Developing and Comparing Data Mining Algorithms That Work Best for Predicting Student Performance

Autor/inn/en	Abdelhafez, Hoda Ahmed; Elmannai, Hela
Titel	Developing and Comparing Data Mining Algorithms That Work Best for Predicting Student Performance
Quelle	In: International Journal of Information and Communication Technology Education, 18 (2022) 1, Artikel 35 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Abdelhafez, Hoda Ahmed) ORCID (Elmannai, Hela)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1550-1876
Schlagwörter	Learning Analytics; Mathematics; Prediction; Academic Achievement; Decision Making; Higher Education; Academic Failure; Academic Standards; Grading; Artificial Intelligence; At Risk Students; College Students; Bayesian Statistics + Suchen Sie Ihr Suchwort? Mathematik; Vorhersage; Schulleistung; Decision-making; Entscheidungsfindung; Hochschulbildung; Hochschulsystem; Hochschulwesen; Notengebung; Schulnote; Künstliche Intelligenz; Collegestudent
Abstract	Learning data analytics improves the learning field in higher education using educational data for extracting useful patterns and making better decisions. Identifying potential at-risk students may help instructors and academic guidance to improve the students' performance and the achievement of learning outcomes. The aim of this research study is to predict at early phases the student's failure in a particular course using the standards-based grading. Several machine learning techniques were implemented to predict the student failure based on support vector machine, multilayer perceptron, naïve bayes, and decision tree. The results on each technique show the ability of machine learning algorithms to predict the student failure accurately after the third week and before the course dropout week. This study provides a strong knowledge for student performance in all courses. It also provides faculty members the ability to help at-risk students by focusing on them and providing necessary support to improve their performance and avoid failure. (As Provided).
Anmerkungen	IGI Global. 701 East Chocolate Avenue, Hershey, PA 17033. Tel: 866-342-6657; Tel: 717-533-8845; Fax: 717-533-8661; Fax: 717-533-7115; e-mail: journals@igi-global.com; Web site: https://www.igi-global.com/journals/
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01