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Record W2088753302 · doi:10.5430/wje.v1n1p28

Towards a Practical Framework of the Remediation of Cognitive Skills at Primary Level

2011· article· en· W2088753302 on OpenAlexvenueno aff
Kaarina Määttä, Outi Kyrö-Ämmälä

Bibliographic record

VenueWorld Journal of Education · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsRemedial educationPsychologyCognitionCognitive skillMathematics educationCognitive developmentTest (biology)Teaching methodCognitive stylePedagogyMedical educationMedicine

Abstract

fetched live from OpenAlex

In this article, the remediation of pupils’ cognitive skills is studied and a practical framework for educational and remedial work at primary level is introduced. This article is based on a doctorate research that studied by means of action research the development and enhancement of the most low-grade school entrants’ reasoning and cognitive skills . Those first grade students (N = 43) who performed the worst in the cognitive skill measures comprised the test and control groups. The pupils in the test group were trained with the methods described in this article for 27 teaching moments during two school years. The results showed that the poor cognitive skills can be rehabilitated. Based on the results, a practical framework is constructed for educational and remedial work at the primary education. In this framework, the nature of cognitive skills and various different factors affecting educational situation are presented. With the help of these factors, the development of cognitive skills can be supported. The development of pupils’ cognitive skills is individualistic, which should be noticed also in teaching. The principles and references are presented, which can help the teachers with their own actions to repair the defective cognitive processes and, at the same time, support the pupils’ learning and working skills.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.998

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)0.0030.000

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 head0.105
GPT teacher head0.428
Teacher spread0.323 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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