Current Stance on Predictive Analytics in Higher Education: Opportunities, Challenges and Future Directions

Autor/inn/en	Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim
Titel	Current Stance on Predictive Analytics in Higher Education: Opportunities, Challenges and Future Directions
Quelle	In: Interactive Learning Environments, 31 (2023) 6, S.3503-3528 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Umer, Rahila) ORCID (Mathrani, Anuradha)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1049-4820
DOI	10.1080/10494820.2021.1933542
Schlagwörter	Prediction; Student Behavior; Models; Academic Achievement; At Risk Students; Learning Problems; Artificial Intelligence; Educational Research; Data Analysis; Data Collection + Suchen Sie Ihr Suchwort? Vorhersage; Student behaviour; Schülerverhalten; Analogiemodell; Schulleistung; Lernproblem; Künstliche Intelligenz; Bildungsforschung; Pädagogische Forschung; Auswertung; Data capture; Datensammlung
Abstract	Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use predictive models to detect learning difficulties faced by students and thereby plan effective interventions to support students. In this paper, we present a systematic literature review on how predictive analytics have been applied in the higher education domain. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we conducted a literature search from 2008 to 2018 and explored current trends in building data-driven predictive models to gauge students' performance. Machine learning techniques and strategies used to build predictive models in prior studies are discussed. Furthermore, limitations encountered in interpreting data are stated and future research directions proposed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01