Looking for learning in teacher learning networks in Kenya
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the findings from a study of teacher professional learning networks in Kenya. Specific areas of focus included network participation, network activities, network leadership, and professional impact on network members and their schools. Design/methodology/approach The research was grounded in the literature on education networks and teacher learning. The research employed a qualitative design and was implemented from September 2015–March 2017, including three two-week field trips to Kenya. Data included network records, 83 personal interviews, 4 focus group interviews, 19 observations of network meetings, and classroom observation of network and non-network teachers in 12 schools. Findings Network participation had positive effects on teachers’ sense of professionalism and commitment to teaching and on their attitudes toward ongoing professional learning and improvement in student learning. Teachers also highlighted network benefits for learning to use new teaching strategies and materials, responding to student misbehavior and misunderstanding, and lesson preparation. Research limitations/implications Research constraints did not permit longitudinal investigation of network activities and outcomes. Practical implications The paper identifies challenges and potential focuses for strengthening the learning potential of network activities, network leadership, and the links between network activity and school improvement. Originality/value Prior research has investigated education networks mostly in North American and similar high income settings. This paper highlights the benefits and challenges for networks as a strategy for continuous teacher development in a low income low resource capacity context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".