Understanding STEM teacher learning in an informal setting: a case study of a novice STEM teacher
Bibliographic record
Abstract
Research into informal STEM education over the past years has shown that informal learning environments increase students’ learning in STEM. However, how STEM teachers learn in an informal setting remains unclear. Such educators who work in informal settings are not all required to have undergone teacher education or professional development, and their progress may differ from other teachers’ experiences. As a result, it is important to observe and understand the path such teachers take to see how they develop their teacher identities. Drawing upon Baxter Magolda’s ( Making their own way: Narratives for transforming higher education to promote self-development , 2004) self-authorship framework, this qualitative case study explores the progress of one informal STEM teacher throughout her first class by qualitatively analyzing her journals, lesson plans, and artifacts. The teacher’s journey progresses towards self-authorship in a nonlinear way with multiple signs of the epistemological, intrapersonal, and interpersonal dimensions of the framework being deeply interconnected to one another. Implications for STEM teacher education within the context of informal STEM education are discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".