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Record W3189412242 · doi:10.5539/jel.v10n5p63

The Effects of a Cognitive Apprenticeship Model on the Argumentative Texts of EFL Learners

2021· article· en· W3189412242 on OpenAlexaffvenue
Ioanna K. Tsiriotakis, Valia Spiliotopoulos, Matthias Grünke, Costas Kokolakis

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArgumentativeSpellingGrammarPsychologyMathematics educationVocabularyControl (management)Test (biology)CognitionCorrective feedbackCurriculumLinguisticsPedagogyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In the present study, a quasi-experimental pre-post test design was used to assess the effects of an argumentative writing strategy (POW+TREE) on the performance of grade five and six students of Greek origin who were learning English as a foreign language (EFL) in a Greek setting. The Self-Regulated Strategy Development (SRSD) cognitive apprenticeship model was utilized to improve the text composition skills of the students. In the experimental group (N=77), participants received instruction on general and genre-specific strategy use for planning and writing argumentative essays, on procedures to apply self-regulation (goal setting, self-monitoring, self- reinforcement, and self-instructions), and on establishing additional skills (vocabulary, grammar-drill instruction, good word choice, interesting openings etc.). The control group (N=100) was supported through a traditional curriculum in writing (focusing on spelling and grammar). Findings of the study showed that strategy instructed students wrote argumentative essays that were schematically stronger, qualitatively better, and longer than those produced by their counterparts in the control group.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.023
GPT teacher head0.358
Teacher spread0.335 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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