An Explainable AI-Based Learning Analytics Framework for Early Identification of At-Risk Students in Higher Education

Background: The widespread adoption of Learning Management Systems (LMS) and digital learning platforms in higher education has produced vast repositories of student behavioural and academic data, creating unprecedented opportunities for predictive Learning Analytics (LA). However, academic failure, disengagement, and dropout remain persistent challenges that undermine institutional effectiveness and student wellbeing. Research problem: Existing Machine Learning (ML)-based student-risk prediction systems are predominantly optimised for predictive accuracy and operate as opaque "black boxes," offering limited insight into the reasons underlying a given risk classification. This lack of interpretability constrains their adoption by faculty, academic advisors, and administrators, who require transparent, actionable, and trustworthy evidence before intervening in a student's academic trajectory. Objective: This paper proposes and conceptually validates 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. Methodology: The framework integrates an eight-stage pipeline spanning data collection, preprocessing, feature engineering, comparative machine learning modelling (Logistic Regression, Random Forest, XGBoost, LightGBM, and a Multilayer Perceptron), rigorous evaluation emphasising recall and F1-score under class imbalance, post-hoc explainability using SHapley Additive exPlanations (SHAP) with a supplementary comparison to Local Interpretable Model-Agnostic Explanations (LIME), tiered risk classification, and structured educational intervention pathways. Contribution: The study synthesises 2020-2026 literature on learning analytics, educational data mining, dropout prediction, and explainable AI to identify a converging research gap concerning interpretability, fairness, and pedagogical actionability, and proposes a conceptual and methodological blueprint - including mathematical formulations, an algorithmic specification, and a proposed experimental protocol using publicly available higher-education datasets - that addresses this gap without prematurely claiming unverified empirical results. Educational significance: By coupling predictive analytics with transparent, instance-level and cohort-level explanations, the proposed framework is intended to support - rather than replace - the professional judgement of educators, enabling earlier, fairer, and more defensible academic interventions.