Debugging behaviors of early childhood teacher candidates with or without scaffolding

Chan Min Kim, Lucas Vasconcelos, Brian R. Belland, Duygu Umutlu, Cory Gleasman

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


It is critical to teach all learners to program and think through programming. But to do so requires that early childhood teacher candidates learn to teach computer science. This in turn requires novel pedagogy that can both help such teachers learn the needed skills, but also provide a model for their future teaching. In this study, we examined how early childhood teacher candidates learned to program and debug block-based code with and without scaffolding. We aimed to see how approaches to debugging vary between early childhood teacher candidates who were provided debugging scaffolds during block-based programming and those who were not. This qualitative case study focused on 13 undergraduates majoring in early childhood education. Data sources included video recording during debugging, semi-structured interviews, and (in the case of those who used scaffolding) scaffold responses. Research team members coded data independently and then came to consensus. With hypothesis-driven scaffolds, participants persisted longer. Use of scaffolds enabled the instructor to allow struggle without immediate help for participants. Collaborative reasoning was observed among the scaffolded participants whereas the participants without scaffolds often debugged alone. Regardless of scaffolds, participants often engaged in embodied debugging and also used trial and error. This study provides evidence that one can find success debugging even when engaging in trial and error. This implies that attempting to prevent trial and error may be counterproductive in some contexts. Rather, computer science educators may be advised to promote productive struggle.

Original languageEnglish (US)
Article number26
JournalInternational Journal of Educational Technology in Higher Education
Issue number1
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • Education
  • Computer Science Applications


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