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Record W2345396613 · doi:10.5539/elt.v9n6p52

Teachers’ Perceptions about Teaching Multimodal Composition: The Case Study of Korean English Teachers at Secondary Schools

2016· article· en· W2345396613 on OpenAlexvenueno aff
Jung O Ryu, George L. Boggs

Bibliographic record

VenueEnglish Language Teaching · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComposition (language)Framing (construction)LiteracyMathematics educationPerceptionPedagogyNegotiationReading (process)SociologyLinguistics

Abstract

fetched live from OpenAlex

<p>Twenty-first-century literacy is not confined to communication based on reading and writing only traditional printed texts. New kinds of literacies extend to multimedia projects and multimodal texts, which include visual, audio, and technological elements to create meanings. The purpose of this study is to explore how Korean secondary English teachers understand the 21<sup>st</sup> literacies and multimodal composition in this era of new types of communication. Framing the study are questions pertaining to what these teachers think about teaching multimodal composition in their writing classrooms. The schools of South Korea, including those in this study, prioritize high-stakes standardized tests, and teachers as well as students and parents gauge success by these test scores. As a result, teachers primarily rely on direct instruction via lectures to provide skills and knowledge to ensure that students will succeed in the high-stakes tests. So while teaching and assessment practices in the classroom still adhere to traditional approaches, ongoing technology outside school has transformed the ways in which young people – the students – generate, communicate, and negotiate meanings via diverse texts. If the primary goal of education is to teach students lifelong skills needed in society, it is the responsibility of schools and teachers to recognize social changes and promote individual learning needs.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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 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

Citations35
Published2016
Admission routes1
Has abstractyes

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