A Decision Support Prototype Tool for Predicting Student Performance in an ODL Environment

Autor/inn/en	Kotsiantis, S. B.; Pintelas, P. E.
Titel	A Decision Support Prototype Tool for Predicting Student Performance in an ODL Environment
Quelle	In: Interactive Technology and Smart Education, 1 (2004) 4, S.253-264 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1741-5659
DOI	10.1108/17415650480000027
Schlagwörter	Distance Education; Dropouts; Academic Achievement; Prediction; Tutors; Predictor Variables; Information Management; Scores; Attendance; Higher Education; Grades (Scholastic); Student Evaluation + Suchen Sie Ihr Suchwort? Distance study; Distance learning; Fernunterricht; Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Schulleistung; Vorhersage; Förderlehrer; Lehrender; Tutor; Prädiktor; Procurement of information; Informationsbeschaffung; Anwesenheit; Hochschulbildung; Hochschulsystem; Hochschulwesen; Notenspiegel; Schulnote; Studentische Bewertung
Abstract	Machine Learning algorithms fed with data sets which include information such as attendance data, test scores and other student information can provide tutors with powerful tools for decision-making. Until now, much of the research has been limited to the relation between single variables and student performance. Combining multiple variables as possible predictors of dropout has generally been overlooked. The aim of this work is to present a high level architecture and a case study for a prototype machine learning tool which can automatically recognize dropout-prone students in university level distance learning classes. Tracking student progress is a time-consuming job which can be handled automatically by such a tool. While the tutors will still have an essential role in monitoring and evaluating student progress, the tool can compile the data required for reasonable and efficient monitoring. What is more, the application of the tool is not restricted to predicting drop-out prone students: it can be also used for the prediction of students' marks, for the prediction of how many students will submit a written assignment, etc. It can also help tutors explore data and build models for prediction, forecasting and classification. Finally, the underlying architecture is independent of the data set and as such it can be used to develop other similar tools. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10