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Record W4388810487 · doi:10.5430/jct.v12n6p206

Development of Literary Competence Through a Semiotic Approach and Technological Resources

2023· article· en· W4388810487 on OpenAlexvenueno aff
Sayra Astrid Sujey Perez Reategui, Kelita Ytamar Rodríguez Rojase, Osmer Campos-Ugaz, Ronald M. Hernández, Miguel A. Saavedra-López, Heriberto Solis Sosa, Lizzeth Aimée García Flores

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsSemioticsCompetence (human resources)NarrativeLikert scaleComprehensionPsychologyInformation and Communications TechnologyMathematics educationPedagogySociologyComputer scienceEpistemologySocial psychologyLiteratureArt

Abstract

fetched live from OpenAlex

Currently, literary competence is scarcely addressed in secondary education teaching. Therefore, the objective of this study was to design a didactic proposal to improve literary competence through a semiotic approach and technological resources in secondary school students of an educational center located in the city of Lambayeque. The research was quantitative, non-experimental and descriptive-propositive design. The instrument used was the "Likert scale to evaluate literary competence", which was applied to a sample of 32 fifth grade students, selected by non-probabilistic sampling. As a result, it was obtained that the students presented low levels of achievement, since 100% of them only evidenced beginning processes. In response to this problem, a didactic proposal for the comprehension and production of narrative discourse was designed and validated from a semiotic perspective using ICT resources. We conclude on the importance of the contributions in the area of communication, which can integrate didactics with technology, contributing to the pedagogical processes for current generations and educational systems of the globalized world.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.278
Teacher spread0.255 · 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 designTheoretical or conceptual
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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