Predictive Analytics Model for Adaptive Teaching in Open and Distance Learning Institutions: Machine Learning Approach

Abstract

The study investigates the application of predictive analytics model in adaptive teaching within Open and Distance Learning (ODL) institutions. The aim of the study lies in addressing the ongoing challenges of high dropout rates and low student engagement, particularly in developing countries. The research gap is the underutilisation of predictive analytics to personalise interventions and enhance learning outcomes in ODL environments. The study employs mixed-method research design including machine learning algorithms with Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and XGBoost, in predicting students at risk of academic failure and providing personalised interventions. A dataset of 5,000 students from the National Open University of Nigeria was used to trained and test the model. Model validation metrics used includes: accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC. More so, (n=1050) participants took part in the experimental and control group including semi-interview, enabling real world application of predictive model. Key findings indicated that Random Forest had the highest ROC-AUC (98.38%), followed by XGBoost (97.76%). Nevertheless, Logistic Regression and SVM outperformed the others in accuracy (97.43%), precision (97.65%), recall (95.95%), and F1-score (96.79%). These results show that adaptive teaching, supported by predictive analytics, is associated with improved student engagement and contributes to reducing dropout rates. The challenges such as data quality, privacy, trust and algorithms bias should be addressed. The study suggest that predictive analytics is capable of transforming teaching methods in ODL institutions, improve personalised and effective learning. Future study should focus on model optimisation and integration with other educational technologies.