This work presents an experimental analysis of first-grade students' block-based programming trajectories. These trajectories consist of edit-level program snapshots that capture learners' problem-solving proc...
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ISBN:
(纸本)9798400706004
This work presents an experimental analysis of first-grade students' block-based programming trajectories. These trajectories consist of edit-level program snapshots that capture learners' problem-solving processes in a navigational microworld. Our results highlight the potential of this fine-grained data capture. Snapshot frequencies in trajectories collected before and after a coding intervention showcase the collective progress of the learners. Graph visualizations, in which nodes represent snapshots and directed edges code edits, highlight strategies, pitfalls and debugging procedures. Individual programming trajectories shed light on details of learners' problem-solving processes that less granular analysis would conceal. Various works in the field of Learning Analytics research show the usefulness of collecting fine-grained process data that proceed from programming activities. However, how to analyze this data is still an open question and research on the subject is in an experimental phase. We contribute to this experimentation by analyzing and discussing results collected from 30 first-grade students in a pretest-posttest study.
With national K-12 education initiatives such as "CSForAll," block-based programming environments have emerged as widely used tools for teaching novice programming. A key challenge presented by block-based p...
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ISBN:
(纸本)9781450358903
With national K-12 education initiatives such as "CSForAll," block-based programming environments have emerged as widely used tools for teaching novice programming. A key challenge presented by block-based programming environments is assessing students' computational thinking (CT) and programming competencies. Developing assessment methods that can evaluate students' use of CT practices such as testing and refining, and developing and using appropriate algorithms, can help teachers evaluate students learning and provide appropriate scaffolding. In this work, we utilize an evidence-centered assessment design approach to devise a three-dimensional assessment to evaluate students' CT competencies based on evidence extracted from their programming trajectories in a block-based programming environment. In this assessment, the first dimension assesses students' knowledge of essential CT concepts, the second dimension assesses students' dynamic testing and refining strategies, and the third dimension assesses their overall problem-solving efficiency. We apply the assessment framework to data collected from students' interactions with a game-based learning environment designed to develop middle-grade students' CT competencies and programming skills. The results demonstrate that students' knowledge of basic CT constructs, such as appropriate use and combination of control structures, serves as the foundation for designing and implementing effective algorithms. Further, we assessed students testing and refining strategies over the three dimensions of novelty, positivity, and scale. The results demonstrate that students with higher algorithmic capabilities tend to make more novel, positive, and small-scale changes. The results reveal distinctive patterns in students' approaches to computational thinking problem solving and make a step toward identifying and assessing productive computational thinking practices.
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