Work In Progress: CodeCapture: A Tool to Attain Insight into the Programming Development Process
Bibliographic record
Abstract
Most introductory computer programming courses focus on giving students a good understanding of fundamentals and programmatic problem solving.Programming assignments are a way for students to reinforce these concepts and for instructors to evaluate students' comprehensions.In a typical assignment, students are given a problem to which they develop a programmatic solution and submit its final iteration for evaluation.Instructors only see this static and final submission, and the decision making or the process of problem solving that led to the final submission is not captured.This paper presents a tool called CodeCapture that periodically captures snapshots of a student's programming assignment source code over the development period and provides insight into their problem-solving process.Instructors can leverage data provided by this tool to identify areas that students have difficulty.Subsequently, instructors can give insightful feedback to students via a combination of metrics describing students' development processes, even in a large-scale classroom.In this paper, a discussion on how CodeCapture overcomes gaps in the existing tools is presented.The technical specifications and the trial results from where CodeCapture was able to enrich the feedback to students regarding their assignments is also presented.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".