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Record W4386119738 · doi:10.5430/jct.v12n4p62

An Activity for Building Teaching Potential Designed on Community of Practice Cooperated with Lesson Study

2023· article· en· W4386119738 on OpenAlexvenueno aff
Kanyarat Cojorn, Kanyarat Sonsupap

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
FundersMahasarakham University
KeywordsDocumentationLesson studyClass (philosophy)Computer scienceMathematics educationFocus groupTeaching methodLearning communityCommunity of practicePedagogyPsychologyProfessional developmentArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes the development of an activity based on the community of practice (CoP) approach in collaboration with lesson study to enhance teaching potential. The CoP approach is utilized to elicit teachers' experiences and facilitate the sharing of teaching guidelines, while the lesson study method enables small groups of teachers to collaboratively design, teach, reflect on, and refine a class lesson. Drawing from semi-structured interviews, classroom observations, documentation, expert field notes, and focus groups, the proposed activity consists of four key components: 1) principle, 2) activity objective, 3) learning activity, and 4) learning evaluation. The learning activity encompasses four steps: educating, innovating, implementing, and reflecting. Each step comprises several sub-activities, with the innovating and implementing steps being iterative. The activity demonstrates a content validity of 0.95 and a suitability rating of 4.88. Furthermore, the participating teachers in this study exhibit increased self-confidence in constructing classroom activities and gained additional pathways for designing effective learning activities. The paper suggests that this approach can effectively foster the acquisition of new knowledge, the development of innovative practices, and the application of effective instructional strategies in the classroom.

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)0.024
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
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 head0.447
Teacher spread0.396 · 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.

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

Citations5
Published2023
Admission routes1
Has abstractyes

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