Towards a new approach to managing teacher online learning: Learning communities as activity systems
Bibliographic record
Abstract
Online learning communities (OLC) are increasingly used for the professional development of teachers; however, it is still unclear how to design effective and sustainable OLC, especially considering the social and cultural differences. This study proposed a practical, theory-driven approach to managing teacher online learning, taking the educational infrastructures and teacher characteristics of rural China into account. We explored the effectiveness of this approach in an OLC that created on a free communication software named QQ. A total of 117 primary school teachers that came from rural China participated in this study for two months. The results demonstrated that the participants had positive perceived ease-of-use, usefulness and satisfaction towards the online learning community. Besides, teachers experienced considerably more positive emotions than negative emotions. In terms of cognition, they involved in the activities of cognitive insight the most. This study informs the effective practice of teacher professional development (TPD) in rural China in several ways, including but not limited to fostering online learning beyond physical knowledge-sharing settings, leveraging the low-cost or free technologies in TPD, and creating reward mechanisms by stakeholders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".