Multiple-choice questions (MCQs) are widely used in higher education because they efficiently assess knowledge and a broad range of cognitive skills across disciplines. This study examines how MCQ-based assessments contribute to predicting final student grades, using data from 2,390 students (comprising 26,698 non-missing attribute values) enrolled in sixteen offerings of three undergraduate and postgraduate economics units at Charles Darwin University (CDU) between 2016 and 2019. Six machine learning classifiers were trained on assessment marks, assessment weightings, final examination performance, and the proportion of MCQs in the examination. Feature importance analysis was then used to q... More >

Depression and academic stress undermine students' attendance, performance, and retention, yet educational institutions lack reliable early-alert tools. Because about three-quarters of lifetime psychiatric conditions emerge before age 25, timely automated screening is critical for early intervention. This paper proposes a multi-source ensemble learning framework for distinguishing {\it depression} (DP) from {\it non-depression} (ND) using 1,547 harmonized samples drawn from six real-world sources: occupational surveys, student academic stress records, Twitter sentiment data, Reddit depression posts, fine-grained emotion corpora, and behavioural monitoring signals. Academic stress and behavio... More >

With the rapid advancement of artificial intelligence and smart education, academic performance prediction has emerged as a critical research direction within educational data mining. This paper provides a systematic review of recent progress in this field, focusing on technological evolution, application scenarios, model adaptation, emerging trends, and persistent challenges. The development of prediction technologies has progressed through three stages: statistical methods, machine learning, and deep learning. These approaches have been widely applied to grade prediction, academic risk and dropout prediction, knowledge tracing, and personalized learning recommendation. Different models var... More >

Educational Data Mining (EDM) has achieved substantial gains in predictive performance, yet many existing approaches remain centered on single-objective optimization, most often accuracy. This does not adequately reflect the multi-dimensional nature of real-world educational decision-making, which requires balancing interpretability, fairness, robustness, efficiency, and timeliness. This perspective advocates a shift toward multi-objective, interpretable, and trustworthy EDM frameworks. We highlight the role of multi-objective optimization in modeling trade-offs through Pareto-optimal solutions and address the challenge of actionable decision-making through bargaining-based mechanisms, such... More >

Personalized learning has become a popular term in education to address learner diversity and enhance student performance. However, its implementation remains a practical challenge in many developing countries due to the lack of fine-grained learning data and platforms. This study proposes a data-driven machine learning approach for personalized learning using routine administrative education data from Rwanda. The approach is scalable, interpretable, and aligned with existing national education information systems. Following a design science research approach, the study combines unsupervised learner profiling via clustering and supervised performance prediction via regression models. The pro... More >

The rapid digitization of education has generated massive, heterogeneous student behavioral data, creating both opportunities and methodological challenges for educational data mining. This perspective paper discusses an emerging paradigm that integrates approximate fast clustering algorithms with deep learning techniques to enable scalable, high-resolution student behavior analysis. By leveraging approximate computation, clustering efficiency can be significantly improved without sacrificing analytical fidelity, while deep learning models facilitate the extraction of high-level representations from multimodal behavioral data. The synergy between these approaches could enable robust applicat... More >

The rapid digitalization of education has led to an explosive growth of multi-source and multi-modal learning data, providing new opportunities for advancing Educational Data Mining (EDM). By learning diverse data such as learning behaviors, assessment records, and interaction logs, EDM enables deeper insights into student learning processes and supports the development of personalized and intelligent education. However, several critical challenges remain, including the heterogeneity and fragmentation of multi-source data, the difficulty of extracting meaningful knowledge through effective data fusion, and the limited interpretability of high-performance predictive models. To address these c... More >

With the development of educational technology and the accumulation of big data, student performance prediction has become a hot topic in the field of education. However, traditional manual statistical methods have limitations in dealing with complex data and are difficult to achieve high-precision prediction. To address this gap, this study proposes a clustering-based feature generation framework to enhance prediction performance. Firstly, the multilayer perceptron (MLP) model is employed to evaluate the effectiveness of the clustering algorithms (K-Means, DBSCAN, and hierarchical clustering) for feature generation. Then, the best clustering algorithm (K-Means) is applied to generate featur... More >