An activity theory approach toward teacher professional development at scale (TPD@Scale): A case study of a teacher learning center in Indonesia
Bibliographic record
Abstract
Abstract Continuous teacher professional development (TPD) ensures that teachers have the capacity to continually plan and implement quality teaching and learning that supports students in achieving their expected program/course learning outcomes. However, teachers’ access to quality TPD is a challenge due to geographical limitations, gender, special needs, marginalized communities, and the government’s policies, or lack of policies, regarding teachers. There are tensions between quality and equity, and cost implications that may hinder the scaling up of quality TPD programs. This paper adopts an activity theory approach to examine how a teacher learning center (TLC) in a regency of Indonesia enhances teachers’ access to quality TPD. The findings reveal that teachers learn in the TLC through different TPD activities. Information and Communication Technologies (ICT) are found to mediate the professional learning activities, learning resources, learning support, and assessments in the TLC. Furthermore, three key stakeholders—the local government, teacher working groups, and school principals—play significant roles in supporting teachers’ professional learning in the TLC.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".