phd thesis on e learning

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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PhD in E-Research and Technology Enhanced Learning

Join our part-time Doctoral Programme in E-Research and Technology Enhanced Learning to carry out your own research and achieve a PhD. (Undertaken over a minimum of four years).

We have designed this programme for anyone in the world who wants to develop their research practice in this area. Although you will mainly learn online, you will also benefit from two short face-to-face residential meetings (one in the first year and one in the second year) where you will get to know your tutors and fellow students, and take part in a range of interactive sessions. These are valuable core elements of the programme.

Download the TEL Enquiry Handbook (2025)

Key information

The programme is purpose-built for professionals responsible for educating or training others in any sector. You will want to study to an advanced level and carry out a piece of research of the highest possible standard in an aspect of your professional practice. We have been running this pioneering programme for 15 years and have had students from a whole spectrum of backgrounds - from computing to law, nursing to higher education. We have found it is very relevant for people wanting to develop distance or blended learning in programmes they manage.

Whatever your background, you will be eager to work towards a PhD and focus on researching educational uses and practices of technology enhanced learning in educational settings and sectors. You’ll finish the programme with new insights, new opportunities, and new career possibilities.

Meet some of our Alumni on the Centre for Technology Enhanced Learning People webpages.

This is not an online version of an existing programme. Our programme was developed as an online programme for part-time professionals who will be studying mainly at a distance. You will be part of a cohort which means you’ll join our online learning community, receiving ongoing feedback from tutors and other students.

We have designed a two-year structured set of modules which introduce research themes in educational uses and practices of technology enhanced learning.

You will decide the focus of your research, but we will be here to advise you every step of the way. For example, past students have researched the use of a virtual learning environment for legal training, technology to assist the elderly at home, and the use of Twitter for early career researchers.

‘Technology enhanced learning' (TEL) encompasses all uses of information and communications technologies in learning and teaching. It is also sometimes referred to as 'e-learning', 'online learning' and 'advanced learning technology'. Many other terms are used around the world to describe this quickly growing and highly impactful phenomenon.

Our programme focuses on 'networked learning' – connections within an online learning community on the Internet. Joining us, you will explore human aspects of technology in learning, the values underpinning the use of technology, and how technology and learning shape each other.

E-Research is an emerging field which involves applying advanced technologies to existing research methods and approaches. Our programme examines trends in this field and explores how to use technologies for research into technology enhanced learning.

E-Research aims to advance and augment rather than replace traditional research methodologies. Improving knowledge in this area helps researchers perform research more creatively, efficiently and collaboratively across long distances, and share their research outcomes.

Although you will join us predominantly online, the residentials are compulsory, and they are an important part of your study. There are two four-day residential meetings in Part One of the programme, one in the first year and another in the second.

You will join us on campus in Lancaster to get to know your tutors and meet other students. At the same time, we will introduce you to wider thinking about e-Research and TEL research, as well as discussing modules, the virtual learning platform and associated technologies.

The first year residential dates for our 2025 intake are 31st March - 3rd April, 2025 The second year residential dates for our 2025 intake are still to be confirmed

The first year residential dates for our 2024 intake are 8th - 11th April 2024 The second year residential dates for our 2024 intake are 1st - 4th April 2025

Further information about timetables, accommodation, travel and visas is available on our Residentials web page.

How to Apply

The next start date is January 2024 (CH17).

Entry requirements

Apply online.

For admission to this programme applicants should normally have:

• a good honours degree from a British university or CNAA, and a good taught Master's degree; or
• qualifications of a comparable standard from a university or recognised degree awarding body in another country.

Preference will be given to applicants who have degrees in cognate areas (normally social science).

Language proficiency

Applicants will need to have an acceptable fluency in written and spoken English.

For students whose first language is not English, an English Language Test Certificate will be required, that is, IELTS Academic with an overall score of 6.5 with at least 6.0 for reading and writing.

Further information can be found at: English Language Requirements .

Please apply using the online system .

The next start date is 1st January, 2025.

Typical Cohort Number: 30

As this is a PhD by coursework and thesis we do not require a research proposal at this stage, but you should explain why you wish to join the programme and how you hope to benefit from it.

Please contact the admissions team if you have any questions about applying to study at Lancaster University.

Fees and funding

The fee for each cohort is set annually by the University and represents the part-time fee for that academic year. Once a student is on the programme the fee will be increased in line with inflation for each subsequent year of the course.

The course fee for:

• 24/25 is £5,410 per academic year, for four years minimum, for UK students and £9,670 per academic year, for four years minimum, for international students.

Fees are subject to a small increase each academic year.

To help finance your postgraduate study at Lancaster, you can apply for funding from charities and other funders: further details are available on the Fees and Funding webpage.

Applicants from the European Union can read more information about Research Fees (from Lancaster University) following the 2016 Referendum.

When you apply you will need to indicate your likely source of funding for your fees. If you are not self-funding you should investigate possible sources of finance as soon as you can for the full period of your study. Many students have been supported by their employing institutions.

Find out what our graduates say about studying on our PhD programmes

Several students have published journal articles arising from their module assignments and theses.

Structure and modules

The programme is divided into two parts and has a modular structure in Part 1. All modules are compulsory and they are assessed along with the thesis proposal and the final thesis.

• Part One (years 1 and 2) consists of four modules that offer participants guided study in key areas of technology enhanced learning research.
• Part Two (year 3 Onwards) - participants carry out an original piece of research under the supervision of a member of staff and produce a thesis with a maximum limit of 50,000 words.

View a list of some of the PhD theses from the TEL programme.

Online and Distance learning

The Educational Research Department has considerable experience of supporting online and distance learners and we aim to make all our students feel that they are full members of the Department and part of the postgraduate community. Wherever possible we facilitate online participation at events and seminars organised by the Department.

All students have access to Moodle which is our Virtual Learning Environment. Many resources, for example, journal articles; required for postgraduate study, will be available online through the OneSearch facility offered by the Library . Additionally, the Library provides extra services for Distance Learners.

Visit our Computer requirements for studying online page for further information.

Rebecca Marsden is the Online Learning Support Officer for the Department of Educational Research and she can be contacted with queries about online learning.

The Academic skills webpages provide a wide range of online study opportunities across a range of topics. These include digital skills, referencing, research training and critical thinking.

Current Programme staff

The staff who teach and supervise programmes and modules can vary due to staff changes including research and other types of leave.

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Dr Brett Bligh

Centre for Higher Education Research and Evaluation, Centre for Technology Enhanced Learning

Dr Kathy Chandler

Centre for Technology Enhanced Learning

Dr Bethan Garrett

Centre for Social Justice and Wellbeing in Education

Dr Katy Jordan

Dr Philip Moffitt

Professor Don Passey

Centre for Social Justice and Wellbeing in Education, Centre for Technology Enhanced Learning

Dr Julie-Ann Sime

If you have any questions, or would like further information about this programme, please contact the Programme Co-ordinator, Alice Jesmont. Tel: +44 (0) 1524 592893 Email: a.jesmont@lancaster.ac.uk

PhDs in Educational Research

PhD - Traditional Route

PhD Education and Social Justice

PhD Educational Research - Higher Education

PhD Higher Ed: Research, Evaluation & Enhancement

ORIGINAL RESEARCH article

E-learning research trends in higher education in light of covid-19: a bibliometric analysis.

• 1 University of Bisha, Bisha, Saudi Arabia
• 2 University of Oum El Bouaghi, Oum El Bouaghi, Algeria
• 3 Binghamton University, Binghamton, NY, United States
• 4 University of Rochester, Rochester, NY, United States

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within “e-learning in higher education.” E-learning as a concept has been widely used in the academic and professional communities and has been approved as an educational approach during COVID-19. This article starts with a literature review of e-learning. Diverse subjects have appeared on the topic of e-learning, which is indicative of the dynamic and multidisciplinary nature of the field. These include analyses of the most influential authors, of models and networks for bibliometric analysis, and progress towards the current research within the most critical areas. A bibliometric review analyzes data of 602 studies published (2020–2021) in the Web of Science (WoS) database to fully understand this field. The data were examined using VOSviewer, CiteSpace, and KnowledgeMatrix Plus to extract networks and bibliometric indicators about keywords, authors, organizations, and countries. The study concluded with several results within higher education. Many converging words or sub-fields of e-learning in higher education included distance learning, distance learning, interactive learning, online learning, virtual learning, computer-based learning, digital learning, and blended learning (hybrid learning). This research is mainly focused on pedagogical techniques, particularly e-learning and collaborative learning, but these are not the only trends developing in this area. The sub-fields of artificial intelligence, machine learning, and deep learning constitute new research directions for e-learning in light of COVID-19 and are suggestive of new approaches for further analysis.

