e learning system thesis

An analysis of students' perspectives on e-learning participation – the case of COVID-19 pandemic

International Journal of Information and Learning Technology

ISSN : 2056-4880

Article publication date: 17 May 2021

Issue publication date: 24 June 2021

During the COVID-19 pandemic, educational institutions were forced to shut down, causing massive disruption of the education system. This paper aims to determine the critical factors for the intention to participate in e-learning during COVID-19.

Design/methodology/approach

Data were collected by surveying 131 university students and structural equation modelling technique using PLS-SEM was employed to analysis the data.

The results showed that the COVID-19 related factors such as perceived challenges and COVID-19 awareness not only directly impact students' intention but also such effects are mediated through perceived usefulness and perceived ease of use of e-learning systems. However, the results showed that the educational institution's preparedness does not directly impact the intention of students to participate in e-learning during COVID-19. The results also showed that the gender and length of the use of e-learning systems impact students' e-learning systems use.

Originality/value

These results demonstrated that, regardless of how well the educational institutions are prepared to promote the use of e-learning systems, other COVID-19-related challenges play a crucial role in forming the intention of students to participate in e-learning during the COVID-19 pandemic. Theoretical and practical implications are provided.

• Distance learning
• Higher education
• Online education

Nikou, S. and Maslov, I. (2021), "An analysis of students' perspectives on e-learning participation – the case of COVID-19 pandemic", International Journal of Information and Learning Technology , Vol. 38 No. 3, pp. 299-315. https://doi.org/10.1108/IJILT-12-2020-0220

Emerald Publishing Limited

Copyright © 2021, Shahrokh Nikou and Ilia Maslov

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The COVID-19 pandemic is the defining global health crisis of our time, and it is adding a fair amount of complexity in how different activities are being conducted ( Adnan and Anwar, 2020 ). Such effects are crucial on higher education, forcing all teaching and learning activities to face a sudden transition to wholly online learning contexts ( Toquero, 2020 ). While the educational environments are still struggling with the digitalisation and digital transformation challenges and finding optimal ways to adapt, the Coronavirus pandemic has fundamentally affected their core: staff and students ( Adedoyin and Soykan, 2020 ; Aristovnik et al. , 2020 ; Strauß and Rummel, 2020 ). For them, the period is inevitably very stressful as all learning and teaching activities – e.g. all classes, meetings, seminars, supervisions and exams were forced to move online within short notice ( Bao, 2020 ; Hodges et al. , 2020 ). Though such transformation is not entirely new for such institutions, they are all now forced to move away from traditional teaching and learning structures to a virtual environment as old education models are no longer adaptable to the challenges of rapidly changing educational environments ( Van Nuland et al. , 2020 ).

In the educational environments, information and communications technology (ICT) has been extensively used to deliver information for education and learning, and e-learning has been an emerging paradigm of modern education ( Sun et al. , 2008 ). E-learning relies on the use of multiple information systems, services and technologies. Information system encompasses information service and information technology (IT), where service is understood as the use of IT. Furthermore, the user experience (UX) and usability of information technology and services also affect e-learning process, not only the technical aspects, but also the social aspects ( Nakamura et al. , 2017 ). Given the relatively recent events in terms of COVID-19 and quarantine situation worldwide, e-learning has become increasingly important as one of the optimal solutions for education ( Radha et al. , 2020 ). We argue that in order to understand better factors influencing individual decision to participate in e-learning in a worldwide quarantine situation, comprehensive research with a holistic approach is needed. Hence, we aim to address this issue by assessing students' experience in their participation in e-learning. Based on this aim, the research question guides this study is What antecedent factors impact students ' intention to participate in e-learning during the COVID-19 quarantine? To answer the stated research question, we develop an integrated theoretical model that encompasses several antecedent factors (perceived challenges during COVID-19, school and teachers' perceived preparedness) and constructs from Technology Acceptance Model (TAM: Davis, 1989 ), perceived usefulness and perceived ease of use ( Yu, 2020 ). We conduct empirical research and collect data through an online survey questionnaire, focusing on university students as the target group. The data will be analysed through structural equation modelling (SEM) using SmartPLS v. 3.

The rest of this paper is structured as follows: Section 2 presents the literature review with the operationalisation of the required terminology and theoretical framework for the study. Section 3 provides the theoretical framework and hypotheses. Section 4 describes the methodology, research design, and data collection. Section 5 provides the results followed by Section 6 , providing discussions. Section 7 concludes the research and outlines the limitations and recommendations for future research.

2. Literature review

2.1 e-learning and participation in e-learning concepts.

To support e-learning, learning management systems (LMS) is increasingly being used, which are e-learning software that can be used to empower teachers to enrich students' learning ( Bansode and Kumbhar, 2012 , p. 415). LMS is a powerful software system enhancing learning and provides automated delivery of the course content and tracking of the learning progress of the students ( Dalsgaard, 2006 ). Sun et al. (2008 , p. 1183) defined e-learning as the use of telecommunication to deliver information for education and training. Garrison and Anderson (2003) defined e-learning participation as teaching and learning facilitated and supported by Internet technologies. In this research, e-learning is defined as the overall technological system to deliver teaching, whereas participation in e-learning is the act of use of telecommunication to deliver teaching and learning within such a system. Khan (2004) defined e-learning as an iterative process that goes from the planning stage through design, production and evaluation to delivery and maintenance stages. However, there are both advantages and disadvantages to e-learning. On a positive side, e-learning allows for a learner-centred, self-paced, cost-effective way of learning and on a negative side, there is a lack of social interactions, higher degrees of frustration and confusion, with higher preparation time for instructors ( Zhang et al. , 2012 ).

Sun et al. (2008) stated that personal perceptions about e-learning could influence attitudes and impact whether a user would intend to use to e-learning in the future. Uppal et al. (2018) and Kim and Frick (2011) mentioned that the supportiveness of the service, information quality and system quality are different aspects of e-learning quality, which could also impact the decision of the users. Moreover, Benigno and Trentin (2000) stated that e-learning is potentially affected by factors such as student characteristics, student-student interaction, learning materials, learning environment, and information technology (IT). Also, Selim (2007) mentioned that there are eight critical success factors of participation in e-learning (e.g. instructor’s attitude towards and control of the technology and student motivation and technical competency). Furthermore, Sun et al. (2008) suggested that perceived e-learning satisfaction is depended on the six dimensions: learner, instructor, course, technology, design and environmental. Sun et al. (2008) concluded that learner computer anxiety, instructor attitude toward e-learning, e-learning course flexibility, e-learning course quality, perceived usefulness, perceived ease of use, and diversity in assessments were the critical factors affecting learner's perceived satisfaction.

Garavan et al. (2010) conceptualised participation in e-learning and quantitatively validated the research model. In their model, the participation in e-learning is formed by the general-person characteristics (e.g. age and social class), motivation to learn and instructional design characteristics of e-learning (content quality and learner support, feedback and recognition). Additionally, the perceived barriers and enablers to e-learning are potentially affected by the proper instructional design of e-learning. Fleming et al. (2017) identified that predictors of future use and overall satisfaction from using e-learning are low perceived complexity of the e-learning system, the knowledge of e-learning, and available technical support for e-learning. Zhang et al. (2012) presented a research model that evaluates the impact of multiple factors on the intention to continue participation in the e-learning systems. Zhang et al. (2012) concluded that the intention to participate depends directly and indirectly on the psychological safety communication climate, on the perceived responsiveness of e-learning system and self-efficacy, as well as satisfaction from the previous use of the system. Furthermore, satisfaction and membership of the community were found to affect the intention to continue participation in e-learning.

2.2 Blended learning: boundaries between physical and virtual

Hrastinski (2008) stated that e-learning participation does not only occur online but also takes place offline. This is mainly due to the fact that e-learning requires time and energy to learn, communication, thinking and assessing what learners have obtained from e-learning communities in more traditional learning settings. Literature on e-learning is primarily on the so-called blended learning of physical and digital learning and Anthony et al. (2020) stated that blended learning (BL) has been increasing in popularity and demand. However, recent literature on the issue seems to be dominated with the factors of educator presence in online settings, interactions between students, teachers and content, and designed connections between online and offline activities as well as between campus-related and practice-related activities.

Wilson (2009 , p. 20) stated that “learning space continuum has two types of conditions at its extremities, wholly independent self-directed unstructured learning at one end and structured teacher-led didactic learning environments at the other”. Furthermore, Wilson (2009) identified different places for learning spectrums, ranging from unstructured that corresponds to home, bar, cafe or gym to lecture theatre and seminar places for holding workshops. The notion of learning space continuum may become necessary when we take into consideration e-learning. As Ellis and Goodyear (2016 , p. 150) identified, the “boundaries” between the physical and the virtual are become less transparent and more permeable, in addition to the greater need of students of being capable of using digital technologies to discover and construct knowledge that is meaningful to them.

Hence, we argue that e-learning participation cannot be defined narrowly as a specific activity in a specific context, but rather a range of activities, some of which may be even blended with the physical (more traditional) learning and interaction with teachers or other students in a more structured or unstructured manner. This could have a significant impact on the way not only e-learning, but the overall learning process is structured, including how the different technologies are used, how the instructional learning programs are structured, what are the social interrelationships between the students, instructors, organisations, and how the success of learning is measured.

2.3 COVID-19, quarantine and e-learning

Kaplan et al. (2020) stated that a third of the global population worldwide was on a quarantine lockdown in order to limit the spread of the COVID-19. This action led to the social distancing and thus fewer social connections, which also included closures of commercial enterprises and higher educations, resulting in limited physical presence and social interactions between the people. The impact of COVID-19 is also seen in the educational environments, with a potential to experience unparalleled transformations, just as many other human spheres of behaviour, which are facilitated by the advents in the development of IT, such as 5G ( Kaplan et al. , 2020 , p. 4). Paraschi (2020 , p. 19) stated that e-learning might even be an alternative activity that is to help communities previously relying on other activities, such as competitive educational and training e-learning programs blended with on-site summer schools in a Greek island as a replacement for tourism, which suffered greatly during the COVID-19 pandemic.

However, there are multiple challenges related to e-learning that come as a result of COVID-19. For instance, Almaiah et al. (2020) identified the critical challenges and factors of e-learning system usage during COVID-19 pandemic. In the research, the authors covered the topics of e-learning system quality, trust, culture, self-efficacy, and issues of financial support, change management and technical maintenance, all of which were mentioned as potentially influential factors of e-learning adoption. Moreover, we argue that COVID-19 pandemic is a challenge impacting the approach to e-learning, thus requiring adaptation and innovation in higher education to cope with the posed challenge. Alea et al. (2020) have evaluated the perceptions among the teachers about the impact of COVID-19 and the community quarantine on the distance learning and found multiple challenges related to it, as well as individual issues with preparedness for delivering distance learning. Also, Abbasi et al. (2020) stated that students did not prefer e-teaching over face-to-face teaching during the lockdown situation, and that administration and faculty members must take necessary measures to improve e-learning during the lockdown. Favale et al. (2020) stated that in the context of 80–90% of people in Italy staying at home during the quarantine, remote working and online collaboration exploded in an Italian university. Thus, the research on participation in e-learning in the context of COVID-19 is very relevant and timely.

2.4 Information service, information systems and information technology

In literature, information service is defined as “a component of an information system representing a well-defined business unit that offers capabilities to realise business activities and owns resources (data, rules, roles) to realise these capabilities” ( Ralyté et al. , 2015 , p. 39). Furthermore, Wijnhoven and Kraaijenbrink (2008 , p. 93) suggested that information services are “services that facilitate the exchange of information goods with or without transforming these goods”. The authors (2008, p. 114) stated that “information services have a lot in common with other types of information systems”, hence implying that the information services are distinct from the information systems. Importantly, it is necessary to outline that information system (IS) is defined as any combination of information technology (IT) and people's activities using that technology ( Gupta, 2000 ).

Accordingly, IT consists of telecommunications, computing, and content, whereby different types of IT are represented at the intersections (e.g. Internet being partly computing, and partly telecommunications). Hence, one may wonder about the exact definitions of an information service, an information system, an information technology and what is the interrelation between them. It is essential to underline that the terms are potentially having blurry boundaries and are hard to define. For the purposes of this particular study, information service is defined as the use of information technology by people. However, the information system of e-learning at large is not considered to be limited only to LMS such as Moodle as there are many other physical and virtual information services that could facilitate e-learning. This study will try to focus on the information services of e-learning that facilitate participation over IT.

