Modeling Student Retention and Success Using Explainable Machine Learning Techniques

How can machine learning personalize educational content generated by LLMs

It is suggested that ML-guided prompt routing can improve perceived relevance of instructional materials while remaining transparent and easy to calibrate and integrate interpretable ML predictions with prompt-engineered LLMs can automate individualized content generation and improve learning outcomes.

A Unified Explainable Predictive Analytics Pipeline for Learning Management Systems

Cross-Institution Student Performance Prediction Using an Explainable Stacking Ensemble Framework

This study presents an explainable machine learning framework for early prediction of student academic performance using a stacking-based ensemble approach. The proposed model integrates Random Forest (RF), XGBoost, and Support Vector Machine (SVM) as base learners, with Logistic Regression (LR) used as a meta-learner. A dataset of 2,392 student records, containing demographic and behavioral features, was used for training and evaluation. Data preprocessing included encoding and normalization, followed by model optimization using cross-validation and grid search. The experimental results demonstrated that the proposed stacking model achieves strong predictive performance, with an accuracy of 95.82% and an Area Under the ROC Curve (AUC) close to 1.0 on the primary dataset, while maintaining strong generalization across external datasets. Model interpretability is enhanced using SHapley Additive exPlanations (SHAP) analysis, which identifies Grade Point Average (GPA), attendance, and study time as the most influential factors. In addition, the framework is implemented as a web-based system for real-time prediction and decision support. The results demonstrate that the proposed approach is a practical solution for educational data analytics.

Explainable machine learning for early classification of middle school student performance

Identifying students at risk of academic underperformance early is a longstanding difficulty for school systems, and the difficulty is sharpest where socioeconomic inequality is severe. This study presents an interpretable machine learning framework for predicting academic outcomes in middle school. The framework was developed on a dataset of 561,538 student records. Preprocessing, feature engineering, and stratified sampling were used to keep the sample representative across subgroups, and class imbalance was handled through class weighting during training together with threshold tuning for each outcome category. Several algorithms were compared, and model selection relied on the Borda Count method, which aggregates each model's ranking across multiple evaluation metrics instead of privileging a single criterion. CatBoost performed best on the test set, with a balanced accuracy of 88.8%, MCC of 70.6%, G-Mean of 88.7%, and F1-score of 91.4%. Explainable AI (XAI) techniques were then applied so that predictions can be interpreted at the global and individual levels. Assessment and examination scores were the strongest predictors of outcomes; poverty and class size mattered too, though less. The practical use of the framework is straightforward: making the model's reasoning visible lets educators and administrators flag at-risk students earlier and direct support to the schools and students most affected by socioeconomic disadvantages.

Early Student Dropout Prediction Using Machine Learning

Student dropout is a significant issue in higher education, affecting both students and institutions. Early identification of at-risk students can help universities improve retention. This study addresses student dropout prediction as a binary classification problem using 4,424 student records. To support realistic early prediction, only features available during the early stages of academic study were used. Three machine learning models were evaluated: Logistic Regression, Random Forest, and XGBoost. Class imbalance was handled through class weighting and parameter adjustment, while stratified 10-fold cross-validation ensured result stability. Experimental results showed that Random Forest achieved the highest accuracy (0.866), whereas XGBoost obtained the best recall (0.81) and ROC-AUC (0.913), making it particularly effective for identifying at-risk students. Logistic Regression provided a reliable and interpretable baseline. Feature importance analysis revealed that academic performance and financial factors were among the strongest predictors of dropout. The findings demonstrate the potential of machine learning as an early warning system for educational decision-making. However, the study is limited to a single dataset and does not include temporal data. Future work may incorporate additional features and evaluate model performance across different institutions.

Interpretable machine learning approaches for student dropout prediction in higher education

Student dropout remains one of the most significant challenges in higher education, affecting academic performance, financial sustainability, and strategic planning within universities. This study presents an approach to predicting student dropout risk using machine learning methods and educational analytics. The research is based on an open-access dataset from the UCI Machine Learning Repository containing 4,424 records and 36 attributes without missing values. Three experimental datasets were constructed using different preprocessing strategies, including class balancing, logarithmic feature transformation, and processing of the Enrolled category. Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbours (k-NN), and Neural Network (NN) models were implemented and compared. The results showed that Logistic Regression and Random Forest achieved the highest performance, with accuracy above 90% and ROC AUC values of up to 0.96. The most significant risk factors included academic performance during the first and second semesters, tuition fee payment status, outstanding debt, scholarship status, age at enrolment, and study programme. The findings indicate that machine learning methods can effectively support early dropout prediction and decision-support systems in higher education institutions.