Applying the J48 classification algorithm in predicting student learning outcomes

Abstract

This paper sets out to use J48 classification algorithm to predict students’ academic performance towards the end of the semester in the Data Structure course under the Computer Science Program. This algorithm aimed to help faculty in forecasting who among the students would likely to fail and who would make it until the end of the semester. In this way, the faculty could make remedial measures to help those struggling students pass the subject and advance to the next level, thus, increasing students’ success rate and retention in a Higher Education Institutions (HEI). This research employed a descriptive correlational design using Exploratory Data Analysis (EDA) for Data Mining in testing and verifying data to generate new information. Data mining is part of the Knowledge Discovery in Databases (KDD) process where it follows six steps: data selection, data pre-processing, data transformation, data mining, interpretation, and knowledge discovery.