Academic performance warning system based on data driven for higher education.

Abstract

Academic probation at universities has become a matter of pressing concern in recent years, as many students face severe consequences of academic probation. We carried out research to find solutions to decrease the situation mentioned above. Our research used the power of massive data sources from the education sector and the modernity of machine learning techniques to build an academic warning system. Our system is based on academic performance that directly reflects students' academic probation status at the university. Through the research process, we provided a dataset that has been extracted and developed from raw data sources, including a wealth of information about students, subjects, and scores. We build a dataset with many features that are extremely useful in predicting students' academic warning status via feature generation techniques and feature selection strategies. Remarkably, the dataset contributed is flexible and scalable because we provided detailed calculation formulas that its materials are found in any university or college in Vietnam. That allows any university to reuse or reconstruct another similar dataset based on their raw academic database. Moreover, we variously combined data, unbalanced data handling techniques, model selection techniques, and research to propose suitable machine learning algorithms to build the best possible warning system. As a result, a two-stage academic performance warning system for higher education was proposed, with the F2-score measure of more than 74% at the beginning of the semester using the algorithm Support Vector Machine and more than 92% before the final examination using the algorithm LightGBM.