Journal
JOURNAL OF COMPUTER ASSISTED LEARNING
Volume 35, Issue 3, Pages 421-434Publisher
WILEY
DOI: 10.1111/jcal.12348
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Funding
- Humanity and Social Science Youth foundation from the Ministry of Education of China [16YJC880085]
- Peak Discipline Construction Project of Education from East China Normal University
- Eastern Scholar Chair Professorship Fund from Shanghai Municipal Education Commission of China [JZ2017005]
- Division of Research on Learning in Formal and Informal Settings [1661036, 1713110]
- Wisconsin Alumni Research Foundation
- Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison
- Direct For Education and Human Resources
- Division Of Research On Learning [1713110, 1661036] Funding Source: National Science Foundation
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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.
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