Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

Autor/in	Calvert, Carol Elaine
Titel	Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics
Quelle	In: Open Learning, 29 (2014) 2, S.160-173 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0268-0513
DOI	10.1080/02680513.2014.931805
Schlagwörter	Academic Achievement; Distance Education; Open Education; Probability; Success; Models; Case Studies; Higher Education; Regression (Statistics); Open Universities; Predictor Variables; School Holding Power; Error of Measurement; Foreign Countries; Curriculum Development; United Kingdom + Suchen Sie Ihr Suchwort? Schulleistung; Distance study; Distance learning; Fernunterricht; Offene Erziehung; Offener Unterricht; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Erfolg; Analogiemodell; Case study; Fallstudie; Case Study; Hochschulbildung; Hochschulsystem; Hochschulwesen; Regression; Regressionsanalyse; Offene Universität; Prädiktor; Messfehler; Ausland; Curriculum; Development; Curriculumentwicklung; Lehrplan; Entwicklung; Großbritannien
Abstract	This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at different milestones identified. The explanatory variables, milestones and reference points when the model is run will be different at other institutions but the approach may be generalised to distance learning institutions and, more broadly, to any HE institution. Institutions, and especially distance education institutions which do not have the advantages of frequently seeing students, need to make full use of any recorded information they hold to try and identify students who are, or become, at potential risk of leaving. The importance of different factors, at different milestones, may help tailor student support to individual students and therefore improve low retention in open access distance education. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10