Analysis of Student Activity in Web-Supported Courses as a Tool for Predicting Dropout

Autor/in	Cohen, Anat
Titel	Analysis of Student Activity in Web-Supported Courses as a Tool for Predicting Dropout
Quelle	In: Educational Technology Research and Development, 65 (2017) 5, S.1285-1304 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1042-1629
DOI	10.1007/s11423-017-9524-3
Schlagwörter	Academic Persistence; Predictor Variables; Dropouts; At Risk Students; Statistical Analysis; Mathematics Education; Statistics; Web Sites; Student Behavior; Dropout Prevention; Integrated Learning Systems; Data Collection; Data Analysis; College Students + Suchen Sie Ihr Suchwort? Prädiktor; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Statistische Analyse; Mathematische Bildung; Statistik; Web-Design; Student behaviour; Schülerverhalten; Data capture; Datensammlung; Auswertung; Collegestudent
Abstract	Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student activity on course websites may assist in providing early identification of learner dropout from specific courses or from degree track studies in general. It was found in this study that identifying the changes in student activity during the course period could help in detecting at-risk learners in real time, before they actually drop out from the course. Data examination on a monthly basis throughout the semester can enable educators and institutions to flag students that have been identified as having unusual behavior, deviating from the course average. It was found that a large percentage of students (66%) who had been marked as at-risk actually did not finish their courses and/or degree. The presented analysis allows instructors to observe website student usage data during a course, and to locate students who are not using the system as expected. Furthermore, it could enable university decision makers to see the information on a campus level for initiating intervention programs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01