Computer Science graduate; machine learning and data science

Feature Engineering and Model Interpretability in Educational Data Mining

In education, feature engineering can be used to enhance classification while model explanations are not addressed in terms of their meaning or reliability.In the education domain, feature engineering has been used to enhance the ability of classifiers to classify, but the meaning and reliability of the model explanations is not addressed. The difference of a methodological re-analysis of published evidence between 2017 and 2024 was explored in this study, focusing on Bangladesh. All of the quan

2026

Early Warning Systems in Higher Education: A Supervised Learning Approach to Dropout Prediction

In higher education, early warning systems have become more commonplace for the identification of students who may need assistance at an opportune time in their academic or institutional journey. But leaving aside the performance of the model on the whole, is it able to make good classification decisions about the small population of students who do drop out? This study reviews this distinction by critical evidence synthesis and secondary quantitative reanalysis of published supervised-learning

2026