Interpretable Clustering of Students’ Solutions in Introductory Programming
| Authors | |
|---|---|
| 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 | |
| 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. |
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