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Record W4402051197 · doi:10.1080/18146627.2024.2386960

The Impact of Educational Innovation on Teachers’ Pedagogical Practices: The Case of the ORELT Project in Kenya

2024· article· en· W4402051197 on OpenAlexaboutno aff
Daniel Ochieng Orwenjo

Bibliographic record

VenueAfrica Education Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyMathematics educationSociologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.159
GPT teacher head0.524
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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