The Potential of Answer Classes in Large-scale Written Computer-Science Exams

Lohr D, Berges MP, Kohlhase M, Rabe F (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Pages Range: 179 - 190

Event location: Aachen DE

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 Professur für Didaktik der Informatik Marc-Pascal Berges Professur für Didaktik der Informatik Michael Kohlhase Professur für Wissensrepräsentation und -verarbeitung Florian Rabe 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., & Rabe, F. (2023). The Potential of Answer Classes in Large-scale Written Computer-Science Exams. In Desel, Jörg; Opel, Simone (Eds.), Proceedings of the Hochschuldidaktik Informatik (HDI) 2021 (pp. 179 - 190). Aachen, DE.

MLA:

Lohr, Dominic, et al. "The Potential of Answer Classes in Large-scale Written Computer-Science Exams." Proceedings of the Hochschuldidaktik Informatik (HDI) 2021, Aachen Ed. Desel, Jörg; Opel, Simone, 2023. 179 - 190.

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