3. Theoretical framework and hypothesis development

Ke and Hoadley (2009) suggested that there is no “one size fits all frameworks” when evaluating online learning communities. From the literature on e-learning, there are a number of identified antecedent factors that could potentially influence participation in e-learning. Besides, factors related to the current situation of pandemic (COVID-19) may also impact the participation in e-learning. The research model for this study is developed based on the literature review outlined above. Firstly, several antecedent factors that may affect participation in e-learning are identified. Secondly, these factors are used to build a theoretical framework which will be evaluated and examined empirically.

3.1 COVID-19 related factors

At the time of writing the paper, the research on the COVID-19 is new, as it is a relatively recent event. Hence, the exploratory purpose of the paper is to identify potential factors that may impact e-learning participation in quarantine time. Therefore, we aim to review the most recently published studies on this topic. For example, Alea et al. (2020) have recently performed a research on the opinions of teachers concerning the preparedness and challenges that the university might face when adopting e-learning in the times of the quarantine. They empirically evaluated the (1) awareness of the COVID-related situation, (2) the teacher's readiness and school's preparedness to conduct distance learning, and (3) perceived challenges in distance learning education ( Musingafi et al. , 2015 ). In this study, nevertheless, as we plan to survey students instead of teachers, we adapt the same survey questions and modify them slightly to fit the context of our study. As such, we use (1) awareness of COVID-19, (2) perceived challenges to participate in e-learning during the quarantine, (3) perceived educational institutions preparedness [perceived teachers' preparedness and perceived school's preparedness] to conduct distance learning, as the COVID-19 related factors to examine the students' intention to e-learning participation.

Awareness of COVID-19 has a positive effect on the intention to e-learning participation.

Awareness of COVID-19 has a positive effect on perceived usefulness.

Awareness of COVID-19 has a positive effect on perceived ease of use.

Perceived challenges during COVID-19 has a negative effect on the intention to e-learning participation.

Perceived challenges during COVID-19 has a negative effect on perceived usefulness.

Perceived challenges during COVID-19 has a negative effect on perceived ease of use.

Perceived educational institutions preparedness during COVID-19 has a positive effect on the intention to e-learning participation.

Perceived educational institutions preparedness during COVID-19 has a positive effect on perceived usefulness.

Perceived educational institutions preparedness during COVID-19 has a positive effect on perceived ease of use.

3.2 Perceived usefulness of e-learning

Perceived usefulness has a significant effect on the intention to e-learning participation.

3.3 Perceived ease of use of e-learning

Perceived ease of use has a significant effect on the intention to e-learning participation.

Perceived ease of use has a significant effect on perceived usefulness.

3.4 Intention to participate in e-learning

In the current study, our dependent variable is e-learning participation, which is measured by the student's intention to participate. There may be multiple different factors that could affect the intention of students to participate in e-learning during the quarantine situation. Prior studies in e-learning research use intention to participate in e-learning ( Masrom, 2007 ; Tselios et al. , 2011 ; Zhang et al. , 2012 ; Park, 2009 ) as the outcome variable.

Moreover, we intend to examine several potential individual characteristics as control variables when assessing the model. We argue that the younger students are more accepting the use of IT for learning. Evidence is paradoxical in this aspect, as Fleming et al. (2017) stated that age does not impact the intention of using e-learning. Ong and Lai (2006) stated that gender might indirectly affect the acceptance of e-learning, as men and women had different perceptions of PU and PEOU of e-learning systems. The theoretical framework model is provided in Figure 1 .

4. Methodology

The data collection was done between 15 August to 15 October 2020 through an online survey when closure of all educational institution, specifically higher education was announced by the Finnish government started from March 2020. Prior to the primary data collection, survey items (instruments) to measure five factors predicting the use of e-learning during COVID-19 among higher education students were adopted from previously validated studies and based on the adaptation process, the items for the current study were slightly modified suit the contexts of the study, COVID-19 and e-learning.

The items for measuring COVID-19 awareness (three items), perceived teachers and school preparedness (six items) and perceived COVID-19 challenges (four items) all were derived from Alea et al. (2020 , pp. 134–136). Survey items for measure perceived usefulness (four items) and perceived ease of use (four items) were derived from Masrom (2007) and Davis (1989) . Finally, items for measuring intention to participate in e-learning during the COVID-19 were derived from Lee et al. (2009) and Davis (1989) . The model measurement and assessment of the constructs were done through the use of SmartPLS 3.2 that was guided by the procedures of Partial Least Squares Structural Equation Modelling (PLS-SEM).

4.1 Data collection

During the school closures, the survey instrument was distributed through an online survey application. The data were obtained only from those respondents who indicated they are currently university students. As mentioned, the data collection was formed in the course of two months, and over 350 invitations were sent. After the closure of the survey, 153 responses were received. Upon further examination of the completeness of the data and removing unengaged responses or those who indicated that they are not currently students, in total, 131 responses were included in the dataset for further analysis.

5.1 Descriptive statistics

Of the respondents, 73 (55.7%) were female, while 56 (42.7%) respondents were males, and two did not want to reveal their gender. The average age of respondents was 25 years old with (standard dev. = 6.1). Moreover, the highest degree of the respondents was as follow: high school diploma ( N  = 63), bachelor's degree ( N  = 40), master's degree ( N  = 19), and PhD or other ( N  = 9). We also asked respondents to indicate how long in total have they been using e-learning systems. The following information was retrieved; less than a year ( N  = 61), between one to three years ( N  = 37), more than three years ( N  = 32) and only one respondent indicated has never used such learning systems. We also asked the respondent to indicate to what extent the instructor's teaching style would impact their decision to participate in e-learning. We asked, “the instructor encourages and motivates me to use e-learning”, or “the instructor's style of presentation holds my interest”. The results showed that 36 students thought the teaching style of the instructor would motivate and encourage them to use e-learning systems and interestingly 23 students mentioned it does not affect their intention or the effect is not considerable. Regarding the second question, we found 28 students who believed that the instructor's presentation style would have a substantial impact on their intention to use e-learning systems to participate in e-learning. The same number of ( N  = 28) students believed that the instructor's presentation style does not at all play a role in their decision to use such systems for e-learning participation, or the effect is somewhat limited.

5.2 Measurement results

In the following, we report on the data analysis at the measurement model, which refers to the assessment of the measures' reliability and their validity. In doing so, we computed: (1) item (indicator) loadings and internal consistency reliability, (2) convergent validity, and (3) discriminant validity ( Hair et al. , 2019 ).

5.2.1 Item loadings and internal consistency reliability

PLS-SEM results were utilised for the item loadings in this study. Table 1 shows the detail of item loadings. As shown in Table 1 , all item loadings (except one item PCHA_2 with the slightly lower value) satisfied the recommended loading values of >0.70 ( Hair et al. , 2019 ). However, from the algorithm process in PLS-SEM, one item (indicator) from the COVID-19 awareness (CAWA_3) was dropped. Therefore, 24 items remained for the next step of the PLS-SEM analysis. Internal consistency reliability refers to the evaluation findings for the statistical consistency across survey items (indicators). According to Hair et al. (2019) , internal consistency reliability should be reported through Cronbach's alpha ( α ) and Composite Reliability (CR). Therefore, we computed these two tests and the values achieved were all above to the recommended threshold of 0.70 ( Hair et al. , 2019 ) providing good internal consistencies.

5.2.2 Convergent validity and discriminant validity

Convergent validity is a statistical measure that assesses the construct validity, and it suggests that assessments having similar or same constructs should be positively related. Regarding the convergent validity, the value s of average variance extracted (AVE) must be reported. As shown in Table 1 , all the AVE values were above the recommended threshold of 0.50.

Discriminant validity test examines the extent to which a construct is different from other constructs ( Hair et al. , 2019 ). In order to report the values, the Fornell Larcker criterion will be used, and the AVE scores of a construct should be lower than the shared variance for all model constructs. As shown in Table 2 , all the AVE scores satisfied this condition, and therefore, the discriminant validity was established based on the evaluation of the Fornell Larcker criterion ( Fornell and Larcker, 1981 ).

However, as we used the PLS-SEM approach to perform the data analysis, we also assessed the discriminant validity through the Heterotrait-Monotrait Ratio of Correlations (HTMT). Discriminant validity problems also appear when HTMT values are higher than 0.90. The construct can be similar if HTMT shows a value of >0.90, which in this case, it indicates the lack of discriminant validity. Table 3 shows the HTMT values, and as it is indicated, all values were lower than 0.90.

We also examined the collinearity by reporting Variance Inflation Factor (VIF) values. The collinearity will be an issue if the VIF value is above 3.00 ( Hair et al. , 2019 ). Perceived usefulness (VIF = 1.663) and perceived ease of use (VIF = 1.559) are the predictor of intention to participate in e-learning during the COVID-19. Moreover, COVID-19 awareness is the predictor of perceived usefulness (VIF = 1.064) and perceived ease of use (VIF = 1.064). Perceived educational institutions preparedness predict perceived usefulness (VIF = 1.087) and perceived ease of use (VIF = 1.087). Perceived COVID-19 challenges predict perceived usefulness (VIF = 1.088) and perceived ease of use (VIF = 1.088). Therefore, the collinearity test results show that collinearity does not emerge as an issue in this study ( Hair et al. , 2019 ).

5.3 Structural results

The structural model assessment was performed following Hair et al. (2019) recommendation. In order to assess the path coefficient between endogenous and exogenous constructs, the sample was bootstrapped through 5.000 sub-sampling. The results of the SRMR indicator estimating the goodness of fit of the structural model was 0.065. The structural results showed that most of the hypotheses were supported ( Table 4 and Figure 2 ). The outcome variable, i.e. intention to participate in e-learning was explained by variance of 69%. Moreover, the perceived usefulness and perceived ease of use were explained by variance of 21% and 15%, respectively. The SEM results showed that the path between COVID-19 awareness to intention to participate in e-learning was significant ( β  = 0.192; t  = 3.220; p  = 0.001); therefore, H1 was supported by the model. The SEM results also showed that the path between COVID-19 awareness to perceived usefulness ( β  = 0.243; t  = 2.748; p  = 0.005) was significant; thus H1a was supported by the model. However, the COVID-19 awareness to perceived ease of use was not significant; thus H1b was rejected by the model.

The SEM results showed that the path between perceived challenges, as expected, negatively impact intention to participate in e-learning ( β  = −0.186; t  = 2.789; p  = 0.005); therefore, H2 was supported by the model. The SEM results also showed that the path between perceived challenges during the COVID-19, as expected, negatively impact both perceived usefulness ( β  = −0.36; t  = 4.599; p  = 0.001) and ( β  = −0.246; t  = 3.167; p  = 0.002), thus H2a and H2b were both supported by the model. In addition, the SEM results showed that the path between perceived educational institutions preparedness to intention to participate in e-learning was not significant; therefore, H3 was rejected by the model. This finding is similar to Zia (2020) who also found that the curriculum and technology have a negative impact on the online classes during the COVID-19 pandemic. Furthermore, the SEM results showed that the path between perceived educational institutions preparedness to PU was also not significant; thus H3a was rejected by the model. However, perceived educational institutions preparedness to PEOU was significant ( β  = 0.235; t  = 2.365; p  = 0.02), thus H3b was supported by the model. Finally, the strongest relationship emerged between the path from perceived usefulness to participate in e-learning ( β  = 0.623; t  = 9.225; p  = 0.001); therefore, H4 was supported by the model. However, the results showed that the path between perceived ease of use to participate in e-learning was significant was not significant; thus, H5 was rejected by the model. As per path between PEOU to PU, the SEM results showed a significant effect of PEOU to PU ( β  = 0.484; t  = 6.220; p  = 0.001); thus H5a was supported by the model.

We also examined the mediating effect of perceived usefulness and ease of use between the COVID-19 related factors and intention to participate in e-learning. To do so, we first accounted for the results of total indirect effects and then examined the specific indirect effects values, as PLS-SEM procedures required. The mediation test results showed the total indirect effects for the paths between COVID-19 awareness ( β  = 0.161; t  = 2.618; p  = 0.01), and perceived challenges ( β  = −0.251; t  = 4.630; p  = 0.001) to intention to participate in e-learning were significant, indicating that there might be mediation effects in these path relationships. Therefore, we checked the specific indirect effects values and found that theses paths are mediated only through perceived usefulness. The result showed that the paths between COVID-19 awareness ( β  = 0.152; t  = 2.553; p  = 0.01) and perceived challenges ( β  = −0.224; t  = 4.187; p  = 0.001) to intention to participate in e-learning were partially mediated through perceived usefulness. Finally, the effect of perceived educational institutions preparedness to intention to participate in e-learning was only realised through the mediating effect of PEOU-PU ( β  = 0.07; t  = 2.218; p  = 0.03).

