HELA: A Novel Hybrid Ensemble Learning Algorithm for Predicting Academic Performance of Students

Autor/inn/en	Keser, Sinem Bozkurt; Aghalarova, Sevda
Titel	HELA: A Novel Hybrid Ensemble Learning Algorithm for Predicting Academic Performance of Students
Quelle	In: Education and Information Technologies, 27 (2022) 4, S.4521-4552 (32 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Keser, Sinem Bozkurt) ORCID (Aghalarova, Sevda)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-021-10780-0
Schlagwörter	Grade Prediction; Academic Achievement; Data Analysis; Data Collection; Mathematics; Evaluation Methods + Suchen Sie Ihr Suchwort? Schulleistung; Auswertung; Data capture; Datensammlung; Mathematik
Abstract	Education plays a major role in the development of the consciousness of the whole society. Education has been improved by analyzing educational data related to student academic performance. By using data mining techniques and algorithms on data from the educational environment, students' performances can be predicted. In this study, a novel Hybrid Ensemble Learning Algorithm (HELA) is proposed to predict the academic performance of students. The prediction results obtained from base classifiers namely Gradient Boosting, Extreme Gradient Boosting, Light Gradient Boosting Machine, and different combinations of these algorithms are given as input to the Super Learner algorithm. Hyper-parameters of base classifiers are optimized with a Random Search algorithm. Students' performances in Math and Portuguese classes are predicted by the proposed algorithm. In the experimental results, 96.6% and 91.2% accuracy values are obtained for the Mathematics course, and the Portuguese course, respectively. This paper is the first study, to our knowledge, to integrate the boosting and stacking-based ensemble learning algorithm for the prediction of students' academic performance that gives better predictive results with high efficiency. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01