Foundations for complete exercise generation

Publikationstyp
Conference Paper
Date Issued
2022-11
Sprache
English
Author(s)
Schmidt, Deniz  
Uzulis, Max Vincent 
Niklas Kersten  orcid-logo
Grasse, Ole  orcid-logo
Hinckeldeyn, Johannes  orcid-logo
Kreutzfeldt, Jochen  orcid-logo
Institut
Technische Logistik W-6  
TORE-URI
http://hdl.handle.net/11420/14529
Citation
20th International Conference on Information Technology Based Higher Education and Training (ITHET 2022)
Contribution to Conference
20th International Conference on Information Technology Based Higher Education and Training, ITHET 2022  
Publisher DOI
10.1109/ITHET56107.2022.10031913
Scopus ID
2-s2.0-85148437060
This paper introduces a new approach to exercise generation for university courses, that aims for high domain coverage, variability as well as adaptability. For this, we utilize a domain-specific language (DSL) which allows the creation of models describing subject matter. These models are further aggregated with a topic-oriented representation of a course and serve as the basis for an exercise generator, which then uses various templates to process different types of knowledge into different types of exercises. This novel approach is initially developed and tested by applying it to two engineering-related university courses.