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This paper aims to examine the state of research on the symbiotic relationship between knowledge management and learning management systems in advancing the mutual strategic agenda of the two initiatives in higher education institutions (HEIs), so as to uncover the themes that have been studied, identify gaps in the existing studies and suggest future areas of research work.

The study adopted systematic literature review (SLR), in which 64 articles published between 2010 and 2022 were identified and analyzed.

Whereas the review revealed some focus areas that have been researched, it also found that only few studies have explicitly explored the symbiotic relationship between knowledge management and learning management systems, with fewer articles exploring this relationship finding their way to mainstream journals. Thus, the findings showed that examination of the interlink between knowledge management and learning management systems in HEIs is still less explored and has multiple possibilities for future research with potential benefits to the higher education industry.

Although different SLRs exist separately in the fields of knowledge management and learning management systems, there seem to be no reviews on the interconnection between the two fields in the context of HEIs. Additionally, this review offers insights into future research avenues for theory, content and context of interplay between knowledge management and learning management systems in HEIs.

The purpose of this study is to propose a hybrid model integrating the expectation-confirmation model with the views of cognitive absorption (CA) theory and updated DeLone and McLean information system success model to examine whether quality factors as antecedents to medical professionals’ beliefs can affect their continuance intention of the cloud-based e-learning system.

This study’s sampling frame was taken from among medical professionals working in hospitals with over 300 beds in Taiwan which had implemented the cloud-based learning management system (LMS) with a blend of asynchronous and synchronous technologies. Sample data for this study were collected from medical professionals at six hospitals in Taiwan. The data for this study were gathered by means of a paper-and-pencil survey, and each sample hospital that participated in this study was asked to identify a contact person who could distribute the survey questionnaires to medical professionals who had experience in using the cloud-based LMS in their learning. A total of 600 questionnaires were distributed, and 378 (63.0%) usable questionnaires were analyzed using structural equation modeling in this study.

This study proved that medical professionals’ perceived learner–content interaction quality, learner–system interaction quality, service quality, cloud storage service quality and learner–human interaction quality all positively caused their perceived usefulness, confirmation and CA elicited by the cloud-based e-learning system, which jointly explained their satisfaction with the system, and resulted in their continuance intention of the system.

Several limitations and suggestions may open avenues for future research. First, the limitation of self-reported measures should be considered; future research may combine with qualitative data (e.g. semi-structured, narrative, in-depth interviews, focus group interviews and open-ended questions) to get more complete interpretations of medical professionals’ cloud-based e-learning continuance intention. Next, this study’s data were collected from hospitals in Taiwan only; given this study’s limited scope, future research may generalize this study’s sample to the respondents of other national cultural backgrounds and make cross-country comparisons to enhance the completeness of this study. Finally, this study’ results were based on cross-sectional data; future research may use a longitudinal analysis by taking into account the evolution of medical professionals’ cloud-based e-learning continuance intention over time.

This study fully evaluates interaction-related and cloud-related quality determinants through an understanding of medical professionals’ state of CA in explaining their cloud-based e-learning system continuance intention that is difficult to expound with only their utilitarian perception of the system. Hence, the results contribute to deep insights into an all-round quality evaluation in the field of medical professionals’ cloud-based e-learning continuance intention, and extrinsic and intrinsic motivators are both taken into consideration in this study’s theoretical development of medical professionals’ cloud-based e-learning continuance intention to acquire a more comprehensive and robust analysis.

This study aimed at determining factors which affect university lecturers’ adoption of the Moodle platform under the conditions of COVID-19. In considering the condition of the COVID-19 pandemic, the unified theory of acceptance and use of technology (UTAUT) model was applied and extended by adding two additional variables of learning demand and time pressure to assess their influence on Moodle platform adoption.

Data were obtained from the 226 participants through an online structured questionnaire. The covariance-based approach of structural equation modeling was used to examine the proposed model. The structural model was tested using the maximum likelihood method of analysis of a moment structures to analyze the study’s hypotheses.

Results suggest that performance expectations have a substantial influence on behavioral intent. The effort expectancy, social effect and facilitative factors have no effects on behavioral intentions. Facilitating conditions directly and significantly affect the actual use of Moodle. The results also reveal that learning demands, which is a salient predictor of perceived time pressure, in turn directly and significantly affects the actual use of Moodle. Finally, the behavioral intention has a strong influence on Moodle’s actual usage.

Although the UTAUT 2 model is considered to be a new and updated version of UTAUT, it has not been used since newly added variables, namely, price, habit and hedonic motivations, are less related to the context and to avoid respondents’ paradox. Moreover, using the Moodle platform in the researched context is compulsory for both students and instructors. Discussion, insights, limitations and recommendations for future studies are suggested.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

Today’s undergraduate students have spent a significant portion of their lives using YouTube for various reasons, whether for entertainment, personal development or academic learning purposes. This study aims to investigate how system factors (i.e. reliability, usability and searchability), interaction factors (i.e. provider and user interactions) and content factors (i.e. format, relevance and coverage) affect undergraduate students’ satisfaction with YouTube and their intentions to adopt YouTube for learning purposes.

