Good Students are Good Students: Student Achievement with Visual versus Textual Programming
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Authors
Joel Coffman
Justin M. Hill
Shannon Beck
Adrian A. De Freitas
Troy Weingart
Issue Date
2022-10
Type
Other
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Keywords
Alternative Title
Abstract
In this full research paper, we compare the impact of learning a visual versus textual programming language in an introductory computing course that is a general education requirement at our institution. We conducted a randomized comparative study with "experimental" sections that were taught using Python instead of RAPTOR, a flowchart-based programming language. The populations of students learning each programming language were similar with respect to gender, race, and predicted performance based upon standardized test scores and prior post-secondary education. Although students' performance on the whole was similar regardless of the programming language taught, predicted performance is correlated with SAT Math scores, grades in mathematics courses (specifically Calculus II), and, for lower-performing students, grades in other courses that satisfy general education requirements. That is, students from these groups who had lower predicted performance and learned Python performed worse on average than their peers who learned RAPTOR, and students with higher predicted performance outperformed (on average) their peers who learned RAPTOR. In addition, students' performance in subsequent computer science courses was not correlated with their performance and the language they learned in our introductory computing course. Our results raise important questions about the role of an introductory computing course in promoting equity and engaging students from historically underrepresented groups in computing fields.
Description
Citation
J. Coffman, J. M. Hill, S. Beck, A. A. De Freitas and T. Weingart, "Good Students are Good Students Student Achievement with Visual versus Textual Programming," 2022 IEEE Frontiers in Education Conference (FIE), Uppsala, Sweden, 2022, pp. 1-9, doi: 10.1109/FIE56618.2022.9962693
Publisher
IEEE