A Framework for Smart Academic Guidance Using Educational Data Mining

Autor/inn/en	Mimis, Mohamed; El Hajji, Mohamed; Es-saady, Youssef; Oueld Guejdi, Abdellah; Douzi, Hassan; Mammass, Driss
Titel	A Framework for Smart Academic Guidance Using Educational Data Mining
Quelle	In: Education and Information Technologies, 24 (2019) 2, S.1379-1393 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (El Hajji, Mohamed)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-018-9838-8
Schlagwörter	Educational Counseling; Guidance; Educational Research; Data Collection; Prediction; Data Analysis; Decision Making; Student Records; Socioeconomic Status; Student Motivation; Models; Foreign Countries; Bayesian Statistics; Accuracy; Morocco + Suchen Sie Ihr Suchwort? Educational counselling; Educational guidance; Bildungsberatung; Erziehungsberatung; Beratung; Bildungsforschung; Pädagogische Forschung; Data capture; Datensammlung; Vorhersage; Auswertung; Decision-making; Entscheidungsfindung; Schülerakte; Socio-economic status; Sozioökonomischer Status; Schulische Motivation; Analogiemodell; Ausland; Marokko
Abstract	The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic guidance recommendation by predicting students' performance which involves analyzing data of students' records, socio-economic data and of course the student's motivation. The proposed model was analyzed and tested using student's data collected from the preparatory classes for "Grandes Ecoles" Reda Slaoui (CPGE) -- Morocco. More specifically, it proposes the use of three models that were applied on real data: Decision tree, Naive Bayes, and Neural networks. The data include the classes period (2012-2014 and 2013-2015) of 330 students in specialty the grade Mathematical Physics (MP) and Engineering Sciences (MPSI). The performance results indicate that our framework can make more accurate predictions of students' performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01