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Analyzing Fine-Grained Skill Development across Computer Science Course Progressions
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DOI：10.1145/3743683.png)
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Abstract
En 中文
Although ample research has focused on computing skill development over a single course or specific programming language, relatively little attention is paid to how computing skills evolve across a program. Our work aims to understand how specific skills develop throughout a progression of CS courses. We use qualitative content analysis to catalog common errors in assignment submissions from four computing courses forming a prerequisite chain: CS1, CS2, Systems Programming (SP), and Operating Systems (OS). We focus on three fine-grained skills encountered in some form in all four courses: (S1) opening and reading data from a file, (S2) storing or organizing data in data structures, and (S3) using the data to implement a solution for a well-defined task. We study how the commonly observed errors or issues evolve across the prerequisite chain, thus analyzing how these skills develop. We notice successful development in most skills, evidenced by a reduction of common errors over the course progression. However, we also notice variability in skill development corresponding to the expected challenges, in working with new techniques (OOP), new languages (C), or concepts (binary files). We also observe an overall lower prevalence of common errors in CS1 and CS2 among students who progress to SP and OS in close succession. We believe that analyzing the evolution of common errors across course progressions would enable educators to gain insight into skills development and if certain outcomes are met more seamlessly than others.
Keywords:
computing skills
skill development
programming education
error analysis
course progression
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539
Citations:
508
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