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1. 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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2. Developing pre-service elementary teacher’s computational thinking knowledge through coding and mathematics pedagogy

   Gleasman, Cory; Kim, ChanMin (January 2020, Research highlights in technology and teacher education 2020)

   Gibson, D. C.; Ochoa, M. N.; Christensen, R.; Cohen, J.; Crawford, D.; Graziano, K.; Langran, E.; Langub, L.; Rutledge, D.; Voogt, J. (Ed.)

   As computer science education standards are disseminated to K-12 school districts nationally, teacher education programs are left with the challenge of ensuring pre-service teachers are prepared to enter their first classroom with the skills and knowledge necessary to align instruction with the new standards. This paper examines the use of a learning intervention called “Block-Based Coding and Computational Thinking for Conceptual Mathematics” (B2C3Math) that aimed to help pre-service teachers majoring in early childhood and elementary education learn and apply computational thinking concepts to their elementary mathematics teaching. Ten pre-service teachers all at the same stage in their teacher preparation program participated in this convergent mixed-methods study. A focus of the research was placed on how participant’s computational thinking knowledge changed following the implementation of B2C3Math. Findings suggest that there were changes in the participants’ views of computational thinking application to elementary mathematics teaching following the implementation of B2C3Math. Implications for research and instructional practices using B2C3Math for teacher education are discussed. 

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3. Objects to debug with: How young children resolve errors with tangible coding toys

   Silvis, D.; Clarke-Midura, J.; Shumway, J.; & Lee, V. R. (January 2021, Proceedings of the International Society of the Learning Sciences (ISLS) Annual Meeting)

   Debugging is an important skill all programmers must learn, including preliterate children who are learning to code in early childhood settings. Despite the fact that early learning environments increasingly incorporate coding curricula, we know little about debugging knowledge in early childhood. One reason is that the tangible programming environments designed for young children entail a layer of material complexity that we have yet to account for in terms of learning to debug. In our study of young children learning to program, we found that in the midst of solving programming tasks and learning to debug, tangible toys presented bugs of their own. This paper analyzes video of Kindergarteners learning to debug errors in the program and errors in the physical materials. We argue that concurrent physical and programming bugs present opportunities for young children to learn about the broader computational system in which they are learning to code. 

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4. Teaching Explicit Programming Strategies to Adolescents

   https://doi.org/10.1145/3287324.3287371  

   Ko, Amy J.; LaToza, Thomas D.; Hull, Stephen; Ko, Ellen A.; Kwok, William; Quichocho, Jane; Akkaraju, Harshitha; Pandit, Rishin (January 2019, Symposium on Computer Science Education)

   One way to teach programming problem solving is to teach explicit, step-by-step strategies. While prior work has shown these to be effective in controlled settings, there has been little work investigating their efficacy in classrooms. We conducted a 5-week case study with 17 students aged 15-18, investigating students' sentiments toward two strategies for debugging and code reuse, students' use of scaffolding to execute these strategies, and associations between students' strategy use and their success at independently writing programs in class. We found that while students reported the strategies to be valuable, many had trouble regulating their choice of strategies, defaulting to ineffective trial and error, even when they knew systematic strategies would be more effective. Students that embraced the debugging strategy completed more features in a game development project, but this association was mediated by other factors, such as reliance on help, strategy self-efficacy, and mastery of the programming language used in the class. These results suggest that teaching of strategies may require more explicit instruction on strategy selection and self-regulation. 

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5. A Randomized Controlled Trial on the Wild Wild West of Scientific Computing with Student Learners

   https://doi.org/10.1145/3291279.3339421  

   Rafalski, Timothy; Uesbeck, P. Merlin; Panks-Meloney, Cristina; Daleiden, Patrick; Allee, William; Mcnamara, Amelia; Stefik, Andreas (January 2019, Proceedings Article published 2019 in Proceedings of the 2019 ACM Conference on International Computing Education Research - ICER '19)

   Scientific computing has become an area of growing importance. Across fields such as biology, education, physics, or others, people are increasingly using scientific computing to model and understand the world around them. Despite the clear need, almost no systematic analysis has been conducted on how students in fields outside of computer science learn to program in the context of scientific computing. Given that many fields do not explicitly teach much programming to their students, they may have to learn this important skill on their own. To help, using rigorous quantitative and qualitative methods, we looked at the process 154 students followed in the context of a randomized controlled trial on alternative styles of programming that can be used in R. Our results suggest that the barriers students face in scientific computing are non-trivial and this work has two core implications: 1) students learning scientific computing on their own struggle significantly in many different ways, even if they have had prior programming training, and 2) the design of the current generation of scientific computing feels like the wild-wild west and the designs can be improved in ways we will enumerate. 

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