A learning trajectory for developing computational thinking and programming This research study identifies the relationship between students’ prior experiences with programming and their development of computational thinking and programming during their first year engineering experience. Many first year programs teach students basic programming constructs using languages like MATLAB or LABView. The reason for this is because the disciplinary schools expect students to transform the constitutive properties that model a system’s behavior into a computer model they can use to analyze a system’s performance. Some undergraduate engineering students are entering college with strong computational backgrounds, while others are not. Peer learning has been used to accommodate the variance is skills between students; however, more needs to be known about the opportunities and issues with helping students develop these skills. This study is the first in a series to better identify students’ transition into developing and reasoning with analytical tools. The initial conjecture is that well balanced teams of novice and expert programmers can have a positive effect on the novice programmer’s development. Further the learning progression across two programming languages is critical to developing a student’s ability to generalize across various computational tools. Self-report background survey, students’ performance on academic assessments and an end of semester exit survey are being analyzed to identify a pattern in the development of novice programmers’ ability to design algorithms and implement them in code. This paper will be of interest to instructors with the objective of developing computational thinking and programming in classrooms with a large variance in students’ backgrounds with programming.
Engineering Education, Computational thinking, Programming constructs, Programming languages
Date of this Version
Brophy, S. P., & Lowe, T. A. (2017, June), A Learning Trajectory for Developing Computational Thinking and Programming Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/27472
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