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1. Debugging behaviors of early childhood teacher candidates with or without scaffolding

   https://doi.org/10.1186/s41239-022-00319-9  

   Kim, ChanMin ; Vasconcelos, Lucas ; Belland, Brian R. ; Umutlu, Duygu ; Gleasman, Cory ( March 2022 , International Journal of Educational Technology in Higher Education)

   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.

    

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2. A study of predictors for debugging quality among preservice, early childhood teachers

   Belland, B. R. ; Kim, C. ; Lee, E. ; Baabdullah, A. ; Zhang, A. Y. ( April 2020 , Annual meeting program American Educational Research Association)

   While there has been much progress in the meaningful integration of computer science within K-12 classrooms, there is a need to promote more equitable participation and to improve teacher preparation. One area in which this is needed is in early childhood education. In this paper, we investigated predictors of debugging quality among 19 pre-service early childhood teachers as they engaged in debugging supported by scaffolding. Bayesian regression indicated the following variables predicted debugging quality: debugging process score, English domain identification, performance approach goals, and sentiment analysis scores applied to what students wrote in response to scaffold prompts. 

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3. ISLSBlending Everyday Movement and Representational Infrastructure: An Interaction Analysis of Kindergarteners Coding Robot Routes

   Silvis, D. ; Lee, V.R. ; Clarke-Midura, J. ; Shumway, J ; Kozlowski, J. ( January 2020 , Proceedings of the International Society of the Learning Sciences (ISLS) Annual Meeting)

   If we want to design for computer science learning in K12 education, then Kindergarten is the place to start. Despite differing formats, early childhood coding tools rely heavily on a similar representational infrastructure. Grids and arrows predominate, introducing a nested series of directional symbols and spatial skills children must learn in order to code. Thus, learning to think computationally is entangled with learning to think spatially and symbolically. This paper analyzes video of Kindergarten students learning to use robot coding toys and examines how they navigated programming’s representational infrastructure. We found that children drew on conventional notions of how objects move, creating a “conceptual blend” for programming robot routes (Fauconnier & Turner, 1998). We argue that coding in Kindergarten requires mapping a series of correspondences from the domain of everyday movements onto the resources available in the representational domain. 

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4. Epistemological Pluralism for Diversifying Preservice Early Childhood Teachers’ Programming Experience

   Kim, C. ; Belland, B. R. ; Umutlu, D. ( April 2020 , Annual meeting program American Educational Research Association)

   Research indicates that computer programming in a bricolage manner is equally strong as structure programming. In this study, we investigated how and why 26 preservice, early childhood teachers learning to program employed diverse approaches to programming. Data included classroom videos, interviews, written reflections, submitted code, and questionnaires. Analysis involved open and axial coding. Findings included (a) all tinkered through trial and error but this does not mean that analytical means were never used, (b) divide-and-conquer was practiced, (c) analytical means were often used in locating the bug whereas tinkering was used mostly in fixing the bug, (d) unnoticing when/where to tinker compromised the programming goal, and (e) robot programming was perceived as creative, artistic, and playful. 

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5. Developing an Introductory Computer Science Course for Pre-service Teachers

   Adler, Rachel F ; Beck, Kristan ( January 2020 , Journal of technology and teacher education)

   Computational thinking (CT) involves breaking a problem into smaller components and solving it using algorithmic thinking and abstraction. CT is no longer exclusively for computer scientists but for everyone. While CT does not necessarily require programming, learning programming to enhance CT skills at a young age can help shape the next generation of children with knowledge that can help them succeed in our technological world. In order to produce teachers who are able to incorporate programming and CT into their future classrooms, we created an introductory Computer Science course (CS0) targeting future K-8 STEM teachers yet open to any student to enroll and learn computer science. We used a mixed-methods approach, examining both quantitative and qualitative data based on self-reported surveys, classroom artifacts, and focus groups from four semesters of data. We found that after taking the course, students’ self-efficacy in CT increased and while education students initially had lower confidence in their computing abilities than computer science students in the course, by the end of the semester there were no differences in their perceived and actual coding abilities when compared with computer science students. Furthermore, education students had many ideas on how to incorporate similar projects into their own future classrooms. 

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