5.4 Multigroup analysis (MGA)

The research model was further investigated to see if the demographic characteristics of the respondents impact the path relationships in the model. To do so, we used the gender, and the average time the participant used the e-learning system in their e-learning activities. These two variables were used as control variables, and then we ran multigroup analysis (MGA) with PLS-SEM. The MGA results showed that respondents are different in some paths (see Table 5 ). For example, the path between perceived teachers and school's preparedness to perceived usefulness was only significant for males ( β  = 0.261; t  = 1.995; p  = 0.05). The MGA results also showed that the path relationships between perceived challenges to (1) intention to participate in e-learning, (2) PU and (3) PEOU, were significant only for females. Therefore, the perceived challenges of COVID-19 could be considered as an important and influential factor influencing directly the decision-making of the students in e-learning participation. Finally, the path between the COVID-19 awareness to PEOU was only significant for females ( β  = 0.332; t  = 3.406; p  = 0.001).

We also divided respondents into two groups based on their use of e-learning systems; group 1 included those who indicated they have experienced and used such systems for less than a year ( N  = 61), group two for those who indicated they have experienced and used such systems for more than one year ( N  = 69). The MGA results showed that the path between perceived educational institutions preparedness and PEOU was only significant for Group 1, those who mentioned that they had used the e-learning system for less than one year. However, more differences were observed in paths between COVID-19 awareness and perceived challenges to intention to participate in e-learning, as well as the path between perceived challenges to PEOU, such that the effects of these two path relationships were only significant for respondents in Group 2 (see Table 5 ).

6. Discussion

The SEM analysis revealed that the students' intention to participate in e-learning is significantly affected by the COVID-19 awareness and perceived challenges of the pandemic. It may be because of the subjective nature of the studied phenomena, which relies on the factors that relate to the individual (i.e. awareness and perceived challenges of the pandemic). These finding are similar to Raza et al. (2020) who also stated that there is need for improving the e-learning experience among students and escalating their intention to use such learning systems. Moreover, the perceived educational institution's preparedness (i.e. teachers and schools) seems to affect the intention to participate in e-learning only through the mediating effect of PEOU-PU. It may suggest that students do not see educational institutions' preparedness by itself as a motivating factor to use the e-learning system. It may also suggest that educational institutions have not been appropriately prepared to fully utilise the functionalities of e-learning systems (e.g. usefulness) facilitating the students' learning.

Moreover, the structure results showed that the awareness of COVID-19 situation might affect the usefulness of e-learning systems, but not the extent to which the use of such systems is easy. Given the pandemic requirements for safety via the social distancing and distance learning, students might consider e-learning systems as a better and safer alternative towards conventional in campus education. In other words, students have no other alternative left other than adapting to the dynamic situation and accepting to use e-learning systems to cope with the changes in their learning modes. Interestingly and as expected, the perceived challenges of COVID-19 situation seem to be a very influential factor determining the perceived value of e-learning systems and the intention to use them, however, it should be noted that the effect is negative. It may suggest that emotional and stress management of students is highly crucial for e-learning in the quarantine times.

Ong and Lai (2006) found that gender might impact the participation in e-learning through the perceived usefulness and perceived ease of use of e-learning systems. In the current paper, it was found the gender of the students impact their decision in e-learning participation. We would suggest that the perceived challenges of COVID-19 situation are having a more pronounced negative effect on female students than on their male counterpart. Plausibly, this might be due to the females' perceptions of their computer self-efficacy, which is crucial for e-learning ( Zhang et al. , 2012 ). In a similar vein, we would argue that the personality variations across genders may affect the results of why COVID-19 awareness has a significant impact on PEOU and the effect is only for females and why perceived preparedness has a significant impact on PU and that the effect is realised only for males. However, the latter may also be explained by the fact that males are more things-oriented, whereas females are people-oriented ( Su et al. , 2009 ). Hence, suggesting that males could potentially see more connections between e-learning systems' functionality (usefulness) and how these were improved by the preparedness of educational institutions.

The fact that the path between perceived educational institutions preparedness and PEOU was significant for those who used e-learning systems for a year or less may indicate that the educational institution's preparedness is only able to help an inexperienced user of e-learning systems by providing sufficient support and relevant information in the times of the pandemic. More experienced users of e-learning systems may have learned how to use them; hence the preparedness did not affect their perception of ease-of-use of e-learning systems. Contrarily, for experienced users who have used e-learning systems longer than a year, it may be that they are able to put the perceived challenges in perspective to the times when e-learning was not the main and the only mode of learning. The experience of use of e-learning systems is logically expected to be highly correlated with the age and the education level; hence, it could be hard to pinpoint whether differences come from the experience or other demographic variables.

7. Conclusions

The education of university students has been interrupted due to COVID-19 pandemic. The current situation has imposed unique challenges of smoothly maintaining the process of teaching and learning, as such e-learning has become an immediate solution to cope with the disruption in higher education. The results of this research revealed several theoretical implications. The first being the extension of the Technology Acceptance Model (TAM: Davis, 1989 ) for making it relevant to the current COVID-19 situation, and its application in the context of higher education to assess students' intention to use e-learning systems. The core theoretical focus of this study was to develop a conceptual model to identify factors impacting the students' intention to e-learning participation during the COVID-19 pandemic. This paper theoretically contributes to the literature by showing that the awareness of and the perceived challenges of the COVID-19 pandemic situation were the most significant factors influencing e-learning participation during the COVID-19 pandemic. As students' awareness of COVID-19 pandemic is increased, they would be more willing to achieve their education goals through the use of e-learning systems, especially when they are socially isolated, campus education is restricted and have to perform their studies mostly online. Moreover, the findings showed that no matter how well prepared the educational institutions (teachers and schools) are, the usefulness of e-learning systems still plays the leading role in enhancing the students' intention to participate in e-learning. Surprisingly, we did not find any direct impact of ease of use of e-learning systems to the intention of e-learning participation. Perhaps, blended learning (offline and online education) could be still the most proffered modes of learning for the students. In other words, a blended approach, where traditional teaching is combined with online teaching, should have ushered the students to participate in e-learning.

Alea et al. (2020) have found that there are multiple challenges in terms of educational preparedness during the COVID-19. However, in this study, it was found that educational institutions preparedness has little to no effect on the intention to participate in e-learning. Thus, the educational institutions are advised to consider the findings of this study to review their approaches to address their politics regarding e-learning in the times of the quarantine. We also found that the effects of the perceived pandemic challenges and educational institutions preparedness are different for experienced and inexperienced users of e-learning systems as well as among female and male students. As such, gender should be considered as a crucial factor in e-learning initiative taken by the educational institutions. Perceived challenges seem to have the most negative impact on women in the pandemic situation and their participation in e-learning. Sun et al. (2008) suggested that personal perceptions about e-learning affect the intention to participate in e-learning. In our study, it seems that the intention to participate in e-learning is affected by the perceptions about the contextual situation, such as about the current pandemic situation, perceived challenges it creates, and how does the educational institution prepare itself to tackle the situation.

7.1 Limitations

One of the drawbacks of the current research is the sample size used that can be expanded to achieve more generalisable findings. The conceptual model was developed for the purpose of this research, and therefore, the structural results and findings should be interpreted carefully. The size of the dataset and the sampling strategy might be other sources of potential errors. Since the data were collected through an online survey and during the COVID-19 pandemic situation, it is very hard to evaluate and assess whether the respondents answered questions as accurate as possible. Finally, this study took place in Finland, and might not apply to other countries due to different COVID-19 situation, regulations and imposed restriction during the current situation.

7.2 Future research

This research has uncovered interesting manifold insights about the different COVID-19 related factors on e-learning at educational institutions. As such, future research may utilise the conceptual model developed in this research and aim to explore further findings in other contexts. For instance, by investigating what encourages students to participate in e-learning more and why education institutions preparedness (both teachers and schools) does not account for higher intention to participate in e-learning. Students' perceptions could also be explored qualitatively. For example, why and how exactly awareness about COVID-19 encourages more intention to use e-learning systems. Future research is also advised on exploring further how educational institutions should become better prepared for future events, if they may occur, such as one we are witnessing in the current pandemic situation.

Theoretical model

Structural model

Reflective indicator loadings and internal consistency reliability

Discriminant validity (HTMT)

Structural results

Multigroup analysis results

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Introduction to E-Learning Systems

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• Aleksandra Klašnja-Milićević 8 ,
• Boban Vesin 9 ,
• Mirjana Ivanović 8 ,
• Zoran Budimac 8 &
• Lakhmi C. Jain 10 , 11  

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 112))

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Recently e-learning systems are experiencing rapid development. The advantages of learning through a global network are manifold and obvious: the independence of time and space, learners can learn at their own pace, learning materials can be organized in one place and used-processed all around the world. One of the most important segments in today’s development and use of the e-learning system is the personalization of content and building of user profiles based on the learning behaviour of each individual user. The personalization options increase efficiency of e-learning, thus justifying the higher initial cost of their construction. In order to personalize the learning process and adapt content to each learner, e-learning systems can use strategies that have the ability to meet the needs of learners. Also, these systems have to use different technologies to change the environment and perform the adaptation of teaching materials based on the needs of learners. The process of adaptation can be in the form of adaptation of content, learning process, feedback or navigation. This chapter introduces the motivation and objectives studied in the subsequently presented research, and presents major standards and specifications in e-learning.

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Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic

• Cedric B. Mpungose 1  

Humanities and Social Sciences Communications volume  7 , Article number:  113 ( 2020 ) Cite this article

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South African universities have been forced to transit from face-to-face to online learning (e-learning) as a result of the coronavirus pandemic (COVID-19). However, various challenges hinder disadvantaged students from realising the full potential of e-learning. Therefore, this study’s main objective is to propose alternative pathways to overcome such challenges for students, to enable them to have access to effective e-learning. This study draws on a two-year postdoctoral qualitative research project conducted at a South African university to explore students’ experiences of the transition from face-to-face to e-learning. Twenty-six students completing a curriculum studies programme were purposively and conveniently sampled to generate data using e-reflective activity, Zoom group meetings and a WhatsApp one-on-one semi-structured interview. Findings articulate the digital divide as a hindrance to students realising the full potential of e-learning, yet lecturers still want students to submit assessment tasks and engage with course activities on the Moodle learning management system. With universities using face-to-face learning becoming vulnerable to the COVID-19 pandemic and other challenges which result in a shutdown of university sites, alternatives need to be sought to allow students, particularly disadvantaged students, to realise e-learning.

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Turki Mesfer Alqahtani, Farrah Dina Yusop & Siti Hajar Halili

Introduction

Since the beginning of higher education, from the time of colonisation to the era of decolonisation, almost all South African universities have been dependent on face-to-face learning (Cuban, 1986 ; Mgqwashu’, 2017 ). Jansen ( 2004 ) argues that face-to-face learning is believed be traditional and excludes students’ experiences, because it occurs in the presence of a lecturer depositing knowledge for students in a demarcated classroom, using traditional methods (lecturer-centred) and traditional resources like textbooks, chats, chalkboards and others. However, these demarcated physical classrooms are not accessible in the case of challenges ranging from student protests to pandemic outbreaks. Face-to-face learning provides real-time contact with resources and others, takes place within a specified contact time, and provides prompt feedback to students (Black and Wiliam, 2006 ; Waghid, 2018 ). That said, e-learning is education that takes place over the Internet is alternatively called online learning, and it is an umbrella term for any learning that takes place across distance and not in a face-to-face platform (Anderson, 2016 ; Mpungose, 2020a ). Furthermore, Choudhury and Pattnaik ( 2020 ) affirm that, e-learning definition evolves with the evolution of Web from Web 0 to 4.0. Thus, “the world was introduced to Internet-based learning with Web 0, which was a read-only site. Thereon, Web (2.0) and Web (3.0) allowed real-time interaction and connected intelligence, respectively. We now witness Web 4.0 where machine and the human brain can directly interact” (Choudhury and Pattnaik, 2020 , p. 2). The concepts of e-learning, distance education, online learning and web-based education are concepts that have been used in the literature. However, Rodrigues et al. ( 2019 , p. 88) affirm that both these concepts share the common feature that “they are a form of instruction that occurs between a learner and an instructor and are held at different times and/or places, using several forms of material”. As such, Arkorful and Abaidoo ( 2015 ) refer to e-learning as the use of educational technologies to enable access to learning and teaching material online. Thus, the importance of e-learning which takes place through the use of the Internet in 21st century university education is undeniable, particularly for the students of today as digital natives (Bennett et al., 2008 ; Prensky, 2001 ). Amory ( 2010 ) and Khoza ( 2019b ) state that e-learning is capable of making course content available online, because of the widespread use of modern technologies such as hardware resources (computers, laptops, mobile phones and others), and software resources (learning management system, software applications, social media sites and others). This suggests that students have freedom to access course information/content anytime and anywhere, irrespective of challenges such as the pandemic outbreak—provided they have access to hardware and software resources.

