ViDA: A virtual debugging advisor for supporting learning in computer programming courses
Bibliographic record
Abstract
Abstract Many students need assistance in debugging to achieve progress when they learn to write computer programs. Face‐to‐face interactions with individual students to give feedback on their programs, although definitely effective in facilitating their learning, are becoming difficult to achieve with ever‐growing class sizes. This paper proposes a novel approach to providing practical automated debugging advice to support students' learning, based on the strong relationship observed between common wrong outputs and the corresponding common bugs in students' programs. To implement the approach, we designed a generic system architecture and process, and developed a tool called Virtual Debugging Advisor (ViDA) that was put into use in classes in a university. To evaluate the effectiveness of ViDA, a controlled experiment and a survey were conducted with first year engineering students in an introductory computer programming course. Results are encouraging, showing that (a) a higher proportion of students could correct their faulty code themselves with ViDA enabled, (b) an overwhelming majority of respondents found ViDA helpful for their learning of programming, and (c) most respondents would like to keep ViDA enabled when they practice writing programs.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".