Data Mining: Will First-Year Results Predict the Likelihood of Completing Subsequent Units in Accounting Programs?

Autor/inn/en	Sithole, Seedwell T. M.; Ran, Guang; de Lange, Paul; Tharapos, Meredith; O'Connell, Brendan; Beatson, Nicola
Titel	Data Mining: Will First-Year Results Predict the Likelihood of Completing Subsequent Units in Accounting Programs?
Quelle	In: Accounting Education, 32 (2023) 4, S.409-444 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sithole, Seedwell T. M.) ORCID (Tharapos, Meredith)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0963-9284
DOI	10.1080/09639284.2022.2075707
Schlagwörter	Data Analysis; Predictor Variables; Accounting; Educational Attainment; Grades (Scholastic); Student Characteristics; College Entrance Examinations; Academic Achievement; Undergraduate Students; Foreign Countries; Grade Prediction; Business Administration Education; College Faculty; Australia + Suchen Sie Ihr Suchwort? Auswertung; Prädiktor; Abrechnung; Buchführung; Buchhaltung; Bildungsabschluss; Bildungsgut; Notenspiegel; Aufnahmeprüfung; Schulleistung; Ausland; Fakultät; Australien
Abstract	This study introduces data mining methods to accounting education scholarship to explore the relationship between accounting students' current academic performance (grades), demographic information, pre-university entrance scores and predicted academic performance. It adopts a C4.5 classification algorithm based on decision-tree analysis to examine 640 accounting students enrolled in an undergraduate accounting program at an Australian university. A significant contribution of this study is improved prediction of academic performance and identification of characteristics of students deemed to be at risk. By partitioning students into sub-groups based on tertiary entrance scores and employing clustering of study units, this study facilitates a more nuanced understanding of predictor attributes. Key findings were the dominance of a cluster of second year units in predicting students' later academic performance; that gender did not influence performance; and that performance in first year at university, rather than secondary school grades, was the most important predictor of subsequent academic performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01