In complicating the above debate, some studies (Liu and Long, 2014 ; Nikoubakht and Kiamanesh, 2019 ) further argue that face-to-face learning is irreplaceable and is the cornerstone of every learning institution, even if the current discourse and technological revolution demand the use of e-learning. The latter studies believe that there is still a conundrum between face-to-face (person-to-person interaction in a live synchronous platform) and e-learning (self-paced learning in an asynchronous platform). As a solution to this conundrum, other scholars (Anderson, 2016 ; Bates, 2018 ; Graham, 2006 ) believe that blended learning which combines online and face-to-face learning is the way to go, so that students can use many ways of accessing course content based on their needs (strengths/limitations).

Nevertheless, there are compelling conditions that can make students choose online over face-to-face learning; this may include violent student protest, pandemic diseases like COVID-19 in the context of this study, and others. According the World Health Organization-WHO ( 2020 ), COVID-19 is a new strain of viruses discovered in 2019, which cause illnesses ranging from the common cold to more severe diseases that can lead to death. They are transmitted between animals and people. Common symptoms of infection include respiratory symptoms, fever, cough, and shortness of breath. As at 31 March 2020, statistics stay at 33 106 deaths globally and in Africa is currently 60 deaths. In other words, this pandemic poses a threat to the face-to-face learning context globally, including in South Africa.

On 11 March 2020 the WHO ( 2020 ) declared COVID-19 a pandemic, and everyone was advised to avoid close contact with anyone showing symptoms. Therefore, universities across the globe have to shut down. In the South African context the President called on all universities to shut down and find ways to offer lectures online as from 18 March 2020 as a precautionary measure (DHET, 2020 ). This call raised questions as to the feasibility of e-learning, particularly at the School of Education in one of the universities in the province of KwaZulu-Natal, because of the extent of inequalities in the South African context. While Mzangwa ( 2019 ) agrees with Bunting ( 2006 ) that since 1994 much has been done in higher education to redress the inequalities of the past through higher education institutions’ policy amendments through the National Plan for Higher Education (Ministry of Education, 2001 ). These amendments have not led to benefits for the majority of previously disadvantaged black South African students in terms of access to e-learning.

In addition, the digital divide—the gap between those who have and do not have access to computers and the Internet—seems to be a huge factor limiting the feasibility of e-learning in a South African context (Van Deursen and van Dijk, 2019 ; Warschauer, 2002 ). These latter studies further assert that issues such as socio-economic factors, race, social class, gender, age, geographical area and educational background determine the level of the digital divide in a university context. While access to the Internet and computers is high in developed European and American universities, African universities—particularly in the South African context—are still battling because of the intensity of the factors which led to the digital divide (Van Deursen and van Dijk, 2019 ). Research shows that various programmes and policies have been developed and implemented to remedy this challenge; hence, universities provide students with free laptops and Wi-Fi (wireless network commonly allows technological devices to interface with internet) access inside the university and residences (Rodrigues et al., 2019 ; Schofield, 2007 ). However, little or no research has been done in the South African context to intervene in addressing university students’ challenges (the digital divide) that hinder them from accessing e-learning from home. This study argues that e-learning while students are at home can never be realised in a South African university context unless the digital divide is addressed. In proposing alternative pathways for South African universities to deal with the digital divide, this study considers a connectivism learning framework.

Conceptualising learning in a digital age

The rapidly evolving technological landscape in the 21st century has meant that university lecturers “have been forced to adapt their teaching approaches without a clear roadmap for attending to students’ various needs” (Kop and Hill, 2008 , p. 2). As a result, connectivism is the promising initial lens through which to conceptualise learning in this digital age, because of its varying attributes from face-to-face to e-learning. Thus, Siemens and Downes ( 2009 ) see learning as the process of crossing boundaries by creating connections or relationships between human and non-human nodes through the setting of an interconnected network. Connectivist learning draws much from available Internet and technological resources to make an effective network that will maximise learning. As a result, connectivity seeks university lecturers to consider the possibilities of Internet access and other technological resources for effective learning, so that each individual student may gather and share information irrespective of challenges (the digital divide) faced (Bell, 2011 ; Kop and Hill, 2008 ). In other words, for effective e-learning to occur even if students are at home, access to the Internet and technological resources should be made available so that they may make connections amongst themselves and the lecturers, irrespective of hindrances faced.

Siemens (2005) further argues that in connectivism, students are not taken as a blank slate or passive recipients of information but are taken as active participants who can nurture, maintain, and traverse network connections to access, share and use information for learning. In order to ensure this, Siemens and Downes ( 2009 ) propose eight principles guiding connectivist learning, as depicted in Table 1 overleaf, which are according to this study are now conceptualised to form dichotomies between F2F learning and e-learning. These principles draw from basic learning frameworks (behaviourism, cognitivism, and constructivism) to incorporate both subject and social experiences for learning. Traditionally, learning is believed to be occurring when the lecturer provides a stimulus (teacher-centred activities) so that students can respond, but the rapid development and implementation of new technologies seeks learning to be individually and socially constructed by students (learner-centred activities) to maintain a diversity of ideas. This suggests that digital learning is more participatory and effective than traditional learning because it seeks lecturers to engage students in a dialogue for social construction of knowledge (Downes, 2010 ). Moreover, Siemens and Downes ( 2009 ) agree with Anderson ( 2016 ) that learning is about creating and connecting to a community (node) of learning within a network. This connection does not only take place within a learning institution, but can also be online so that students at home or in their residences can access learning. In other words, connectivism prioritises e-learning as the first and best option for students to access learning, if there are forceful or compelling conditions that hinder face-to-face learning.

Siemens and Downes ( 2009 ) further argue in principle that traditional resources such as books, chats, chalkboard and others form the core of learning, but the digital age needs them to be supplemented by modern resources like the Internet, computers, mobile phones and others for students to make connections and share information amongst themselves and others. In other words, modern resources enhance active student participation and the capacity to know more; thus the active student has the ability to use resources provided to seek out current information from primary and secondary resources, as compared to being a passive student (Downes, 2010 ). This suggests that in connectivist learning, it is not enough for a student to depend only on the prescribed readings, taught content, consultation with one lecturer and students in a particular subject/module. However, connectivists seek students to enjoy exploring the world in order to connect with other people outside the normal context, through the use of search engines, social media and other means, because learning is about not only knowledge consumption but construction (Anderson, 2016 ).

The manner in which students are assessed depends on the ability to see connections between subject fields, ideas, and concepts (Siemens and Downes, 2009 ). In other words, assessment must be made enjoyable to students because it is not done for the purpose of grading but for developmental purposes (Black and William, 2009 ). The content (objectives) taught during the official time in the lecture may change over time, based on new contributions in a subject; this requires students to be driven by a professional and social rationale in making decisions as to what to learn and how to make meaning out of it (Downes, 2010 ). Therefore, just lecture contact time is not enough for students, and it should be supplemented with students’ extra time so that learning outcomes can be achieved.

Furthermore, review of research done by Damşa et al. ( 2015 ) on quality in Norwegian Higher Education, outlines dichotomous aspect of F2F learning and e-learning. The study aimed at identified important contributors to enhance of quality learning in higher education, and to identify the knowledge gaps in the literature. It was found that, in as much as both platforms (F2F learning and e-learning) share the same aspect in communication, collaboration, and supervision and interaction. However, e-learning provides much of these aspect than F2F learning since it creates more intense atmosphere from synchronous to asynchronous teaching and learning aspect. This suggests that the development use of educational technology (videos, smart phones, learning management systems and social media sites) raises quality learning on e-learning as compared to F2F environment. Thus, e-learning advocates for student-centredness versus teacher-centeredness in teaching and learning of the content because “students learn together online, support mechanisms such as guiding questions generally influence the way students interact…” (Damşa et al., 2015 , p. 56).

Review of the literature: technology in and of learning in a digital age

While there are various definitions of educational technology, a narrow definition refers to educational technology as “the effective use of technological tools in teaching and learning” by bringing in students’ experiences (Govender’ and Khoza, 2017 , p. 67). These studies (Amory, 2010 ; Khoza, 2019b ) are pessimistic in tone, further pioneering the most narrow and concise definition of educational technology, that it is there because of technology in education (software and hardware resources in learning) and technology of education (pedagogical resources in learning). Thus, according to the context of this study, educational technology is all physical resources and online resources used in learning, and ideological resources behind the use of both physical resources and online resources.

Nocar et al. ( 2016 ) conducted a qualitative case study in China and the Czech Republic to outline the importance of physical resources. Findings outlined that the use of both traditional physical resources and modern physical resources for teaching display a fruitful result for students’ knowledge acquisition. Moreover, some scholars believe that traditional physical resources (traditional education), like stationary desks, books, chalkboard and others, enhance students’ task to memorise and recite content during learning, and its use still symbolises the principle of slavery (Cuban, 1986 ; Freire, 1972 ). However, the use of traditional physical resources promotes a teacher-centred method, which is the most direct and effective way for teaching students because it provides face-to-face interaction (Hoadley and Jansen, 2014 ). As such, Liu and Long ( 2014 ) further argue that traditional physical resources, sometimes referred to as ‘old technology’ (television, chats, radio, posters and others) is irreplaceable and the cornerstone of every learning institution, even if the current discourse demands the use of modern physical resources.

Furthermore, the importance and usage of modern physical resources (technological tools) is witnessed in every corner of each university. A study conducted by Keengwe, Onchwari, and Wachira ( 2008 ), to provide a literature review on the use of modern physical resources (computers, mobile phones and others) for teaching and learning university courses, affirmed this. The study outlined that modern physical resources provide opportunities to support students’ learning and need good and strategic planning for maximum integration into the curriculum. Consequently, in the past two decades universities have begun to integrate modern physical resources into the curriculum for effective learning (Khoza, 2019a ; Mpungose’, 2019a ). This suggests that students should be provided with relevant technological devices, which may include but are not limited to netbooks, iPads, webcams, laptops and desktop computers, mobile phones and others. These kinds of new technology have made life easier for students, because they would find notes and all course information stored electronically and easily accessible (Amory, 2010 ; Waghid, 2018 ). In other words, that the accessibility of modern physical resources give students options to use any available resources in order to access online resources.

van de Heyde and Siebrits ( 2019 ) revealed that online resources are software resources in education that help physical resources to communicate learning. This includes but is not limited to application software packages (Microsoft Office 365), Internet browsers (Firefox, Chrome), social media sites (Twitter, Facebook), and learning management systems (Moodle, Canvas) (Anderson, 2016 ; Bates, 2018 ). In the context of this study, the focus is more on learning management systems and social media sites to enhance e-learning. As such, the importance of e-learning is witnessed in study conducted Swinnerton et al. ( 2018 ) in unbundled University project exploring digitalisation and marketisation of higher education in both United Kingdom and South Africa. The study revealed that irrespective of existing inequalities, but the use of e-learning for teaching and learning university courses is significantly the effective way to ensure relationships between universities and private sector. In other words, if students does not have access to technological resources for e-learning they are less likely to be unemployed after receiving their qualification because of the lack of technological skills applicable in the workspace.

