Beyond Verbal Self-Explanations: Student Annotations of a Code-Tracing Example Produced by ChatGPT
Bibliographic record
Abstract
Prior research shows that in order for students to learn from examples, they need to actively process the example solutions, for instance by self-explaining them.The majority of this research focused on student explanations expressed in words (either spoken or written) and so less is known about other forms of expression, such as ones involving spatial elements (e.g., flowcharts, drawings).I used a qualitative approach to (1) identify strategies students used to annotate a code-tracing example produced by ChatGPT and (2) assess the quality of student annotations, including how constructive they were.The annotation activity took place in a first-year programming class.I report on the results related to these variables (strategies used, quality, level of construction), including their relationship to course grades.Overall, students used a variety of verbal and visual strategies to annotate the code-tracing example produced by ChatGPT, with flowcharts used more frequently by students who received the highest quality scores.Quality scores were significantly correlated to course grades, but construction scores were not.This work proposes a novel approach for coding student construction that can be applied to both verbal and visual representations.It also contributes to the domain of programming education by examining the effectiveness of different student annotation strategies on code-tracing examples, emphasizing the benefits of visual tools like flowcharts.i I would also like to thank my committee member, Dr. Rebecca Merkley, who has always been available to discuss the complexities of educational research (and practice).I appreciate your openness and thoughtfulness.Thank you also to Dr. Elizabeth Stobert,
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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 | 0.013 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".