Application of the Classification Tree Model in Predicting Learner Dropout Behaviour in Open and Distance Learning

Autor/in	Yasmin, Dr.
Titel	Application of the Classification Tree Model in Predicting Learner Dropout Behaviour in Open and Distance Learning
Quelle	In: Distance Education, 34 (2013) 2, S.218-231 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0158-7919
DOI	10.1080/01587919.2013.793642
Schlagwörter	Distance Education; Open Universities; Predictor Variables; Student Behavior; Dropouts; Foreign Countries; Statistical Analysis; Student Characteristics; Gender Differences; Marital Status; Employment Level; Majors (Students); Social Status; Age Differences; Income; At Risk Students; Place of Residence; Access to Education; Classification; Models; Data Analysis; India + Suchen Sie Ihr Suchwort? Distance study; Distance learning; Fernunterricht; Offene Universität; Prädiktor; Student behaviour; Schülerverhalten; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Ausland; Statistische Analyse; Geschlechterkonflikt; Familienstand; Beschäftigungsgrad; Sozialer Status; Age; Difference; Age difference; Altersunterschied; Einkommen; Wohnort; Education; Access; Bildung; Zugang; Bildungszugang; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Auswertung; Indien
Abstract	This paper demonstrates the meaningful application of learning analytics for determining dropout predictors in the context of open and distance learning in a large developing country. The study was conducted at the Directorate of Distance Education at the University of North Bengal, West Bengal, India. This study employed a quantitative research design using a data mining approach to examine the predictive relationship between pre-entry demographic variables of learners with their dropout behaviour. Demographic and academic variables of learners, such as gender, marital and employment status, subject chosen, social status, age and income status were taken as independent or explanatory variables for predicting the response variables. Data analysis showed that the pattern of learner attrition is strongly biased towards a relatively disadvantaged category of learners, namely married and employed learners and those belonging to a higher age group. It also indicated that employed men or married women are more likely to leave due to factors such as pregnancy or relocation, and that remoteness of location of residence contributed to a high dropout rate. The results of this study provide important input for counsellors and faculty members to advise learners for best possible completion options. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10