Data Mining Approach to Predicting the Performance of First Year Student in a University Using the Admission Requirements

Autor/inn/en	Adekitan, Aderibigbe Israel; Noma-Osaghae, Etinosa
Titel	Data Mining Approach to Predicting the Performance of First Year Student in a University Using the Admission Requirements
Quelle	In: Education and Information Technologies, 24 (2019) 2, S.1527-1543 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Adekitan, Aderibigbe Israel)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-018-9839-7
Schlagwörter	Foreign Countries; Data Collection; Educational Research; Prediction; Academic Achievement; College Freshmen; College Admission; Admission Criteria; Grade Point Average; Accuracy; Mathematics; Nigeria + Suchen Sie Ihr Suchwort? Ausland; Data capture; Datensammlung; Bildungsforschung; Pädagogische Forschung; Vorhersage; Schulleistung; Studienanfänger; Hochschulzugang; Hochschulzulassung; Zulassung; Admission; Admission procedures; Zulassungsbedingung; Zulassungsverfahren; Mathematik
Abstract	The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic, and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students' performance in their first year is not fully explained by cognitive entry requirements. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01