The importance of self-regulation in a MOOC has been extensively discussed in research studies that provide evidence about the significant relationship between self-regulated learning and success in an e-learning environment. Learners with high self-regulated learning are more independent in regulating their learning and have a greater probability of success in their online courses. This study identifies factors that influence self-regulated learning and determines relationships between these factors and self-regulated learning. A conceptual model is proposed for combining success factors for self-regulated learning in a MOOC environment. A research instrument based on the model was designed and administered to six hundred and twenty-two MOOC students enrolled in five universities. Relationships between relevant factors and selfregulated learning were examined using a Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, and the statistical findings revealed that three factors-service quality, attitude, and course quality-influence self-regulated learning in a MOOC.

In higher education learning, e-learning systems have become renowned tools worldwide. The evident importance of e-learning in higher education has resulted in a prenominal increase in the number of e-learning systems delivering various forms of services, especially when traditional education (face-to-face) was suddenly forced to move online due to the COVID-19 outbreak. Accordingly, assessing e-learning systems is pivotal in the interest of effective use and successful implementation. By relying on the related literature review, an extensive model is developed by integrating the information system success model (ISSM) and the technology acceptance model (TAM) to illustrate key factors that influence the success of e-learning systems. Based on the proposed model, theory-based hypotheses are tested through structural equation modeling employing empirical data gathered through a survey questionnaire of 537 students from three private universities in Jordan. The findings demonstrate that quality factors, including instructor, technical system, support service, educational systems, and course content quality, have a direct positive influence on students’ satisfaction, perceived usefulness, and system use. Moreover, self-regulated learning negatively affects students’ satisfaction, perceived usefulness, and system use. Students’ satisfaction, perceived usefulness, and system use are key predictors of their academic performance. These findings provide e-learning stakeholders with important implications that guarantee the effective, successful use of e-learning that positively affects students’ learning.

Massive Open Online Courses (MOOC) is a new phenomenon in online learning that has aroused increasing interest by researchers as a significant contribution to improving educational system quality and openness. The purpose of this paper is to compile and analyze MOOC research that has been published between 2012 and 2016. A systematic analysis technique was employed and Template Analysis (TA) approach was used for mapping MOOC research into three dimensions in accordance with the Biggs 3P model. First dimension is Presage, include the following factors: Learners' characteristics with sub-factors (learner demographics, learner motivation, and interactivity) and instructor. Second, Process, including factors of pedagogy, pattern of engagement, instructional design, assessment, credit, plagiarism, sustainability, and learning analytics. Third dimension is Product, including factors of student dropout rate and MOOC quality. This classification is aimed at providing a comprehensive overview for readers interested in MOOCs who seek to understand the critical success factors influencing MOOC success.

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Metaverse, which combines a number of information technologies, is the Internet of the future. A media for immersive learning, metaverse could set future educational trends and lead to significant reform in education. Although the metaverse has the potential to improve the effectiveness of online learning experiences, metaverse-based educational implementations are still in their infancy. Additionally, what factors impact higher education students’ adoption of the educational metaverse remains unclear. Consequently, the aim of this study is to explore the main factors that affect higher education students’ behavioral intentions to adopt metaverse technology for education. This study has proposed an extended Technology Acceptance Model (TAM) to achieve this aim. The novelty of this study resides in its conceptual model, which incorporates both technological, personal, and inhibiting/enabling factors. The empirical data were collected via online questionnaires from 574 students in both private and public universities in Jordan. Based on the PLS-SEM analysis, the study identifies perceived usefulness, personal innovativeness in IT, and perceived enjoyment as key enablers of students’ behavioral intentions to adopt the metaverse. Additionally, perceived cyber risk is found as the main inhibitor of students’ metaverse adoption intentions. Surprisingly, the effect of perceived ease of use on metaverse adoption intentions is found to be insignificant. Furthermore, it is found that self-efficacy, personal innovativeness, and perceived cyber risk are the main determinants of perceived usefulness and perceived ease of use. While the findings of this study contribute to the extension of the TAM model, the practical value of these findings is significant since they will help educational authorities understand each factor’s role and enable them to plan their future strategies.

Aim/Purpose: This study seeks to investigate the factors that influence online students’ continued usage intention toward e-learning systems by presenting an extended model that is based on the Delone and McLean (2003) IS success model (D&M ISS model). Background: The use of e-learning systems in this era has become a vital element of delivering higher education. Learning via e-learning systems has significant benefits that support conventional learning. Thus, it is crucial to measure the success of e-learning systems’ implementation. Methodology: This study was conducted with 590 undergraduate and postgraduate students from three private universities in Jordan, and data was gathered via an online self-report questionnaire. Contribution: Theoretically, this study advances the literature and empirically examines a modified version of the D&M ISS model by including context-specific factors that are drivers of successful implementations of e-learning systems. Findings: The path analysis with structural equation modelling confirms that students’ satisfaction and their continued usage intention regarding the e-learning system are positively related to service quality, system quality, and information quality. Self-directed learning, however, has a negative effect on satisfaction and continued usage intention. Furthermore, the findings reveal that both satisfaction and continued usage intention positively influence students’ perceptions of perceived academic performance. Recommendations for Practitioners: The quality of learning content format and design are recognized as fundamental factors for e-learning success. Thus, both instructors and e-learning developers should provide reliable, accurate, and up-to-date learning materials. This directs e-learning developers toward designing systems with simple and useful functionalities that embrace the essential features that enable students to perform the required tasks effectively and to access and share learning materials flexibly. Furthermore, the current study reveals that self-directed learning (SDL) is a key barrier to successful e-learning system employment. It has a negative impact on satisfaction (SAT) and continued usage intention (CUI). Thus, developing students’ skills related to SDL is deemed a necessity. This could be attained by designing contemporary pedagogical curricula that are based on student-centered learning. This approach to learning encourages students to acquire self-regulatory skills and be accountable for their learning. This environment has to be supported by pedagogical tools (e.g., synchronous/asynchronous communication channels and multimedia tools) to enable effective interaction between instructors and students. Recommendation for Researchers: The current study does not investigate the role of potential moderators that might influence the research model’s relationships. Future studies might tackle such limitation by examining the moderating effect of computer self-efficacy and culture. Impact on Society: This study reveals that the success of e-learning systems depends not only on the quality of the information, system, and service but also on student self-directed learning. Future Research: The sample employed for this study was selected from three private universities in Jordan; consequently, the results cannot be generalized to the entire student population of Jordan. Further research, therefore, should focus on targeting a larger scope by including public universities, which in turn would enhance the generalizability of the findings. In addition, this cross-sectional study was conducted using a quantitative method based on the use of self-reported online survey to gather data. Thus, future research should consider longitudinal study that employs a mixed methods approach to reveal additional constructs and insights regarding e-learning system adoption by students.

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