Year: 2025
Type: article
Abstract: This study investigates the application of supervised machine learning methods for predicting student learning outcomes based on academic performance in previous semesters. The experimental data comprises of 2344 graduated from the CQ56 and CQ57 (2022 and 2023) cohorts across all majors at the Academy of Finance. The result indicates that J48 decision tree algorithm achieved the highest prediction accuracy for graduation classification, both with the original data and the preprocessed data that addresses missing values. This preliminary study demonstrates the potential for an effective application of machine learning to student data mining at the Academy of Finance.
Authors: Thu Thuy Cu, Duc Thanh Nguyen
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4409564518