81; SD  = 1.80). The students have been engaged in online learning for at least two terms in both synchronous and asynchronous modes. The students belonged to low- and middle-income groups but were equipped with the basic online learning equipment (e.g., computer, headset, speakers) and computer skills necessary for their participation in online classes. Table 1 shows the primary and secondary platforms that students used during their online classes. The primary platforms are those that are formally adopted by teachers and students in a structured academic context, whereas the secondary platforms are those that are informally and spontaneously used by students and teachers for informal learning and to supplement instructional delivery. Note that almost all students identified MS Teams as their primary platform because it is the official learning management system of the university.

Informed consent was sought from the participants prior to their involvement. Before students signed the informed consent form, they were oriented about the objectives of the study and the extent of their involvement. They were also briefed about the confidentiality of information, their anonymity, and their right to refuse to participate in the investigation. Finally, the participants were informed that they would incur no additional cost from their participation.

3.2 Instrument and data collection

The data were collected using a retrospective self-report questionnaire and a focused group discussion (FGD). A self-report questionnaire was considered appropriate because the indicators relate to affective responses and attitude (Araujo et al., 2017 ; Barrot, 2016 ; Spector, 1994 ). Although the participants may tell more than what they know or do in a self-report survey (Matsumoto, 1994 ), this challenge was addressed by explaining to them in detail each of the indicators and using methodological triangulation through FGD. The questionnaire was divided into four sections: (1) participant’s personal information section, (2) the background information on the online learning environment, (3) the rating scale section for the online learning challenges, (4) the open-ended section. The personal information section asked about the students’ personal information (name, school, course, age, and sex), while the background information section explored the online learning mode and platforms (primary and secondary) used in class, and students’ length of engagement in online classes. The rating scale section contained 37 items that relate to SRC (6 items), TLCC (10 items), SIC (4 items), TSC (6 items), TCC (3 items), LRC (4 items), and LEC (4 items). The Likert scale uses six scores (i.e., 5– to a very great extent , 4– to a great extent , 3– to a moderate extent , 2– to some extent , 1– to a small extent , and 0 –not at all/negligible ) assigned to each of the 37 items. Finally, the open-ended questions asked about other challenges that students experienced, the impact of the pandemic on the intensity or extent of the challenges they experienced, and the strategies that the participants employed to overcome the eight different types of challenges during online learning. Two experienced educators and researchers reviewed the questionnaire for clarity, accuracy, and content and face validity. The piloting of the instrument revealed that the tool had good internal consistency (Cronbach’s α = 0.96).

The FGD protocol contains two major sections: the participants’ background information and the main questions. The background information section asked about the students’ names, age, courses being taken, online learning mode used in class. The items in the main questions section covered questions relating to the students’ overall attitude toward online learning during the pandemic, the reasons for the scores they assigned to each of the challenges they experienced, the impact of the pandemic on students’ challenges, and the strategies they employed to address the challenges. The same experts identified above validated the FGD protocol.

Both the questionnaire and the FGD were conducted online via Google survey and MS Teams, respectively. It took approximately 20 min to complete the questionnaire, while the FGD lasted for about 90 min. Students were allowed to ask for clarification and additional explanations relating to the questionnaire content, FGD, and procedure. Online surveys and interview were used because of the ongoing lockdown in the city. For the purpose of triangulation, 20 (10 from Psychology and 10 from Physical Education and Sports Management) randomly selected students were invited to participate in the FGD. Two separate FGDs were scheduled for each group and were facilitated by researcher 2 and researcher 3, respectively. The interviewers ensured that the participants were comfortable and open to talk freely during the FGD to avoid social desirability biases (Bergen & Labonté, 2020 ). These were done by informing the participants that there are no wrong responses and that their identity and responses would be handled with the utmost confidentiality. With the permission of the participants, the FGD was recorded to ensure that all relevant information was accurately captured for transcription and analysis.

3.3 Data analysis

To address the research questions, we used both quantitative and qualitative analyses. For the quantitative analysis, we entered all the data into an excel spreadsheet. Then, we computed the mean scores ( M ) and standard deviations ( SD ) to determine the level of challenges experienced by students during online learning. The mean score for each descriptor was interpreted using the following scheme: 4.18 to 5.00 ( to a very great extent ), 3.34 to 4.17 ( to a great extent ), 2.51 to 3.33 ( to a moderate extent ), 1.68 to 2.50 ( to some extent ), 0.84 to 1.67 ( to a small extent ), and 0 to 0.83 ( not at all/negligible ). The equal interval was adopted because it produces more reliable and valid information than other types of scales (Cicchetti et al., 2006 ).

For the qualitative data, we analyzed the students’ responses in the open-ended questions and the transcribed FGD using the predetermined categories in the conceptual framework. Specifically, we used multilevel coding in classifying the codes from the transcripts (Birks & Mills, 2011 ). To do this, we identified the relevant codes from the responses of the participants and categorized these codes based on the similarities or relatedness of their properties and dimensions. Then, we performed a constant comparative and progressive analysis of cases to allow the initially identified subcategories to emerge and take shape. To ensure the reliability of the analysis, two coders independently analyzed the qualitative data. Both coders familiarize themselves with the purpose, research questions, research method, and codes and coding scheme of the study. They also had a calibration session and discussed ways on how they could consistently analyze the qualitative data. Percent of agreement between the two coders was 86 percent. Any disagreements in the analysis were discussed by the coders until an agreement was achieved.

This study investigated students’ online learning experience in higher education within the context of the pandemic. Specifically, we identified the extent of challenges that students experienced, how the COVID-19 pandemic impacted their online learning experience, and the strategies that they used to confront these challenges.

4.1 The extent of students’ online learning challenges

Table 2 presents the mean scores and SD for the extent of challenges that students’ experienced during online learning. Overall, the students experienced the identified challenges to a moderate extent ( x̅  = 2.62, SD  = 1.03) with scores ranging from x̅  = 1.72 ( to some extent ) to x̅  = 3.58 ( to a great extent ). More specifically, the greatest challenge that students experienced was related to the learning environment ( x̅  = 3.49, SD  = 1.27), particularly on distractions at home, limitations in completing the requirements for certain subjects, and difficulties in selecting the learning areas and study schedule. It is, however, found that the least challenge was on technological literacy and competency ( x̅  = 2.10, SD  = 1.13), particularly on knowledge and training in the use of technology, technological intimidation, and resistance to learning technologies. Other areas that students experienced the least challenge are Internet access under TSC and procrastination under SRC. Nonetheless, nearly half of the students’ responses per indicator rated the challenges they experienced as moderate (14 of the 37 indicators), particularly in TCC ( x̅  = 2.51, SD  = 1.31), SIC ( x̅  = 2.77, SD  = 1.34), and LRC ( x̅  = 2.93, SD  = 1.31).

Out of 200 students, 181 responded to the question about other challenges that they experienced. Most of their responses were already covered by the seven predetermined categories, except for 18 responses related to physical discomfort ( N  = 5) and financial challenges ( N  = 13). For instance, S108 commented that “when it comes to eyes and head, my eyes and head get ache if the session of class was 3 h straight in front of my gadget.” In the same vein, S194 reported that “the long exposure to gadgets especially laptop, resulting in body pain & headaches.” With reference to physical financial challenges, S66 noted that “not all the time I have money to load”, while S121 claimed that “I don't know until when are we going to afford budgeting our money instead of buying essentials.”

4.2 Impact of the pandemic on students’ online learning challenges

Another objective of this study was to identify how COVID-19 influenced the online learning challenges that students experienced. As shown in Table 3 , most of the students’ responses were related to teaching and learning quality ( N  = 86) and anxiety and other mental health issues ( N  = 52). Regarding the adverse impact on teaching and learning quality, most of the comments relate to the lack of preparation for the transition to online platforms (e.g., S23, S64), limited infrastructure (e.g., S13, S65, S99, S117), and poor Internet service (e.g., S3, S9, S17, S41, S65, S99). For the anxiety and mental health issues, most students reported that the anxiety, boredom, sadness, and isolation they experienced had adversely impacted the way they learn (e.g., S11, S130), completing their tasks/activities (e.g., S56, S156), and their motivation to continue studying (e.g., S122, S192). The data also reveal that COVID-19 aggravated the financial difficulties experienced by some students ( N  = 16), consequently affecting their online learning experience. This financial impact mainly revolved around the lack of funding for their online classes as a result of their parents’ unemployment and the high cost of Internet data (e.g., S18, S113, S167). Meanwhile, few concerns were raised in relation to COVID-19’s impact on mobility ( N  = 7) and face-to-face interactions ( N  = 7). For instance, some commented that the lack of face-to-face interaction with her classmates had a detrimental effect on her learning (S46) and socialization skills (S36), while others reported that restrictions in mobility limited their learning experience (S78, S110). Very few comments were related to no effect ( N  = 4) and positive effect ( N  = 2). The above findings suggest the pandemic had additive adverse effects on students’ online learning experience.

