Epistemological Appropriation in One High School Student’s Learning in Cooperative Education
Bibliographic record
Abstract
In this study, detailed observations and interviews from a high school student’s semester-long cooperative (co-op) placement in a dental practice are used to exemplify Hung’s theoretical approach to understanding situated learning. Using Hung’s theory of epistemological appropriation in an analysis of the coop supervisor’s regulatory behaviors (scaffolding, modeling, and coaching) and of the novice’s corresponding regulatory behaviors (submitting, mirroring, and constructing) helped to explain the developments in this student’s learning, actions, and beliefs. In contrast to the progression suggested by Hung’s theory, this study reports daily examples of all types of regulatory behaviors, with scaffolding/submitting being most prominent. The discussion focuses on how Hung’s theory of regulatory behaviors informs supervisors’ improving opportunities for novices’ learning and informs novices’ engagement in epistemological appropriation in work-based learning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".