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Record W6957675957 · doi:10.60692/4aa0a-jv637

Assessing blended and online-only delivery formats for teacher professional development in Kenya

2023· article· en· W6957675957 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsConcordia UniversityWilfrid Laurier University
Fundersnot available
KeywordsKenyaBlended learningProfessional developmentLiteracyFaculty developmentTechnology integrationSample (material)Plan (archaeology)Development plan

Abstract

fetched live from OpenAlex

The present study compared the learning and experiences of Kenyan teachers randomly assigned to either an online or a blended 12-week intensive teacher professional development program (TPD). The TPD addressed the fundamentals of early literacy development as well as how to use early literacy software to support students learning. TPD outcomes were assessed through surveys, course performance and discussion elements. Teachers demonstrated pre- to post-test gains in domain knowledge, lesson plan construction and comfort teaching early literacy skills. Few differences were observed between the online versus blended formats. However, teachers endorsed a blended instructional format over online-only or in-person formats. Challenges regarding resources and infrastructure were identified as barriers to technology integration within the classroom. Some cultural challenges were identified as potential barriers for young learners using software developed in Western countries. Overall, both online and blended formats appear to be effective TPD delivery systems for Kenyan teachers, however, findings highlighted challenges that need to be addressed to optimize learning when using technology. Future research recommendations include broadening the teacher sample to assess potential differences due to regionalism, associated differences in access to resources, and further examination of teaching experience on learning in the two types of online formats.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.258
Teacher spread0.207 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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