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Using multimodal learning analytics to understand effects of block‐based and text‐based modalities on computer programming

计算机科学 模式 学习分析 调试 模态(人机交互) 块(置换群论) 过程(计算) 人工智能 数据科学 程序设计语言 社会科学 几何学 数学 社会学
作者
Dan Sun,Fan Ouyang,Yan Li,Chengcong Zhu,Yang Zhou
出处
期刊:Journal of Computer Assisted Learning [Wiley]
卷期号:40 (3): 1123-1136 被引量:2
标识
DOI:10.1111/jcal.12939
摘要

Abstract Background With the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives To further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school. Methods An online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions The results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality.
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