Cavus and Zabadi ( 2014 ) argue that in trying to move away from the traditional paper and pen environment (face-to-face), learning management systems (web-based learning environment to disseminate content) is one of the most highly adopted and used online environments in higher education institutions for e-learning. This includes open-source software learning management systems (free of charge, where the source code can be changed) such as Moodle, Open edX and Chamilo, and cloud-based learning management systems (with a start-up cost and source code that cannot be changed) such as Canvas, Sakai, dot Learn and others. Ajlan and Pontes ( 2012 ) outline that almost all learning management systems have common features, which include pedagogy, learner environment, instructor tools, course and curriculum design, administrator tools, and technical specifications. However, their efficiency can be different because of various factors such as being unclear to users, bandwidth requirements, take-up and maintenance cost, manuals, customisation and adaptation to the local environment (Anderson, 2016 ). However, this needs effective e-learning policies in place in order to address the needs of students and lecturers as according to the recent study conducted by Swartz et al. ( 2019 ) to explore the core business in contemporary South African universities.

In exploring first-year students’ use of social media sites at one South African university of technology, Basitere and Mapatagane ( 2018 ) confirmed that students become more interactive when they use platforms that they are familiar with, such as social media sites, compared to learning management systems imposed by the university. Social media sites are referred to as Internet-influenced Web 2.0 technologies that allow users to create social networks to share content based on personal experiences, education and society. Hence, social media sites users can be referred to as ‘prosumers’ because they produce (create) and consume (share) information (Clement, 2020 ; Ritzer and Jurgenson, 2010 ). Moreover, a recent review conducted by Manca ( 2020 ) on the integration of social media sites into learning, revealed that both Twitter and Facebook are the most used social media sites in higher education, compared to Instagram, WhatsApp, Pinterest, Snapchat and others. In addition, social media sites content is easily accessible because it is compatible with both computers and mobile devices, and this makes life easier for students (Clement, 2020 ; Dlamini and Nkambule, 2019 ; Manca, 2020 ).

With all of the above being said about the use both physical resources (traditional and modern) and online resources (learning management system and social media sites) for learning, but digital divide remains the major issue. As such,Van Deursen and van Dijk ( 2019 ) assert that the digital divide is one of the big limitations on the use of educational technology globally. These authors’ study further argues that the digital divide is a real phenomenon that is here to stay in developed countries, but is worse in developing ones—not only in terms of the first digital divide (access to Internet), but also in terms of the second digital divide (attitude, skills, type of use) and third digital divide (Internet outcomes/benefits). This suggests that even though universities can provide free access to Wi-Fi within their perimeters and students’ residences, including free laptops, there will be some students (residing in rental rooms or at home) who might not have access to the Internet. Similarly, some students would prefer to use other resources, based on their strengths or limitations. Hence, this paper argues for alternatives to be made available by lecturers or university management, so that all students can have the same access to e-learning irrespective of their geographical area, culture, race, socio-economic factors and others.

Selwyn ( 2004 ) further argue that the dichotomous aspect of digital divide clearly reveals the ones that either have access or do not have access to technological resources, and this influence the status of connectedness (either connected or not connected). The latter author assert that this situation is termed as ‘haves’ and ‘have-nots’. Consequently, the latter author concludes that the digital divide is a critical issue in higher education landscape that is not just technological but it is also social, economic, cultural and political. This suggests that in mitigating digital divide, universities, communities, churches, political figures, businessman and others seek to collaborate and come up with both practical and theoretical solution in order to enhance effective e-learning in pre, during and post pandemic outbreak.

Research context and method

Study context.

LMS have been adopted by most South African universities to cope with the demands for accessible and more flexible online content dissemination (Amory, 2010 ; Mpungose, 2019b ). In transitioning from the paper (face-to-face) to the paperless (online) environment, the University of KwaZulu-Natal in South Africa adopted the Moodle LMS in 2010; it was made compulsory in 2016 for first-year students and fully implemented at the fourth-year level in 2019 (University Moodle Training Guide, 2017 ). Unavailability of a guiding online learning policy and lack of training for lecturers ignited challenges, which were evident in the use of learning management systems by students (Mpungose, 2019b ).

To this end, from 2019 to 2020 I conducted a postdoctoral research project on students’ experiences with the use of a learning management system in a School of Education. From the project, I extracted a case of 26 students’ experiences of the use of the LMS. A South African University at School of Education offers a broad range of degree programme courses across various fields of study. It prepares mostly disadvantaged black students, followed by other minorities (Indian, coloured (mixed race) and white students) for professional teaching careers in Education Studies and other disciplines. The School of Education mainly offers all lectures in face-to-face form, while the learning management system is used as an online resources depository (holding lecturers’ notes) for student access. The eruption of the COVID-19 pandemic forced the School of Education to move all lectures totally online. However, the majority of registered students in School of Education at South African universities are victims of the digital divide, and this hinders their access to e-learning (Bunting, 2006 ; Dlamini and Nkambule, 2019 ). Therefore, this study’s main objective is to propose alternative pathways to overcome hindrances to students’ access to effective e-learning.

Research methods and data collection

This is a qualitative interpretive case study of 26 students who were purposively and conveniently selected because they were accessible; they were attending face-to-face lectures and then transitioned to e-learning due to the COVID-19 pandemic. After recruiting students through an electronic flyer, they signed consent forms with details of ethical issues (confidentiality, anonymity, and beneficence). I used interpretivism not to predict what students experience, but to understand and describe how they make meaning of their actions during the transition period in their own context of the School of Education shutdown (Creswell, 2014 ). Through the use of a more explorative case study design, I generated a rich and deep description of students’ experiences, which resulted in pioneering alternatives to overcome hindrances in realising e-learning (Yin, 2013 ).

Students were given an e-reflective activity to be completed in two weeks’ time, two sessions of Zoom group meetings for a period of 40 min each, and a WhatsApp one-on-one semi-structured interview for 35 min (Creswell, 2014 ; Yin, 2013 ). iCloud was used to record meetings and interviews for direct transcription to ensure trustworthiness (transferability, dependability, confirmability and credibility).

Data were thematically analysed using inductive and deductive reasoning (Creswell and Poth, 2017 ). The data generated by the three instruments were recorded and not transcribed, but directly and openly coded from the recorded source in order to avoid loss of meaning during transcription. Open coding was used to connect codes to categories. I deductively mapped the codes onto the set categories (from the theoretical framework and the literature) to form themes. However, I sought to use an inductive process to recapture the remaining codes, which were not deductively analysed during the prior analysis, to form categories. After using these processes as a guide, categories were focused and sharpened to form three themes, as indicated in the findings section

Consequently, two research questions were unpacked, namely: what are students’ experiences of the transition from face-to-face to e-learning and why their experiences are in particular ways when learning online. The first question gave answers to the first objective of the study, which is to understand students’ experiences of the transition from face-to-face to e-learning, and the second question addresses the second study’s objective, which is to find reasons that informs students’ experiences. This is elaborated in findings and discussion section in order to propose alternatives that can assist or allow students, particularly disadvantaged students, to realise or enjoy benefits of e-learning.

Presentation of findings

In this section, I present the key findings on students’ experiences of the transition from face-to-face to e-learning. I articulate the use of online resources and physical resources before crafting the alternative pathways through themes and its respective categories

Theme 1: Experiences of the use of online resources

Mpungose ( 2019b ) Agrees with Selwyn and Stirling ( 2016 ) that accessibility to online resources enhances effective e-learning. This suggests that e-learning is only possible provided students have access to online resources ranging from emails, software applications, learning management systems, social media sites and others. As such, Student 1 articulated, “ I keep on receiving emails saying the assignment that is due needs to be submitted on Moodle … I was informed that lectures will be recorded and posted on Moodle [learning management system]”. However, digital divides limits most students for effective e-learning particularly those staying in remote areas. Moreover, Student 4 confirmed this “… I only check my emails from the community library with internet access because I have no internet access and network service at home, but I can sometimes only receive voice calls and text messages from my phone… ”.

Internet access seem to play a major role in order to observe effective e-learning, but this can never be achieved if students have limited or no access. For instance, Student 7 asserted, “ I do not have data bandwidth [Internet access] at home …submitting assignment is impossible …”. This assertion shows that online assessment is impossible if the students have no access to the internet. Student get frustrated if lecturers keeps on demanding students to meet due dates while students have no internet access. As shown by Student 24 who articulated, “… having limited internet access but I am expected to submit an assignment next Friday, in a week’s time …a lecturer is briefing us to download resources from Moodle ”.

Furthermore, Selwyn ( 2016 ), as well as Khoza and Biyela ( 2019 ) share the same sentiment that social media sites plays a huge role in mitigating digital divide in order to realise e-learning in this digital age. As such, Student 5 indicated, “ since there is no Internet café by home, I use free Facebook or WhatsApp data bundles to communicate with other students …” This suggests that most students have access to social media sites because of free data bundle access provided by network service providers (Vodacom, Telkom, Cell C and others in a South African context), and this helps student to communicate learning. Consequently, Khoza ( 2019b ) further argue that having access to online resources without pedagogy behind the use can limit effective e-learning. This is witnessed by Student 12 who opined, “ I am so disappointed of this sudden shutdown without having proper ways or training in place to access lectures online … ” Similarly, Student 15 said, “W e are still not told which online platform will be used for online lectures … ” In other words, students seek adequate training on the use of online resources so that they can be well informed to avoid confusion. Evidently, Student 9 showed confusion by outlining that “… university informed us that lectures will be online, but they did not tell us the online platform is going to be used ”.

Theme 2: Experiences on the use of physical resources

Makumane and Khoza ( 2020 ) argue that traditional physical resources is influenced by professional reasoning in order to attain specific discipline goals during curriculum implementation. This suggests that traditional physical resources are fundamentals in addressing the module needs in e-learning. For instance, most of the students agreed with Student 23 who posited, “ I am currently depending on the hard copy of module outline and recommended books for studying because even libraries with Internet at home are also closed” . In other words, traditional physical resources like textbooks, module/course packs, and other hardcopies can act as an alternative pathway in case students have no internet access. While it is valuable for students to have access to modern physical resources like laptops, smartphones, Wi-Fi routers and others in order to enhance e-learning, but affordability to possess such resources remains a question because of social divide (poor socio-economic background). Thus, this remains the burden of the university to provide modern physical resources to students for successful e-learning. As such, student 14 asserted, “ …We were promised to get laptops when the academic calendar commences but still there are no laptop, and I end up using my smart phones for correspondence ”.

Similarly, Student 17 said, “ This shutdown will affect me because I am staying in remote areas away from campus and do not have funds to access Wi-Fi hotspot spaces like community libraries … and there are no funds provided for to support us… ” While the shutdown demands all lectures to be online and universities are also demanded to put measures in place for effective e-learning, but failure to provide all necessary resources to students can bring more frustration in the process. Evidently, Student 11 shared the same sentiment with other international students “ I will be suffering to find the transport to go and come back from home … Shutting down face-to-face lectures causes chaos since I do not have necessary equipment for learning”.

Discussion of findings

The adoption and use of online resources in a South African university shows the critical need to serve students for e-learning (van de Heyde and Siebrits, 2019 ). Van de Heyde and Siebrits ( 2019 ) further argue that online resources like learning management systems are highly used by universities for online lectures, but the form of customisation to adapt them to a local context may hinder learning. This is evident from students’ accounts on the use of Moodle for e-learning, where they stated that only a few students had access to the Moodle learning management system to download readings, slides and others during the transition from face-to-face to e-learning (at home). This suggests that Moodle was customised as a depository, and not to provide asynchronous online lectures. In other words, there was poor customisation of the Moodle learning management system to link with other online resources for chatting (Pear Deck), video conferencing (Zoom), and recording (CamStudio) and others (Anderson, 2016 ). Consequently, the findings indicate the general consensus that the Moodle learning management system alone is not capable of offering online lectures, but needs to be supplemented by other online software and social media sites. This suggests that, universities should start to think out of the box to consider social media site as an official platform to supplement learning management system to offer lecturers online.

Consequently, students therefore preferred social media sites (Facebook and WhatsApp) for communication, which were not officially adopted by universities for e-learning. In support of this, ‘prosumers’(students) as digital natives who are techno-savvy enjoy the use of Web 2.0 applications with good user-friendliness and swift communication (Clement, 2020 ; Ritzer and Jurgenson, 2010 ). Findings showed that even if students have limited access to internet but free data bundles form their social media sites account, they could access each other for content discussion and communication. As a result, Hamidi and Chavoshi ( 2018 ) further argue that if students can use social media sites successfully, universities should consider bringing social media sites (Snapchat, WhatsApp, Facebook, Instagram, twitter and others for e-learning.