This research uses the information systems success model as its theoretical framework to explore the system, interaction and content factors associated with undergraduate students’ satisfaction with YouTube and their intentions to use YouTube for learning. The proposed hypotheses were examined by the structural equation modelling technique based on a survey with 345 undergraduate students at a Southeastern institution in the USA.

The results indicate that both system factors (including reliability, usability and searchability) and content factors (including format, relevance and coverage) have a statistically significant effect on students’ satisfaction with YouTube. This study also demonstrates that students’ satisfaction with YouTube significantly influences their intentions to use the platform for learning purposes.

The proposed research model provides a novel perspective in understanding the complex nature of students’ adoption of YouTube for learning purposes, led by both system and content factors mediated by satisfaction with YouTube.

This study suggests that when YouTube is intuitive to use and relevant content is added to the platform regularly, students are more likely to adopt this platform for learning purposes. As a result, it is critical that librarians remain aware of information-seeking practices and platforms used by students to tailor approaches to teaching information literacy to help students understand how to use the platform effectively.

Using the information systems success model, this research sheds light on the roles of system and content factors in undergraduate students’ satisfaction with YouTube and their intentions to use it for learning.

Digitalisation is reshaping the learning process, shifting it towards personalisation and online learning, and fuelling the growth of massive open online courses (MOOCs) and micro-credentials. Despite being a popular global trend, limited studies have looked at micro-credentials and their impact on learners' behavioural outcomes. The purpose of this study is to investigate the impact of programme design factors on learning experience, as well as the inter-relationships between programme design, learning experience and behavioural responses (e.g. engagement and willingness to pay more (WTPM)) towards micro-credentials. In addition, the study aims to investigate learning enjoyment as a moderator.

A survey questionnaire was used to collect data from 354 respondents who are working adults living in the major economic states in Malaysia. Data analysis was performed using the analysis of a moment structures (AMOS) statistical software and SPSS (Statistical Package for the Social Sciences) PROCESS macro.

The results show the significance of programme design factors (i.e., flexibility, system quality and content quality) in determining the learning experience. The learning experience is found as a mediator in the relationship between programme design factors and learner engagement and WTPM. In addition, the moderation assessment confirms that enjoyment during learning strengthens the relationship between experience and behavioural responses.

This study is amongst a few selected studies that focus on engagement in and WTPM for micro-credentials. In addition, it emphasises the mediating role of the learning experience and the moderating role of enjoyment in understanding the impact of programme design on learners' experiences and behavioural responses.

This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms.

The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model’s reliability and validity. Machine learning algorithms were used to determine the relationships among the constructs in the proposed research model.

The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students’ continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS.

The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context.

The purpose of this study is to propose the research model integrating the expectation-confirmation model with the views of learning engagement (LE) and extending DeLone and McLean information systems (IS) success model to examine whether quality determinants as antecedents to students' beliefs can influence students' continuance intention of massive open online courses (MOOCs).

Sample data for this study were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed, and 363 (60.5%) useable questionnaires were analyzed using structural equation modeling in this study.

This study proved that students' perceived knowledge quality, system quality, interface design quality, learner–instructor interaction quality, and collaboration quality all positively caused students' perceived usefulness, confirmation and LE in MOOCs, which jointly explained students' satisfaction with MOOCs and subsequently resulted in students' continuance intention of MOOCs.

This study fully evaluates IS-related and interaction-related quality determinants via an understanding of students' state of LE in explaining students' continuance intention of MOOCs that is difficult to expound with only their utilitarian perception of MOOCs. Hence, this study contributes to deep insights into an all-round quality evaluation in the field of MOOCs continuance intention and takes extrinsic and intrinsic motivators into account in the theoretical development of MOOCs continuance intention to acquire a more comprehensive and robust analysis.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

The increasing need for student engagement and the wide availability of digital teaching resources are providing opportunities for careful consideration and planning of assignments within and among business management courses. This paper aims to examine implementation strategies for integrating multiple business simulations with gradually increased assignment weighting and coordinated implementation in a university business curriculum.

A case study research design with a pattern-matching logic is used to represent a critical test of formulated programmatic and learning theories that have a set of propositions and circumstances with which the proposals are believed to be true. Applied digital management education tools used in a graduated weighting scheme compare an empirically-based pattern with a predicted pattern.

Systemic program-wide implementation of teaching resources such as simulations, microsimulations and application-based activity role-playing assignments can deliver engaging internal course and coordinated management program experiential-type learning. Carefully planning graduated assignment weights can be a practical strategy for using a low-risk approach to enhance experiential learning.

The strategies proposed provide a practical approach for controlling the learning pace and facilitating low-risk experiential learning through the modern digital business education landscape.

This paper investigates innovative implementation ideas to strategically arrange simulation assignments that can enhance success and prepare students future management work-based training. It explores the value of incorporating different types of business simulations and advanced active learning activities to provide students with engaging experiential learning experiences.