Toward an Understanding of "Teaching in the Making:" Explaining Instructional Decision Making by Analyzing a Geology Instructor's Use of Metaphors.
Bibliographic record
Abstract
There is a need to enhance science and geoscience literacy. Effective instruction allows students opportunity to build their own models, test them, make their own arguments, and discern reliability of the claims and arguments of others. Attempts at designing and importing such instruction have shown limited implementation fidelity, even with attached professional development. Up to present, attempts to understand the problem of implementation sought to focused on the context of the teacher (beliefs, knowledges, and motivations) to explain teacher practice, and results indicate great complexity. Maintaining a similar focus, this investigation analyzes a geology instructor's use of metaphor, when talking about teaching, learning, and knowledge, to understand and explain the factors involved in his instructional decision making. Eric (pseudonym), a geology professor, implemented a curricular intervention in two successive introductory geology classes. However, Eric selected and amended only particular facets of the intervention. The research utilizes classroom observations and multiple audio recorded meetings with Eric to understand why he chose and amended certain parts of the intervention and not others. Results show that Eric described his teaching in terms of two metaphors: the puzzle metaphor and the field trip metaphor. The metaphors paralleled each other in terms how Eric saw his role, his students' role and the role and the nature of knowledge, and therefore influenced what and how he taught. This study suggests that curriculum designers need to take instructor context into consideration when designing curricular interventions and analyzing for the use of metaphor may be an effective way to discern that context.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".