Engaging students in computational modeling: The role of an external audience in shaping conceptual learning, model quality, and classroom discourse
Bibliographic record
Abstract
Abstract Research suggests that designing for an external audience may support conceptual understanding by offering students increased opportunities to reframe perspectival thinking in ways that support domain‐specific reasoning. While this argument is theoretically compelling, to our knowledge, it has not been empirically tested in terms of comparing the conceptual growth of students designing computational models for an external audience to the conceptual growth of students designing computational models for a classroom audience of their teacher and peers. In a constructionist agent‐based computational modeling environment, we compare the conceptual understanding, artifact quality, and discourse of 6th grade students designing models of tides primarily for an external audience of younger students (i.e., designing for 5th graders) to the conceptual understanding of 6th grade students designing models for a classroom audience. We found that students who designed for an external audience of younger children displayed greater conceptual growth about the mechanisms that cause tidal bulges as evidenced by students’ pre–post assessments, models, and user guides/reports. Our analysis of classroom discourse suggests that designing for an audience of younger children may have facilitated domain‐specific reasoning in whole‐class discussions by creating opportunities for shifts between students’ own perspectives and the perspectives of their anticipated audience.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".