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doi:10.22028/D291-31045
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: | https://link.springer.com/chapter/10.1007/978-3-319-74781-1_3 |
Link to this record: | hdl:20.500.11880/29196 http://dx.doi.org/10.22028/D291-31045 |
ISBN: | 978-3-319-74781-1 978-3-319-74780-4 |
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: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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