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• W2965576334 abstract "Block-based programing languages (BBPL) provide effective scaffolding for K-12 students to learn computational thinking. However, the output-based assessment in BBPL learning is insufficient as we can not understand how students learn and what mistakes they have had. This study aims to propose a data-driven method that provides insight into students' problem-solving process in a game-based BBPL practice. Based on a large-scale programing dataset generated by 131,770 students in solving a classical maze game with BBPL in Hour of Code, we first conducted statistical analysis to extract the most common mistakes and correction trajectories students had. Furthermore, we proposed a novel program representation method based on tree edit distance of abstract syntax tree to represent students' programing trajectories, then applied a hierarchical agglomerative clustering algorithm to find the hidden patterns behind these trajectories. The experimental results revealed four qualitatively different clusters: quitters, approachers, solvers and knowers. The further statistical analysis indicated the significant difference on the overall performance among different clusters. This work provides not only a new method to represent students' programing trajectories but also an efficient approach to interpret students' final performance from the perspective of programing process." @default.
• W2965576334 created "2019-08-13" @default.
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• W2965576334 date "2019-07-27" @default.
• W2965576334 modified "2023-10-18" @default.
• W2965576334 title "Programming trajectories analytics in block-based programming language learning" @default.
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• W2965576334 doi "https://doi.org/10.1080/10494820.2019.1643741" @default.
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