Promoting teachers' learning and knowledge building in a socio-technical system
Bibliographic record
Abstract
The study proposes a way in which the learning and knowledge building (LKB) framework, which is consistent with the knowledge conversion phases proposed by Nonaka and Takeuchi, supports teachers’ informal and self-directed workplace learning. An LKB framework in a socio-technical system was developed to support professional development in an extended professional community. The LKB framework was implemented and formatively evaluated in the in-service course that prepares teachers for accreditation in an e-portfolio community. The extended community consisted of 16 participants, in-service teachers and domain experts. The evaluation considered (a) how the LKB practices of the framework became actualized among the community members and (b) what supported these LKB practices. Data were collected from log-files of the portfolio system. Correlation analysis and Bayesian dependency modelling revealed the way in which the bottom-up peer scaffolding from community members influences teachers’ LKB practices. As a result, the study proposes that a socio-technical system might promote LKB in a professional community.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".