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Record W2912189094 · doi:10.5539/hes.v9n2p1

Primary Teachers Knowledge of Quadrilaterals

2019· article· en· W2912189094 on OpenAlexvenueno aff
Yasin Gökbulut, Mustafa Kemal Şen

Bibliographic record

VenueHigher Education Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsQuadrilateralContext (archaeology)Mathematics educationMathematicsParallelogramSubject (documents)PsychologyComputer scienceEngineeringArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This paper aims to reveal primary teachers knowledge of the quadrilaterals about the concept of quadrilaterals by examining the definitions of the quadrilaterals. A total of four primary teachers, 1 female and 3 male teachers, participated in the study which determined by maximum diversity sampling. The data collection tool consisted of four open-ended questionnaires, which were not based on mathematical procedural knowledge of the teachers and aims to reveal the subject matter knowledge of quadrilaterals. The first question is about the definition of the quadrilaterals (square, rectangle, trapezoid, parallelogram, rhombus and deltoid), the second question is the determination of the characteristics of the quadrilaterals, the 3rd question is the comparison of the characteristics of the quadrilaterals and the 4th question is about related to the nomenclature of the quadrilaterals. The data were analyzed by using descriptive analysis method (Zazkis & Leikin, 2008). As a result of analyzes, it was seen that the subject area information of the participants was insufficient. In order to be successful in the teaching of geometry, the in-service training activities should be organized in order to eliminate the deficiencies in the subject matter knowledge and the necessity of reviewing the mathematics courses they took at the university, which can be corrected first, in the context of the content.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.776

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)0.0010.000

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 head0.079
GPT teacher head0.444
Teacher spread0.366 · 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
Published2019
Admission routes1
Has abstractyes

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