Analysis of Interdisciplinary STEM Lessons Generated by Pre-Service and Inservice Teachers in the United States
Bibliographic record
Abstract
This study views STEM as a space with the potential to dismantle the narrow disciplinary silos that have led to inequitable gaps in achievement. Responding to the latest recommendations for teachers from NCTM and NSTA, this study aims to examine the instructional design of secondary mathematics and science pre-service teachers in an interdisciplinary STEM lesson about a pandemic. Starting with two images, teachers are asked to design the associated activities that they would implement in the classroom. Researchers utilized a qualitative methodology based on the categories of the 5E Model: Engage, Explore, Explain, Elaborate and Evaluate. The findings highlight responses to each of the 5E factors are very mixed. Teaching strategies consisting of posing questions predominate, promoting the Engage factor; while the Explore factor is barely considered, which could hinder the incorporation of group skills and critical thinking. This study offers a pathway on how to assess the professional learning of novice teachers following the 5E model through a contextualized activity with bacterial growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".