Student Profile Modeling Using Boosting Algorithms

Autor/inn/en	Hamim, Touria; Benabbou, Faouzia; Sael, Nawal
Titel	Student Profile Modeling Using Boosting Algorithms
Quelle	In: International Journal of Web-Based Learning and Teaching Technologies, 17 (2022) 5, Artikel 4 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1548-1093
Schlagwörter	Mathematics; Artificial Intelligence; Man Machine Systems; Student Characteristics; Profiles; Mathematics Achievement; Prediction + Suchen Sie Ihr Suchwort? Mathematik; Künstliche Intelligenz; Mensch-Maschine-System; Charakterisierung; Profilanalyse; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Vorhersage
Abstract	The student profile has become an important component of education systems. Many systems objectives, as e-recommendation, e-orientation, e-recruitment and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose. In this paper, the authors present a comparative study between different boosting algorithms which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher Score, Information Gain combined with Recursive Feature Elimination to enhance the preprocessing task and models' performances. Using multi-label dataset predict the class of the student performance in mathematics, this article results show that the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best performance when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01