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Record W2587742554 · doi:10.5539/ijel.v7n3p103

The Impact of Metacognition Strategies in Teaching Mathematics among Innovative Thinking Students in Primary School, Rafha, KSA

2017· article· en· W2587742554 on OpenAlexvenueno aff
Nahed Mokhtar Hassan Rizk, Khaled Ahmed Mahmoud Attia, Alaa Ahmed Hassan Al-Jundi

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
FundersNorthern Border University
KeywordsMetacognitionMathematics educationPsychologyCreative thinkingClass (philosophy)CreativityCognitionComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The study aims at finding out the impact of metacognition strategies in the teaching of mathematics in developing creative thinking among gifted primary school students in Rafha province in the Kingdom of Saudi Arabia. They are defined in this study as the students whose IQ score is (120 and above) according to the Wakslar measurement for children intelligence and are selected by their teachers. The study sample consists of 40 male and female students from the fifth class in the primary stage. They were divided randomly into two groups; experimental group which was taught by the methods and strategies of the suggested teaching program and a control group upon whom the ordinary method was applied. Each group included 20 male and female students. For the purpose of the study, a creative thinking measurement in mathematics, designed by the researchers, was used for data collecting. The program was implemented for three successive weeks, three sessions per week each one lasting for one period. After finishing the program, a post measurement was conducted for all the study variables for both the experimental and control groups. The results show statistically significant differences at the significance level (0.05) between using the metacognition strategies on one hand and the ordinary method on the other. The differences were in favor of the metacognition strategies.

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)0.005
metaresearch head score (Gemma)0.154
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.154
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.029
GPT teacher head0.411
Teacher spread0.382 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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