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Record W3017296919 · doi:10.21083/ajote.v9i0.5731

Affordances and Constraints of Implementing Lesson Study for Teachers’ Professional Development: A Review

2020· review· en· W3017296919 on OpenAlexvenueno aff
Jimmy Ezekiel Kihwele

Bibliographic record

VenueAfrican Journal of Teacher Education · 2020
Typereview
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAffordanceUnderpinningLesson studySubject matterProfessional developmentMathematics educationConstraint (computer-aided design)Key (lock)Computer sciencePedagogyPsychologyEngineering

Abstract

fetched live from OpenAlex

Lesson Study (LS) has been adopted worldwide as a research tool for teachers who teach the same subjects to cooperate in identifying problems, planning a lesson, teaching that lesson and coming up with solutions to the problem identified. This helps them grow professionally. This study is a review of approaches to implementing LS in different places, of opportunities that the use of LS provides, and of constraints on its effectiveness and sustainability. In this study, 30 published research articles were thoroughly studied and analyzed so as to answer three key questions that serve as its underpinning. The findings show that the methods used to implement LS vary in many places. However, in several cases, LS practitioners try to implement the original structure of LS as it was implemented in Japan in the 1960s. The findings show that LS affords more pedagogical opportunities than constraint: teachers learn new professional skills and knowledge and they improve their understanding of the subject matter and change their attitudes, beliefs and views on collaborative working.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.227
GPT teacher head0.505
Teacher spread0.278 · 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 designOther design
Domainnot available
GenreReview

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

Citations4
Published2020
Admission routes1
Has abstractyes

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