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Abstract This study indicates the most effective combinations of scaffolding features within computer science and technology education settings. It addresses the research question, “What combinations of scaffolding characteristics, contexts of use, and assessment levels lead to medium and large effect sizes among college‐ and graduate‐level engineering and technology learners?” To do so, studies in which scaffolding led to a medium or large effect size within the context of technology and engineering education were identified within a scaffolding meta‐analysis data set. Next, two‐step cluster analysis in SPSS 24 was used to identify distinct groups of scaffolding attributes tailored to learning computer science at the undergraduate and graduate levels. Input variables included different scaffolding characteristics, the context of use, education level, and effect size. There was an eight‐cluster solution: five clusters were associated with large effect size, two with medium effect size, and one with both medium and large effect size. The three most important predictors were the context in which scaffolding was used, if and how scaffolding is customized over time and the decision rules that govern scaffolding change. Notably, highly effective scaffolding clusters are associated with most levels of each predictor. 

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. 

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. 

This paper took up the tradition of Critical Feminism and Ethnography to examine early childhood education (ECE) pre-service teachers’ perspectives on STEM and robotics integration. The central research questions are (1) How can we make sense of preservice teachers’ formation of STEM/STEAM teacher identity while participating in our robotic unit from a Critical Feminist perspective? (2) What are preservice teachers’ perceptions of benefits, barriers and concerns (both structural level and individual level), and recommendations for pedagogical practice for STEM and robotics integration in ECE? (3) How can we better prepare and support pre-service teachers, largely women and non-STEM-majors, for STEM and robotics content integration in their classrooms? To answer the above questions, we collected interview data from 76 informants from a large public university in the Southeastern United States. Each informant designed a lesson plan on teaching with robots and completed approximately 30-minute structured interviews. We focused on our informants' lived experiences and centered their voices while conducting and analyzing the interviews via thematic coding and category analysis. Analysis of the interview stories indicated that our informants considered the robotics module in their pre-service training as a valuable learning experience of STEM/robotics integration in ECE. The three most commonly perceived benefits of STEM/robotics integration by pre-service teachers are early exposure helps build a STEM knowledge foundation (n = 66), STEM and robotics content effectively increases students’ motivation and engagement (n = 60), and bridging the gender gap in STEM as historically male-dominated fields (n = 27). The three most commonly perceived barriers are concern about age-appropriateness of robots (n = 53), time/state standard constraints (n = 35), and funding/resources available and support from the school and local district (n = 18). Our findings indicate structural and institutional barriers are still present and can potentially deter ECE teachers from implementing STEM/robotics content in their classrooms. We thus call for attention from a structural level instead of shifting the burdens onto both pre-service and in-service teachers. Employing a conscious effort of being self-reflexive, critical, and counter-hegemonic in our practices, this article is one of the first to approach motivation from a Critical Feminism perspective in the field and provides tangible implications for both engineering education research and practice. 

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.