Analysing & Predicting Students Performance in an Introductory Computer Science Course
- 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.
Author details: | Alexander Tillmann, Detlef Krömker, Florian Horn, Thorsten Gattinger |
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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 |