Introduction

The idea of e-learning was originated in the 1990s to explain learning thoroughly through technical advances. When instructional architecture and technologies have advanced, more attention has been paid to studying with the pedagogy. University education, further education, and e-learning have also recently adopted prominent roles in e-learning, too. It is now possible to provide e-learning for off-the-formal training through the internet. It also increased the need for personalization and advanced social people’s tools ( Siemens, 2005 ). In addition, it is often referred to as being able to read. It will help mix much learning more conveniently, but it has to be done, given the success of “traditional” e-learning pages. When the educational and technological assets join, this will be something more than a personal matter.

The COVID-19 pandemic has forced the closure of many activities, especially educational activities. To limit the spread of the pandemic, universities, institutes, and academic schools had to switch to e-learning using the available educational platforms. Social distancing is critical, and the COVID-19 pandemic has brought an end to face-to-face education, negatively impacting educational activities ( Maatuk et al., 2021 ). This closure has stimulated the growth of distance education activities as an alternative to face-to-face education in their various forms. Accordingly, many universities have shared the best ways to deliver course materials remotely, engage students, and conduct assessments.

The concept of e-learning, although widely known has not yet been fully explored ( Nicholson, 2007 ). Many countries designed and deployed distance education systems during the COVID-19 pandemic to ensure that higher education could continue without interruption ( Tesar, 2020 ). Several opportunities and challenges related to e-learning, higher education, and COVID-19 arose as a result of this, prompting a flurry of research into the area. When looking at the scientific studies published during the COVID-19 pandemic, it shows clearly that many international journals have published a large number of academic articles about e-learning in higher education during COVID-19 ( Karakose and Demirkol, 2021 ). Furthermore, a vast amount of bibliometric research has been carried out in this field. However, there is very little research focused entirely on the relationship between e-learning, higher education, and COVID-19, using scientometric or bibliometric analysis ( Furstenau et al., 2021 ).

This paper will discuss bibliometric indicators for e-learning in higher education during COVID-19 studies and proceed with a network analysis to define the most important sub-areas in this topic. To define the trends of e-learning in higher education during COVID-19, the following questions are proposed:

Q1: What are the most important sub-fields of e-learning in higher education in light of COVID-19?

Q2: Who are the most influential authors on the subject of e-learning in higher education in light of COVID-19?

Q3: What countries and research institutions are the most referenced for research on the subject of e-learning in higher education in light of COVID-19?

Q4: What are the research gaps and recent trends in the subject of e-learning in higher education in light of COVID-19?

An analysis was conducted to provide a broad and long-term perspective on the vocabulary of learning publications. It helps to recognize emerging problems within the multifaceted and increasing study fields of the world of e-learning. Newly published studies can improve knowledge and bridge the knowledge gap through findings regarding e-learning trends; this applies particularly to higher education due to the importance of knowing the latest information about distance learning and its methods. For this reason, the research is valuable for analyzing the volume of publications that have been made on the subject matter and to solidify the knowledge base on what has been studied by different expert researchers in education. So this will create new progress and new proposals to improve education in the event of a future pandemic.

In recent years, there has been an increasing interest in research within areas related to e-learning: online learning, blended learning, technology acceptance model, smart learning, interactive learning environments, intelligent tutoring systems, digital learning were reported ( Oprea, 2014 ; Castro-Schez et al., 2020 ; de Moura et al., 2020 ; Kao, 2020 ; Nylund and Lanz, 2020 ; Pal and Vanijja, 2020 ; Patricia, 2020 ; Şerban and Ioan, 2020 ).

A substantial quantity of literature has been written and published on the bibliometric analysis of e-learning. These studies mainly aim to identify the most critical areas (keywords) of e-learning. Networks such as that conducted by Chiang et al. (2010) showed that the significant research areas in e-learning are as follows: Education and Educational Research, Information Science and Library Science, and Computer.

Science/Multidisciplinary Applications

Cheng et al. (2014) analyzed data from 324 articles published between 2000 and 2012 in academic journals and conference proceedings from 2000 to 2012 to determine the vital research areas (the results identify six research themes in the field e-learning). Tibaná-Herrera et al. (2018a) used VOSViewer to conduct a bibliometric analysis of SCOPUS and SCImago Journal & Country Rank to establish the “e-learning” thematic category of scientific publications, thereby contributing to the discipline’s consolidation, accessibility, and development by researchers.

Bai et al. (2020) have also pursued similar work in analyzing 7,214 articles published in 10 journals on the subject of e-learning from 1999 to 2018; this study offers valuable hints on the future direction of how e-learning may evolve. Fatima and Abu (2019) examined 9,826 records from the Web of Science (WoS) database between 1989 to 2018 to identify significant contributions to the area of e-learning. The findings of this study show that the United States and the United Kingdom have contributed more than half of the research in e-Learning. According to a recent survey by Mashroofa et al. (2020) , the University of London is the most prolific institution globally. According to the WoS database, the institution has published 131 studies on e-learning; the bibliometric analysis of 6,934 results revealed that the publications received 59,784 citations.

Hung (2012) employed text mining and bibliometrics to examine 689 refereed journal articles and proceedings, comparing them to these research results. These works are divided into two domains, each of which has four groups. The study’s findings now offer evidence that e-learning methods vary across top countries and early adopter countries.

There have been multiple previous attempts to do a systematic review of e-learning publications ( Lahti et al., 2014 ; Zare et al., 2016 ; Garcia et al., 2018 ; Rodrigues et al., 2019 ; Araka et al., 2020 ; Valverde-Berrocoso et al., 2020 ), these studies mainly aimed to identify research areas, the most used and most important methods, and tools in e-learning.

Many studies have examined the results of e-learning publications through meta-analysis ( ŠUmak et al., 2011 ; Lahti et al., 2014 ; Mothibi, 2015 ; Cabero-Almenara et al., 2016 ; Yuwono and Sujono, 2018 ).

The study’s contribution is that no controlled studies have compared differences in networks, models, and software outputs to define the most critical research areas in e-learning and the most influenced authors, organizations, and countries.

The study makes an important contribution to the analysis of current models and networks of e-learning in higher education during the COVID-19 pandemic, aiming to define the most critical research areas in e-learning and the most influenced authors, organizations, and countries. In addition, it looks at the framework of e-learning and its future research trends in light of COVID-19. This has been done through numerous investigations ( Tibaná-Herrera et al., 2018a , c ; Hilmi and Mustapha, 2020 ; López-Belmonte et al., 2021 ).

Materials and Methods

Bibliometric data.

We retrieved published research via a topic search of the Science e-learning in higher education during the COVID-19 pandemic using the WoS database on August 12, 2021. The following search terms were used: topic = (“e-learning” “COVID-19” “higher education”), in title-abs-key from 2020 to 2021, and were 602 studies (475 articles, 80 articles; early access, 25 proceedings paper, 22 reviews) distributed over 2 years, as shown in Figure 1 .

Figure 1. Publications per year (KnowledgeMatrix Plus outputs).

The following selection criteria were used to choose the studies. First, for the title, we looked at the following: the studies that looked at the topic of e-learning in higher education during COVID-19. Second, for the abstract, we looked at the following: the studies that addressed the problem of e-learning in COVID-19. Third, for the keyword, we looked at the following: the studies that included e-learning, higher education, universities, and COVID-19. Fourth, the subject areas were limited to a selection of works that dealt with this subject in the following disciplines: business management and accounting, educational sciences, social sciences, and psychology.

The bibliometric study data represents the overall research on “E-learning in higher education in light of the COVID-19” in the WoS database. These data covered the last 2 years (2020 and 2021) in which the use of e-learning was expected due to the closure and quarantine procedures.

