Weekly Featured Article

Aggregating Time Series and Tabular Data in Deep Learning Model for University Students’ GPA Prediction

Harjanto Prabowo¹, Alam Ahmad Hidayat², Tjeng Wawan Cenggoro^2,3, Reza Rahutomo², Kartika Purwandari², And Bens Pardamean ^2,4

¹Management Department, BINUS Business School Undergraduate Program, Bina Nusantara University, Jakarta 11480, Indonesia
²Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta 11480, Indonesia
³Computer Science Department, School of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia
⁴Computer Science Department, BINUS Graduate ProgramMaster of Computer Science Program, Bina Nusantara University, Jakarta 11480, Indonesia

Abstract

Current approaches of university students’ Grade Point Average (GPA) prediction rely on the use of tabular data as input. Intuitively, adding historical GPA data can help to improve the performance of a GPA prediction model. In this study, we present a dual-input deep learning model that is able to simultaneously process time-series and tabular data for predicting student GPA. Our proposed model achieved the best performance among all tested models with 0.4142 MSE (Mean Squared Error) and 0.418 MAE (Mean Absolute Error) for GPA with a 4.0 scale. It also has the best R 2 -score of 0.4879, which means it explains the true distribution of students’ GPA better than other models.

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https://ieeexplore.ieee.org/document/9452125

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