Analyzing Fine-Grained Skill Development across Computer Science Course Progressions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".