Please use this identifier to cite or link to this item: doi:10.22028/D291-31129
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Title: Data-Driven Approach Towards a Personalized Curriculum
Author(s): Backenköhler, Michael
Scherzinger, Felix
Singla, Adish
Wolf, Verena
Editor(s): Yudelson, Michael
Boyer, Kristy Elizabeth
Language: English
Title: Proceedings of the 11th International Conference on Educational Data Mining
Pages: 6
Publisher/Platform: International Educational Data Mining Society
Year of Publication: 2018
Title of the Conference: EDM 2018
Place of the conference: Buffalo, New York, USA
Publikation type: Conference Paper
Abstract: Course selection can be a daunting task, especially for first-year students. Sub-optimal selection can lead to bad performance of students and increase the dropout rate. Given the availability of historic data about student performances, it is possible to aid students in the selection of appropriate courses. Here, we propose a method to compose a personal-ized curriculum for a given student. We develop a modular approach that combines a context-aware grade prediction with statistical information on the useful temporal ordering of courses. This allows for meaningful course recommendations , both for fresh and senior students. We demonstrate the approach using the data of the computer science Bachelor students at Saarland University.
Link to this record: hdl:20.500.11880/29230
Date of registration: 8-Jun-2020
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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