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  1. Analogical reasoning is considered to be a critical cognitive skill in programming. However, it has been rarely studied in a block-based programming context, especially involving both virtual and physical objects. In this multi-case study, we examined how novice programming learners majoring in early childhood education used analogical reasoning while debugging block code to make a robot perform properly. Screen recordings, scaffolding entries, reflections, and block code were analyzed. The cross-case analysis suggested multimodal objects enabled the novice programming learners to identify and use structural relations. The use of a robot eased the verification process by enabling them to test their analogies immediately after the analogy application. Noticing similar functional analogies led to noticing similarities in the relation between block code as well as between block code and the robot, guiding to locate bugs. Implications and directions for future educational computing research are discussed.

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    Free, publicly-accessible full text available September 1, 2024
  2. This article reports the analysis of data from five different studies to identify predictors of preservice, early childhood teachers’ views of (a) the nature of coding, (b) integration of coding into preschool classrooms, and (c) relation of coding to fields other than computer science (CS). Significant changes in views of coding were predicted by time, prior robot programming experience, and perceptions of the value of coding. Notably, prior programming knowledge and positive perceptions of mathematics predicted decreases in views of coding from pre- to post-survey.

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    Free, publicly-accessible full text available June 13, 2024
  3. Building causal knowledge is critical to science learning and scientific explanations that require one to understand the how and why of a phenomenon. In the present study, we focused on writing about the how and why of a phenomenon. We used natural language processing (NLP) to provide automated feedback on middle school students’ writing about an underlying principle (the law of conservation of energy) and its related concepts. We report the role of understanding the underlying principle in writing based on NLP-generated feedback. 
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    Free, publicly-accessible full text available July 1, 2024
  4. This is a contribution to a Symposium This symposium will provide opportunities for discussion about how Artificial Intelligence can support ambitious learning practices in CSCL. To the extent that CSCL can be a lever for educational equitable educational change, AI needs to be able to support the kinds of practices that afford agency to students and teachers. However, AI also brings to the fore the need to consider equity and ethics. This interactive session will provide opportunities to discuss these issues in the context of the examples presented here. Our contribution is focused on two participatory design studies we conducted with 14 teachers to understand the kinds of automatic feedback they thought would support their students’ science explanation writing as well as how they would like summaries of information from students’ writing presented in a teacher’s dashboard. We also discuss how we developed our system, PyrEval, for automated writing support based on historical data and scoring from manual coding rubrics. 
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  5. In principle, educators can use writing to scaffold students’ understanding of increasingly complex science ideas. In practice, formative assessment of students’ science writing is very labor intensive. We present PyrEval+CR, an automated tool for formative assessment of middle school students’ science essays. It identifies each idea in a student’s science essay, and its importance in the curriculum. 
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  6. Science writing skills depend on a student’s ability to co-ordinate conceptual understanding of science with the ability to articulate ideas independently, and to distinguish between gradations of importance in ideas. Real-time scaffolding of student writing during and immediately after the writing process could ease the cognitive burden of learning to co-ordinate these skills and enhance student learning of science. This paper presents a design process for automated support of real-time scaffolding of middle school students’ science explanations. We describe our adaptation of an existing tool for automatic content assessment to align more closely with a rubric, and our reliance on data mining of historical examples of middle school science writing. On a reserved test set of semi-synthetic examples of science explanations, the modified tool demonstrated high correlation with the manual rubric. We conclude the tool can support a wide range of design options for customized student feedback in real time. 
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  7. Abstract

    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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