4.3 Students’ strategies to overcome challenges in an online learning environment

The third objective of this study is to identify the strategies that students employed to overcome the different online learning challenges they experienced. Table 4 presents that the most commonly used strategies used by students were resource management and utilization ( N  = 181), help-seeking ( N  = 155), technical aptitude enhancement ( N  = 122), time management ( N  = 98), and learning environment control ( N  = 73). Not surprisingly, the top two strategies were also the most consistently used across different challenges. However, looking closely at each of the seven challenges, the frequency of using a particular strategy varies. For TSC and LRC, the most frequently used strategy was resource management and utilization ( N  = 52, N  = 89, respectively), whereas technical aptitude enhancement was the students’ most preferred strategy to address TLCC ( N  = 77) and TCC ( N  = 38). In the case of SRC, SIC, and LEC, the most frequently employed strategies were time management ( N  = 71), psychological support ( N  = 53), and learning environment control ( N  = 60). In terms of consistency, help-seeking appears to be the most consistent across the different challenges in an online learning environment. Table 4 further reveals that strategies used by students within a specific type of challenge vary.

5 Discussion and conclusions

The current study explores the challenges that students experienced in an online learning environment and how the pandemic impacted their online learning experience. The findings revealed that the online learning challenges of students varied in terms of type and extent. Their greatest challenge was linked to their learning environment at home, while their least challenge was technological literacy and competency. Based on the students’ responses, their challenges were also found to be aggravated by the pandemic, especially in terms of quality of learning experience, mental health, finances, interaction, and mobility. With reference to previous studies (i.e., Adarkwah, 2021 ; Copeland et al., 2021 ; Day et al., 2021 ; Fawaz et al., 2021 ; Kapasia et al., 2020 ; Khalil et al., 2020 ; Singh et al., 2020 ), the current study has complemented their findings on the pedagogical, logistical, socioeconomic, technological, and psychosocial online learning challenges that students experience within the context of the COVID-19 pandemic. Further, this study extended previous studies and our understanding of students’ online learning experience by identifying both the presence and extent of online learning challenges and by shedding light on the specific strategies they employed to overcome them.

Overall findings indicate that the extent of challenges and strategies varied from one student to another. Hence, they should be viewed as a consequence of interaction several many factors. Students’ responses suggest that their online learning challenges and strategies were mediated by the resources available to them, their interaction with their teachers and peers, and the school’s existing policies and guidelines for online learning. In the context of the pandemic, the imposed lockdowns and students’ socioeconomic condition aggravated the challenges that students experience.

While most studies revealed that technology use and competency were the most common challenges that students face during the online classes (see Rasheed et al., 2020 ), the case is a bit different in developing countries in times of pandemic. As the findings have shown, the learning environment is the greatest challenge that students needed to hurdle, particularly distractions at home (e.g., noise) and limitations in learning space and facilities. This data suggests that online learning challenges during the pandemic somehow vary from the typical challenges that students experience in a pre-pandemic online learning environment. One possible explanation for this result is that restriction in mobility may have aggravated this challenge since they could not go to the school or other learning spaces beyond the vicinity of their respective houses. As shown in the data, the imposition of lockdown restricted students’ learning experience (e.g., internship and laboratory experiments), limited their interaction with peers and teachers, caused depression, stress, and anxiety among students, and depleted the financial resources of those who belong to lower-income group. All of these adversely impacted students’ learning experience. This finding complemented earlier reports on the adverse impact of lockdown on students’ learning experience and the challenges posed by the home learning environment (e.g., Day et al., 2021 ; Kapasia et al., 2020 ). Nonetheless, further studies are required to validate the impact of restrictions on mobility on students’ online learning experience. The second reason that may explain the findings relates to students’ socioeconomic profile. Consistent with the findings of Adarkwah ( 2021 ) and Day et al. ( 2021 ), the current study reveals that the pandemic somehow exposed the many inequities in the educational systems within and across countries. In the case of a developing country, families from lower socioeconomic strata (as in the case of the students in this study) have limited learning space at home, access to quality Internet service, and online learning resources. This is the reason the learning environment and learning resources recorded the highest level of challenges. The socioeconomic profile of the students (i.e., low and middle-income group) is the same reason financial problems frequently surfaced from their responses. These students frequently linked the lack of financial resources to their access to the Internet, educational materials, and equipment necessary for online learning. Therefore, caution should be made when interpreting and extending the findings of this study to other contexts, particularly those from higher socioeconomic strata.

Among all the different online learning challenges, the students experienced the least challenge on technological literacy and competency. This is not surprising considering a plethora of research confirming Gen Z students’ (born since 1996) high technological and digital literacy (Barrot, 2018 ; Ng, 2012 ; Roblek et al., 2019 ). Regarding the impact of COVID-19 on students’ online learning experience, the findings reveal that teaching and learning quality and students’ mental health were the most affected. The anxiety that students experienced does not only come from the threats of COVID-19 itself but also from social and physical restrictions, unfamiliarity with new learning platforms, technical issues, and concerns about financial resources. These findings are consistent with that of Copeland et al. ( 2021 ) and Fawaz et al. ( 2021 ), who reported the adverse effects of the pandemic on students’ mental and emotional well-being. This data highlights the need to provide serious attention to the mediating effects of mental health, restrictions in mobility, and preparedness in delivering online learning.

Nonetheless, students employed a variety of strategies to overcome the challenges they faced during online learning. For instance, to address the home learning environment problems, students talked to their family (e.g., S12, S24), transferred to a quieter place (e.g., S7, S 26), studied at late night where all family members are sleeping already (e.g., S51), and consulted with their classmates and teachers (e.g., S3, S9, S156, S193). To overcome the challenges in learning resources, students used the Internet (e.g., S20, S27, S54, S91), joined Facebook groups that share free resources (e.g., S5), asked help from family members (e.g., S16), used resources available at home (e.g., S32), and consulted with the teachers (e.g., S124). The varying strategies of students confirmed earlier reports on the active orientation that students take when faced with academic- and non-academic-related issues in an online learning space (see Fawaz et al., 2021 ). The specific strategies that each student adopted may have been shaped by different factors surrounding him/her, such as available resources, student personality, family structure, relationship with peers and teacher, and aptitude. To expand this study, researchers may further investigate this area and explore how and why different factors shape their use of certain strategies.

Several implications can be drawn from the findings of this study. First, this study highlighted the importance of emergency response capability and readiness of higher education institutions in case another crisis strikes again. Critical areas that need utmost attention include (but not limited to) national and institutional policies, protocol and guidelines, technological infrastructure and resources, instructional delivery, staff development, potential inequalities, and collaboration among key stakeholders (i.e., parents, students, teachers, school leaders, industry, government education agencies, and community). Second, the findings have expanded our understanding of the different challenges that students might confront when we abruptly shift to full online learning, particularly those from countries with limited resources, poor Internet infrastructure, and poor home learning environment. Schools with a similar learning context could use the findings of this study in developing and enhancing their respective learning continuity plans to mitigate the adverse impact of the pandemic. This study would also provide students relevant information needed to reflect on the possible strategies that they may employ to overcome the challenges. These are critical information necessary for effective policymaking, decision-making, and future implementation of online learning. Third, teachers may find the results useful in providing proper interventions to address the reported challenges, particularly in the most critical areas. Finally, the findings provided us a nuanced understanding of the interdependence of learning tools, learners, and learning outcomes within an online learning environment; thus, giving us a multiperspective of hows and whys of a successful migration to full online learning.

Some limitations in this study need to be acknowledged and addressed in future studies. One limitation of this study is that it exclusively focused on students’ perspectives. Future studies may widen the sample by including all other actors taking part in the teaching–learning process. Researchers may go deeper by investigating teachers’ views and experience to have a complete view of the situation and how different elements interact between them or affect the others. Future studies may also identify some teacher-related factors that could influence students’ online learning experience. In the case of students, their age, sex, and degree programs may be examined in relation to the specific challenges and strategies they experience. Although the study involved a relatively large sample size, the participants were limited to college students from a Philippine university. To increase the robustness of the findings, future studies may expand the learning context to K-12 and several higher education institutions from different geographical regions. As a final note, this pandemic has undoubtedly reshaped and pushed the education system to its limits. However, this unprecedented event is the same thing that will make the education system stronger and survive future threats.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Jessie S. Barrot, Ian I. Llenares & Leo S. del Rosario

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Barrot, J.S., Llenares, I.I. & del Rosario, L.S. Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines. Educ Inf Technol 26 , 7321–7338 (2021). https://doi.org/10.1007/s10639-021-10589-x

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DOI : https://doi.org/10.1007/s10639-021-10589-x

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Epekto Ng Paggamit Ng Online Class Sa Pag- Aaral Ng Piling Mag-Aaral Sa Unang Taon Sa Medyor Sa Filipino

• Stephanie S. Alba Bestlink College of the Philippines
• Jenalyn C. Arsaga Bestlink College of the Philippines
• Mark Angelo V. Bacatio Bestlink College of the Philippines
• Janeth Ross L. De Vera Bestlink College of the Philippines
• Mhica Shaine O. Mogarte Bestlink College of the Philippines

Ang online class ay umusbong nang magsimulang magkaroon ng pandemya sa ating bansa nga COVID-19 noong taong 2020. Naging daan ang online class upang maipagpatuloy ang pag aaral ng mga mag aaral kahit hindi pumupunta sa paaralan partikular na sa Bestlink College of the Philippines. Ito'y nangangailangan lamang ng mga kagamitan tulad ng kompyuter, o kahit anong gadyet at internet. 