The reasons for choosing this database over others, particularly Scopus and ScienceDirect, are due to several considerations; due to WoS data, the field of scientometrics has advanced significantly. WoS is more than simply a database of academic papers. Many information objectives are supported by this selected, organized, and balanced database, including full citation links and improved metadata ( Birkle et al., 2020 ). WoS databases include high-quality research covering Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Emerging Sources Citation Index (ESCI) ( Falagas et al., 2008 ).

Figure 1 illustrates how interest in e-learning research has increased in recent years, particularly between 2020 and 2021. Among the 602 studies with 4,280 citations, 230 in 2020 (1,400 citations), and 372 in 2021 (2,880 citations), the importance of higher education institutions, including universities, in this modern teaching and learning approach and their significance in the educational process during COVID-19 is evident. They are different from the periods approved in the previous studies ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ). Therefore, this field of research (e-learning) has been renewed, and researchers should pay more attention to it to provide effective methods and approaches in light of the continuing epidemic.

Methods and Tools

According to the methods and approaches of bibliometric analysis (see: Zupic and Čater, 2015 , p. 04). the study relied on the co-occurrence indicator (co-word) to find out the main keywords on which previous studies focused as well as the co-authorship, publications, and citations indicators to find prominent authors, organizations, and countries in the topic of e-learning in higher education in COVID-19.

Following the methodology of preparing the bibliometric study in management and organization, which was explained by Zupic and Čater (2015) , the bibliometric analysis was carried out by completing the following steps: research design, study questions, and analysis approach selection (co-occurrence, publication, citation, and co-authorship); bibliometric data compilation, selection, and filtration, analysis (choosing the appropriate bibliometric software, clean the data, and generate networks); visualization, and interpretation.

The bibliometric analysis was performed to design networks of e-learning and define the most frequent keywords and the most cited authors, organizations, and countries to explain new and current trends within this topic. This is achieved depending on different software: CiteSpace converts research domain concepts into mapping functions between research frontiers and intellectual bases and is effective for information visualization ( Chen, 2016 ); VOSviewer is used to design the networks and is a powerful function for co-occurrence analysis and citation analysis ( Van Eck and Waltman, 2013 ). KnowledgeMatrix Plus is a powerful tool for analyzing frequency and statistics ( Chen and Song, 2017 ). This software was not used in previous studies ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ).

Results and Discussion

Keywords frequency.

Figures 2A,B and Supplementary Table 1 present the most frequent keywords that have been repeated more than five, which amounted to 131.

Figure 2. (A) Network of keywords (VOSviewer outputs). (B) Network of keywords (CiteSpace outputs).

Figure 2A shows nine sub-areas (clusters) for research in e-learning within higher education during the era of COVID-19. First, the red cluster shows searches related to the following: higher education, students, motivation, attitudes, systems, technology acceptance model, and user acceptance. Second, the green cluster shows searches related to the pandemic, blended learning, online learning, hybrid learning, flipped classrooms, virtual learning, and distance education. Third, the navy-blue cluster shows searches related to higher education online, online teaching, online assessment, formative assessment. Fourth, the yellow cluster relates to stress, health, care, quarantine, mental health, anxiety, college students, adults, children. Fifth, the violet cluster shows searches related to surgery, surgical education, skills, strategies, student satisfaction, and simulation. Sixth, the light blue cluster shows searches related to e-learning, performance, quality, remote learning, digital learning, assessment, evaluation. Seventh, the orange cluster shows searches related to education, Covid-19, coronavirus, sars-cov-2, distance learning, medical education. Eighth, the brown cluster included: computer-based learning, self-instruction/distance learning, internet/web-based education, curriculum, knowledge, science, and technology. Finally, the pink clusters showed searches related to artificial intelligence, machine learning, and deep learning. The researcher can also take these subfields as topics for research in e-learning, especially the last cluster, which formed a recent research trend for many scholars ( Bhardwaj et al., 2021 ; Kashive et al., 2021 ; Rasheed and Wahid, 2021 ).

Figure 2B shows that the research on this topic requires focusing on several issues. These are the most frequently mentioned keywords in Supplementary Table 1 , including COVID-19 crisis, technology acceptance model (TAM), distance education, stress, ICT, special education needs, mental health, student satisfaction, surgical teaching, self-efficacy, technology adoption, using the machine, and e-learning. At the same time, many studies used different terms to express the same meaning, such as interactive learning, online learning, and Distance learning. This is similar to what was found in previous studies on e-learning ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ).

Reference Authors

Figures 3A–C show the network of the most referenced authors on the topic of “E-learning in higher education in COVID-19” based on co-authorship:

Figure 3. (A) Network of authors (VOSviewer outputs). (B) Publications and citations per author (KnowledgeMatrix Plus outputs). (C) Network of cited authors in COVID-19 (CiteSpace outputs).

Figure 3A shows that there is a research partnership between eight authors. The co-authorship is the affiliation and the country: Fernando Augusto Bozza, Rosana Souza Rodrigues, Walter Araujo Zin, Alan Guimaraes and Gabriel Madeira Werberich, Federal University of Rio de Janeiro, Brazil. Joana Sofia F. Pinto, Willian Reboucas Schmitt and Manuela Franca, Complexo Hosp Univ Porto, Radiol Dept, Porto, Portugal. As for the rest, they have separate and individual publications. Figures 3A–C present the top authors based on publications and citations.

Figure 3B shows that the first author on this topic on “E-learning in higher education in COVID-19” is Antonio José Moreno-Guerrero, Univ Granada, Dept Didact & Sch Org, Spain. Among this research, we find “Impact of Educational Stage in the Application of Flipped Learning: A Contrasting Analysis with Traditional Teaching” ( Pozo Sánchez et al., 2019 ). We also find research on e-learning in mathematics teaching: an educational experience in adult high school ( Moreno-Guerrero et al., 2020 ) as well as research on the following: the effectiveness of innovating educational practices with flipped learning and remote sensing in earth and environmental sciences ( López Núñez et al., 2020 ); machine learning and big data and their impact on literature; a bibliometric review with scientific mapping in WoS; and a flipped learning approach as an educational innovation in water literacy ( López Belmonte et al., 2020 ; López Núñez et al., 2020 ). Moreno-Guerrero talked about e-learning and did not discuss the COVID-19 ( Moreno-Guerrero et al., 2020 ); otherwise, Lüftenegger discussed e-learning and COVID-19 ( Holzer et al., 2021 ; Korlat et al., 2021 ; Pelikan et al., 2021 ).

Figure 3C shows that the most important authors searched in COVID-19 and touched on e-learning are Maram Meccawy, Isabel Chiyon, and Anand Nayyar among others.

Reference Organizations

Figures 4A–C displays the most referenced organizations on the topic of “E-learning in higher education in COVID-19” based on publications, citations, and co-authorship.

Figure 4. (A) Network of organizations (VOSviewer outputs). (B) Network of organizations (CiteSpace outputs). (C) Citations per publications by the organization (KnowledgeMatrix Plus outputs).

Figures 4A–C demonstrate that the leading research organization for publications, citations, and co-authorship on this topic is the University of Toronto with 16 publications and 207 citations, followed by the University of King Abdulaziz with 15 publications and 57 citations the Jordan University of Science and Technology with 11 publications and 115 citations, then the University of Vienna with 10 publications and 30 citations, then the University of Sharjah with 10 publications and 20 citations, then the University of Granada with 9 publications and 79 citations, then the University of Porto with 9 publications and 14 citations, then Monterrey Institute of Technology and Graduate Studies with 9 publications and 2 citations, then the University of Jordan with 8 publications and 46 citations, and finally, the University of Colorado with 8 publications and 16 citations. That is due to several reasons, including the interest of these organizations in publishing in the WoS database. Then their interest in publishing in the subject of the study. We thus find it among the top 500 universities. 1

Reference Countries

Figures 5A–C display the most referenced countries on the topic of “E-learning in higher education in COVID-19” based on publications, citations, and co-authorship.

Figure 5. (A) Network of countries (VOSviewer outputs). (B) Network of countries (CiteSpace outputs). (C) Citations per publications by country (KnowledgeMatrix Plus outputs).

