Since the outbreak of the pandemic in 2019, pandemic prevention and control has gradually become normal, and internet-based massive open online courses (MOOCs) have become widely available. Aiming to give an insight into learner engagement, motivation, and learning performance in MOOCs in the post-pandemic time, this study reviewed 52 articles…

This study aimed to understand the relationship between course activities and learning progress among students enrolled in the MicroMasters certificate program offered in an affordable MOOC-based learning platform. In order to capture the relationship, the differences between the engagement patterns of learners in the MicroMasters program compared…

The purpose of this study was to evaluate the factors that influence behavioural intention (BI) to use the Online Summary-with Automated Feedback (OSAF) in a MOOCs platform. Task-Technology Fit (TTF) was the main framework used to analyse the match between task requirements and technology characteristics, predictng the utilisation of the…

The research aims to establish a conceptual framework to design a MOOC to promote media literacy in the context of information disorder. Information disorder is a phenomenon that mainly occurs from the large flow of false or incorrect content posted on the internet. Promoting media literacy is a way of helping citizens to face the challenges of…

Despite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high…

Instructors are the most important component for the development of a Massive Open Online Courses (MOOCs) because of many MOOCs have been developed by instructors and offered free of charge. In this study we examine the benefits of sharing and course design actions during the enforcement of a MOOC. In this study a qualitative research design, case…

High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…

Traditionally, assessment professionals use analyses relying upon null hypothesis significance testing (NHST), but those tools have limitations when analyzing small samples or disaggregated data. This study used common NHST analytical techniques, compared their results, and then explored an alternative technique that perhaps allows for a more…

This paper explores the development and evaluation of a MOOCs-based virtual physics measurement laboratory module designed to overcome common barriers in physics education, such as limited physical lab access, high costs, and safety concerns. Utilizing the Borg and Gall Research and Development model, this module integrates structured layouts,…

This reflection on practice presents an exemplar of an integrated distributed leadership and reciprocal peer review of an academic development initiative. Distributed leadership, characterised by collaborative responsibility, is flexible and adaptive and -- when integrated with reciprocal peer review of teaching -- fosters quality learning…

This study examines the potential of inclusive design in Massive Open Online Courses (MOOCs) to address the impact of stereotype threat, a significant barrier encountered by underrepresented groups, particularly in STEM education. As online learning opportunities grow, the demographic composition of course participants frequently does not reflect…

Drawing on the technology acceptance model (TAM), self-determination theory (SDT), expectation-confirmation model (ECM), and massive open online courses (MOOCs) design effects, the present work introduced a conceptual model for understanding the fine-grained mechanism underlying learner satisfaction and continuance intention. A structural equation…

This study proposes a knowledge graph-based big data analysis model for course quality evaluation, aiming to address issues in online education course evaluations such as semantic bias, grammatical deficiencies, vocabulary limitations, false evaluations, information distortion, and imbalanced evaluation categories. The model incorporates three…

This study introduces a novel Learner-Intention Continuum, spanning from curiosity and exploration to purposeful, goal-directed learning. This continuum fills a critical gap in understanding the diverse motivations of informal and semi-formal learners, specifically those who enroll in massive open online courses (MOOCs). Through latent class…

This research aims to conduct a systematic and rigorous review of published articles on how the diagnosis and the design of micro-courses can enhance teachers' digital competence in universities and higher education institutions. The objective is to synthesize existing evidence on training strategies for Teacher Digital Competence in higher…