Predicting academic performance for students' university: case study from Saint Cloud State University.

ABSTRACT: Predicting students' performance is one of the essential educational data mining approaches aimed at observing learning outcomes. Predicting grade point average (GPA) helps to monitor academic performance and assists advisors in identifying students at risk of failure, major changes, or dropout. To enhance prediction performance, this study employs a long short-term memory (LSTM) model using a rich set of academic and demographic features. The dataset, drawn from 29,455 students at Saint Cloud State University (SCSU) over eight years (2016-2024), was carefully preprocessed by eliminating irrelevant and missing data, encoding categorical variables, and normalizing numerical features. Feature importance was determined using a permutation-based method to identify the most impactful variables

SUBMITTER: Al-Ahmad BI 

PROVIDER: S-EPMC12453804 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature