Juliana Ngozi Ndunagu

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.

This study investigates how Artificial Intelligence (AI)-based Tutoring Systems can be used to provide secondary school students with personalized learning feedback and to enhance their academic performance. The two main empirical objectives were to assess individualized formative assessment in classroom settings and to examine teachers’ and students’ perceptions of AI adoption. A mixed-methods quasi-experimental design was employed, integrating quantitative pre-/post-tests, interviews, and focus groups across six schools (n = 600). This approach enabled triangulation between measurable learning outcomes and contextual perception data for robust validation. Quantitative data were analyzed using Python (t-tests, ANCOVA), while thematic coding in NVivo was applied to qualitative data. Expert review, pilot testing, and Cronbach’s α (>0.80) were used to validate the instruments and ensure reliability, including pre-/post-tests and engagement scales. Findings revealed that students who received AI-based interventions achieved significantly higher academic performance (Cohen’s d = 1.05) and engagement (d = 0.72) compared with control groups. Teachers with AI exposure reported greater preparedness (mean = 3.4) and fewer perceived barriers. The study provides empirical evidence on the pedagogical viability of AI tutoring in under-resourced contexts, contributing to self-regulated and socio-technical learning theories. It also recommends enhanced systemic teacher education, ethical leadership, and structural support to foster equitable adoption of AI in education. The findings carry strong implications for policy development and educational innovation in promoting data-driven, inclusive learning within Nigeria’s secondary education system