Analysing & Predicting Students Performance in an Introductory Computer Science Course

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Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.

Metadaten
Author details:	Alexander Tillmann, Detlef Krömker, Florian Horn, Thorsten Gattinger
URN:	urn:nbn:de:kobv:517-opus4-416307
Title of parent work (German):	Commentarii informaticae didacticae
Publisher:	Universitätsverlag Potsdam
Place of publishing:	Potsdam
Publication type:	Article
Language:	English
Date of first publication:	2018/08/28
Publication year:	2018
Publishing institution:	Universität Potsdam
Publishing institution:	Universitätsverlag Potsdam
Release date:	2018/09/19
Tag:	Blended learning; Dispositional learning analytics; Formative assessment; Learning analytics; Learning dispositions; heterogeneity
Issue:	12
First page:	29
Last Page:	45
RVK - Regensburg classification:	SR 910
Organizational units:	Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC classification:	0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Publishing method:	Open Access
	Universitätsverlag Potsdam
Peer review:	Nicht ermittelbar
Collection(s):	Universität Potsdam / Schriftenreihen / Commentarii informaticae didacticae (CID) / CID (2018) 12 / Inhalte und Kompetenzen der Informatik
License (German):	CC-BY - Namensnennung 4.0 International