Naging malaking hamon ang panibagong paraan ng pag-aaral na ito sa ibat ibang larangan. Anuman ang husay ng mga guro sa kanilang pamamaraan at kagamitang panturo ay hindi pa rin magiging epektibo ang pagkatuto ng mga bata kung wala ang mga magulang na nagmomonitor sa kanilang mga anak sa bahay habang nasa online class ang bata. Mahalagang makipagugnayan ang mga guro sa mga magulang dahil ito ay isa sa mga estratehiya upang maging aktibo ang mga bata sa kanilang pagaaral. Sa pag-aaral na ito'y nalaman ng mga mananaliksik ang epekto ng paggamit ng online class sa mga piling mag-aaral mula sa unang taon sa kolehiyo sa BSED medyor sa Filipino, natukoy at nailahad sa pag-aral na ito kung angkop bang gamitin ang online class sa makabagong panahon bilang ganap na pantulong sa pagtuturo.

Ang isinagawang pananaliksik na ito ay patungkol sa epekto ng paggamit ng Online class sa piling mag-aaral mula sa unang taon sa kolehiyo sa Medyor sa Filipino. Ito ay ginamitan ng deskriptong metodolohiya, pinili ng mga mananaliksik. Ang descriptive survey research design na gumagamit ng talatanungan upang malikom ng mga datos. Ang mga mananaliksik ay naniniwala na ang disenyong ginamit ay ang angkop para sa paksa dahil mas mapapadali ang pangangalap ng mga datos na isinasagawa at naiintindihan ng mga mananaliksik sa pag-aaral na ito ang descriptive survey research design ay nababagay sa pagaaral na isinasagawa kahit limitado lamang ang kanilang respondente. Ito ay dahil hindi lamang sila nakadenpende sa mga sagot sa kanilang talatanungan kundi maaari rin silang magsagawa ng panayam at obserbasyon upang idagdag sa mga nakalap nilang datos at impormasyon. Ang mga mananaliksik ay nangalap ng datos sa paaaralan ng Bestlink College of the Philippines. Ang mga ito ay nagpasagot sa apatnapu (40) na mga mag-aaral sa unang taon ng BSED medyor sa Filipino Panuruang taon 2022-2023. Sila ang aming napiling respondente dahil nais naming malaman kung paano nakaka-apekto sa kanila, Bilang isang mag aaral ang pag gamit ng Online class Mula sa unang taon sa kolehiyo sa Medyor sa Filipino. Makikita sa bahaging ito ang frequency at bahagdan ng bawat respondente ayon sa kanilang edad na 35 o 87.5% ng mga estudayante na kabilang sa edad na 20 na taong gulang, kung saan ito ay mayroong pinakamataas na bahagdan. At mula sa ikalawa pinapakita na ang frequency at bahagdan ng respondente ayon sa kanilang mga kasarian 34 o 85% na babae ang pinakamataas na sumagot sa talatanungan at may pinaka mataas na ranggo. At mula sa ikatlo, Matatagpuan ang frequency at bahagdan ng mga respondente ayon sa kanilang mga 17 o 42.5% ang pinakamataas na nakuha sa paggugol ng oras sa Online class ng mga estudyante sa paggamit ng online learning mode. Dulot ng makabagong paraan ng pag-aaral ito ay nagpapakita sa bawat talahanayan ng weighted mean at bahagdan ng mga respondente ayon sa epektong dulot ng makabagong paraan ng pag-aaral 3.35 ang grand mean nito. At 3.625 ang mean at nakakuha ng pinaka mataas na ranggo. Sa pananaw sa Online class ito ay nagpapakita ng weighted mean ay nakakuha ng mataas na Mean na may 4.45 na lubos na nakaaapekto sa paggamit ng online learning mode, weighted mean at bahagdan ng mga respondente ayon sa ayon sa pananaw ng online class at sa bawat na nararamdaman ng mga estudyante sa iba't-ibang platform extensions for Google Classroom, Google Meet, and Zoom. Ipinapakita sa talahanayan ng weighted mean at bawat bahagdan ng bawat respondente na nakakuha na Mean 3.55 na kailangan pang linangin ang kanilang kaalaman sa paggamit ng online learning mode sa online class. Sa pagpapaunlad ng kaalaman at kahusayan sa pag-aaral ng asignaturang Filipino hinggil sa epekto nito. Ang talahanayan ay nagpapakita ng weighted mean at bahagdan ng mga respondente ayon sa mula sa unang aytem nito ay nakakuha ng may Mean na 4 ito ay madalas na nakaaapekto ayon sa pagpapaunlad ng kaalaman. Panghuli na posibleng rekomendasyon ang bawat talahanayan ay nagpapakita ng weighted mean at bawat bahagdan ng bawat respondente ayon sa mula sa unang aytem ito ay nakakuha ng mayroong mean na 3.475 na naaayon sa kanilang isinagot sa talatanungan.

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The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

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Farah lalani.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

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Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

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Online classes and learning in the Philippines during the Covid-19 Pandemic

2021, International journal on integrated education

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Students’ acceptance of online learning in developing nations: scale development and validation

Mehdi rajeb, laura m morett.

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Corresponding author.

Accepted 2022 Oct 9; Issue date 2023.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

Most education systems were severely impacted by the COVID-19 pandemic, and as a result, learning shifted from face-to-face to online in higher education institutions. This unprecedented shift in the learning environment caused substantial challenges for students. The situation was more severe in developing nations such as Bangladesh, which lacked available resources and knowledge of online education to support their students. Recent studies suggest that students resisted online learning in various developing nations. To support online learning in developing nations, this study develops the Acceptance of Online Learning (AOL) scale comprised of both institutional and student-related factors. To validate the AOL scale, the study collected data from 441 students across 30 higher education institutions in Bangladesh to determine the factors explaining students’ acceptance of online learning using AOL measurements. The results showed that institutional factors, such as technological sufficiency, instructor efficiency, and technical assistance play significant roles in students’ acceptance of online learning in developing nations. These findings will help education policymakers and administrators in developing nations to assess the needs of students with respect to online learning, and the AOL scale will assist in the evaluation of students’ acceptance of online learning in these nations.

Keywords: Online education, Student acceptance, Assessment, Structural equation modeling, Developing nation

Introduction

According to the United Nations (UN, 2020 ), approximately 1.6 billion students worldwide were affected by the COVID-19 pandemic. Moreover, the International Association of Universities (Marinoni et al., 2020 ) reported that 1.54 billion university students experienced negative impacts of the pandemic, and many universities across the world have suffered extensively. Situations are far worse in developing countries, in which students often lack computer accessibility, technical infrastructure, and competency in distance learning (Alibudbud, 2021 ). Online learning was rarely a regular component of Higher Education Institutions (HEI) in developing nations; however, due to COVID-19, HEIs had to provide it on short notice. This study explores students’ acceptance of online learning practices implemented in response to the COVID-19 pandemic in Bangladesh, a developing country in South Asia.

In many cases, online learning was adopted by HEIs due to the public health crisis caused by COVID-19, which prevented students from safely gathering to learn in person. Even developed nations with relatively better technological infrastructure suffered from these sudden changes, and developing nations demonstrated even less capacity to cope with the situation (Marinoni et al., 2020 ). Many students in developing nations have slow internet access, limited digital skills, insufficient technological infrastructure and support, instructors insufficiently trained in providing online instruction, and other socio-economic issues (Adnan & Anwar, 2020 ; Agormedah et al., 2020 ; Muthuprasad et al., 2021 ; Shrestha et al., 2021 ; Simamora, 2020 ). The context of higher education in developing nations is significantly different from developed nations in terms of the student population, recourses, and instructor abilities. Several studies suggest that the impact of COVID-19 on higher education was severe in developing countries such as Sri Lanka (Rameez et al., 2020 ), India (Jena, 2020 ), Pakistan (Adnan & Anwar, 2020 ), and Bangladesh (Saha et al., 2021 ; Shrestha et al., 2021 ).

Similar to the rest of the world, Bangladesh observed an instantaneous shift in the higher education system brought on by the large-scale adoption of online learning in response to COVID-19. This study attempted to use Bangladesh’s scenario to demonstrate the difficulties many developing countries faced and propose a potential online learning evaluation model. Bangladesh has a population of more than 164 million (World Bank, 2020) and steady economic growth (Andaleeb et al., 2012 ). Currently, 130 universities are operating in Bangladesh (Chowdhury & Sarkar, 2018 ), enrolling 7.1 million students (UGC, 2018 ). Similar to other developing countries, Bangladesh has a high economic vulnerability, a low human asset index, and low per capita income (Davidson et al., 2014 ).

