Please use this identifier to cite or link to this item: doi:10.22028/D291-31045
Volltext verfügbar? / Dokumentlieferung
Title: Student Performance Prediction and Optimal Course Selection: An MDP Approach
Author(s): Backenköhler, Michael
Wolf, Verena
Editor(s): Cerone, Antonio
Roveri, Marco
Language: English
Title: Software Engineering and Formal Methods : SEFM 2017 Collocated Workshops: DataMod, FAACS, MSE, CoSim-CPS, and FOCLASA
Startpage: 40
Endpage: 47
Publisher/Platform: Springer
Year of Publication: 2018
Place of publication: Cham
Title of the Conference: SEFM 2017
Place of the conference: Trento, Italy
Publikation type: Conference Paper
Abstract: Improving the performance of students is an important challenge for higher education institutions. At most European universities, duration and completion rate of degrees are highly varying and consulting services are offered to increase student achievement. Here, we propose a data analytics approach to determine optimal choices for the courses of the next term. We use machine learning techniques to predict the performance of a student in upcoming courses. These prediction form the transition probabilities of a Markov decision process (MDP) that describes the course of studies of a student. Using this model we plan to explore the effect of different strategies on student performance.
DOI of the first publication: 10.1007/978-3-319-74781-1_3
URL of the first publication:
Link to this record: hdl:20.500.11880/29196
ISBN: 978-3-319-74781-1
Date of registration: 28-May-2020
Notes: Lecture notes in computer science ; volume 10729
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Verena Wolf
Collections:Die Universitätsbibliographie

Files for this record:
There are no files associated with this item.

Items in SciDok are protected by copyright, with all rights reserved, unless otherwise indicated.