Enhancing the Prediction of Student Performance Based on the Machine Learning XGBoost Algorithm

Autor/inn/en	Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib
Titel	Enhancing the Prediction of Student Performance Based on the Machine Learning XGBoost Algorithm
Quelle	In: Interactive Learning Environments, 31 (2023) 6, S.3360-3379 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Asselman, Amal) ORCID (Khaldi, Mohamed) ORCID (Aammou, Souhaib)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2021.1928235
Schlagwörter	Algorithms; Artificial Intelligence; Factor Analysis; Student Behavior; Accuracy; Prediction; Academic Achievement; Mathematics Instruction; Data Analysis; Middle School Students; High School Students; Physics + Suchen Sie Ihr Suchwort? Algorithm; Algorithmus; Künstliche Intelligenz; Faktorenanalyse; Student behaviour; Schülerverhalten; Vorhersage; Schulleistung; Mathematics lessons; Mathematikunterricht; Auswertung; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; High school; High schools; Oberschule; Studentin; Physik
Abstract	Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by inventing several advanced extensions. However, most of the proposed extensions have exclusively been improved in a pedagogical sense, as the improvements have mostly been limited to the analysis of students' behaviour during their learning process. In contrast, Machine Learning provides many powerful methods that could be efficient to enhance, in the technical sense, the prediction of student performance. Our goal is to focus on the exploitation of Ensemble Learning methods as an extremely effective Machine Learning paradigm used to create many advanced solutions in several fields. In this sense, we propose a new PFA approach based on different models (Random Forest, AdaBoost, and XGBoost) in order to increase the predictive accuracy of student performance. Our models have been evaluated on three different datasets. The experimental results show that the scalable XGBoost has outperformed the other evaluated models and substantially improved the performance prediction compared to the original PFA algorithm. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01