Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms

Soheila Gheisari; Hamid Salarian

cs.CYMay 24, 2026

Analysis and Prediction of At-Risk Students Using Machine Learning Algorithms

Authors: Soheila Gheisari, Hamid Salarian

Organizations: Department of Information Technology, Sydney International School of Technology and Commerce, Sydney, NSW 2000, Australia

Abstract

Student attrition represents a significant challenge for higher education institutions because it impacts both academic results and financial viability. Machine learning provides an effective solution to identify students who require assistance before they leave their academic programs. The research investigates how machine learning approaches enable institutions to predict student withdrawal and enrollment cancellation through data-driven insights for strategic decisionmaking. The evaluation of models includes Logistic Regression, Random Forest, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) based on academic performance and demographic data and enrollment records. The results show that logistic regression and linear SVM models produced the highest accuracy which demonstrates ML's capability to detect students at risk.

Explore similar work

Jointly Predicting Courses and Grades Using a Transformer-Based Model
Aug 13, 2026Paul SavalaSequence Modeling Grading
Fairness Audits of Institutional Risk Models in Deployed ML Pipelines
Apr 21, 2026Kelly McConvey, Dipto Das, Maya Ghai +3Fairness Audits Institutional Composition
Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning
Jun 12, 2026Lala Yamazaki, Ramchandra RimalPhysiological Data Machine Learning

Aug 13, 2026 · cs.AIJ/K move · Enter open · S save

Jointly Predicting Courses and Grades Using a Transformer-Based Model

Paul Savala

Mathematics, St. Edward’s University, 3001 S Congress Ave, Austin, 78704, TX, USA. · S Congress Ave

Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses taken within a semester. This simplification can lead to inaccurate performance predictions, particularly for students with heavy or challenging course loads. This paper introduces a TRansformer for Academic Course-grade Estimation (TRACE) that addresses this limitation by jointly predicting both the set of courses a student will take and their corresponding grades for an upcoming semester. Our approach encodes courses on a per-semester basis to capture the effects of course concurrency and utilizes a novel loss function combining course-set prediction with grade prediction. We demonstrate that predicting courses taken in addition to the grades in those courses leads to significant improvements in prediction quality. Trained on ten years of institutional data, our joint prediction model reduces mean absolute error by nearly 50% compared to an identical architecture that predicts grades alone. The model also outperforms traditional LSTM-based sequential models, as well as graph neural network-based approaches, and offers natural ways to incorporate student attribute data. This work demonstrates the utility of modern neural architectures for creating interpretable models that can be adapted to new institutions via retraining and recalibration, as well as the importance of key techniques, such as predicting courses taken during training. We discuss how this model could be incorporated into early detection systems at institutions of higher education.

Sequence Modeling Grading

Open paper Ask about it PDF

Abstract

Explore similar work

Jointly Predicting Courses and Grades Using a Transformer-Based Model

Fairness Audits of Institutional Risk Models in Deployed ML Pipelines

Leveraging Physiological Signals to Predict Exam Outcomes with Machine Learning