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Record W2980983177 · doi:10.5430/wje.v9n5p51

Mathematics Teachers’ Early Lesson Study Experiences in Turkey: Challenges and Advantages

2019· article· en· W2980983177 on OpenAlexvenueno aff
Mesut Bütün

Bibliographic record

VenueWorld Journal of Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsLesson studyTurkishCurriculumMathematics educationPsychologyFocus groupProfessional developmentSchool teachersPedagogyQualitative researchFaculty developmentSociology

Abstract

fetched live from OpenAlex

The purpose of this study was to reveal the challenges and advantages of the implementation of the lesson studymodel in Turkish middle schools. This study was a case study and the participants of the study was 11 middle schoolmathematics teachers in three different school in a city at central Anatolia region of Turkey. Three lesson studycycles were carried out at one month. The data were collected with field notes, focus group interviews and anopen-ended questionnaire. Data were analysed by using content analysis. Moreover, direct quotations from teachers’views were also included. Findings indicated that teachers had prejudice that the lesson study implementations wouldnot help their professional development. However, their prejudices were positively changed and they began to showinterest when they attended the implementations of lesson study. Implementation of the lesson study model enabledthe teachers to share experiences, to focus on students’ thinking / understanding, it also enabled teachers to be moreactive, to examine the curriculum and resources related to instructional materials in depth. On the other hand,challenges in the process of lesson study implementation were as follows: time problem, teachers' ego and fear ofbeing observed, adaptation of students in research lessons, differences between researcher and teachers’ views aboutmathematics teaching, the intensity of the curriculum and the pressure of the central exam.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.414
Teacher spread0.356 · 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 designQualitative
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

Citations7
Published2019
Admission routes1
Has abstractyes

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