The recommendation is an active area of scientific research; it is also a challenging and fundamental problem in online education. However, classical recommender systems usually suffer from item cold-start issues. Besides, unlike other fields like e-commerce or entertainment, e-learning recommendations must ensure that learners have the adequate…

MOOC platforms provide a means of communication through forums, allowing learners to express their difficulties and challenges while studying various courses. Within these forums, some posts require urgent attention from instructors. Failing to respond promptly to these posts can contribute to higher dropout rates and lower course completion…

Higher education institutions have consistently strived to provide students with top-notch education. To achieve better outcomes, machine learning (ML) algorithms greatly simplify the prediction process. ML can be utilized by academicians to obtain insight into student data and mine data for forecasting the performance. In this paper, the authors…

Prior research on the "Community of Inquiry" (CoI) framework has a limited amount of work which uses structural techniques to confirm the factorial structure of the CoI. The current study investigates the structural relationships among the three elements of the CoI framework (cognitive presence, teaching presence, and social presence),…

There is a growing need to empirically evaluate the quality of online instructional interventions at scale. In response, some online learning platforms have begun to implement rapid A/B testing of instructional interventions. In these scenarios, students participate in series of randomized experiments that evaluate problem-level interventions in…

Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…

In e-learning environments, most adaptive systems do not consider the learner's emotional state when recommending activities for learning difficulties, blockages, or demotivation. In this paper, we propose a new approach of emotion-based adaptation in e-learning environments. The system will allow recommendation resources/activities to motivate…

This research addresses the pressing need for innovative educational frameworks that foster creativity and innovation in online learning environments. The study develops and validates a comprehensive model integrating STEAM education, micro-learning principles, and augmented reality (AR) technology within massive open online courses (MOOCs).…

Online learning communities play a crucial role in delivering high-quality courses to a large number of learners. However, to maintain an economically sustainable and constantly evolving online learning ecosystem, it is essential to create a virtuous cycle from knowledge production to knowledge consumption by charging learners to incentivize…

In the context of virtual learning communities (VLCs), where the participants may not know each other, it is necessary to have a mechanism to help when deciding who to work with and what reliable contents and information sources are. This study aims to design a generic trust model, named T-VLC, applicable to VLCs, which can be adapted to different…

In this study, 210 articles were reviewed from two databases--Web of Science and Scopus--using the systematic literature review method, following the PRISMA (preferred reporting items for systematic reviews and meta-analyses) selection and analysis process. Through systematic literature review and statistical analysis of empirical research…

Explainable Artificial Intelligence (XAI) refers to systems that make AI models more transparent, helping users understand how outputs are generated. XAI algorithms are considered valuable in educational research, supporting outcomes like student success, trust, and motivation. Their potential to enhance transparency and reliability in online…

This study advances the emerging research on shared metacognition through the lens of the community of inquiry framework. It seeks components and utterances of the community of inquiry and shared metacognition in online collaborative learning environments to bring an instructional design model to the fore. A three-cycle design-based research…

The aim of research is to reveal the effect of a training program based on the TAWOCK model for teaching computational thinking skills on teaching self-efficacy among computer teachers. It used the quasi-experimental approach, with a pre-test and post-test design with a control group. An electronic training program based on the TAWOCK model was…

This study innovates English network teaching by applying a refined Association Rule Mining (ARM) algorithm. It integrates an "interest" parameter into ARM, dynamically adapting content to individual learners' profiles, improving engagement and outcomes. Controlled experiments, spanning diverse online platforms, validate the ARM model's…