With the background in mind, many students in Bangladesh are unfamiliar with online education compared to students in developed countries, leading to resistance to online learning. A student survey in Bangladesh, conducted in June 2020 (3 months after the official closure of academic intuitions), indicated that 40% of university-level students were engaged in online teaching and learning and that a majority of these students were skeptical of several aspects of it (Islam et al., 2020 ). Moreover, another study identified an array of problems (e.g., adaptation to online learning, internet issues, lack of digital knowledge) associated with online teaching practices in Bangladesh (Al-Amin et al., 2021 ). Hence, to alleviate issues related with online learning, research is needed to explore how students are accepting the online learning processes implemented by the HEIs in developing nations.

Purpose of the study

Considering students’ opinions as a valid measure of the effectiveness of online learning (Gatian, 1994 ; Srinivasan, 1985 ; Tai et al., 2019 ), this study aims to (a) develop the Acceptance of Online Learning (AOL) scale, a comprehensive evaluation tool to assess students’ online learning acceptance, (b) test the reliability and validity of AOL scale, and (c) identify factors influencing the efficacy of online education in Bangladesh based on the measures obtained by the AOL scale. The findings provide empirical evidence on how to evaluate online teaching and learning effectiveness, especially in developing countries.

Literature review

This study proposes an integrated theoretical framework combining both student-related factors and institutional factors to measure students’ acceptance of online learning in HEIs in developing nations (see Fig.  1 ). The study was initiated due to students’ resistance to online learning in Bangladesh. Students in Bangladesh were utterly dissatisfied with online learning and unwilling to engage in it (Islam et al., 2020 ; Saha et al., 2021 ). Hence, this study attempts to develop an assessment scale to identify crucial components contributing to students’ acceptance of online learning.

Theoretical framework for AOL scale in developing nations

Most studies assessing students’ acceptance of online learning focused on one type of factor and rarely considered more than one dimension of students’ acceptance. Previous studies have emphasized the importance of students’ acceptance of online learning (Aguilera-Hermida, 2020 ; Pal & Vanijja, 2020 ) and focused separately on institutional factors (e.g., facilitating conditions, support system, instructional quality), student-related factors (e.g., self-efficacy, motivation, experience), and socio-cultural factors (e.g., culture, demography) (Abbasi, 2011 ; Alenezi et al., 2011 ; Nichols, 2008 ; Priatna et al., 2020 ; Tarhini et al., 2016 ). However, institutional factors and student-related factors are dependent on each other as some student-related factors are heavily influenced by institutional interventions, and student-level efficacies also affect the perceived efficiency of institutional factors. For instance, perceived usability of the online learning process is explained by Lee ( 2010 ), relating both student-level characteristics and institutional factors. Furthermore, to enhance students’ acceptance of online learning, students need both individual and institutional-level support (Lee, 2010 ). Several studies considered different institutional factors and various student-related factors to explain students’ acceptance of online learning (Alenezi et al., 2011 ; Hong & Kim, 2018 ; Valencia-Arias et al., 2019 ). Also, Tarhini et al. ( 2016 ) emphasized how socioeconomic and cultural differences affect online learning acceptance.

A number of studies focused solely on institutional factors explaining students’ acceptance of online learning. For instance, Teo ( 2010 ) developed an E-Learning Measurement (ELAM) scale consisting of instructor quality, perceived usefulness, and facilitating conditions. Larmuseau et al. ( 2019 ) have explained students’ acceptance of online learning through instructional quality, and Alenezi et al. ( 2011 ) have focused extensively on institutional support.

On the other hand, several studies focused solely on student-related factors to explain online learning acceptance. For example, Hong and Kim ( 2018 ) proposed a Digital Readiness for Academic Engagement (DRAE) scale conceptualizing the user's individuality considering the students' behavioral traits. Similarly, Shen et al. ( 2013 ) focused on user characteristics (i.e., self-efficacy) to explain online learning experience and acceptance. Moreover, several other studies have considered socioeconomic, demographic, and psychological factors affecting acceptance of online learning (Francis et al., 2019 ; Tarhini et al., 2016 ; Tsai et al., 2020 ; Tzafilkou et al., 2021 ).

Above all, a multidimensional perspective is needed to evaluate and improve online learning. Moreover, research on students’ acceptance of online learning in developing nations is quite limited (Ayodele et al., 2018 ). Such research is necessary, as education infrastructure and academic culture are different in developing compared to developed nations (Asabere, 2013 ). Thus, attitudes toward online learning could also be utterly different, and are still going through a transformation (Phutela & Dwivedi, 2020 ) in developing nations.

Research on online learning is extremely limited in Bangladesh; hence, this study is conceptualized based on online learning acceptance studies conducted in other countries (please see Table 1 ). Moreover, opinions from students and experts were sought, and six factors were identified to be included in the study based on their relevance, applicability, and necessity. Hence, this study will introduce a measurement scale of online learning acceptance for developing nations exploring six aspects (see Fig.  1 for theoretical framework): (a) student factors: students’ acceptance, digital literacy, resistance to change; (b) institutional factors: technological sufficiency, instructor efficiency, and technical assistance. Secondly, the context of online education in developing nations will be discussed, especially current conditions in Bangladesh relating to the six factors stated above.

List of studies reviewed to develop AOL scale

Student-related factors

Students’ acceptance of online learning.

Students’ acceptance of online learning could be defined as an overall indicator of how comfortable students are in participating online learning process. The concept of students’ acceptance of online learning is not new and can be defined as a strong indicator of usability of the online learning process (Casaló et al., 2008 ). Students’ acceptance of online learning is assessed through widely used multidimensional components, including overall satisfaction (Casaló et al., 2008 ; Frøkjær et al., 2000 ; Lee, 2010 ), willingness to participate in the future (Beldad & Hegner, 2018 ; Lee, 2010 ; Tang & Chaw, 2016 ), user recommendations (Zhang et al., 2019 ), perceived convenience (Chang et al., 2012 ), and overall motivation to use the system (Venkatesh, 2000 ). Overall satisfaction is influenced by accumulated experiences with online learning (Parasuraman et al., 1994 ). Willingness to participate in the future is another indicator of acceptance of online learning (Beldad & Hegner, 2018 ). For instance, if a student is satisfied with the online learning process, the student will be more willing to participate in it. User recommendations are a strong indicator of satisfaction, as well. If students are willing to recommend an online learning system to other students, it reflects that students were satisfied and accepted the new learning process as an useful one (Lee, 2010 ; Singh et al., 2020 ). Perceived convenience is a measure of the usability of the online system. Specifically, if the new learning system is inconvenient, it is less usable (Chang et al., 2012 ), so the current study also considered students’ perceived convenience. Based on self-determination theory, Chen and Jang ( 2010 ) have identified extensive implications of the motivation behind acceptance of the online learning process. Hence, this study also incorporated motivation as a contributing element to measure overall student acceptance.

Resistance to change

The sudden disruption of the traditional teaching system and shift towards online learning may incur resistance to change among students (Vivolo, 2016 ). Resistance to change is an essential concept in explaining the students’ acceptance of online learning (Barak, 2018 ; Vivolo, 2016 ). Barak ( 2018 ) extensively reviewed students’ resistance to change and explained how rigidity might affect key academic skills. Such skills are necessary for the practical implementation of knowledge. Due to the COVID-19 pandemic, resistance was apparent when HEIs initiated online learning in 2020. Therefore, institutions had to motivate their students to participate in online learning in Bangladesh (Majed et al., 2020 ; Saha et al., 2021 ). Resistance to change is critical factor to explain students’ acceptance of online learning. A student’s resistance to accept online learning may also be dependent on multiple aspects including academic culture, socioeconomic, and psychological conditions. In a developing nation, students’ resistance to accept online learning may occur due to students’ lack of adaptability, facilitating conditions, self-efficacy, or community (Dhawan, 2020 ; George & Camarata, 1996 ). In the theoretical model informing this study, resistance to change has been considered as one of the explanatory constructs behind students’ satisfaction. It is assumed to be related to digital literacy, technological sufficiency, instructor’s efficiency, and technical assistance.

Digital literacy

Digital literacy supports students’ efficiency in using new technology. This study adopted digital literacy as a crucial factor behind students’ acceptance of online learning, reasoning that if students can engage in online learning effectively, they will show greater acceptance of it (Holden & Rada, 2011 ). The impact of digital literacy may vary due to differences in target populations and technologies. Moreover, digital literacy relates to institutional factors as HEIs are playing an important role in mentoring young students (Reddy et al., 2021 ). Also, digital literacy may affect students’ perceptions of technological conditions, technical support systems, instructor competency, and resistance towards online learning. As empirical measures of digital literacy vary (Lyons et al., 2019 ), the current study modified previous measurements and developed a new measure to explore digital literacy. This new measure is described in the Method section.

Institutional factors

Technological sufficiency.

