Quantified Analysis of Analogical Reasoning During Debugging Among Novice Programing Learners (Poster 8)

• In Event: AERA Poster Session Wednesday 4:20 p.m.
  In Poster Session: Technology, Instruction, Cognition, and Learning (TICL) Poster Session

Abstract

Analogical reasoning has long been known to be integral to problem solving. This is no less true in computer science. In this study, we coded actions made by preservice teachers as they learned to debug block-based programming. We then ran mediation analysis and constructed multilevel mixed models to address the question “In what ways do novice programming learners use analogy in debugging?” We found that using similar/dissimilar/unrelated analogies correctly led to successful debugging, successful use of similarities and dissimilarities prompted pre-service teachers to further work on their code, and the successful use of similarities was significantly and positively associated with pre-service teachers testing code and confirming code meeting programming goals. Further discussion will be present in the full paper.

Authors

• ChanMin Kim, Pennsylvania State University

• Yinqi Zhang, Pennsylvania State University

• Eunseo Lee, Pennsylvania State University

• Emre Dinç, Pennsylvania State University

• Afaf Baabdullah, King Saud University

• Brian R. Belland, Pennsylvania State University