Stump-the-Teacher: Using Student-generated Examples during Explicit Debugging Instruction
Bibliographic record
Abstract
As the number of upper-elementary students (grades 4-7) interested in computer programming increases, there is growing interest in age-appropriate pedagogical approaches to debugging instruction. However, previous research findings with younger novice learners are limited, and research with explicit debugging instruction has shown limited uptake by students. This experience report describes two novel classroom activities undertaken as part of an investigation into explicit debugging instruction with upper-elementary-aged students: student-generated examples (SGE), in which students modified working programs by purposefully introducing bugs; and stump-the-teacher, in which the teacher demonstrates their expert debugging approach explicitly by thinking aloud while attempting to debug the SGEs. Students demonstrated both an eagerness to craft examples that they hoped would stump the teacher and actively engaged with the live-debugging exercise. Students were also asked to compare and describe their own debugging approach to the teacher's debugging approach. Analysis of the bugs deliberately introduced by students found that they were primarily syntax-related, in line with early novice programmer error patterns. Additionally, student comparisons of their debugging approaches demonstrated awareness and engagement by most students, as well as possible early indications for disengaged or overwhelmed students. Further, analysis of later student debugging behavior on exercises showed students following the "run first, run often" approach demonstrated by the teacher. These findings suggest that engaging students in explicit debugging instruction can provide insights into their engagement and confidence levels, as well as encourage them to self-reflect and improve their debugging approaches.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".