Moreover, the findings show that the university did not have any policy in place guiding the use of e-learning and nor was training provided. This situation as according to Yu ( 2016 ) is termed to be influence that leads to students’ technostress caused by the misfit between environmental demands (e-learning) and students abilities (access to online resources). In other words, the shutdown that occurred because of pandemic outbreak (COVID-19) demanded student to have access to online resources in order to take their lectures online while most of them are from remote areas having no internet access, and are still battling to use the newly introduced software for e-learning (video conference software like Zoom). As such, students were confused as to what resources were available for e-learning and how they will transition from face-to-face to e-learning. This was worsen by the unavailability of the guiding e-learning policy in place and no instructional designers employed by the university to provide relevant capacity building for students. As such, Mpungose ( 2019b ) assert that the power lies with the university management to use e-learning policy that can address issues on content dissemination, execution of assessment, and online resources in order to equip students with necessary skills for effective e-learning. This suggesst that policy viability on the use of online resources also give direction to both students and lecturers so that they can know their roles.

Several students agreed that traditional physical resources is the core of learning at the university, even if there are challenges hindering e-learning, because they relied on recommended books, module outlines, written notes and others. This proves that the old technology is irreplaceable, and that it acts as a back-up to e-learning. Thus textbooks, posters, charts and others must be made available to support students’ learning (Cuban, 1986 ; Freire, 1972 ). This suggests that traditional physical resources may be most useful to those students who have no or limited access to internet. As such, each module/course seek the need to have these resources in place even if the module/course is offered online. The use of traditional physical resources for learning displays a fruitful result for students’ knowledge acquisition (Simmonds and Le Grange, 2019 ). Moreover, traditional learning is vertical (formal) and driven by student knowledge for learning in a demarcated environment (Khoza and Biyela, 2019 ). This allows students have control over “selection of the content (selection), when and how they learn (pedagogy and sequence), as well as how quickly they learn (pace)” (Hoadley and Jansen, 2014 , p. 102). As result, students preferred and opted to use the nearest local community libraries with access to Wi-Fi rather than staying at home (often with no Internet) in order to access online resources irrespective of difficulties faced at home.

Most students did not have laptops, even though these were provided free of charge by the university (many had been sold for personal benefit). They preferred to use mobile phones with free network data bandwidth for communicating amongst themselves. In other words, the use of modern physical resources provides an easy way to ensure e-learning, because it provides access to recorded lectures and electronic resources like videos, but it needs good planning (Keengwe et al., 2008 ; Mpungose’, 2019a ). The main concern that hindered students from realising the full potential of e-learning was the expensive cost of Internet infrastructure such as Wi-Fi routers, laptops, mobile phones and access to data bandwidth. Consequently, Van Deursen and van Dijk ( 2019 ) argue that Internet access and technological resources (the first digital divide) is the main limiting factor in universities from developing countries like South Africa, even though students do have skills (the second first digital divide) to benefit from e-learning (the third first digital divide). In other words, the use (ideological resources) of any available physical resources is not a problem to students (digital natives) in a digital age—the problem is the affordability and availability of those physical resources for e-learning.

Towards alternative pathways for e-learning

This study explored students’ experiences during the transition from face-to-face to e-learning in a School of Education at a South African university. Based on the case study and the literature, including the guiding theoretical framework, the study identified benefits, challenges, and other related issues on the use of physical resources and online resources to realise e-learning. Most importantly, the interpretation of empirical data generated provides a summary of proposed alternative pathways and implications related to the use of physical resources and online resources to enhance effective e-learning. On the first hand, findings suggest that students are influenced by formal experiences (hardware), which seek students to use traditional physical resources to enhance e-learning. On the other hand, students are also influenced by informal experiences (software), seeking them to use online resources for effective e-learning. In complication this findings, students seem to miss non-formal experience (pedagogy), which seek them to use their own identities (love, passion, values, self-direction and others) to find thousand ways or theories to enhance a successful e-learning. Moreover, it is proven that e-learning resides in human and non-human appliances (Siemens and Downes ( 2009 ); thus students should be provided with relevant traditional resources (books, manuals, chats, posts and others) and modern resources (laptops, mobile phones/tablets, mobile Wi-Fi routers and others). In addition, free monthly Wi-Fi data bandwidth should be provided to students so that they may access e-learning, since this seems to be the main challenge to achieving e-learning in the South African context.

Downes ( 2010 ) argues that e-learning needs connectedness of specialised nodes or information sources, so that students can learn anyhow, anywhere and independently, at their own pace. To achieve this, this study therefore holds that the Moodle learning management system should not be used as a depository, but should be customised to be linked to social media sites (WhatsApp/Facebook), lecture-recording software (CamStudio), video and audio conferencing (Zoom, YouTube live, Skype, Microsoft Teams) and other learning resources in order to provide interactive lectures (both synchronous and asynchronous). This will serve to eliminate the dichotomy between face-to-face and e-learning, because the learning taking place when at the university should be the same as that which is available when students are at home.

The findings indicate that fully equipped university information centres should be identified and used to provide blended lectures, through the special arrangement of community libraries (since even these are not accessible now owing to COVID-19), in order to meet the needs of students coming from remote areas halfway. The findings also show that without proper planning, e-learning will never be achieved at a university. Hence, a university should have an e-learning policy, intense scheduled online learning capacity building, and allocated instructional designers (not technicians) to capacitate both lecturers and students.

All learning management system share the same features: pedagogy, learner environment, instructor tools, course and curriculum design, administrator tools, and technical specification features (Cavus and Zabadi ( 2014 ). However, the findings showed that the learning management system is missing the personal feature for students that will motivate them to love and have a passion for using online resources. This study posits that in order to leverage the potential of the Moodle learning management system, it should be linked with software that provides educational videos (NBC Learn), games for student-centred activities (game-based learning software), Edublogs (assessment for learning) and others. In other words, choosing what resources to use and learning to offer depends on rationale, time management and goals to be achieved during e-learning. This will assist students to incorporate both physical and online resources to achieve effective e-learning for these digital natives (Mpungose’, 2019a ; Prensky, 2001 ).

Despite challenges experienced by students in transitioning from face-to-face to e-learning—in particular, the prominence of the digital divide as the main hindrance to students realising effective e-learning—overall the customisation of the Moodle LMS to meet the local needs of disadvantaged students is beneficial to realise e-learning. Moreover, the findings indicate that while there may be many challenges that can hinder students from realising the full potential of e-learning, alternative pathways like the provision of free data bandwidth, free physical resources and online resources, and the use of an information centre for blended learning and others, seem to be the solution in the context of COVID-19.

However, it must be taken into consideration that while this can be the solution, students are unevenly challenged, and therefore still need capacity building on the use of learning management systems and other newly adopted online learning software. It is also imperative that university-wide teaching and learning pedagogy, instructional designers and e-learning policy consider the potential benefits and challenges when encouraging the use of e-learning.

Within the South African context, there is a critical need for increased investment in upgrading resources, both in universities and at community level, because of the digital divide. While there is still a need for further research, this article emphasises the both practical and theoretical alternative pathways that can be used to enable university students to realise the full potential of e-learning. Universities need to plan ahead of hindrances to learning such as a pandemic outbreak, student protests and others, and be abreast of the current literature on the rapidly evolving discipline of ET.

Data availability

The datasets used and/or analysed during this study are available from the authors on reasonable request.

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Acknowledgements

I want to thank Prof. Simon Bheki Khoza for his supervision in to construct this article from a PhD research and Post-doctoral project, as well as Leverne Gething language for editing. Furthermore, I want to acknowledge support and advancement from the National Research Foundation (NRF) and the Fulbright scholarship within the framework of the Research and innovation.

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Mpungose, C.B. Emergent transition from face-to-face to online learning in a South African University in the context of the Coronavirus pandemic. Humanit Soc Sci Commun 7 , 113 (2020). https://doi.org/10.1057/s41599-020-00603-x

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The adoption of an e-learning system using information systems success model: a case study of Jazan University

Bassam al-shargabi.

1 Faculty of IT, Middle East University, Amman, Jordan

2 Management Information System, Jazan University, Jazan, Saudi Arabia

Shadi Aljawarneh

3 Faculty of IT, Jordan University of Science and Technology, Irbid, Jordan

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Raw data is available as a Supplemental File .

The e-learning system has gained a phenomenal significance than ever before in the present COVID-19 crisis. The E-learning delivery mechanisms have evolved to enhanced levels facilitating the education delivery with greater penetration and access to mass student population worldwide. Nevertheless, there is still scope to conduct further research in order to innovate and improve higher quality delivery mechanism using the state-of-the-art information and communication technologies (ICT) available today. In the present pandemic crisis all the stakeholders in the higher education system, i.e ., the governments, institutions, and the students expect seamless and efficient content delivery via e-learning platforms. This study proposes the adoption of the e-learning system by the integration of the model proposed by Delon and Mcclean “Information System Success Model” in Jazan University, Kingdom of Saudi Arabia (KSA) and further attempts to identify the factors affecting E-learning applications' success among the students.

The data were gathered from 568 respondents. The Statistical Package for the Social Sciences version 26 (SPSS v.26.0) was used for the data analysis and one-way ANOVA is applied to test the hypothesis.

The overall results of this study allude to the fact that there is a significant relationship between Information system Success Model factors and the adoption of e-learning systems. The research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities.

Introduction

With the rapid growth of the Internet, prodigious information and communication technology (ICT) improvements have influenced practically all aspects of modern life. The information system (IS) has deeply penetrated and integrated into almost all the sectors ranging from organizations, industries, the education sector, and business activities in order to accomplish the desired objectives and to gain the associated benefits thereof ( Ibrahim, 2018 ). The education sector is among such promising and lucrative sectors which are most impacted by technology adoption due to its enhanced capability of offering high-quality teaching. However, the e-learning environment is influenced by the degree of e-learning adoption ( Lee & Lee, 2008 ; Al-Asmari & Rabb Khan, 2014 ). Presently, most universities and their managements worldwide have trusted in the IS and the Internet in their educational functions since the Internet has facilitated unbounded academic operations irrespective of geographical separations ( Martins et al., 2019 ). Given that e-learning system is a blended combination of both students and instructors, the Internet usage by both has demonstrated that it can alter the traditional learning methods used with an interactive online system, yet most researches focus more on student perspectives ( Wang, Solan & Ghods, 2010 ; Hassanzadeh, Kanaani & Elahi, 2012 ; Salam & Farooq, 2020 ; Martin, Modi & Feldman, 2021 ). The e-learning system (ELS) has now begun to play its role beyond teaching since it enables access to learning resources without any time or location limitations ( Al-Fraihat et al., 2020 ). Teaching and learning systems have undergone phenomenal transformations over the past decade which is also established in the literature ( Al-Asmari & Rabb Khan, 2014 ; Al-Harrasi, Al-Khanjari & Sarrab, 2015 ; Alhabeeb & Rowley, 2017 ; Martins et al., 2019 ). ELS in the education sector is being used globally for many years now. During the last two decades, e-learning is being used in communication course formats, audio-video tapes, videoconferencing, and TV broadcasts. Presently, the Internet is the optimal medium for e-learning; Internet-based distance education is considered to be the greatest common e-learning technological implementation of ICT ( Martins et al., 2019 ). Also, the rapid growth in technology has gained traction due to the exponential growth usage of smart devices such as smartphones and hi-tech laptops at significant scales. Recent technologies and applications in smart devices have become the key elements of e-learning, communication, resource sharing, and management for both students and instructors. ELS such as Blackboard have completely redefined and transformed the traditional classes into web presentations. It provides direct links to conduct live classes, sessions, exams, upload and download files, discussions, and also enable students for asking questions and providing feedback ( Liu, Huang & Lin, 2012 ; Alhomod & Shafi, 2013 ; Almaiah, Jalil & Man, 2016 ; Alksasbeh et al., 2019 ).

