Analysing computational thinking in collaborative programming: A quantitative ethnography approach. (12th February 2019)

• Record Type: Journal Article
• Authors: Wu, Bian; Hu, Yiling; Ruis, A.R.; Wang, Minhong
• Is Part Of: Journal of computer assisted learning. Volume 35:Number 3(2019)
• Page Start: 421
• Page End: 434
• Publication Date: 2019-02-12
• Subjects: Computer-assisted instruction -- Periodicals; 371.334
• DOI: 10.1111/jcal.12348
• Languages: English
• ISSNs: 0266-4909

Abstract

Abstract: Computational thinking (CT), the ability to devise computational solutions for real‐life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high‐performing and a low‐performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high‐performing team exhibited CT that was systematic, whereas the CT of the low‐performing team was characterized by tinkering or guess‐and‐check approaches. However, the low‐performing group's CT development trajectory ultimately converged towards the high‐performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem‐solving contexts. Lay Description: What is already known about this topic: Computational thinking competence includes computational concepts, computational practices, and computational perspectives. Collaborative programming has the potential to foster computational thinking competence development. What this paper adds: The low‐performing student group adopted a bottom‐up perspective at the beginning but transformed to a top‐down approach by the end. The low‐performing group's computational thinking development trajectory had convergent evolution towards the high‐performing group's trajectory. Implications for practice and/or policy: Computational thinking competence can be developed in collaborative contexts even when students begin with a bottom‐up approach. Computational thinking should go beyond the cognitive development of computational concepts to connect computational practice and computational identity. Quantitative ethnography approach has potential to model computational thinking competence in collaborative programming context.