Alenezi et al. ( 2011 ) has emphasized technological sufficiency as a core institutional factor in implementing online learning. Technological sufficiency (i.e., availability of necessary hardware, software, and supporting infrastructure) is a primary issue affecting the acceptance of online learning (Tarhini et al., 2016 ; Teo, 2010 ). Technological sufficiency is a core concern in a developing nation such as Bangladesh. Lack of technological sufficiency may lead to perceived difficulty of use and may negatively impact the acceptance of the new technology. Moreover, technological sufficiency is critical for many students to successfully participate in online classes. Technological sufficiency is also a major component in explaining the digital divide in developing nations (Saha et al., 2021 ). Technological sufficiency influences the feasibility of online learning for students and affects digital literacy, technical assistance, and instructor efficiency. For instance, customized math learning software will aid instructors in communicating with students in online classes. In this study, technological sufficiency is measured through availability of hardware (Alshare et al., 2011 ; Blocher et al., 2002 ; Lee, 2008 ), availability of software (Almaiah et al., 2019 ; Lee, 2008 ; Ohliati & Abbas, 2019 ), and quality of internet access (Dhawan, 2020 ; Nugroho et al., 2021 ).

Instructor efficiency

Instructor efficiency is often mentioned as a major institutional factor affecting acceptance of online learning (Arghode et al., 2018 ; Ouyang & Scharber, 2017 ; Rios et al., 2018 ). Ouyang and Scharber ( 2017 ) extensively discuss instructor influence of online learning and conclude that instructors’ lesson plans and facilitation significantly influence acceptance of online learning. Moreover, instructor efficiency is a multi-dimensional concept that depends on several factors, including communication, feedback, critical discourse, and building connections with learners (Arghode et al., 2018 ). Thus, instructor efficiency is a potent catalyst affecting both perceived ease of use and acceptance of online teaching in Bangladesh (Al-Amin et al., 2021 ).

Technical assistance

Technical assistance, a core institutional factor, is associated positively with perceived usefulness, and perceived support service quality affects online learning acceptance (Lee, 2010 ). Furthermore, the ideology of technical assistance is directly connected to the concept of perceived ease of use. A student will find online learning more accessible if the student receives technical assistance when needed (Cheng et al., 2012 ). This study investigates whether technical assistance influences students’ acceptance of online learning. If technical support is overused, however, students will be underprepared and will be dependent on it.

Online learning in developing nations: current conditions

With increasing accessibility of communication technology in developing nations, online teaching offers certain advantages over traditional face-to-face teaching (e.g., access, cost, convenience). However, online learning may not be equally effective for all students in underdeveloped educational systems (Gulati, 2008 ). In developing nations, educational and communication infrastructure has yet to allow all students to participate in it (Jaffer et al., 2007 ). Indeed, multiple studies have noted obstacles related to online learning in developing nations. For instance, students in India lacked digital knowledge, access to high-speed internet, and necessary infrastructure and were unsatisfied with quality of learning and technical efficiency of teachers (Jena, 2020 ; Kumar, 2021 ; Muthuprasad et al., 2021 ). Moreover, online learning failed to produce desired results in Pakistan due to limitations in access to high-speed internet, technological infrastructure, and student finances (Adnan & Anwar, 2020 ). Furthermore, only a few educational institutions in Pakistan implemented effective online instruction during the COVID-19 pandemic (Ullah et al., 2021 ). Similar circumstances were evident in Sri Lanka (Haththotuwa & Rupasinghe, 2021 ; Howshigan & Nadesan, 2021 ). In Nepal, almost half of online classes were hampered by unreliable electricity and internet (Subedi et al., 2020 ). Students in Sub-Saharan developing countries such as Kenya, Ghana, and South Africa faced issues such as limited access to technological infrastructure, high cost and low reliability of internet service, and low digital proficiency (Pete & Soko, 2020 ). In sum, many developing countries have limited infrastructure, accessibility to digital devices, and digital proficiency of students and instructors. All of these may significantly influence the efficiency of online learning and students’ acceptance of it.

As a developing nation, Bangladesh is facing similar obstacles. Prior to the COVID-19 pandemic, online learning was not institutionalized in Bangladesh. On May 7th, 2020, HEIs in Bangladesh were formally granted permission to deliver classes and examinations online (Abdullah, 2020 ). The transition from face-to-face to online learning was not easy; indeed, the Bangladeshi authority tasked with ensuring quality of education in HEIs in Bangladesh expressed concerns about the quality of academic activities in online learning (Riyasad, 2020 ). Moreover, students were stressed and started to express resistance (Kabir et al., 2021 ). At that point in time, the whole education system in Bangladesh was under tremendous pressure.

The difficulty of transitioning to online learning in Bangladesh was due to several reasons. Firstly, the country was never prepared for such a major technological shift in higher education. Secondly, almost no research on online learning in Bangladesh had been conducted before its implementation there. Also, most of the HEIs in Bangladesh used synchronous general online meeting and conversation platforms such as WhatsApp, Facebook messenger, and Zoom to deliver online classes. Saha et al. ( 2021 ) concluded that the remote instruction implemented by HEIs in Bangladesh was unsatisfactory, creating a digital divide among students. Moreover, Shahriar et al. ( 2021 ) concluded that students’ and teachers’ lack of digital literacy created inertia in online classes. In addition, several studies have reported that issues such as poor technological infrastructure and limited access to devices and internet accessibility caused substantial obstacles for HEIs in operating online classes in Bangladesh, similar to other developing countries (Islam et al., 2020 ; Shahriar et al., 2021 ). However, few studies have discussed how to improve the quality of online teaching to promote students’ acceptance of it in developing nations such as Bangladesh.

Hence, along with identifying factors affecting online learning acceptance, it is necessary to assess how to support online learning in developing countries. Supporting and enhancing the quality of online learning in developing nations may seem difficult as the process requires sizable investments in technology and other related sectors. On the other hand, if HEIs in developing nations start to assess their respective online learning process, it will provide necessary indications regarding what needs to be improved. Some online learning factors (e.g., facilitating condition or technological sufficiency) may not be improved instantly, but other factors, such as instructor efficiency or digital literacy, could be systematically improved with a reasonable amount of time and effort. Technological insufficiency is the main reason behind the digital divide in developing nations (Saha et al., 2021 ) and may require national level interventions to ensure digital fairness among students in developing nations. However, technological innovations in designing online learning delivery may reduce resource requirements for students to participate in online learning process in developing nations. For instance, Zhang et al. ( 2017 ) have discussed the possibility of customization in mobile phone-based curriculum integration for online learning, which could be a breakthrough for developing nations to overcome technological insufficiencies.

Need for an integrated scale for online learning acceptances in developing nations

The need for an integrated multidimensional framework of online learning acceptance is supported by previous research. First, recent studies indicate that acceptance of online learning is not a unidimensional construct (Larmuseau et al., 2019 ; Sivo et al., 2018 ). Secondly, the perspectives of students from developing nations should be considered when constructing an instrument to assess online learning acceptance. At present, however, there are only a few established online learning assessments appropriate for use in developing nations. Moreover, studies measuring acceptance of online learning in developing nations vary widely in number, focus, and conceptualizations of relevant constructs. Third, online learning is often implemented in conjunction with traditional face-to-face learning; thus, context-specific assessment processes for online learning are more important than ever. Fourth, most extant scales and questionnaires related to online learning (see Table 1 ) were constructed for developed nations, whose circumstances differ from those of developing nations. In developed nations, online learning has been implemented for decades and evolved as an accepted learning system. For instance, student enrollment in online education in the U.S. has been steadily increasing for last 14 years (Palvia et al., 2018 ). Similar growth in online student enrollment was observed in Australia during the same time frame (Greenland, 2011 ). Over the years, institutions in developed nations amassed experience in online teaching and learning, which influenced HEIs in developed nations to develop curriculum, tools, and programs for online learning. Moreover, the facilitating conditions necessary for online learning are far better in developed nations compared to those in developing nations. On the contrary, online learning is hardly institutionalized by HEIs in many developing nations, and many HEIs in developing nations have little experience in developing online teaching and learning process Hence, the online learning capacities of developing nations are far behind those of developed nations. The uniqueness of these conditions in developing nations may require different resolutions when building and assessing online learning acceptance.

Lastly, socioeconomic, and cultural issues have crucial effects on online learning acceptance assessments. Students in developed nations have different socioeconomic and cultural norms compared to students in developing nations. For instance, the ELAM scale (Teo, 2010 ) was a good fit for British environment but was found inefficient for Lebanese environment (Tarhini et al., 2016 ). According to Tarhini et al. ( 2016 ), cultural differences and unknown factors may have caused goodness of fit issues with the ELAM scale in the Lebanese environment. Additionally, Aguilera-Hermida ( 2020 ) assessed students’ attitude and motivation towards online learning in USA, Mexico, Peru, and Turkey, and concluded that both attitude and motivation differ from country to country. Moreover, item wording, item format and assessment perspectives may vary widely from one culture to another (Tarhini et al., 2016 ).

Hence, this study introduces a new scale formed from reframing and combining prior scales to measure institutional and student factors affecting online learning acceptance in developing nations.

Research method

To date, little systematic research has been conducted on online learning acceptance in Bangladesh. Hence, the initial theoretical framework guiding this study was developed based on studies conducted in other nations. In addition, opinions from students and experts were sought, and six factors were identified in this study for inclusion in the AOL scale based on their informational and applicability implications.

