The Reciprocal Nature of Pedagogical and Technical Knowledge and Skill Development between Experts and Novices
Bibliographic record
Abstract
This paper outlines the findings of a study focused on the impact an expert teacher’s pedagogical and technical knowledge and skill may have on the pedagogical and technical development of pre-service technology education teachers. Specifically, this inquiry falls within the context of traditional wooden boat building in Newfoundland and Labrador, Canada. Understanding the relationship between an expert’s knowledge and skill, and the development of a novice’s knowledge and skill is vitally important for institutions charged with graduating technology education teachers. Exploring the impact of pre-service teachers’ pedagogical and technical development was considered in relation to an expert teacher’s pedagogical content knowledge, and the nuance between declarative and procedural knowledge within technological activity. Data were collected from semi-structured interviews, workshop session observations, and researcher/participant journal entries. The sample was purposeful as the participants were recruited from boat building workshops between 2017 and 2019 and the 2017-2018 technology education diploma program cohort from Memorial University. Thematic analysis was used to identify major themes within the data. A descriptive visual framework based on the data analysis was constructed to highlight the complexities of teaching and learning within the multifaceted setting of a technical activity. An analysis of the data indicates that fostering and maintaining reciprocal interpersonal relationships between experts, novices, and peers are critical for the development of pre-service teacher technical and pedagogical knowledge and skill
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".