The Y-Model - Formalization of Computer-Science Tasks in the Context of Adaptive Learning Systems

Lohr D, Berges MP, Kohlhase M, Müller D, Rapp M (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Event location: Berlin DE

DOI: 10.1109/GECon58119.2023.10295148

Abstract

Students' answers to tasks provide a valuable source of information in teaching as they result from applying cognitive processes to a learning content addressed in the task. Due to steadily increasing course sizes, analyzing student answers is frequently the only means of obtaining evidence about student performance. However, in many cases, resources are limited, and when evaluating exams, the focus is solely on identifying correct or incorrect answers. This overlooks the value of analyzing incorrect answers, which can help improve teaching strategies or identify misconceptions to be addressed in the next cohort.  


In teacher training for secondary education, assessment guidelines are mandatory for every exam, including anticipated errors and misconceptions. We applied this concept to a university exam with 462 students and 41 tasks. For each task, the instructors developed answer classes -- classes of expected responses, to which student answers were mapped during the exam correction process. The experiment resulted in a shift in mindset among the tutors and instructors responsible for the course: after initially having great reservations about whether the significant additional effort would yield an appropriate benefit, the procedure was subsequently found to be extremely valuable. 

Authors with CRIS profile

Dominic Lohr Lehrstuhl für Angewandte Mathematik (Wissenschaftliches Rechnen) Marc-Pascal Berges Professur für Didaktik der Informatik Michael Kohlhase Professur für Wissensrepräsentation und -verarbeitung Dennis Müller Professur für Wissensrepräsentation und -verarbeitung Max Rapp Professur für Wissensrepräsentation und -verarbeitung

Related research project(s)

Domain and competency modelling (VoLL-KI) Von Lernenden Lernen: Ganzheitliche daten- und wissensunterstützte Hochschulbildung und deren Gestaltung - VoLL-KI Dec. 1, 2021 - Nov. 30, 2025

How to cite

APA:

Lohr, D., Berges, M.-P., Kohlhase, M., Müller, D., & Rapp, M. (2023). The Y-Model - Formalization of Computer-Science Tasks in the Context of Adaptive Learning Systems. In Proceedings of the IEEE German Education Conference (GeCon). Berlin, DE.

MLA:

Lohr, Dominic, et al. "The Y-Model - Formalization of Computer-Science Tasks in the Context of Adaptive Learning Systems." Proceedings of the IEEE German Education Conference (GeCon), Berlin 2023.

BibTeX: Download