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Record W3116286347 · doi:10.5539/jel.v10n1p55

The Contribution of Meta-Cognitive Guidance to Building Geometry Teaching Units and Improving Classroom Teaching Processes

2020· article· en· W3116286347 on OpenAlexvenueno aff
Erez Nahmias, Mina Teicher

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionMathematics educationNinthSubject matterInstructional designPsychologyCognitionTeaching methodPedagogyCurriculum

Abstract

fetched live from OpenAlex

In this paper, we examine the importance of building instructional units that incorporate metacognition intent processes that contribute to the development of geometric thinking. We show that the implementation of metacognition processes in the initial stages of constructing tailored instructional units will improve students’ geometric ability. The study was performed on middle school mathematics teachers of the ninth grade. The experiment we conducted shows that building instructional units that incorporate metacognition intent benefit learning processes on two levels: First, in the subject matter. Second, they contribute to a deeper understanding that improves student’s ability to connect related subjects to mathematical geometry. moreover, we will present a practical model that incorporates different aspects that could operate a guideline for middle school mathematics teachers.

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)0.008
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.054
GPT teacher head0.397
Teacher spread0.343 · 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 designOther design
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
Published2020
Admission routes1
Has abstractyes

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