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To use robots within early childhood education requires the preparation of early childhood teachers to use and teach block-based programming. We used a hierarchical linear model approach to address our research question: How can study cohort, cognitive challenge types, and motivational challenge types be used to predict lesson plan quality? Positive motivational challenge predictors were task value of programming, task value of teaching, mastery goals of programming, belonging in teaching, and autonomy in robotics. Negative motivational challenge predictors were mastery goals of teaching, belonging in robotics, self-efficacy in teaching, autonomy in programming, and autonomy in teaching. Positive cognitive challenge predictors were technical issues, problem solving - higher-order skills, and lesson design - other issues. 

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. 

This paper explores customized scaffolding for pre-service teachers’ problem-solving in technology and engineering discipline. We used clustering analysis to discover natural groupings of scaffolding characteristics which were used in 144 computer-based scaffolding studies from the previous meta-analysis. We first selected input variables based on our research questions which include different scaffolding characteristics, context of use, education level, and effect size. Next, using a two-step clustering algorithm, we found four clusters based on the predominant scaffolding characteristics and profiled each cluster in terms of scaffolding characteristics and their context of use. The resulting cluster solutions indicate what combination of scaffolding characteristics used in different types of problem-centered learning context would be effective for pre-service teachers’ technology- and engineering-related problem-solving. 

It is often said that computer science is for all students. This implies that it is also for early childhood students, including preschoolers, kindergarteners, and early elementary schoolers. To integrate computer science education into early childhood education, it is necessary to prepare early childhood teachers to do so. In this study, we investigated how and why 15 preservice, early childhood teachers reacted to and addressed challenges when creating block-based programming to control robots. Data sources included classroom recordings, interviews, lesson artifacts, and questionnaires. Analysis strategies included open and axial coding. Findings on hypothesis generation, guess-and-check practice, stereotypical conception, and adaptive attribution to success in programming are discussed. 

This research examined how undergraduates majoring in Early Childhood Education (a) engaged with robot coding and (b) designed to engage preschoolers in learning with robots. A symbolic interactionism perspective was applied to examine how and why these future early childhood education teachers learned to code and planned lessons integrating robots into preschoolers’ play. The central research question was: How and why do pre-service early childhood teachers use robotics in preschoolers’ play?