Interpretable Clustering of Students’ Solutions in Introductory Programming

Warning

This publication doesn't include Faculty of Arts. It includes Faculty of Informatics. Official publication website can be found on muni.cz.

Authors

EFFENBERGER Tomáš PELÁNEK Radek

Year of publication 2021
Type Article in Proceedings
Conference Artificial Intelligence in Education. AIED 2021. Lecture Notes in Computer Science, vol 12748
MU Faculty or unit

Faculty of Informatics

Citation
Web https://doi.org/10.1007/978-3-030-78292-4_9
Doi http://dx.doi.org/10.1007/978-3-030-78292-4_9
Keywords interpretable clustering; pattern mining; introductory programming; problem solving
Description In introductory programming and other problem-solving activities, students can create many variants of a solution. For teachers, content developers, or applications in student modeling, it is useful to find structure in the set of all submitted solutions. We propose a generic, modular algorithm for the construction of interpretable clustering of students’ solutions in problem-solving activities. We describe a specific realization of the algorithm for introductory Python programming and report results of the evaluation on a diverse set of problems.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.