Abstract
This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students' computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners' groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.
Highlights
Identifying solutions to complex problems in our society demands engineering graduates who possess the technical knowledge and skill set of the discipline and the professional mindset needed to shape the future
Our research questions are: RQ I : What are the differences in students’ computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? our goal is to identify and analyze patterns of the initial ability of the students in Computational Thinking (CT) practices and their relationship with the progress of other CT practices in a first-year engineering course
The importance of acquiring a strong foundational knowledge of computational problem-solving practices is critical to support the learning of advanced practices
Summary
Identifying solutions to complex problems in our society demands engineering graduates who possess the technical knowledge and skill set of the discipline and the professional mindset needed to shape the future. The Engineer 2020 proposes traits such as strong analytical skills, creativity, ingenuity, professionalism, and leadership as aspirations needed to operate in societal, geopolitical, and professional contexts within which engineering and its technologies will occur [1]. Weintrop et al [13] reviewed the existing literature on CT They performed open coding on 32 in-classroom activities and used external reviewers to propose a taxonomy that includes four major categories: (1) Data practices, (2) Modeling and Simulations Practices, (3) Computational problem-solving practices, and (4) System thinking practices. Weintrop and colleagues’ framework was chosen because it is comprehensive and grounded on evidence, that unlike many other taxonomies, has been based on collected data from classrooms and expert reviewers
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