The Impact of Educational Innovation on Teachers’ Pedagogical Practices: The Case of the ORELT Project in Kenya
Bibliographic record
Abstract
This paper reports the findings of a pilot project, Open Resources for English Language Teaching (ORELT), that aimed to introduce open resources for teaching English in Kenyan junior secondary schools alongside the traditional textbooks that until now have been the only teaching resources available to teachers and learners. The ORELT materials, which consisted of open content in the form of DVDs, books, and online content were developed by the Commonwealth of Learning (CoL), Canada, for use throughout the Commonwealth of Nations. Before the rollout of these materials, English teachers from selected schools were enlisted for an in-service workshop where they were trained on the materials and the concept of open resources, and were given a hands-on familiarisation with the resources. Two ORELT workshops were conducted for four days each between 18–21 March 2013, and 6–9 May 2013 at the Kenyatta University Conference Centre (KUCC). Participants of the first workshop were drawn from secondary schools within the urban and peri-urban areas of Nairobi. Participants for the second workshop were drawn from rural schools. This article views the introduction of these open resources in Kenyan schools as an educational innovation and investigates the effects of such an innovation on teachers’ pedagogical practice. The study found that this innovation had a generally positive impact on the teachers’ pedagogical practice in terms of their pedagogical content knowledge and skills, teaching methodology, and professional growth.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| 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".