Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors

Autor/in	Rowtho, Vikash
Titel	Early Detection of At-Risk Undergraduate Students through Academic Performance Predictors
Quelle	In: Higher Education Studies, 7 (2017) 3, S.42-54 (13 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1925-4741
Schlagwörter	Foreign Countries; Undergraduate Students; Identification; At Risk Students; Low Achievement; Academic Failure; Predictor Variables; Predictive Measurement; Early Intervention; Potential Dropouts; Dropout Prevention; Personality Traits; Cognitive Style; Socioeconomic Status; Learner Engagement; Demography; Likert Scales; Correlation; Factor Analysis; Componential Analysis; Grade Point Average; Student Surveys; Multiple Regression Analysis; Monte Carlo Methods; Mauritania + Suchen Sie Ihr Suchwort? Ausland; Identifikation; Identifizierung; Unterdurchschnittliche Leistung; Prädiktor; Individual characteristics; Personality characteristic; Persönlichkeitsmerkmal; Cognitive styles; Kognitiver Stil; Socio-economic status; Sozioökonomischer Status; Demografie; Likert-Skala; Korrelation; Faktorenanalyse; Schülerbefragung; Monte-Carlo-Methode; Mauretanien
Abstract	Undergraduate student dropout is gradually becoming a global problem and the 39 Small Islands Developing States (SIDS) are no exception to this trend. The purpose of this research was to develop a method that can be used for early detection of students who are at-risk of performing poorly in their undergraduate studies. A sample of 279 students participated in the study conducted in a Mauritian private tertiary academic institution. Results of regression analyses identified the variables having a significant influence on academic performance. These variables were used in a linear discriminant analysis where 74 percent of the students could be correctly classified into three categories: at-risk, pass or fail. In conclusion, this study has proposed a new technique that can be used by institutions to determine significant academic performance predictors and then identify at-risk students upon whom interventions can be implemented prior to exams to address the problem of dropouts. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01