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Record W3114213175 · doi:10.5539/ies.v14n1p12

Impacts of Metacognition Management System (MMS) Training Course on Metacognitive Competencies

2020· article· en· W3114213175 on OpenAlexvenueno aff
Fariba Dezhbankhan, Diana Lea Baranovich, Nabeel Abedalaziz, Soraya Dezhbankhan

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionCLARITYPsychologyMathematics educationTest (biology)Cognition

Abstract

fetched live from OpenAlex

Metacognition is one of the vital competencies to seize the opportunities and overcome the challenges of twenty-first century; however, there is no precise definition of metacognition and it is a fuzzy concept. While classic, descriptive and procedural models try to describe the nature and ingredients of metacognition, theoretical clarity in terms of better definition and representation of its components is needed. This study by adopting theoretical models of metacognition through the Plan-Do-Check-Act principles (as a management instrument) proposes a conceptual framework, “Metacognition Management System (MMS)” that consolidates components, functions, and processes of metacognition in a single window. Then, impacts of a multidimensional intervention designed based on the MMS concept (MMS Training Course) provided in 12 hours, on 31 students’ metacognitive competencies was investigated using quasi-experimental pre-test, post-test design. The large effect size (Partial η2 = .939, 95% confidence interval) implied that MMS training course has a statistically significant impact on metacognitive competencies. This study has implications for further theoretical and experimental researches on the configuration and application of the MMS as well as designing multidimensional metacognitive intervention.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.231
GPT teacher head0.490
Teacher spread0.259 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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