Lessons on and from the dihybrid cross: An activity–theoretical study of learning in coteaching
Bibliographic record
Abstract
Abstract During their training, future teachers usually learn the subject matter of science. However, they are largely left on their own when it comes to figuring out how to teach this subject matter, that is, how to find appropriate pedagogical forms. In this article we present a model of collective teaching and learning, which we term coteaching/cogenerative dialoguing , as a way to build deep learning of science concepts while learning about alternative ways to teach the same subject matter. As praxis, coteaching brings about a unity between teaching and learning to teach; cogenerative dialoguing brings about a unity between teaching and researching. Both are potential sites for deep learning. We articulate coteaching/cogenerative dialoguing in terms of activity theory and the associated first‐person research methodology that has been developed by critical psychologists as a method of choice for dealing with the theory–praxis gap. Our detailed case study highlights opportunities of learning subject matter and pedagogy by university professors who participate in coteaching/cogenerative dialoguing in an urban high school. © 2002 Wiley Periodicals, Inc. J Res Sci Teach 39: 253–282, 2002
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".