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Title: Discovery of Similarities Across Debugging Tasks in Relations Within and Between Virtual and Physical Objects
Authors: Kim, Chanmin
Dinç, Emre
Lee, Eunseo
Baabdullah, Afaf
Zhang, Anna Y.
Belland, Brian R.
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Kim, C., Dinç, E., Lee, E., Baabdullah, A., Zhang, A. Y., & Belland, B. R. (2022). Discovery of similarities across debugging tasks in relations within and between virtual and physical objects. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 1185-1188). International Society of the Learning Sciences.
Abstract: Analogical reasoning is critical in debugging. Still, the literature is unclear on how non-CS major novice programming learners draw inferences from previous debugging tasks when attempting to understand the present debugging task. We addressed the research question How do novice programming learners discover relational commonalities across debugging tasks in which block-code contains structural relations within itself and with a corresponding robot? We used a theory-informed coding scheme to analyze interviews and screencasting data. We found that (a) noticing similarities in function between the target task and the source task(s) guided learners to discover relational commonalities within block code between the source task(s) and the target task, (b) functional analogy was not necessary for discovery of every relational commonality between the target task and the source task in block code, and (c) noticing and using relational commonalities did not always lead to successful debugging.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

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