View Proposal

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Proposer

Zi Hau Chin

Title

Explainable Machine-Learning Models for Predicting Students at Risk of Course Failure

Goal

Automatic Classification and Prioritisation of Student Support Requests Using Machine Learning

Description

Build a system that identifies students who may require academic support and explains the factors contributing to each prediction. The purpose should be early support, not punishment, exclusion, or automated academic decisions. Which model provides the best balance between predictive performance and interpretability? -How early can course-failure risk be predicted reliably? -Are explanations stable and actionable? -Does predictive performance differ across relevant student groups? -How does prediction change when earlier assessment grades are excluded?

Resources

UCI Student Performance Dataset, OULAD, Student-Performance-Prediction-ML-Comparison

Background

Python, data analysis, supervised machine learning and classification, Data preprocessing and feature engineering, explainable AI

Url

Difficulty Level

Moderate

Ethical Approval

Full

Number Of Students

1

Supervisor

Zi Hau Chin

Keywords

learning analytics, early-warning system, explainable ai, counterfactual explanation, imbalanced classification, calibration, algorithmic fairness, educational data mining

Degrees

Bachelor of Science in Computing Science