Figures 5A–C illustrate that the top countries for publications, citations, and co-authorship in this topic are as follows: the United States with 344 publications and 1,167 citations, the United Kingdom with 132 publications and 530 citations, China with 117 publications and 592 citations, Spain with 104 publications and 321 citations, Italy with 98 publications and 175 citations, Brazil with 74 publications and 224 citations, Canada with 67 publications and 368 citations, India with 64 publications and 139 citations, Saudi Arabia with 60 publications and 216 citations, and Germany with 59 publications and 133 citations. These show extensive collaboration, especially between the United States and the United Kingdom with 11 collaborations, between the United States and Canada with 10 collaborations, and between the United States and China with 9 collaborations; other countries show an average of 3–5 collaborations.

The results of the bibliometric analysis showed that there are nine sub-fields of research within a topic: motivation and students’ attitudes to e-learning systems in higher education (technology acceptance model), comparison between blended learning and virtual learning, online assessment versus formative assessment of students in higher education, stress, anxiety, and mental health of college students in COVID-19, surgical education strategies to develop students’ skills, quality and performance of higher education strategies of e-learning in COVID-19, challenges of medical education and distance learning during COVID-19, and changing higher education curricula using technology.

Finally, using artificial intelligence, machine learning, and deep learning to transform the e-learning Industry, this final sub-field formed a recent research trend for many scholars ( Bhardwaj et al., 2021 ; Kashive et al., 2021 ; Rasheed and Wahid, 2021 ).

The bibliometric study shows that the first author in e-learning is Antonio José Moreno-Guerrero, Univ Granada, Dept Didact & Sch Org, and Spain. His writings ( Pozo Sánchez et al., 2019 ; López Núñez et al., 2020 ; Moreno-Guerrero et al., 2020 ) are considered a useful reference in e-learning and blended learning. Therefore, Marko Lüftenegger is one of the most influential author in the topic of “E-learning in higher education in COVID-19” ( Holzer et al., 2021 ; Korlat et al., 2021 ; Pelikan et al., 2021 )

The results of the bibliometric analysis showed that the top research organizations in this domain are as follows: the University of Toronto, the University of King Abdulaziz, Jordan University of Science and Technology, the University of Vienna, the University of Sharjah, the University of Granada, the University of Porto, Monterrey Institute of Technology and Graduate Studies, the University of Jordan, and the University of Colorado. The results also illustrate that the top countries are: United States, United Kingdom, China, Spain, Italy, Brazil, Canada, India, Saudi Arabia, Germany, due to several reasons, including the interest of these organizations and countries in publishing in the Web of Science database and their interest in publishing in the subject of the study.

Our research overlaps with that of López-Belmonte et al. (2021) , who tried to investigate the development of e-learning in higher education in the academic literature listed on the WoS. The same analysis, as well as bibliometric analysis, was carried out. The findings revealed no set path for research because of the research on e-learning in higher education, recent creation, and a scarcity of relevant research. According to the results of the bibliometric analysis, the study was aimed at determining acceptance and implementation of the educational curriculum in the teaching and learning processes.

This paper discusses the use of a bibliometric approach to track e-learning trends in higher education during the COVID-19 pandemic through the WoS database. From a methodological perspective, our proposed approach can visually represent the temporal links of the most cited articles internally in various streams and provide a comprehensive overview of the evolution of topics in the WoS database. Also, direct citation network analysis enables researchers to test articles important in e-learning and get a comprehensive overview of the issues published.

The study provided an insight into the world’s e-learning research in terms of mapping research publications. A scientific study was conducted using 602 e-learning documents from 2020 to 2021, and these were obtained through the WoS database. Over the years, the analysis identified trends in contributions in this area and headline sources for most researchers and leading institutions. The study is convergent with many previous studies in this area, including Chiang et al. (2010) , Hung (2012) , Cheng et al. (2014) , Tibaná-Herrera et al. (2018b) , Fatima and Abu (2019) , Bai et al. (2020) , and Mashroofa et al. (2020) . However, our study relies on many software to compare various theoretical models and networks of e-learning.

Based on the analysis data’s inference, growth trends in research publishing in e-learning of different forms have increased in recent years, especially so for the last 2 years (230 in 2020 and 386 in 2021). The significant findings of the bibliometric analysis are as follows: there are nine sub-fields of study in the subject of “E-learning in higher education in COVID-19,” and the prominent authors in this area are as follows: Antonio José Moreno-Guerrero and Marko Lüftenegger; the University of Toronto Canada is the most frequently cited organization in this domain; the United States is the leading country in terms of publications and citations; and the sub-field of artificial intelligence, machine learning, and deep learning to transform the eLearning Industry has emerged as a recent research trend for many scholars.

The study examined a very important topic, which is one of the current topics, “e-learning in higher education during COVID-19,” using bibliometric analysis of 602 studies published in Web of Science databases from 2020 to 2021. We found that the study sample should be larger; it needs further studies and a longer time, especially when we analyze citation, and research on this topic will thus continue in future years. Also, there are many tools and methods used in the bibliometric analysis that were not used in our study, including what has been mentioned ( Tibaná-Herrera et al., 2018b ; Gul et al., 2020 ; López-Belmonte et al., 2021 ; Rashid et al., 2021 ).

The findings of this study will assist interested academics and educational policymakers ( Brika et al., 2021 ) in the field of e-learning in understanding the current state of e-learning and identifying the different research trends in light of COVID-19. Additionally, it will serve as the beginning point for new research during the COVID-19 crisis, which will examine various problems and trends.

The findings of this research may help evaluate e-learning institutions’ quality and promote future educational trends. The findings may be utilized by e-learning institutions to evaluate quality as strategic dimensions and policy makers’ vision.

Data Availability Statement

The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.

Author Contributions

All authors contributed to the design and implementation of the research, performed the revision, verified the analytical methods, supervised the findings of this work, discussed the results, and contributed to the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, to fund this research work through the project number (UB-56-1442).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.762819/full#supplementary-material

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Oprea, C. L. (2014). The Internet-a tool for interactive learning. Procedia Soc. Behav. Sci. 142, 786–792. doi: 10.1016/j.sbspro.2014.07.617

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Patricia, A. (2020). College students’ use and acceptance of emergency online learning due to Covid-19. Int. J. Educ. Res. Open 1:100011. doi: 10.1016/j.ijedro.2020.100011

Pelikan, E. R., Lüftenegger, M., Holzer, J., Korlat, S., Spiel, C., and Schober, B. (2021). Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination for perceived competence. Z. Erziehungswiss. 24, 393–418. doi: 10.1007/s11618-021-01002-x

Pozo Sánchez, S., López Belmonte, J., Moreno Guerrero, A. J., and López Núñez, J. A. (2019). Impact of educational stage in the application of flipped learning: a contrasting analysis with traditional teaching. Sustainability 11:5968. doi: 10.3390/su11215968

Rasheed, F., and Wahid, A. (2021). Learning style detection in E-learning systems using machine learning techniques. Expert Syst. Appl. 174:114774. doi: 10.1016/j.eswa.2021.114774

Rashid, S., Rehman, S. U., Ashiq, M., and Khattak, A. (2021). A scientometric analysis of forty-three years of research in social support in education (1977–2020). Educ. Sci. 11:149. doi: 10.3390/educsci11040149

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Valverde-Berrocoso, J., Garrido-Arroyo, M. D. C., Burgos-Videla, C., and Morales-Cevallos, M. B. (2020). Trends in educational research about e-learning: a systematic literature Review (2009–2018). Sustainability 12:5153.

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Keywords : e-learning, higher education, COVID-19, bibliometric analysis, Web of Science (WoS) database

Citation: Brika SKM, Chergui K, Algamdi A, Musa AA and Zouaghi R (2022) E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis. Front. Psychol. 12:762819. doi: 10.3389/fpsyg.2021.762819

Received: 22 August 2021; Accepted: 31 December 2021; Published: 03 March 2022.

