Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

In the context of massive open online courses (MOOCs), searching and retrieving information can be challenging because there is a huge amount of unstructured content, which creates a problem and makes it difficult for users to quickly find relevant lessons or resources. As a result, learners and teachers face significant barriers to accessing the…

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…

Visual data have dramatically increased in quantity in the digital age, presenting new opportunities for social science research. However, the extensive time and labor costs to process and analyze these data with existing approaches limit their use. Computer vision methods hold promise but often require large and nonexistent training data to…

Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…

In response to the COVID-19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacks in knowledge is how effective the anti-COVID measures were in maintaining quality education.…

Entrepreneurship education has evolved to meet the demands of a dynamic business environment, necessitating innovative teaching methods to prepare entrepreneurs for market uncertainties. Large Language Models (LLMs) like the Generative Pre-trained Transformer 4 (GPT-4), recognized for their exceptional performance on public datasets, are examined…

The development of critical data and artificial intelligence (AI) literacy has become a key focus in current discussions in Higher Education, thus it is necessary to develop and advance capacity building, reflectiveness and awareness across disciplines to critically address the possibilities and challenges presented by data and AI. In this paper,…

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which…

This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI)…

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and…

Multimodal learning analytics (MMLA) has increasingly been a topic of discussion within the learning analytics community. The Society of Learning Analytics Research is home to the CrossMMLA Special Interest Group and regularly hosts workshops on MMLA during the Learning Analytics Summer Institute (LASI). In this paper, we articulate a set of 12…

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of…

This paper presents "the DataBuzz Project." "DataBuzz" is a high-tech, mobile educational lab, which is housed in a 13-meter electric bus. Its specific goal is to increase the data literacy of different segments of society in the Brussels region through inclusive and participatory games and workshops. In this paper, we will…