Research steps

Based on previous literature, this study (a) develops a multidimensional scale to measure students’ acceptance of online learning in developing nations; (b) tests the reliability and validity of this assessment; and (c) uses it to characterize students’ acceptance of online learning in Bangladesh. This assessment is entitled Acceptance of Online Learning (AOL).

The majority of studies of technology acceptance focus on system efficiency or acceptance and usability. This study argues that acceptance depends not only on the efficiency of the system, but also the individuals who use the system, their perceptions of it, and related issues. Above all, students’ resistance to change may impede their acceptance of online learning, whereas students’ digital literacy and technological sufficiency, instructor efficiency, and institutional technical support may promote acceptance toward online learning in Bangladesh. Moreover, socio-economic, and cultural norms may affect online learning acceptance, as well.

Measurements and item generation

Overall, online learning acceptance was measured using five items. Of these items, three items were conceptually reframed from Teo Gopal et al. ( 2021 ), Casaló et al. ( 2008 ), and Swan ( 2001 ). Technological sufficiency was measured using four items, of which two items were obtained from the instrument developed by Sultana and Khan ( 2019 ). Instructor efficiency was measured using eight items, of which three items were adopted from instruments developed by Gopal et al. ( 2021 ), Chen and Chen ( 2007 ), and Swan ( 2001 ). Digital literacy was measured using four items, of which three items were obtained from Tang and Chaw ( 2016 ). Resistance to change was measured using two items obtained conceptually from the conclusions of Barak ( 2018 ). Technical assistance was measured using three items motivated by the work of Green and Denton ( 2012 ). Item formatting was changed to optimize acquisition of information from students in Bangladesh. All items utilized a 7-point Likert scale in which 1 represents strongly disagree and 7 represents strongly agree.

Scale development and pre-testing

The AOL scale was developed using a total of 35 questions, of which nine were demographic questions and 26 were items contributing to different constructs, such as overall acceptance, technological sufficiency, instructor efficiency, resistance to change, and digital literacy. These items were initially developed based on available literature reviews and issues identified in focus group discussions. To improve the quality of the items developed for the AOL scale, a small pilot test was conducted to ensure clarity and conciseness. A group of 15 students were given the AOL instrument and requested to complete the survey. Students were then interviewed about the clarity of each question in the survey. The wording, length and format of the items were further adjusted based on the responses acquired in the pilot test. The items of the AOL scale were developed in English as the respondents are usually taught in English. These items are given in Table 2 .

Item pool for AOL scale

Study design, sampling, and data collection

To conduct the study, a cross-sectional study design was implemented. Initially, the target population of this study was all university-level students in Bangladesh. At the time the survey was administered, however, only private universities in Bangladesh had implemented online learning. Hence, students from public universities were excluded as they were not exposed to online learning. There is no accessible student database for college or university students in Bangladesh; hence, this study adopted a convenience sampling procedure to collect data from 441 students of private HEIs in Bangladesh. Of the 105 private universities in Bangladesh (Hasan & Islam, 2020 ), this study collected data from students of 30 universities across four divisions, 1 i.e., Barisal, Chittagong, Dhaka and Rangpur. Table 3 contains sample characteristics. A similar sampling process is widely used in the current literature for scale development and validation (please see: Bhagat et al., 2016 ; Glassman et al., 2021 ; Sun & Rogers, 2021 ). Moreover, within the student population of Bangladesh, participants are heterogenous in nature and capable of providing multidimensional perspectives from students (see Table 3 ).

Distribution of students’ demographic characteristics in the sample (n = 441)

Due to restrictions on face-to-face data collection from the COVID-19 pandemic, data was collected via an online survey. This survey was sent to respondents via email, and they were requested to complete it outside of class and work. The survey response rate is approximately 25%. Students were informed that the survey was anonymous, and they were requested to ignore any questions that they did not feel comfortable answering and informed that they could withdraw at any time. Furthermore, students were informed that submission of the survey entails implied consent to participate in the study.

Missing value replacement

After data collection, it was observed that the data contained several missing values across different variables. For instance, item T1r had five missing values (out of 441 responses), items T2r, E3r, Cl1r, AOA1r, AOA3r and A4r had three missing values (out of 441 responses), item T3r had two missing values (out of 441 responses), and items T4r, E4r, AOA2r, A1r, and A3r had one missing value (out of 441 responses) (see item details in Table 2 ). To evaluate whether these missing values occurred randomly or not, a test of Missing Completely at Random (MCAR; (Little, 1988 ) was performed. The MCAR value was not significant, indicating that the missing values occurred at random. As SEM is sensitive to missing values, they were replaced with median values for respective items. A similar missing value replacement methodology was suggested by Maniruzzaman et al. ( 2018 ), Farrell ( 2010 ), and Gómez-Carracedo et al. ( 2014 ).

Analyses were conducted in IBM AMOS 20 and were divided into two parts. In the initial stage, the study employed Confirmatory Factor Analysis (CFA) to validate the AOL measurement scale. In the second stage, a Structural Equation Model (SEM) was developed to explore causal relations of each construct.

To validate the scale, a CFA approach was taken considering six constructs and 26 items to develop the AOL scale (see Table 2 for details). The first CFA model failed to meet the required goodness of fit measures. After careful consideration and stepwise deduction of each construct and respective items in the initial CFA model, one construct (i.e., resistance to change) and six items were dropped in the final model due to unsatisfactory factor loadings (< .6), as suggested by (Lopez et al., 2021 ). All items contributing to resistance to change (i.e., RC1r, RC2r) were dropped, as respective factor loadings failed to meet the cutoff point. Furthermore, one item (CL4r) contributing to the construct of digital literacy and three items (E6r, E7r, E8r) contributing to instructor efficiency were dropped due to unsatisfactory factor loadings. Therefore, the final CFA model was constructed using five constructs (i.e., overall acceptance, technological sufficiency, instructor efficiency, and digital literacy) and 20 items contributing to these constructs. Based on the covariance structure of the primary CFA model, errors for two items of Efficiency (i.e., E1r and E3r) and two items of Acceptance (i.e., A2r and A3r) are correlated. To constrain the effects of these correlated errors, the study allowed co-variation between the respective error terms of these items. These correlated items had similar wordings though their contents differed.

The CFA model fit indices (CFI = .951, RMSEA = .06, NFI = .926, GFI = .912, χ 158 2 = 439.2 , p  < .01) surpass their respective cut off points (Nunnally & Bernstein, 1994 ). Moreover, all standardized loadings for each item obtained in this model are high and positively significant. Furthermore, the composite reliability (i.e., internal consistency reliability) value for each construct exceeded the cutoff point of .7 (Nunnally & Bernstein, 1994 ). The study also obtained measures for convergent validity by using Average Variance Extracted (AVE) (see Table 4 for more details). Computed AVE values for each construct exceeded the threshold value of .50 (Bagozzi & Yi, 1988 ). Discriminant validity of the CFA model was assessed through comparisons between respective square root of AVE and correlations between constructs (Fornell & Larcker, 1981 ). The highest correlations between constructs were always less than the square root of the AVE of each construct (Farrell, 2010 ) (see Table 5 for detailed measures of discriminant validity). All available reliability and validity values for the measurement model indicate that the scale met acceptable psychometric criteria with sufficient validity and reliability. Standardized factor loadings as well as AVE and Composite Reliability values are presented in Table 6 , and the estimated CFA model is provided in Fig.  2 .

Convergent validity measures for CFA model

Discriminant value (DV) for each factor and their bivariate correlations

Constructs with items with standardized factor loadings, item means and standard deviations for AOL scale

** r indicates recoded variables went through general missing value analysis

Estimated five factor CFA model (item definitions are provided in Table 2 )

In the second phase of the analysis, a structural equation model was obtained, with student acceptance of online learning as the outcome variable. The structural model indicates sufficient fit of the data fit (CFI = .94, NFI = .92, RMSEA = .06, SRMR = .05, DF = 2.93, p  < .01) . The squared multiple correlation coefficient obtained from the model indicates that 48.9% of the variation in acceptance of online learning is explained by institutional and student related factors provided in Fig.  3 . The Maximum Likelihood (ML) regression coefficient estimate for technological sufficiency is .51, p  < .01, indicating that this factor had the strongest impact on students’ acceptance of online learning in Bangladesh. The ML regression coefficient estimates for instructor efficiency is .305, p  < .01, and for technical assistance is .157, p  < .05. On the other hand, the ML regression coefficient estimate for digital literacy is statistically non-significant ( p  > .05) (see Table 7 for details). Hence, the model indicates that technological sufficiency, instructor efficiency, and technical assistance have a significant and positive impact on acceptance of online learning. In contrast, students’ digital literacy failed to significantly affect acceptance of online learning. The fitted SEM model is available in Table 7 and Fig.  3 . In summary, from CFA measures, this study concludes that the AOL scale developed in this study meets all major psychometric requirements, including internal consistency, composite reliability, convergent validity, and discriminant validity. Furthermore, the SEM model estimated the impacts of technological sufficiency, instructor efficiency, and technical assistance on students’ acceptance of online learning in Bangladesh.

