Automated Questions about Learners' Own Code Help to Detect Fragile Prerequisite Knowledge

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openAccess
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2023-06-29
Major/Subject
Mcode
Degree programme
Language
en
Pages
7
505-511
Series
ITiCSE 2023 - Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education
Abstract
Students are able to produce correctly functioning program code even though they have a fragile understanding of how it actually works. Questions derived automatically from individual exercise submissions (QLC) can probe if and how well the students understand the structure and logic of the code they just created. Prior research studied this approach in the context of the first programming course. We replicate the study on a follow-up programming course for engineering students which contains a recap of general concepts in CS1. The task was the classic rainfall problem which was solved by 90% of the students. The QLCs generated from each passing submission were kept intentionally simple, yet 27% of the students failed in at least one of them. Students who struggled with questions about their own program logic had a lower median for overall course points than students who answered correctly.
Description
Publisher Copyright: © 2023 Owner/Author.
Keywords
online education, prerequisite knowledge, program comprehension, QLC
Other note
Citation
Lehtinen, T, Seppälä, O & Korhonen, A 2023, Automated Questions about Learners' Own Code Help to Detect Fragile Prerequisite Knowledge . in ITiCSE 2023 - Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education . ACM, pp. 505-511, Annual Conference on Innovation and Technology in Computer Science Education, Turku, Finland, 08/07/2023 . https://doi.org/10.1145/3587102.3588787