Reviewed by:

Copyright © 2022 Brika, Chergui, Algamdi, Musa and Zouaghi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Said Khalfa Mokhtar Brika, [email protected]

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PhD thesis: The Use of Learning Styles in Adaptive Hypermedia

""Computer-based learning has become a common phenomenon in the modern age. Many distance-learning systems distribute educational resources on the Internet and indeed entire study programmes are now widely available online. Such a large amount of content and information can be intimidating to learners, who may exhibit different individual characteristics, such as variation in goals, interests, motivation and/or learning preferences. This suggests that a uniform approach taken by learning environments to deliver materials and resources to students is not appropriate and that personalisation of such materials/resources should address users’ differences to provide a customised learning experience, thus enhancing its effectiveness, lowering drop-out rates and maintaining high student motivation. This thesis addresses the latter issue of learning preferences, specifically investigating learning styles as an adaptation mechanism for personalised computer-based learning. A number of previous studies indicated the positive effect that this kind of adaptation provides, but under closer examination these were not conducted in a scientifically rigorous manner and thus their findings are somewhat limited. This research utilises a quantitative and highly objective approach to investigate visual/verbal and sequential/global learning styles in different user groups. Three user trials were carried out to discover whether there were any benefits to using these learning styles for studying in an adapted environment. Overall, no statistically significant benefits were found and these findings now shed doubt as to whether learning styles are indeed an effective mechanism for personalised learning.""

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Digital Commons @ USF > College of Education > Teaching and Learning > Theses and Dissertations

Teaching and Learning Theses and Dissertations

Theses/dissertations from 2023 2023.

Saudi Parents as Advocates for Their Young Children with Disabilities: Reflections on The Journey , Sadeem A. Alolayan

Theses/Dissertations from 2022 2022

Graduate Teaching Assistants’ Knowledge and Attitudes Toward Students with Disabilities in Higher Education , Yanlys De La Caridad Palacios

High School Teachers’ Perceptions of Promoting Student Motivation and Creativity through Career Education , Kyeonghyeon Park

The Specifics of Specific Learning Disability: An Analysis of State-Level Eligibility Criteria and Response to Intervention Practices , Lora M. Williams

Theses/Dissertations from 2021 2021

Saudi Early Childhood Educators' Perceptions of Gender Roles in Children's Dramatic Play , Dalal Alanazi

Barriers to Reducing the Assistive Technology use for Students with Autism as Perceived by Special Education Teachers in Saudi Arabia , Othman Ahmed Alasmari

Saudi Teachers’ Perspectives on Implementing Evidence-Based Practices Specifically Designed for Students with Autism Spectrum Disorder , Ahmad Saad Alghamdi

Perceptions of Preservice Teachers of Students with Intellectual Disabilities About their Preparation for Inclusive Education , Abdullah Aljudaya

Experiences of Saudi Arabian Mothers of Young Children with Disabilities: An Exploratory Study , Samirah Bahkali

Persistence Like a Mother: Nursing the Narrative toward Doctoral Completion in English Education—A Poetic Autoethnography , Krista S. Mallo

Warming Up and Cooling Down: Perceptions and Behaviors Associated with Aerobic Exercise , Balea J. Schumacher

A Multimodal Literacy Exploration: Lived Experiences of Haitian Immigrant Adolescent Girls in The Bahamas , Natasha Swann

Theses/Dissertations from 2020 2020

Perceptions of Preservice Teachers of Students with Autism and Intellectual Disabilities in their Teacher Preparation Programs in Saudi Arabia , Salman Almughyiri

Mapping Narrative Transactions: A Method/Framework for Exploring Multimodal Documents as Social Semiotic Sites for Ethnographic Study , Anne W. Anderson

The Effects of Augmented Reality (AR)-infused Idiom Material on Iranian Students’ Idiom Achievements, Motivation, and Perceptions , Babak Khoshnevisan

An Examination of Changes in Muscle Thickness, Isometric Strength, and Body Water Throughout the Menstrual Cycle , Tayla E. Kuehne

How the Use of Learner-Generated Images and Authentic Materials Affects the Comprehension and Production of Vivid Phrasal Idioms in L2 English Learners , Melissa Larsen-Walker

Explore L2 Chinese Learners' Motivation through L2MSS: Selves, Mental Imagery, and Pedagogical Implications , Yao Liu

Exploring Adult Indigenous Latinxs’ English Language Identity Expressions and Agency: A MALP®-informed Photovoice Study , Andrea Enikő Lypka

Theses/Dissertations from 2019 2019

The Use of Assistive Technology with Students with Severe Intellectual and Developmental Disabilities in Saudi Arabia: Teachers’ Perspectives , Khalid Mohammed Abu Alghayth

Saudi Special Education Preservice Teachers’ Perspective towards Inclusion , Sarah Binmahfooz

The Teacher Evaluation Conundrum: Examining the Perceptions of Special Education Teachers , Gordon Brobbey

Illuminating Changes in Preservice Teachers’ Perceptions about Teaching Elementary Mathematicsin an Introductory Methods Course , Elaine Cerrato

International Teaching Assistants’ Perceptions of English and Spanish Language Use at the University of Puerto Rico-Mayagüez , Edward G. Contreras Santiago

Psychological Responses to High-Intensity Interval Training Exercise: A Comparison of Ungraded Running and Graded Walking , Abby Fleming

The Effects and Students’ Views of Teachers' Coded Written Corrective Feedback: A Multiple-Case Study of Online Multiple-draft Chinese Writing , Jining Han

Autism and Inclusion in England’s Multi Academy Trust: A Case Study of a Senior Leadership Team , Danielle Lane

Promoting L2 Idiomatic Competence among Chinese College Students via WeChat , Zhengjie Li

EFL Student Collaborative Writing in Google Docs: A Multiple Case Study , Quang Nam Pham

Threats to Teaching: An Investigation Into the Constructs of Compassion Fatigue in the Classroom , April M. Steen

A New Literacy Coach and Two English Language Arts Teachers Learn Together: A Narrative Inquiry , Christiana C. Succar

Theses/Dissertations from 2018 2018

General Education Teachers’ Perceptions of Response to Intervention Implementation: A Qualitative Interview Study , Adhwaa Alahmari

A Study of Ghanaian Kindergarten Teachers' Use of Bilingual and Translanguaging Practices , Joyce Esi Bronteng

Deaf Lesbian Identity , Noël E. Cherasaro

Beyond Replicative Technology: The Digital Practices of Students with Literacy-Related Learning Difficulties Engaged in Productive Technologies , Aimee Frier

Once Upon a Genre: Distant Reading, the Newbery Medal, and the Affordances of Interdisciplinary Paradigms for Understanding Children’s Literature , Melanie Griffin

Learning in the Margins: The Educational Experiences of an African American Male with Disabilities , Aisha Holmes

Including children with learning differences: Experiences of independent school teachers , Lisa M. Lockhart

The Effects of Music Choice on Perceptual and Physiological Responses to Treadmill Exercise , Taylor A. Shimshock

Theses/Dissertations from 2017 2017

Perceptions of Arab American Mothers of Children with Autism Spectrum Disorder: An Exploratory Study , Haifa Alsayyari

It’s Not All Sunflowers and Roses at Home: A Narrative Inquiry of At-Risk Girls and Their Perceptions of Their Educational Experiences , Jessica Aggeles Curtis

Exploring Mathematics Teacher Education Fieldwork Experiences through Storytelling , Melody Jeane Elrod

Improving Reading Comprehension of Children with ASD: Implication of Anaphoric Reference Support with Computer Programming , Seda Karayazi Ozsayin

A Qualitative Content Analysis of Early Algebra Education iOS Apps for Primary Children , Lissa S. Ledbetter

Cultivating Peace via Language Teaching: Pre-Service Teachers' Beliefs and Emotions in an EFL Argentine Practicum , María Matilde Olivero

Collaboration with Families: Perceptions of Special Education Preservice Teachers and Teacher Preparation , Mehmet Emin Ozturk

Perspectives of AP U.S. History Teachers in Title I Schools , Mark Lance Rowland

What Does It Mean to Be a Service-Learning Teacher? - An Autoethnography , Kristy Causey Verdi

Early Childhood Mathematics Through a Social Justice Lens: An Autoethnography , Jennifer Ward

Theses/Dissertations from 2016 2016

Urban English Language Arts Teachers’ Stories of Technology Use: A Narrative Inquiry , Bridget Abbas

