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Predicting students' academic performance and main behavioral features using data mining techniques

Almutairi, Suad, Shaiba, Hadil and Bezbradica, Marija orcid logoORCID: 0000-0001-9366-5113 (2019) Predicting students' academic performance and main behavioral features using data mining techniques. In: Advances in Data Science, Cyber Security and IT Applications. ICC 2019., 10-12 Dec 2019, Riyadh, Saudi Arabia. ISBN 978-3-030-36364-2

Abstract
Creating learning environments, where students, parents, and teachers are linked to a learning process, helps study their overall impact on the students’ performance. Data mining can analyze these inter-relationships and thus enable the prediction of academic performance to improve the student’s academic level. The main factors that affect the student’s performance were selected using feature selection methods. An analysis of the crucial features was investigated to better understand the data. One of the main outcomes found is the impact of the behavioral features on the students’ academic performance. Moreover, gender and relation demographical features are another important features found. It was evedent that there is an academic disparity between genders, as females constitute the most outstanding students. Furthermore, mothers have a clear role in student academic excellence. Six machine learning methods were used and tested to predict the studnet’s performance, namely random forest, logistic regression, XGBoost, MLP, and ensemble learning using bagging and voting. Of all the methods, the random forest got the highest accuracy with 10-best selected features that reached 77%. Overfitting was addressed successfully by tuning the hyper-parameters. The results show that data mining can accurately predict the students’ performance level, as well as highlight the most influential features.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Educational data mining; machine learning; deep learning; learning analytics
Subjects:Computer Science > Artificial intelligence
Computer Science > Computational linguistics
Computer Science > Computer simulation
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science 1097. Springer, Cham. ISBN 978-3-030-36364-2
Publisher:Springer, Cham
Official URL:https://dx.doi.org/10.1007/978-3-030-36365-9_21
Copyright Information:© 2020 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:26154
Deposited On:06 Sep 2021 13:55 by Marija Bezbradica . Last Modified 06 Sep 2021 13:55
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