TL;DR
A unified predictive analytics pipeline for LMS data is proposed that integrates systematic preprocessing, feature engineering and selection, multi-model benchmarking, handling of class imbalance, and Explainable Artificial Intelligence (XAI) within a unified decision-support architecture.
This study proposes an explainability-driven framework for student outcome prediction by comparing a black-box high-performance model with an inherently interpretable model. While machine learning models are increasingly used for academic earlywarning systems, their lack of transparency limits real-world deployment...
This study develops and comparatively evaluates four supervised machine learning models for multi-class prediction of final student outcome on the Open University Learning Analytics Dataset (OULAD), demonstrating that aggregate accuracy substantially overstates the usefulness of these models for risk detection and that...
The Explainable Learning Analytics Framework (ELAF) as an integrated eight-layer conceptual architecture is suggested as an integrated eight-layer conceptual architecture to cover this persistent gap between the prediction accuracy of algorithms and the use of the algorithms in the educational domain.
Objectives: To develop BDEN, a scalable intelligent tutoring framework for personalized learning analytics, learner mastery prediction, and early identification of at-risk students. Method: BDEN integrates Kafka–Spark distributed processing with an attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) model...
A context-aware Machine Learning framework for predicting student performance and dropout in online learning environments affected by insurgency-related disruptions and supports resilient, adaptive, and equitable online education in crisis-affected environments.
An Explainable Artificial Intelligence (XAI)-based Learning Analytics Framework designed to identify at-risk students at an early stage of a programme or semester while providing interpretable, human-understandable justifications for each prediction is proposed.
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