Teachers’ Third Eye: Using Video Elicitation Interviews To Facilitate Kuwaiti Early Childhood Preservice Teachers’ Reflections , Hessa Alsuhail

Foreign Language College Achievement and the Infusion of Three Selected Web 2.0 Technologies: A Mixed Method Case Study , Eulises Avellaneda

Emotional Self-Regulation: Voices and Perspectives of Teachers within Diverse Socio-Cultural Contexts , Anna Paula Peixoto Da Silva

The Effect of Exercise Order on Body Fat Loss During Concurrent Training , Tonya Lee Davis-Miller

Subtext of Decisions: Literacy Practices in the Context of Coding , Julia Hagge

The Role of Prep Schools in the Middle to High School Transition of Students in Southeastern Turkey , Mucahit Kocak

“It’s Not Pixie Dust”: An Exploratory Qualitative Case Study of a School-Based Multimodal Tablet Initiative , Erin Elizabeth Margarella

Influence of Language Arts Instructional Practices on Early Adolescents’ Motivation to Read: Measuring Student and Teacher Perceptions , Sarah E. Pennington

Educators' Oral Histories of Tampa Bay Area Writing Project Involvement , Margaret Hoffman Saturley

Anti-Fat Attitudes and Weight Bias Internalization: An Investigation of How BMI Impacts Perceptions, Opinions and Attitudes , Laurie Schrider

Use of a Game-Based App as a Learning Tool for Students with Mathematics Learning Disabilities to Increase Fraction Knowledge/Skill , Orhan Simsek

Theses/Dissertations from 2015 2015

Examining Experiences of Early Intervention Providers Serving Culturally Diverse Families: A Multiple Case Study Analysis , Wendy Lea Bradshaw

"I want to be the Sun": Tableau as an Embodied Representation of Main Ideas in Science Information Texts , Margaret Branscombe

A Case Study of Teachers' in Professional Learning Communities in a Campus Preschool , Victoria Jacqueline Damjanovic

Student-teacher Interaction Through Online Reflective Journals in a High School Science Classroom: What Have We Learned? , Megan Elizabeth Ehlers

Novice Teachers' Stories of Solving Problems of Practice , Yvonne Franco

Facilitating Motivation in a Virtual World Within a Second Language Acquisition Classroom , Andrew Warren Gump

IWitness and Student Empathy: Perspectives from USC Shoah Foundation Master Teachers , Brandon Jerome Haas

Precalculus Students' Achievement When Learning Functions: Influences of Opportunity to Learn and Technology from a University of Chicago School Mathematics Project Study , Laura A. Hauser

The Role of the Interruption in Young Adult Epistolary Novels , Betty J. Herzhauser

A Conceptual Analysis of Perspective Taking in Support of Socioscientific Reasoning , Sami Kahn

Restricted and Repetitive Behaviors as Strengths, not Weaknesses: Evaluating the Use of Social Stories that Embed Restricted Interests on the Social Skills of Children with Autism Spectrum Disorder , Maya Nasr

Job Satisfaction of Adjunct Faculty Who Teach Standardized Online Courses , Claudia A. Ruiz

Relationships between the Algebraic Performance of Students in Subject-Specific and Integrated Course Pathways , Derrick Saddler

The Common Core State Standards: Its Reported Effects on the Instructional Decision Making of Middle School Social Studies Teachers , Tracy Tilotta

The Influence of Types of Homework on Opportunity to Learn and Students' Mathematics Achievement: Examples from the University of Chicago School Mathematics Project , Yiting Yu

Theses/Dissertations from 2014 2014

Picturing the Reader: English Education Pre-service Teachers' Beliefs About Reading Using Photovoice , Michael Dicicco

The Effect of Music Cadence on Step Frequency in the Recreational Runner , Micaela A. Galosky

Balanced Artistry: Describing and Explaining Expert Teacher Practice as Adaptive Expertise , Nina Graham

The Fight Within: Experiences of School District Employees Who Advocate for the Rights of Their Own Children with Disabilities Inside the Districts Where They Work, a Heuristic Case Study , Keri Haley

A Phenomenological Study of the Experiences of Higher Education Students with Disabilities , Allen J. Heindel

Constructing an "Appropriate" Education in Florida Special Education Due Process Final Orders , Michelle Henry

The Effect of Teachers' Epistemological Beliefs on Practice , Milton David Huling

Perceptions, Beliefs and Practices about Technology among Teachers in a Jamaican Infant School , Suzette Anissia Kelly

"Choosing My Words Carefully": Observing, Debriefing, and Coaching Four Literacy Teachers' Through Their Lessons , Iveta Maska

Presentation of Civic Identity in Online High School Social Studies Discussion Forums , Holly Mcbride

In Our Image: The Attempted Reshaping of the Cuban Education System by the United States Government, 1898-1912 , Mario John Minichino

The Hypertrophic Effects of Practical Vascular Blood Flow Restriction Training , John Francis O'halloran

Science Teachers' Understandings of Science Practices before and after the Participation in an Environmental Engineering Research Experiences for Teachers (RET) Program , Dilek Özalp

The Effects of Emotive Reasoning on Secondary School Students' Decision-Making in the Context of Socioscientific Issues , Wardell Anthony Powell

Interagency Collaboration for the Provision of Services to Migrant Children with Disabilities: An Exploratory Study , Georgina Rivera-Singletary

Reflections in the Classroom: Perspectives on Teaching for Social Justice from Secondary Social Studies Educators , Gregory Lee Samuels

A Case Study of the Roles and Perceptions of Writing Coaches , Amy June Schechter

Genres of Children's Websites: A Comprehensive Methodology for Analyzing Digital Texts , James L. Welsh

Theses/Dissertations from 2013 2013

Attitude Toward Digital and Print-Based Reading: A Survey for Elementary Students , Diedre D. Allen

Playing in Trelis Weyr: Investigating Collaborative Practices in a Dragons of Pern Role-Play-Game Forum , Kathleen Marie Alley

Curriculum Gatekeeping in Global Education: Global Educators' Perspectives , Robert Wayne Bailey

Reading Assessment Practices of Elementary General Education Teachers: A Descriptive Study , Sarah Mirlenbrink Bombly

An Investigation of the Effects of an Authentic Science Experience Among Urban High School Students , Angela Chapman

Social Studies Teachers in an Evaluative Role: The Peer Evaluator Experience in the Accountability Era , Martha Barnes Ford

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This collection contains University of Texas at Austin electronic theses and dissertations (ETDs). The collection includes ETDs primarily from 2001 to the present. Some pre-2001 theses and dissertations have been digitized and added to this collection, but those are uncommon. The library catalog is the most comprehensive list of UT Austin theses and dissertations.

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• UT Electronic Theses and Dissertations   30995

• Open access
• Published: 08 May 2024

Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

BMC Medical Education volume  24 , Article number:  512 ( 2024 ) Cite this article

225 Accesses

Metrics details

Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

Peer Review reports

A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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Philip, M. Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework. BMC Med Educ 24 , 512 (2024). https://doi.org/10.1186/s12909-024-05487-y

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E- Learning

Paper instructions: I need a research paper on an evaluation plan to be conducted on a model for assessing readiness of faculty members in crises countries such as Libya, Tunisia and Egypt. The model has been developed with its Assessment Measurement Instrument and the methods chosen for evaluation are case studies.

However, in my report I am following a Design Science Research Methodology, where evaluation of models is carried out in two parts: 1.    Artificial evaluation- Judges a model in a contrived and non-realistic way (A single case study to be used in a private University). 2.    Naturalistic evaluation- Naturalistic evaluation considers real users in real contexts, i.e. evaluation “within an organisation” (Multiple case studies and focus group discussion to be used). Case studies- Three universities U1, U2 and U3 have been chosen to be used as in Libya, Tunisia and Egypt, respectively. Focus groups discussions- to be used at the end to discuss the result of implementing the model and the instrument in real context.

What is needed is a plan or the steps to be carried out as part of evaluating the readiness model and its assessment instrument using the two above approaches. In other word, what do you usually to test something using case studies? I have attached the model and the instrument in File 1 and two examples, Ex1 and Ex2.

