The proliferation of digital learning platforms has revolutionized the generation, accessibility, and dissemination of educational resources, fostered collaborative learning environments and producing vast amounts of interaction data. Machine learning (ML) algorithms have emerged as powerful tools for analyzing these complex datasets, uncovering…

Purpose: Predicting learner outcomes in blended learning (BL) is a new problem with many challenges, as learner data must be collected in both face-to-face and online environments. The purpose of this article is to identify the best method for building a model to predict student performance in BL and to determine the appropriate time for early…

The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality.…

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and…

The use of educational data mining and machine learning to analyse large data sets collected by educational institutions has the potential to discover valuable insights for decision-making. One such area that has gained attention is to predict student performance by analysing large educational data sets. In the relevant literature, many studies…

The digital trails that students leave behind on e-learning environments have attracted considerable attention in the past decade. Typically, some of these traces involve the production of different kinds of texts. While students routinely produce a bulk of texts in online learning settings, the potential of such linguistic features has not been…

The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to…

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…

This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses,…

The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment, learning takes place via Communities of Practice (CoP). The learning success factors in online CoPs are learners' similarity in learning interests and…

One of the keys to the success of collaborative learning is balanced participation by all learners, but this does not always happen naturally. Pedagogical robots have the potential to facilitate balance. However, it remains unclear what participation balance robots should aim at; various metrics have been proposed, but it is still an open question…

This paper presents an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We address four research questions related to the impact of tutoring materials, skill mastery, feature extraction, and graph representation learning. To investigate the impact of tutoring materials, we analyze the…

The COVID-19 outbreak necessitated a rapid transition to eLearning in higher education institutions worldwide, including Uganda, where infrastructural and digital literacy challenges compounded this shift. Predicting student satisfaction with eLearning systems helps institutions evaluate how well these platforms are working, assess their future…

Algorithms and machine learning models are being used more frequently in educational settings, but there are concerns that they may discriminate against certain groups. While there is some research on algorithmic fairness, there are two main issues with the current research. Firstly, it often focuses on gender and race and ignores other groups.…

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…