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Record W2887064837 · doi:10.5539/elt.v11n9p102

Building Writing Skills in English in Fifth Graders: Analysis of Strategies Based on Literature and Creativity

2018· article· en· W2887064837 on OpenAlexvenueno aff
Fernando Lopez Niño, Martha Elizabeth Varón Páez

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsnot available
Fundersnot available
KeywordsCreativityPsychologyComic stripMathematics educationComicsWriting processAction researchQualitative researchForeign languageProcess (computing)PedagogyComputer scienceSociologySocial psychology

Abstract

fetched live from OpenAlex

The present article is centered on the description and analysis of the process of action-research followed by a group of twenty eight fifth graders of primary level in a public school in Colombia who improved their writing ability in English as a Foreign Language through the application of several creativity writing strategies. Among those strategies we can count the use of acrostics, calligrams, comic strips and posters, connecting children with fiction and real information taken from subjects taught at school. This research was designed with the objective of developing writing skills in a creative way, based on qualitative and quantitative methods by using surveys, checklists, field notes and a final interview to collect data. Findings revealed that writing mistakes were diminished after each one of the sessions application. Additionally, children were motivated to write in English and to assume different positions about topics of their interest from the advantages provided from new knowledge acquired about diverse topics related to their lives.

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)0.002
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 score0.440
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.353
Teacher spread0.341 · 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

Citations26
Published2018
Admission routes1
Has abstractyes

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