Predicting Academic Outcomes: A Survey from 2007 till 2018

Autor/inn/en	Alturki, Sarah; Hulpu?, Ioana; Stuckenschmidt, Heiner
Titel	Predicting Academic Outcomes: A Survey from 2007 till 2018
Quelle	In: Technology, Knowledge and Learning, 27 (2022) 1, S.275-307 (33 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Alturki, Sarah)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2211-1662
DOI	10.1007/s10758-020-09476-0
Schlagwörter	Grade Prediction; Academic Achievement; Data Use; Dropout Rate; Teacher Evaluation; Instructional Improvement; Data Analysis; Literature Reviews; Outcomes of Education + Suchen Sie Ihr Suchwort? Schulleistung; Teacher appraisal; Lehrerbeurteilung; Unterrichtsqualität; Auswertung; Lernleistung; Schulerfolg
Abstract	The tremendous growth of educational institutions' electronic data provides the opportunity to extract information that can be used to predict students' overall success, predict students' dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students' needs, and much more. This paper aims to review the latest trends in predicting students' performance in higher education. We provide a comprehensive background for understanding Educational Data Mining (EDM). We also explain the measures of determining academic success and highlight the strengths and weaknesses of the most common data mining (DM) tools and methods used nowadays. Moreover, we provide a rich literature review of the EDM work that has been published during the past 12 years (2007-2018) with focus on the prediction of academic performance in higher education. We analyze the most commonly used features and methods in predicting academic achievement, and highlight the benefits of the mostly used DM tools in EDM. The results of this paper could assist researchers and educational planners who are attempting to carry out EDM solutions in the domain of higher education as we highlight the type of features that the previous researches found to have significant impact on the prediction, as well as the benefits and drawbacks of the DM methods and tools used for predicting academic outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01