The Kingdom of Saudi Arabia (KSA) is one of the leading countries in the education sector that has implemented e-learning in light of the (COVID-19) pandemic. It has shifted the whole education processes over to e-learning systems. The Saudi government was being proactive in supporting and recommending the adoption of e-learning for both students and instructors through the workshops and training. Consequently, Jazan University was very proactive in implementing the government’s recommendations for all program courses delivery via the ELS. It has integrated e-learning into its educational processes for the majority of its courses. Intensive workshops and training sessions were conducted from the beginning for students and staff about the functioning of ELS (Blackboard). The results proved to have achieved the desired successful expectation which allowed the creation of dedicated departments and deanships to support and strengthen e-learning based educational systems ( Martins et al., 2019 ; Salam & Farooq, 2020 ; Alhabeeb & Rowley, 2017 ; Duygu, Alkiş & Ozkan-Yildirim, 2018 ). Many Saudi universities are now integrating their learning processes and several applications systems such as management learning systems (MLS) and blackboard ( Adeyinka & Mutula, 2010 ; Eom et al., 2012 ; Lin, 2013 ). Therefore, information and communication technology (ICT) has improved the collaboration between students and instructors, mutual interactions, management communications, and also educational performance as a whole. Additionally, many researchers have confirmed that ELS is a highly beneficial medium for distance education. ELS could be defined as “the combined use of modern information and communications technology (ICT) and computers to deliver instruction, information, and learning content” ( Donovan et al., 2018 ). Alternatively, ELS is defined as a form of Internet technology based information system (IS) that delivers an unbounded, independent, and flexible training and learning opportunity to the learner ( Duygu, Alkiş & Ozkan-Yildirim, 2018 ; Eom & Ashill, 2018 ). This technology-based system has rather simplified the learning processes ( Al-Shargabi & Sabri, 2016 ).

The elements such as benchmarks, learning environment, learning outcomes, cost-benefit analysis, and IS models form the key constituents of an ELS. It is a combined scholarly suggestion for a necessity of a general model assessment and evaluation of e-learning programs eloquence ( Liu, Huang & Lin, 2012 ; Alhomod & Shafi, 2013 ; Al-Harrasi, Al-Khanjari & Sarrab, 2015 ; Al-Shargabi & Sabri, 2016 ; Alhabeeb & Rowley, 2017 ; Yakubu & Dasuki, 2018 ; Alksasbeh et al., 2019 ). The scholars have proposed and evaluated models based on information system theory adapted from the information system success model (ISSM) and technology acceptance model (TAM), respectively ( Liu, Huang & Lin, 2012 ; Alhomod & Shafi, 2013 ; Al-Harrasi, Al-Khanjari & Sarrab, 2015 ; Al-Shargabi & Sabri, 2016 ; Alhabeeb & Rowley, 2017 ; Yakubu & Dasuki, 2018 ; Alksasbeh et al., 2019 ). The outcome of their studies strongly urged for a necessity to implement an open-systems perspective based on general systems theory which operates on largely accepted concepts and principles with an arranged and interactive knowledge transfer ( Wang, Solan & Ghods, 2010 ; Hassanzadeh, Kanaani & Elahi, 2012 ; Sabri, 2016 ; Opoku, Pobee & Owusu Okyireh, 2020 ; Martin, Modi & Feldman, 2021 ; Rouibah, Lowry & Almutairi, 2015 ; Marjanovic, Delić & Lalic, 2016 ).

The updated ISSM proposed by DeLone & McLean (1992) describes information systems success considering the factors of information quality, system, and services. These factors influenced the model usage and user satisfaction which eventually led to net benefits at reduced costs. The model has been used in the IS literature but sparingly in the ELS context. Hence, numerous studies endeavored to fill that gap by using IS variables of success model in the e-learning system context. This study is considered to be as one of the few studies that investigates university students’ e-learning adoption in Saudi Arabia’ by the implementation of ISSM ( Al-Kofahi, Hassan & Mohamad, 2020 ; Ahmed & Seliaman, 2017 ). Therefore, it contributes to bridging the e-learning gap in the ELS studies which is already established in Saudi Arabia ( Ahmed & Seliaman, 2017 ; Lin, 2013 ). As argued by DeLone & McLean (1992) , the applications of IS are successful if businesses receive the net information systems benefits based on the usage of such systems and how satisfied the users are while using them. Therefore, this study is based on this model proposed by DeLone and McLean, to determine the success factors responsible for the ELS (Blackboard) acceptance at Jazan University where currently most of the universities in Saudi Arabic are shifting for online learning even after the disappearance of the COVID-19 pandemic. On the premise of the existing literature, background information, and the observed gap in the study, the research question for this study is: “What are the factors affecting the e-learning system to get the net cost-effective benefits at Jazan University?” Henceforth, the study was undertaken in order to corroborate the research question.

The rest of this paper is organized as follows: “Background” introduces the background of ISSM and ELS along with the most recent related work. “Materials and Methods” describes the data gathering, analysis, and hypothesis testing of the proposed model. The result and discussion of the proposed model for adoption of the E-learning system by the integration of the model proposed by Delon and McLean can be found in “Results and Discussion”. The conclusion was drawn and insight into future work are presented in the “Conclusion”.

A few studies with a limited scope have focused on the integration between the adoption of e-learning and the information system models, which touched upon the technical and management facets ( Rouibah, Lowry & Almutairi, 2015 ; Marjanovic, Delić & Lalic, 2016 ; Martin, Modi & Feldman, 2021 ; Al-Kofahi, Hassan & Mohamad, 2020 ). This study attempts to provide a theoretical model from the information system and education fields. The theory used in this study includes the ISSM, which is associated with educational viewpoints and frequently deliberated in technology adoption studies. Before the introduction of this theory, a brief review of the current use of ELS and ISSM is provided as a background for the study.

E-learning system (ELS)

Presently, the e-learning system is supposedly the most prevalent Internet based learning setting which aids in efficient time usage and boundary less learning ( Sabri, 2016 ; Martin, Modi & Feldman, 2021 ). Nevertheless, these systems are successful subject the user acceptance and satisfaction. ELS users can access the system via Internet portals to reap the benefits of the information, lessons learned, knowledge, and skills. E-learners can access the courses either directly (live) or by the uploaded and posted content on the portal (accessed offline at a later time). These are later assessed by a variety of different methods on the knowledge obtained. This establishes the fact that ELS proves to be the best effective learning milieu. During the learning process, different system users have the flexibility of direct or indirect interactions with their peers ( Alhomod & Shafi, 2013 ; Yakubu & Dasuki, 2018 ; Sabri, 2016 ; Opoku, Pobee & Owusu Okyireh, 2020 ; DeLone & McLean, 1992 ).

In the learning context, students and universities combined have several added advantages with the ELS service offerings. These advantages summarized as the following:

• Faster web access to information
• Enhanced upload and download contents
• Content standardization and accountability
• Availability
• Interactivity
• Higher user satisfaction
• Improved opportunities for career growth and flexible learning for students
• Increased innovation
• Superior operational efficiency and
• Cost savings

However, the advantages of ELS are dependent on users’ satisfaction, incessant system use, and intent to use. Therefore, it is highly imperative to comprehend users’ adoption to use ELS with reference to the predictive factors of behavioral intention theories ( Selim, 2007 ; Sabri, 2016 ). These advantages aid universities to become more efficiently optimize their academic business procedures and operating costs. In order to determine the impacting factors towards developing the framework for this study, the analysis is undertaken for the previous works accomplished on models, case studies, and focus groups to investigate ELS miscellany ( Al-Asmari & Rabb Khan, 2014 ; Alhabeeb & Rowley, 2017 ; Alhomod & Shafi, 2013 ; Alksasbeh et al., 2019 ; Yakubu & Dasuki, 2018 ). Owing to the ELS benefits and service offerings to universities and students, many universities have either increased their spending or allocated additional funds in order to upgrade the learning process systems. However, it is still vague to determine whether those factors had any influence on the success of ELS.

Information system success model (ISSM)

In 1992, DeLone & McLean (1992) had proposed an (ISSM) for measuring IS success in organizations to acquire the net benefit. They suggested that IS success is a multifaceted and symbiotic paradigm. Therefore, it is indispensable to study the interrelationships among those dimensions and control them. Subsequently, numerous scholars suggested some reforms to this model ( Al-Shargabi & Sabri, 2016 ; Sabri, 2016 ; Altameem, 2013 ; Al-Shargabi & Sabri, 2015 ). Consequently, in 2003, DeLone & McLean (2016) incorporated some of the changes that scholars suggested and accordingly restructured their old model with the updated (ISSM) as illustrated in Fig. 1 . They decided to augment the dimension of service quality, user satisfaction, intent to use, and net benefit thereof. The new model cited that service, system, information quality, system use, and user satisfaction are the critical success factors that lead to net benefits of using IS. The researchers argued if IS success evaluation is desired then, service, system, information, and quality is the impacting factor of its subsequent use. User satisfaction is the outcome of positive or negative benefits which will govern to promote the use of IS ( DeLone & McLean, 2016 ).

Furthermore, this model illustrated in Fig. 1 is not limited to SME proprietor contentment only, yet it could be implemented and extended to placate the other users in industries, enterprises, and organizations by toting up other dynamics like hardware, software, network, security, policy, privacy, upper administration support, and structural traditional dynamics that influence IS evaluation ( Kademeteme & Twinomurinzi, 2019 ; Adeyinka & Mutula, 2010 ; Li et al., 2012 ; Lin & Chen, 2012 ).

The updated ISSM and its success dimensions are listed as below:

• Information Quality: The level at which specific information obtained from the IS come to an agreement with the users’ anticipations and desires in terms of timeliness, accurateness, safety, significance, intuitiveness, and information consistency.
• System Quality: The level at which the IS system executes and functions aligned with the accord, convenience, simplicity of use, browsing, and learning, response time, and effectual consumption.

Service quality: The level at which the ICT center provides the system support which lays emphasis on the learning process and service accurateness and sustainability. While IS runs for steadiness, dependability, swift retrieval, speediness, and fortification of the communal networking system.

• System use: The function quality acuity for communal networking, proficient penetrating, information interchange, identity management, background cognizance, network cognizance, and contact management.
• User satisfaction: The degree of gratification provided to students regarding usefulness and efficacy by a comprehensive social networking quality system.
• Intention to use: The user’s learning alacrity to use and continue to use e-learning applications.
• Net benefits: It is the critical measure of triumph since it amplifies the negative and positive steadiness triumph of the system upon the users.

The IS recognizes the ISSM to be one of the most prominent models around. Further, its expediency has been quoted, verified, and established in several varied sectors including the academic institutions ( Wang, Solan & Ghods, 2010 ; Martin, Modi & Feldman, 2021 ; Donovan et al., 2018 ; Al-Shargabi & Sabri, 2015 ; Hamidi & Jahanshaheefard, 2019 ). Notably, this is the most popular model ever established that has been in use on a frequent basis for testing the gratification levels of users, owners, and customers. Technology and human communication have a forecast divergence which is the subject of key disagreement between IS and universities ( Kock, 2015 ; Al-Shargabi & Sabri, 2016 ; Yakubu & Dasuki, 2018 ). The universities’ key objective in establishing didactic guidance to both technology and management is based on e-learning acceptance by students’ affirmative attitude and willingness. Consequently, this study aims to offer an ISSM based evaluation model. Figure 1 above, illustrates the proposed model in this study for e-learning success evaluation from the student’s viewpoint. It describes and evolves measures of information, service, and system excellence and correlating impacts. There is an emergent requisite for e-learning adoption assessment based on previous studies and discourse. The objective of this study is focused on investigating student gratification with ELS’ adoption at Jazan University. The proposed model (as illustrated in Fig. 1 ) is deemed to be the most apposite model taking the developments in ELS adoption, Internet usage, and latest applications into account.

This study has evolved six sets of ISSM based factors that contribute to e-learning satisfaction at Jazan University. Those factors ( Fig. 2 ) are information quality, system quality, service quality, student satisfaction, system use, and net benefit to using ELS. According to the literature ( Yakubu & Dasuki, 2018 ; Al Zoubib & Jali, 2014 ; Martins et al., 2019 ; Opoku, Pobee & Owusu Okyireh, 2020 ), it is believed that there is a scarcity in the literature case studies in Arab countries and further debate about the e-learning system with an Information system is a prerequisite. The implementation of the model should be bespoke according to the socio-economic, cultural, psychographic, geographic, and demographic conditions in the Arab countries in order to circumvent failures keeping in view the inferences of the literature ( Alhomod & Shafi, 2013 ; Altameem, 2013 ; Al-Kofahi, Hassan & Mohamad, 2020 ; AlMulhem, 2020 ).

The primary research objective is to propose a research model which applies Information system adoption combined with literacy philosophies for further usage comprehension. To accomplish this objective, an empirical study was conducted on students at Jazan University, Saudi Arabia.

A systematic literature review of some articles related to e-learning systems adoption and information system success model (ISSM) is summarized as a Theoretical foundation as shown in Table 1 below. By utilizing the Scopus database. This study is based on 40 articles and conference papers from the years 2013 to 2021.

