Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters.
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The findings reveal the promising role of computer-based scaffolding in making complex learning with real-world projects accomplishable by a wide range of students and reducing the gaps between high- and low-achieving students.ĭata workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. Despite academic achievement discrepancies among the participants at the beginning of the course, low-achievers’ post-study performance in product quality and programming thinking skills became similar to that of high-achievers, and their post-study performance in subject knowledge became similar to that of medium-achievers. At the end of the course, their performance was improved by 35.49% in product quality and 38.98% in subject knowledge their programming thinking skills were improved by 20.91% in problem understanding, 21.86% in modular design, and 25.70% in process design.
The results show that with the support of computer-based scaffolding, all participants maintained a high level of motivation during the course. The scaffolding was designed based on the four-component instructional design (4C/ID) model. This study incorporated computer-based scaffolding into a project-based programming course to make complex project learning accessible to students. However, completing an authentic project involves a complex process, which may pose challenges to many students, especially low-achievers. Project-based learning, in which students engage in meaningful learning with authentic projects and building agency and autonomy for sustainable learning, has been increasingly promoted in higher education.
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