A Systematic Literature Review of Student' Performance Prediction Using Machine Learning Techniques

Autor/inn/en	Albreiki, Balqis; Zaki, Nazar; Alashwal, Hany
Titel	A Systematic Literature Review of Student' Performance Prediction Using Machine Learning Techniques
Quelle	In: Education Sciences, 11 (2021), Artikel 552 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Zaki, Nazar)
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2227-7102
Schlagwörter	Literature Reviews; Grade Prediction; Artificial Intelligence; Educational Environment; Data Collection; College Students; School Holding Power; Dropouts; Online Courses; Academic Achievement + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Data capture; Datensammlung; Collegestudent; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Online course; Online-Kurs; Schulleistung
Abstract	Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students' performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students' performance. (As Provided).
Anmerkungen	MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: education@mdpi.com; e-mail: indexing@mdpi.com; Web site: https://www.mdpi.com/journal/education
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01