Learning study is “hard”: case of pre-service biology teachers in British Columbia
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe a pilot learning study (LS) comprising of three biology pre-service teachers (PSTs) in British Columbia, which took place during an initial teacher education (ITE) course and school-based practicum. The study explored PSTs’ learning experiences, and identified conditions that supported and challenged their engagement with the LS discourse. Design/methodology/approach Drawing from a variety of methods including teacher semi-structured interviews and reflective entries, the PSTs’ experiences of teaching and reflection were described and themes were constructed; course assignments, classroom materials, meeting notes and fieldnotes served triangulation purposes. Variation theory framed the LS and analysis of this case study. Findings Findings highlight how the PSTs developed comfort with the tension of making mistakes that supported their interpretation of classroom pedagogy and refining of instructional strategies. As the study alluded to how LS is “hard,” the PSTs demonstrated how positive experiences in the course-based cycle sustained their pursuit of learning despite challenges faced in the school-based practicum. Research limitations/implications This small-scale study has limited generalizability. Practical implications Exposing PSTs to a variety of “mistakes” in ITE and to approach them not merely as ontological objects of pedagogical shortcomings are discussed together with factors that promoted teacher learning. Originality/value This study contributes to literature exploring the organization of LS within ITE, as situated in educational contexts where LS is unfamiliar and organizational structures are not readily in place to fully support its implementation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".