Structural Model explaining students’ acceptance towards online learning platforms (item definitions are provided in Table 2 )

Path co-efficient of different factors explaining effects on students' acceptance of online learning in Bangladesh

Online learning will be a critical part of HEIs in Bangladesh, so students’ acceptance of it has become immensely important. This study integrated both student and institutional factors to develop the Acceptance of Online Learning (AOL) scale to assess students’ acceptance of online learning in Bangladesh. The AOL scale was examined and validated using confirmatory factor analysis. AOL is a five-factor (i.e., overall acceptance, technological sufficiency, instructor efficiency, technical assistance, and digital literacy) model and AOL constructs were developed focusing on online learning conditions in developing countries. All constructs adopted by AOL are associated with each other, which indicates that online learning acceptance depends on an integrated combination of both institutional and student-related factors. For instance, technological sufficiency is highly correlated with digital literacy of students, r  =  . 73 , p  <  . 01 (please see Table 5 ), which indicates that digital literacy of students is correlated with their access to digital equipment and internet facilities. Moreover, a student with better digital literacy will perceive online learning as more acceptable. Also, technical assistance for online education and instructor efficiency are strongly correlated, r  =  . 68 , p  <  . 01 (see Table 5 ), which indicates that instructors may fail to provide efficient lessons in class unless institutions provide sufficient technical support. For instance, in absence of an appropriate learning management system, it is very difficult for a faculty member to provide resources to students, which in turn perceive the instructor as less efficient. Furthermore, AOL is developed to assess the feasibility of online learning for students in developing nations and adopted a holistic approach to assessment.

AOL was used to explore critical factors affecting students’ acceptance of online learning in Bangladesh using structural equation modeling (SEM). The results showed that three major factors had significant impacts on students’ acceptance of online teaching and learning in Bangladesh: technological sufficiency, instructor efficiency, and technical assistance.

The findings were consistent with previous studies conducted in developing nations. For instance, Ambarwati et al. ( 2020 ) concluded that technological conditions affected online learning in Indonesia. In addition, institutional factors such as technological infrastructure, internet access, and access to supporting devices significantly impacted the behavioral intentions of online learners. In Malaysia, Goh and Blake ( 2021 ) revealed significant impacts of e-learning infrastructure on e-learning acceptance. Furthermore, in four countries in Southeast Asia, infrastructure, institutional service quality, and instructor efficiency contributed to e-learning success (Bhuasiri et al. ( 2012 ). These findings are aligned with those of other studies regarding instructor efficiency and technical assistance (Arghode et al., 2018 ; Chang et al., 2012 ; Larmuseau et al., 2019 ; Lee, 2010 ; Ouyang & Scharber, 2017 ; Rios et al., 2018 ; Teo, 2010 ).

However, the combination of AOL constructs is unique from current online learning acceptance scales for developing nations. Moreover, the items for each construct and the assessment dimensions of AOL differs from current scales. Also, some findings from Bangladesh measured using the AOL scale are not aligned with online learning acceptance scales or questionnaires developed in other developing nations. For instance, Al-Gahtani ( 2016 ) concluded that computer self-efficacy significantly affects students’ intentions to use online learning, but in Bangladesh, digital literacy of students did not significantly affect online learning acceptance. This may indicate that the digital literacy levels of Bangladeshi students are similar. Bhuasiri et al. ( 2012 ) explored critical success factors behind e-learning in developing nations and revealed that changing learners’ behavior plays an important role in successful e-learning implementation. The AOL scale failed to accommodate students’ behavioral change even though resistance to change factor was incorporated in the initial theoretical model.

Conclusion and contributions

This study investigated students’ perspectives towards online learning in a developing nation and integrated the findings of previous studies to develop a new assessment tool to evaluate online learning acceptance in developing nations. As this study is based on students’ acceptance of online learning in Bangladesh, a developing nation, the multidimensional framework developed in this study is applicable to many similar developing nations.

The first contribution of this study is the development of a comprehensive scale to assess students’ acceptance of online learning to support it in developing nations. The AOL scale will contribute to research and practice in two ways: (1) it will measure both feasibility conditions and overall student acceptance of online learning, and (2) it is developed for recurrent applications. The AOL scale can be used as a base by future researchers to add more dimensions to the theoretical framework of online learning acceptance. Even though the combination of factors incorporated in the AOL scale is unique, all factors included in it are grounded in similar previous scales, such as the ELAM scale (Teo, 2010 ), the DRAE scale (Hong & Kim, 2018 ), the measurement instrument developed by Shen et al. ( 2013 ), and the measurement instrument developed by Larmuseau et al. ( 2019 ).

Moreover, the AOL scale was designed and developed considering perspectives of students from Bangladesh, a developing nation, which are overlooked in the previous literature. At present, HEIs in Bangladesh are still at a very early stage of implementing online learning (Sarker et al., 2019 ). As online learning has not previously been considered a regular component of learning in developing nations such as Bangladesh, little research has assessed students’ acceptance of online learning in these nations. Due the COVID 19 pandemic, many HEIs in Bangladesh and in other developing nations adopted online learning; hence, a validated scale for assessing students’ acceptance of it is needed to ensure achievement of learning outcomes in online learning environments.

Practically, the AOL scale and the findings from this study will aid academic administrators in implementing and maintaining an effective program of online learning. Acceptance of online learning can be assessed at regular intervals using the AOL scale. The overall acceptance score of the AOL scale indicates whether students are comfortable with online learning or not. Similarly, the AOL score for technological sufficiency indicates whether sufficient technological infrastructure is available for students to engage in online learning. Also, the AOL score for technical assistance indicates whether students are adequately supported to engage in online learning by respective HEIs. The AOL score for digital literacy indicates whether students are sufficiently digitally literate to accept online learning. Lastly, the AOL score for instructor efficiency indicates whether instructors are efficient enough to conduct online classes. Scores for each construct in the AOL scale indicates students’ acceptance of online learning, as implemented by their respective HEIs. Moreover, the AOL scale is also suitable for longitudinal applications. This scale can be used as a base to customize and develop new scales according to the specific needs of different HEIs in developing nations such as Bangladesh.

Limitation and future research

A limitation of the study is that data was gathered from students of private HEIs 2 in Bangladesh, as only they had implemented online learning at the time of data collection. Hence, it would be ideal for future studies to incorporate responses from students of public HEIs in Bangladesh and other developing nations. Moreover, this study did not examine causal relations between constructs. Lastly, measures were directly obtained from respondents through a self-administered online survey, as opposed to a trained data enumerator-administered survey. Despite these limitations, the AOL scale is expected to improve the feasibility and quality of online learning in HEIs in Bangladesh and other developing nations.

Biographies

is an Assistant Professor (on leave) in the ULAB School of Business, the University of Liberal Arts Bangladesh. He is also a doctoral student, and Graduate Council Fellow in the department of educational studies in Psychology, Research Methodology, and Counselling at The University of Alabama. He served in several research positions in Center for Enterprise and Society and served as research consultant in several projects initiated by Bath University, UK/Dutch Funding Agency, USDA, and IFC-SEDF (World Bank Group). His research interest is focused on Bayesian approach in psychometric analysis, educational research methodology, statistical predictive modeling, and statistical applications in educational research and social science.

Dr. Yurou Wang, Ph.D.

is a Clinical Assistant Professor of Educational Psychology and Residential M.A. in Educational Psychology Program Coordinator at the University of Alabama (UA). She received her Ph.D. in Educational Psychology (Learning and Development) from the University of Kansas in 2019. She endeavors to help disadvantaged and low-motivation students. Her research agenda coalesces around self-determination theory, achievement emotions, and academic persistence. She has amalgamated technology into the exploration of academic motivation and emotion by building the Micro-facial Expression Tracking (MET) Lab.

Dr. Kaiwen Man, Ph.D.

was appointed as an Assistant Professor in the Department of Educational Studies in Psychology, Research Methodology, and Counseling at the University of Alabama. Also, Kaiwen has held many positions as Researchers including at the Association of American Medical College, at the Educational Testing Service, and at the Charted Financial Analyst. His research explores questions on the boundaries and interactions of the educational statistics, biometrics, and behavioral research literature with particular attention to models for eye-tracking data, responding process data, Bayesian statistics, and data mining. His works has been published in many peer-reviewed flagship quantitative journals such as Educational and Psychological Measurement, Journal of Educational Measurement, Journal of Educational and Behavioral Statistics, and Applied Psychological Measurement. Furthermore, his projects have been externally funded by the ETS Harold Gulliksen Psychometric Research Fellowship program.

Dr. Laura Morett, Ph.D.

is Assistant Professor of Educational Psychology at the University of Alabama. Dr. Morett uses research approaches from cognitive neuroscience, educational psychology, and developmental science to investigate how the neurobiological organization of language develops and how it contributes to learning, with a particular focus on gesture and its relationship to speech.

Availability of data and materials

The data set analyzed in this study is available from the corresponding author upon request.

