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Record W4388731715 · doi:10.23856/5918

MENTAL SIMULATION SELF-REFLECTION TASKS FOR INCREASED LEARNING OF ENGLISH

2023· article· en· W4388731715 on OpenAlexaboutno aff
Nataliia Tarasiuk

Bibliographic record

VenuePolonia University Scientific Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychology of Development and Education
Canadian institutionsnot available
Fundersnot available
KeywordsReflection (computer programming)Computer scienceOutcome (game theory)Exploratory researchEmpirical researchGeneralizationCognitionCognitive psychologyPsychologyMathematics educationCognitive scienceArtificial intelligenceEpistemologyMathematics

Abstract

fetched live from OpenAlex

The notions of mental simulation and self-reflection have become established in neuroscience. Numerous line of research suggest that mental simulation and self reflection promote enhanced learning in general, specifically foreign languages. Our study explores and discusses these notions for their deeper understanding. The research encompasses smooth simulation with negative outcome and challenging simulation with positive outcome. The paper demonstrates mental simulation self-reflection tasks in correlation with cognitive skill planning. They are embedded within such specific conditions for inducing exploratory behavior as error-approach instruction, tasks with complex and dynamic decision-making characteristics, specific stimulus information (what, when, where) in life situations and peer feedback. The following methods have been used: theoretical methods (analysis, interpretation and generalization), empirical methods (observation). The author provides an example of mental simulation self-reflection tasks with and without problem-solving case study within conditions for exploratory behavior for the topic “Planning a trip to Canada”.

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 score0.296
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.325
Teacher spread0.290 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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