Predictive Modelling of Student Performance in MMU Based on Machine Learning Approach

DOI

10.2991/978-94-6463-094-7_21How to use a DOI?

Keywords

Student performance; Boruta feature selection; Machine learning; Data mining

Abstract

This research work is to identify and forecast student performance in Multimedia University (MMU) based on machine learning approach. Surveys had been carried out to collect students’ cumulative grade point average, background, history grades, opinion towards MMU environment, and lifestyle. In this study, two datasets, namely Dataset 1 (1288 records) and Dataset 2 (945 records) have been collected from two trimesters. Following that, data pre-processing and data transformation are carried out. Next, feature selection is performed to eliminate the irrelevant features. Finally, the classification models of Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN) origins are designed, and their performances evaluated. The classification was performed with 80–20, 70–30, 60–40 and 50–50 train-test splits and 10-fold cross validation on Merge Dataset. GridSearchCV was applied to perform hyperparameter tuning. The performance metrics are accuracy, precision, recall and F1-Score. RF obtains the best accuracy results among the six models in Merged dataset classification, RF scores 0.9093 for 5-Level classification with oversampling and 1.0 for binary classification with oversampling.

Copyright

© 2022 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jun Yang Chan
AU  - Hu Ng
AU  - Timothy Tzen Vun Yap
AU  - Vik Tor Goh
PY  - 2022
DA  - 2022/12/27
TI  - Predictive Modelling of Student Performance in MMU Based on Machine Learning Approach
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 258
EP  - 278
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_21
DO  - 10.2991/978-94-6463-094-7_21
ID  - Chan2022
ER  -