Based on the review of e-Learning Readiness Models, the artefact (Model) eLearning Readiness Assessment Model illustrated in Figure 1 below is developed to guide the study. The proposed model is an eclectic model for assessing faculty e-Learning readiness.  The five main parameters that are used to develop the hybrid model are; Personal readiness, attitudinal readiness, Institutional readiness, Technological readiness and cultural readiness (social readiness).

Appendix 1 lists the finalised items constituting the Assessment Measurement Instrument. Participants will report their perceptions on these items with a five-point Likert-scale from 1 to 5, where 1 being “strongly disagrees” and 5 being “strongly agree”. The aspects of readiness can be measured with the readiness level being bench marked as: ‘not ready lot of work to be done’ for items between 1 and 2.6, ‘not ready some work to be done’ for items between 2.6 and 3.4, ‘expected level of readiness’ for items on 3.4, ‘ready but need some improvements’ for items between 3.4 and 4.2 and ‘ready to go’ for items between 4.2 and 5. Aydın and Tasci identification of “expected level of readiness” for e-Learning which is defined as the mean score of 3.40 will be adopted in order to evaluate the survey results as part of the artefacts evaluation. Figure 2 below illustrates the levels of measuring readiness. Figure 3: Levels of measuring e-readiness (Aydın and Tasci, 2005)

APPENDIX 1: Faculty Readiness Assessment Measurements Instrument

Readiness Category    Readiness Factor    No.    Items Personal    ITE    1    I use e-mail as the main communication tool with my students and colleagues 2    I use office software (e.g., MS Word, MS Power Point, etc.) for content delivery and demonstration 3    I use social network sites (e.g., Facebook, Twitter, Skype, etc.) ATT    4    I am aware of what e-Learning is and of its benefits 5    I support and intend to use e-Learning in my teaching 6    E-Learning will create more stress for me as an instructor SE    7    I feel confident in my ability to use e-Learning in teaching 8    I can teach myself most of the things I need to know about using e-learning 9    I am hesitate to use e-Learning for fear of making mistakes G    10    Gender dissimilarity makes no difference in the adoption of e-Learning 11    E-Learning  is a good teaching tool to resolve gender segregation in HEIs, in the region 12    Female faculty members most beneficiary of using e-Learning Attitudinal Beliefs    PU    13    Adopting e-Learning will allow me to accomplish teaching tasks effectively and quicker 14    Using e-Learning system will improve my teaching performance 15    Adoption of e-Learning in the region will have a positive effect on  the educational process PEOU    16    Learning the use of e-Learning will be easy for me 17    Using e-Learning systems is easy to use 18    Using e-Learning will make it easier for me to teach course content PF    19    Adopting e-Learning will offer me flexibility in learning as to time and place 20    Adopting e-Learning will save me time and effort commuting to the university 21    Adopting e-Learning will allow me control over my teaching Institutional    PD    22    I need extensive training to develop e-content 23    My university provides training to prepare faculty using e-Learning 24    My university is willing to invest in my future professional development LS    25    University management  is enthusiastic about e-Learning 26    Management  support is strong at my college/department 27    My college/department has adequate financial resources to develop technology-based initiatives IN    28    Greater financial rewards are needed to get faculty to participate in e-Learning 29    Greater release time is needed to get faculty to design and deliver e-Learning courses 30    I am morally motivated and encouraged to use e-Learning Technological    ACC    31    The cost of connecting to the internet is affordable 32    I have an easy access to the internet with good speed in the university 33    I am very satisfied with the networked infrastructure at my university TS    34    I do not face any technical problems while using the internet 35    A help desk is available at my department when there is technical problem 36    An able and effective technical team is always available at my university ISS    37    Information Security is a major concern with e-Learning systems 38    Extra security precaution is needed with e-Learning before I would feel comfortable using e-Learning in my teaching 39    I am satisfied with the security provided by my university to protect information Cultural    LANG    40    Use of local Arabic Language is important for e-Content development and teaching 41    Use of foreign Language such English and French simplifies and improves teaching online 42    My students won’t accept learning by languages other than Arabic SC    43    E-Learning is seen as a cultural invasion by society in the region 44    Religious view in the region could be an obstacle to adopting e-Learning 45    My university culture does not dupport e-Learning SI    46    I will take my family view regarding using e-Learning 47    People whom opinions I value would think I should use e-Learning in my teaching 48    Whether I decide to adopt e-Learning for teaching is entirely up to me

Demonstration and Evaluation The aim of the demonstration and evaluation phase of DS methodology is to demonstrate that the artefact (DQ methodology) feasibly works to achieve its objectives (stated in section 3) in at least one context. Additionally, it considers how well developed DQ method support solution to the problem. The utility, quality, and efficacy of the method must be rigorously demonstrated via well-executed evaluation methods. In order to give answer to the third research question (RQ3)(Section 3) a combination of quantitative and qualitative analysis techniques are and will be used. The main techniques for evaluating this research are set to be case studies and focus groups [68]. Further information about evaluating and expected outcomes will be given in the following section.

Case Studies To objectively evaluate the research artifact, a case study method has been selected. Case study is an empirical inquiry that investigates a phenomenon within its real-life context [68]. To evaluate the DQ methodology in an operational manner I plan to engage it into a real world cases. Part of the methodology is expected to be implemented as prototypical software tool. This tool can be exclusively implemented for the particular case (as it will be shown later) or brought of the shell, following the DQ requirements and available SOA infrastructure. Data collection from executing different scenarios will be done following the general principles of conducting case studies. More particularly information will be gather in form of personal recording, open ended, formal and informal interviews, charts graphs as well as environmental observations. 2.1.1    Case study Dynamic Open Home-Automation (DOHA). At present time, I am working in close cooperation with the University of Granada on a project called Dynamic Open Home-Automation (DOHA) [77]. DOHA is a SOA-based platform for the access, control and management of home-automated systems, composed of a set of lightweight and independent services (devices). The collaboration between services in DOHA is resolved based on the Peer-to-Peer (P2P) orchestration principles in which the interaction control and execution order of operations, messages and transactions required in the collaboration, is the responsibility of each service. The DOHA composition is shown on fig. 8 divided into service, virtual and physical layers. Since the goal is to detect semantic data inaccuracies, of interest in this case is the service level. I intend to apply my methodology in the following way: Information will be profiled for the given case using the template proposed in Section 5.1. Next, in the definition stage, profiled data will be used to construct the predicates. The process of construction will be empowered by ontology about the environment. At this stage, I plan that ontology will be built by administrator using Protégé [78] software. After predicates are built and stored in a comprehensive repository, the execution is followed. The process of execution will transform the predicates into readable for services envelops. Then the latter will be dispatched through the infrastructure using JXTA [79] message standard. Then the results of the execution will be saved in to log files. The process concludes with reading the aforementioned and generating reports that will be analysed. To encapsulate everything, in this case, an exclusive module (“quality predicate manager” fig. 8) needs to be implemented and integrated within the DOHA middleware that will eventually ensure efficient detecting of poor semantic data. Except for evaluation the artifact I am also planning to apply the practitioner cycle (See figure 3) in a way of constant discussions with the partners from University of Granada, which would positively increase the quality of the artifact. 2.1.2    Case study – Goods Retailer Web service Composition Another case that I might apply this methodology is presented in Appendix 3. The diagram depicts a web service oriented configuration. In this case, the cooperation between services based on the centralized orchestration principles (ESB). Also, instead of storing the information about each process in every service, a special module (BPE) is responsible for managing and execution all the processes. The latter are also stored in a business process repository. Additional information about every process is stored in business rule repository. The communication protocol in this case is SOAP. Following DQ methodology, in the preparation stage, quality predicates will be composed based on the information stored for particular process. Then predicates will be converted in SOAP envelops and dispatched using the BPE. Consequently, they will be stored in practical oriented log file. 2.2    Focus groups After a case is deployed, a number of scenarios will be developed. Based on them, operational criteria such as usability, efficiency and effectiveness will be evaluated using focus groups. In order to provide more objective results, people from these groups will be system architects manager, administrators and developers in charge of setting and implementing the methodology. A summary table with the criteria and method chosen is presented below. Criteria    Description    Method    Example Usability    The extent to which a methodology can be used by specified users to achieve specified goals in a specified case (employed by [80]) Modified positive System Usability Scale (SUS) approach [81] Quantifying the usability by collecting simple pass/fail metrics and reporting completion rates    Profiling data: Did the user/administrator identify the service name in the given scenario? Building quality predicates: Did the user/administrator managed to build the quality rule to ensure the office temperature? Efficiency        Time required to deploy the methodology    Measuring the total efforts needed in time and expenses.    Time necessary to implement the module of constructing the quality predicates Time required to execute the methodology    Measuring the total  time for execution including: -time for profiling data -time for building quality statements -time for execution by the middleware and environment -time for analysing     Measuring the time needed to different users to perform same task/scenario, executed by the same composition. Measuring the time needed to the system execute same task on the same scenario. Services’ response times are constant. Effectiveness    The extent to which a methodology can detect inaccurate semantic data    analysing the content of the reports create in the end of the execution stage    Analysing the report created by the user/administrator containing service name, process involved into error, violated value, etc.