Materials & methods

The objective of this research is to examine the aspects accountable for the use of an ELS (Blackboard) application at Jazan University. Blackboard is the ELS being used in Jazan University that enables students to attend their classes online at the beginning of the semester, download course materials, engage in group discussions, submit assignments, and attend online quizzes and tests, and communicate with their instructors. A survey was developed and conducted in the study to generate quantitative descriptions of the respondents and test the relationship among the constructs (factors) used. The study’s respondents were Jazan University students because Students have shown and indicated that they were technologically adept and willing to employ any technological tool that may help them learn more effectively. This approach also have exploited and proved to be suitable as in Opoku, Pobee & Owusu Okyireh (2020) . The data was gathered from 568 respondents and examined the hypotheses and the associated factors using SPSS (v26). One-way ANOVA is applied to test the hypothesis. A purposive sampling technique was used to select students who were using ELS. Data analyzed using the SPSS v 26 application. The study tested the hypothesis, checked for the reliability, and validity of the measurement model. A number of 27 items were designed, used, and analyzed for the study.

Research model

This study has adopted the updated DeLone and McLean IS success model to examine the e-learning system’s net benefit from the students’ perspective to improve the understanding of e-learning adoption in Jazan University. This is a very popular model and easy to understand the technology adoption as discussed in the literature review. Hence, the main objectives of the research model are:

• To identify the quality factors impacting e-learning system benefits in Jazan University.
• To develop an empirically proposed model and provide support for the university to improve ELS and student benefits.

For this reason, it is necessary to copiously comprehend the relations between information systems success factors in ELS as shown in Fig. 2 .

The proposed model illustrated in Fig. 2 examines the relationship between information quality, system quality, and service quality as antecedent variables that are expected to have a positive impact on user satisfaction and system use, which in turn affect the net benefit to use ELS by students who are using ELS. Therefore, the proposed model has six hypotheses to test:

H1a: Information quality will have a positive impact on system use.

H1b: Information quality will have a positive impact on student satisfaction

H2a: System quality will have a positive impact on student satisfaction.

H2b: System quality will have a positive impact on system use.

H3a: Service quality will have a positive influence on student satisfaction

H3b: Service quality will have a positive impact on system use

H4: Student satisfaction will have a positive impact on system use

H5: Student satisfaction will have a positive influence on the Net benefit of using ELS

H6: System use will have a positive impact on the Net benefit to use ELS

Data collection

This study was conducted using both primary and secondary data. Firstly, the primary data based on e-learning and DeLone and McLean Information system success Model were collected. Literature reviews of 43 articles were accessed from several leading databases available at the Saudi Digital Library (SDL) such as Cambridge University Press, Emerald, EBSCOHost, Springer, Wiley Online, and Proquest. The cited articles were published within the range of 1992–2020. The secondary data were used to identify the independent variables discussed to affect the net benefit to ELS. The three independent variables are information quality (IQ), system quality (SQ), and service quality (Ser.Q) are the factors impacting system use and student satisfaction (St.S), which in turn impact the net benefit to using ELS (NBELS). To examine this, an online survey (Google form) contains a detailed questionnaire was created and sent out to students of Jazan university to answer the questionnaire which was used in the collection of primary data. The questionnaire was created using questions with validity and reliability of which have been solidly evidenced in papers in high-impact journals ( Raman et al., 2018 ). Pre-testing of the questionnaire was conducted on a sample of 700 participants; a total number of 568 usable responses after excluding incomplete responses, resulting in a response rate of 81.147%. The survey questionnaire was divided into three-parts, the first part requested approval from the participants to confirm confidentiality, the second part focused on demographic information, and the third part contained statements concerning the conceptual framework of the study. A five-point Likert scale was used to measure the indicators (anchored at 1 = strongly disagree and 5 = strongly agree). Table 2 represented the demographic characteristic of the respondents where ( N = 568).

Profile of the Respondents’

The demographic profile of the responses, split by gender frequency is shown in the Table 2 above.

Data analysis

Before conducting the data analysis, data must be screened to ensure the usability, validity, and reliability of the data before proceeding to the statistical analysis of such data. The data were collected on MS-EXCEL, where information was collect by using a structured questionnaire developed using Google forms for conducting an online Survey. 568 completed forms were selected and completed by respondents. In this paper, for analyzing the collected data, the Statistical Package for the Social Sciences version 26 (SPSS v.26.0) is exploited. The data analysis methods that we used in this paper are a descriptive, reliable, exploratory factor, correlation, and regression. These techniques are used for testing the proposed model based on students’ perceptions ( Alksasbeh et al., 2019 ; Sabri, Hakim & Zaila, 2020 ).

Descriptive statistics of constructs

Descriptive Statistics is a prerequisite for a clear conceptualization of the constructs for the purpose of study. Table 3 below shows the descriptive statistics mean and standard deviation of each parameter within each construct (variables). All the questions which were measured on a five-point Likert Scale were transformed into Z-Scores with mean zero and standard deviation one. The results suggested that all the means were positive. The positive average Z-Score inferred that the respondent on the average (majority) have affirmatively responded.

Model reliability and discriminant validity evaluation

The reflective model was utilized to ensure the reliability and validity of the construct measures and to provide support for the suitability of their inclusion in the path model. Following the recommendation in ( Kock, 2015 ; Alksasbeh et al., 2019 ), measurement, reliability is assessed with Cronbach’s alpha coefficient, and Dillon–Goldstein rho coefficient (DG’s rho) to tests the internal consistency for items in the same construct. The accepted value of Cronbach’s alpha (α) must be the minimum threshold (0.70) as suggested by Nguyen (2020) . As shown in Table 4 , the Cronbach’s alpha value was more than 0.7. As ( Chin, 1998 ) considers Dillon–Goldstein’s rho to be a better indicator than Cronbach’s alpha, Dillon-Goldstein’s rho values higher than 0.8 suggests unidimensional, which we have in the latent constructs. Besides, the higher value of Cronbach’s alpha coefficient and Dillon–Goldstein rho coefficient (DG’s rho) is a better indicator of a reflective construct which indicates satisfactory reliability for all five latent constructs. Here the bock is unidimensional, as the first eigenvalue is greater than one and the second is less than one. Again, Average Variance Extracted (AVE) of all constructs in the model exceeded 0.50, which is the recommended threshold ( Fornell & Larcker, 1981 ). Further, Table 4 demonstrates an AVE of all the latent variables to be higher than its correlations. Thus the requirement of discriminant validity is also met.

Hypotheses testing: correlation and regression analysis

The proposed hypotheses are evaluated by using correlation and regression analysis. Correlation analysis examines the association among dependent and independent variables and hence determines the criteria to accept or reject the proposed hypotheses. In Table 5 below, the lower-triangular format depicts the correlation that establishes an association among the latent variables. The outcomes demonstrate the correlation to be greater than 0.50 (>0.50) which establishes a positive association among independent and dependent variables in the research model as shown in Table 5 . Henceforth, it is obvious that outlier constructs are resilient and buttressed.

The quality influencing factors on student satisfaction such as information, system, and service respectively are tested by using regression analysis which may impact the use of ELS. Hence, based on this the linear regression tested the initial regression model in Table 6 . The outcomes demonstrated that information quality (H1, β = 0.451, p < 0.01), system quality (H2, β = 0.462, p < 0.01), service quality (H3, β = 0.242, p < 0.05) caused a significant impact on the student satisfaction and system use. Additionally, the dependent variable of student satisfaction R2 value is 0.872. This implies that there is a variance of 82.5% in the ELS student satisfaction which corroborates and explains information quality, system quality, and service quality impact factors within the proposed model.

Besides, the following hypotheses are also buttressed by the first-generation regression model:

Structural model path and testing of hypothesis

This research has investigated the causal relationship among the constructs. Here indirect effects denote mediation effects whereas the direct effect is the hypothesized relationship between two constructs. The sum of the direct effect and the indirect effect of a variable on another variable is called the total effect ( Yakubu & Dasuki, 2018 ). The outcomes suggest that student satisfaction and system use gratification towards the implementation of e-learning system (ELS) are positively associated with information quality as information output in terms of accuracy, relevance, consistency, and completeness. Additionally, it is also evident and established that the system quality component which complies with the managerial decision-making characteristics of reliability, access, efficiency, and ease of use is also positively associated with student satisfaction and system use. Similarly, the service quality component which complies with the information technology adequacy measuring the client’s emotional evaluation of the expected satisfying service is certainly coupled with student satisfaction which is a performance measure of the system performance measurement. Table 6 , illustrates the rest results of the specific hypothesis.

Results and discussion

The primary contribution of this study is to be able to establish the notion that e-learning system implementation is impacted by ISSM which allows knowledge increment in further understanding of the ISSM model proposed by DeLone and McLean and how it impacts the e-learning system implementation in Saudi universities. The ISSM model is investigated in this study which leads to establishing the facts regarding the level of readiness of Saudi universities in order to implement ELS and further determining other contributing factors which are significant in positively impacting the successful ELS implementation in Saudi universities.

It is established from the results of this study that information quality has a positive effect on student satisfaction and system use. Videlicet, 75% strongly agree or partially agree on the awareness concept.

In system quality terms, the results suggest a positive impact on the readiness of Saudi universities towards ELS implementation. Principally, 82% of the participants either agree or partially agree to the impact of this factor on ELS implementation. Furthermore, the results corroborate nearly all the previous literature and allude to the advancement of commitment to change, chaos reduction, and resistance to change caused by the communication between the system and universities.

Additionally, results of this study have revealed that Saudi universities’ readiness to implement ELS is significantly impacted by service quality which is buttressed by 88% of respondents who either agree or strongly agree to the question. This is an indication to the point that older generations will encounter phenomenal challenges towards generating interest in adopting automation technologies. This also establishes that the higher the level of educational attainment in the society, the stronger the impacts of service quality on broadband adoption.

Besides, the odds of implementing ELS successfully are directly proportional to increased student satisfaction. Nevertheless, 18% of the respondents have negated based on the ELS privacy concerns and technology adoption and mellowness. Hence, it is of prime importance to address the issues of location-based applications and privacy violations. Besides, some respondents argued about the negative effects of ELS in terms of causing distractions and investing huge amounts of time adopting new applications and technology which is an arduous task and interferes with the traditional learning process.

Finally, the results revealed that Saudi universities’ readiness to implement ELS is also significantly impacted by system use which is buttressed by 82% of respondents who either agree. This is explained by the survey results based on the people’s awareness about the future of ELS in Saudi universities. This finding is in congruence and consistent with the researcher’s viewpoints that system use plays a critical role and determines the student’s behavior toward implementing ELS.

The results of this research study consistently buttress the literature ( Martins et al., 2019 ; Salam & Farooq, 2020 ; Alhabeeb & Rowley, 2017 ; Duygu, Alkiş & Ozkan-Yildirim, 2018 ; Altameem, 2013 ; Maatuk et al., 2021 ; Aljawarneh et al., 2015 ). Unfortunately, there no researches regarding using ISSM for measuring adoption of ELS in Arab countries for comparing their outcomes with the obtained results in this study Furthermore, the outcomes of this endeavor can be utilized as a reference towards implementing ELS in other universities across Saudi Arabia or the Middle East and also by the other researchers and implementers in order to upgrade Arabic universities’ readiness levels in developing countries for new technologies adoption. Finally, this study can allow the top-level administrators to assist them in identifying the important factors affecting the success of implementation and introduction of a novel information system.

Conclusions

The overall results of this study allude to the fact that there is a significant relationship between Information System Success Model factors and the adoption of e-learning systems. It is indicated that the study suggests that student satisfaction, system use, all quality components of information system and associated services are strongly leaned towards the implementation of e-learning system across the university. It is further revealed about Saudi universities’ readiness to implement ELS is also significantly impacted by system use. Therefore, the research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities. The proposed recommendation in this study is to enhance system services to make students more agile in using the e-learning system.

Supplemental Information

Supplemental information 1, supplemental information 2, funding statement.

The author received financial support to cover the publication fee of this research article from the Middle East University, Amman, Jordan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Additional Information and Declarations

Shadi Aljawarneh is an Academic Editor for PeerJ.

Bassam Al-shargabi conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Omar Sabri conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Shadi Aljawarneh conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the paper, and approved the final draft.

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E-LEARNING SYSTEM

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