Bangladesh is geographically divided into eight divisions, and all HEIs are located in different divisions.

Private universities in Bangladesh have a similar academic structure to public universities but are administered by non-government entities.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Mehdi Rajeb, Email: [email protected].

Yurou Wang, Email: [email protected].

Kaiwen Man, Email: [email protected].

Laura M. Morett, Email: [email protected]

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Top 5 Advantages and Disadvantages of Online Classes in the Philippines

• Course Finder
• June 24, 2022

Online learning has been the primary mode of acquiring education for most Filipino students over the last two years. But since DepEd recently announced that all schools in the country can do face-to-face classes as early as next school year, many people are now thinking of shifting back to the traditional form of learning. Isa ka ba sa kanila? Are you trying to decide whether it’s worth continuing your classes in an online setting or not? If so, then let CourseFinder help you out. We listed some of the top advantages and disadvantages of online classes in the Philippines that you should be aware of. And on that note, let’s begin.

Advantages and Disadvantages of Online Classes in the Philippines

There are a couple of good and bad effects of online classes in the Philippines that we would like to share with you today. We’ll start with the pros of online learning:

Benefits of Online Classes in the Philippines

• One of the best advantages of online learning is that you don't have to leave the comfort and safety of your own home ( taong bahay yarn? ) just to study. This will also lessen your chances of exposure to COVID-19 and other illnesses that may affect your performance as a student.
• You will be able to save time and money too, because there's no need for you to commute or drive to school every day.
• This depends upon the university, but a lot of online classes in the Philippines work on a Flexi-schedule scheme. With this, you can learn whenever you're free to. Something that you can't do with the traditional way of learning.
• Another one of its best advantages is that you get to spend more time with your family. Yes, you're studying and all, but you're also doing it while in their company. The presence of your loved ones can encourage you to study harder and feel less stressed.
• You will also be introduced to different programs and communication platforms your professors use to conduct their online classes. The software you'll work with may vary depending on what you're studying, but some of the ones you may have to be familiar with include Microsoft Word, Google Sheets, Skype, Adobe, Google Meet, Telegram, Zoom, and more.

Struggles of Online Classes in the Philippines

• Although it’s nice that you can attend classes without leaving your home, all that time you spend away from your teacher and classmates may make you feel isolated. And when taken for granted, that feeling may worsen and cause distinct effects on your mental health.
• Whether it be the laugh of your loved ones, the sound of the television, your dogs barking, or even the sound of jeepneys on the street, there are so many distractions when it comes to doing online classes in the Philippines. It also doesn't help that most houses of ordinary Filipino families aren't built to have an extra room that can serve as a quiet study area.
• One of the most significant struggles of online classes in the Philippines is the unreliable internet connection. Unless you’re connected to the most expensive package from the most reliable carriers, you're probably used to dealing with connectivity disruptions at this point.
• Yes, you can attend your classes with just a smartphone. But to do well, you need to have a device that can keep up with the audio, video, and program requirements of your online class. If you can afford a computer or laptop then that's great, but what if you don’t?
• Another downside of doing online classes in the Philippines is that you might not be able to develop a proper sense of responsibility and self-discipline. Since you’re not in the usual classroom setting, you may find it easy to neglect your school work and not miss deadlines.

Final Words

These are the top 5 advantages and disadvantages of online classes in the Philippines that you should always be aware of. Online learning has plenty of benefits and may be the better choice if you have all the resources and like to stay safe from all virus threats. However, if you feel unable to take in all the lessons, and you're scared on missing out on all the quintessential estudyante moments, the traditional face-to-face classes will work for you.

That's it for this article. We hope that by sharing the pros and cons of online classes in the Philippines, you'll be able to decide which mode of learning works the best. If you want to read more blogs about similar topics, just head over to the articles section of our website. We have a collection of informative and easy-to-read articles about school, career, and life in general.

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Skill Our Future: Digital Learning Platform Officially Launches in the Philippines, Empowering Youth Towards Employability

November 10, 2024.

The Skill Our Future (SOF), an initiative that aims to expand access to digital skills, artificial intelligence (AI), and employment opportunities for young people, including those from underserved communities in the Asia-Pacific region, was recently launched in the Philippines. 

The event brought together 14 community partners from higher education institutions, civil society organizations, government agencies and enterprises to support and collaborate in expanding youth’s access to educational opportunities enhancing their employability in the job market. 

Jointly led by the United Nations Development Programme (UNDP), Microsoft, and the Islamic Development Bank (IsDB),  Skill Our Future is a free online learning platform that aims to improve the youth’s interpersonal, digital and employability skills, opening doors to employment opportunities and empowering them to contribute to the sustainable development of their communities and beyond. The platform has been strategically designed, providing holistic pathways to address the growing digital divide. 

According to the 2020 data from Heinrich Böll Foundation, despite high internet usage, 2.8 million Filipino students still lack adequate access to technology and digital literacy. This deepens socioeconomic inequality and risks leaving many youth behind in the digital era. Aligned with RA 11927, the Philippine Digital Workforce Competitiveness Act, the SOF online learning platform addresses this gap by offering courses that equip youth with essential digital, employability, and 21 st  century skills to prepare them for the future of work. 

Dr. Selva Ramachandran , Resident Representative of UNDP Philippines, highlighted the impact of collaboration in ensuring the youth are empowered to succeed in their careers and drive change, stating, “With the help of our partners, we can  bridge this gap, to foster inclusive growth and ensure that young people can participate fully in economic and technological advancements. We encourage you to join us in creating a brighter future where every young individual has the opportunity to succeed and contribute to sustainable development. Together, we can bridge the skills gap and empower the next generation to become innovators, leaders, and changemakers. Let’s invest in their potential and build a sustainable future for everyone.”

The official launch of Skill Our Future (SOF) in the Philippines offered users a chance to share their experiences and the platforms impact on their personal growth. 

Joemelyn Alindayo , an SOF user and Movers Fellow who completed all the soft skills courses, shared that: “ The courses improved my leadership as one of the student leaders in our school. It helped me in leading and navigating challenges, and in becoming a more sufficient student. “ Joemelyn echoed this sentiment, saying, “We’re moving forward, not backward. This free platform gives us a chance to learn and prepare for the future. It benefits everyone. Let’s give it a try. ”

Another user of the SOF platform and Movers Fellow, Berwin Incio , highlighted the platform’s relevance in today’s digital landscape. “ In the digital age, we must keep up with the demands of education and jobs, and Skill Our Future offers courses to help meet these demands. The Basic Microsoft courses enhanced my skills at work allowing me to help my classmates with Microsoft tools. ”

Kahren Wayet , an educator and entrepreneurship professor, emphasized the role of platforms like SOF supporting classroom education. “ In the classroom, we can’t always monitor every student. Platforms like SOF can help students learn necessary skills for the future, especially soft skills applicable across professions .”

The initiative will be expanded to grassroots communities in the Philippines, academic institutions, and out-of-school youth through robust collaboration with community partners like the Movers Programme, Higher Education Institutions (HEI’s), Civil Society Organizations (CSO), and government agencies, envisioning to integrate the learning platform to their own initiatives. 

About Skill Our Future (SOF) Skill Our Future (SOF) aims to bridge the digital divide by empowering underserved youth across the Asia-Pacific with essential digital and 21st-century skills. The program equips young people for the evolving job market by offering capacity-building opportunities and fostering an innovation-driven ecosystem that supports partnerships and sustainable development.  

About UNDP 

UNDP is the leading United Nations organization fighting to end the injustice of poverty, inequality, and climate change. Working with our broad network of experts and partners in 170 countries, we help nations to build integraed, lasting solutions for people and the planet. Learn more at  www.undp.org /philippines or follow @UNDPPH

Media Contact UNDP:  [email protected]

UNDP is the leading United Nations organization fighting to end the injustice of poverty, inequality, and climate change. Working with our broad network of experts and partners in 170 countries, we help nations to build integrated, lasting solutions for people and planet. 

Learn more at ph.undp.org or follow at @UNDPPH.

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Regulatory Alert - Asia - Doing Business

Philippines: foreign digital services vat law enacted.

• HKTDC Research

The Philippines has signed into law a bill imposing value‑added tax (VAT) on digital services supplied by foreign providers. Republic Act (RA) No. 12023 , enacted on 2 October 2024, imposes 12% VAT on cross‑border digital services consumed in the country. The law covers streaming platforms, online marketplaces, digital advertising, the sale of digital goods, and cloud services, among others. Exempted from VAT are digital educational services, including webinars and online courses, and online subscription‑based services supplied to educational entities registered with government agencies.

Non‑resident digital service providers (DSPs) must register for VAT if their total sales exceed PHP3 million (US$51,900); collect VAT on the services they provide to end consumers (B2C sales); and appoint a representative office or agent to assist them in complying with the law. Non‑compliance will result in temporary suspension.

The law will take effect 15 days after its publication on 3 October. However, foreign DSPs will only be subject to VAT 120 days after the Implementing Rules and Regulations (IRR) are issued, which must be within 90 days from the law’s effective date.

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