Level    Syntactic and Semantic(Rigorous)    Pragmatic(Relevance) Evaluation Factors

Artifact Packages     Process quality requirements    Research output requirements Correctness    Flexibility    Understandability    Simplicity    Implementability    Support  Semi -structured Data    Support distributive, cooperative, web and P2P types of IS    Detect semantic inaccuracy Profiling Information    ▲    ▲    ∆    ▲    ?    ▲    n/a    n/a Preparation Stage    ∆    ▲    ∆    x    ?    ?    ▲, ?    n/a Execution Stage    ▲    ▲    ∆    x    ?    ?    ▲, ?    ∆ Analysing Stage    ▲    ▲    ▲    ▲    ?    ?    ▲, ?    ▲, ? ▲ – Fulfil the requirement; ∆ – partly fulfil the requirement   x – do not fulfil the requirement;  ?- results in progress  n/a – not applicable

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A Chicago teen entered college at 10. At 17, she earned a doctorate from Arizona State

Dorothy Jean Tillman II participates in Arizona State University’s commencement, May 6, 2024, in Tempe, Ariz. Tillman, 18, earned her doctoral degree in integrated behavioral health in December at age 17 from the school. Tillman, of Chicago, began taking college courses at age 10. She earned her associate’s, bachelor’s and master’s degrees before she turned 17. (Tillman Family via AP)

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CHICAGO (AP) — Dorothy Jean Tillman II’s participation in Arizona State University’s May 6 commencement was the latest step on a higher-education journey the Chicago teen started when she took her first college course at age 10.

In between came associate’s, bachelor’s and master’s degrees.

When Tillman successfully defended her dissertation in December, she became the youngest person — at age 17 — to earn a doctoral degree in integrated behavioral health at Arizona State, associate professor Leslie Manson told ABC’s “Good Morning America” for a story Monday.

“It’s a wonderful celebration, and we hope ... that Dorothy Jean inspires more students,” Manson said. “But this is still something so rare and unique.”

Tillman, called “Dorothy Jeanius” by family and friends, is the granddaughter of former Chicago Alderwoman Dorothy Tillman.

When most students are just learning to navigate middle school, her mother enrolled Tillman in classes through the College of Lake County in northern Illinois, where she majored in psychology and completed her associate’s degree in 2016, according to her biography.

Tillman earned a bachelor’s in humanities from New York’s Excelsior College in 2018. About two years later, she earned her master’s of science from Unity College in Maine before being accepted in 2021 into Arizona State’s Behavioral Health Management Program.

Most of her classwork was done remotely and online. Tillman did attend her Arizona State commencement in person and addressed the graduating class during the ceremony.

Tillman told The Associated Press on Tuesday that she credits her grandmother and trusting in her mother’s guidance for her educational pursuits and successes.

“Everything that we were doing didn’t seem abnormal to me or out of the ordinary until it started getting all of the attention,” said Tillman, now 18.

There have been sacrifices, though.

“I didn’t have the everyday school things like homecoming dances or spirit weeks or just school pictures and things like that ... that kind of create unity with my peers,” she said.

She has found time to dance and do choreography. Tillman also is founder and chief executive of the Dorothyjeanius STEAM Leadership Institute. The program includes summer camps designed to help young people in the arts and STEM subjects.

She said her plans include public speaking engagements and fundraising for the camp, which Tillman said she hopes to franchise one day.

Tillman is motivated and has innovative ideas, said Manson, adding, “And truly, I think what is inspiring is that she embodies that meaning of being a true leader.”

Jimalita Tillman said she is most impressed with her daughter’s ability to show herself and her successes with grace, but to also understand when to “put her foot down” when choosing between social outings and her education.

Associated Press researcher Jennifer Farrar in New York contributed to this report.

Teen walks at graduation after completing doctoral degree at 17

Dorothy Jean Tillman II was 10 when she entered college as a freshman.

A teenager from Chicago walked in her graduation ceremony this month after earning her doctoral degree at 17.

Dorothy Jean Tillman II told " Good Morning America " that she was homeschooled in her early years before entering college at age 10.

In 2020, she said she earned a Master of Science degree, and then, one year later, at age 15, was accepted into the Doctorate of Behavioral Health Management program at Arizona State University.

In December 2023, at 17, Tillman successfully defended her dissertation to earn her doctoral degree in integrated behavioral health from ASU's College of Health Solutions.

On May 6, she walked at ASU's spring commencement ceremony.

Tillman told "GMA" she has always held education in such high regard in part due to her family's background.

"People in my life like my grandmother, who was part of the Civil Rights movement, she of course harped on the importance of education and consistently learning something always," Tillman said. "But the way I always held education so high on my own, aside from being raised that way, was finding different things to be educated about."

She continued, "I feel like that urge to learn something new just never didn't exist for me."

Teen who battled leukemia and homelessness as a child graduates college at 18

Dr. Lesley Manson, a clinical associate professor at ASU, told "GMA" that Tillman is the youngest person in school history to earn a doctoral degree in integrated behavioral health.

Manson said she oversaw Tillman's dissertation for the doctoral program offered through ASU Online.

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During her studies, Tillman wrote a journal article of her dissertation and completed an internship at a university student health center, according to Manson.

"She really led change and worked on different forms of management to really reduce healthcare stigma and improve that student population there to be able to enter and accept student health services," she said of Tillman. "It was wonderful to see her and help her navigate some of those personal and professional interactions and grow through those experiences."

Manson described Tillman as an "inquisitive" and "innovative" student, and emphasized just how rare it is to accomplish what she has so far.

"It's a wonderful celebration ... but this is still something so rare and unique," she said. "She has innovative ideas and motivation, which is wonderful, and truly, I think what is inspiring is that she embodies that meaning of being a true leader."

Manson said she hopes Tillman continues to inspire people with her love of learning, saying, "That curiosity is always there, and I think all learners come with that, but it's great to be able to see it in someone so young as well."

Her inspiration and how she gives back to community

Tillman said her own journey wouldn't be possible without the support of her mom, who she said is one of her biggest motivators.

"Seeing my mother consistently work so hard to continuously uphold our family's legacy, and be that person that everyone was able to go to, if they needed anything ... always seeing [her] like [a] 'wonder woman' definitely made me want to grow up [into] an accomplished person," she said.

An advocate for education, Tillman is also the founder and CEO of a leadership institute that emphasizes the arts and STEM.

"I feel like adding art and putting a focus on it throughout science, technology, engineering and math makes the kids excited to learn all those things," she said. "And it opens them up to all of the possibilities and all the knowledge provided in that area of just STEM."

'Super dad' graduates with master's while working 3 jobs

As for her plans after graduation, Tillman said she is "just like any other teenager, still figuring out what my specific dreams and goals are."

"I'm really just grateful that the world is my oyster, and that I've done so much so young," she said. "And I have time to kind of think that through."

Tillman added that she hopes young people will take away from her story that it's OK to continually figure out what you want to do in life.

"Always remember that everyone has points in their life where they feel like they're figuring it out," she said. "And so figuring things out, not knowing what you want isn't a bad thing. But making the choice not to sit down and try to figure it out is."

Editor's note: This story has been updated with additional quotes from Tillman since its original publish date of May 13, 2024.

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