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Record W2618077836 · doi:10.5539/elt.v10n6p53

Visual Aids and Multimedia in Second Language Acquisition

2017· article· en· W2618077836 on OpenAlexvenueno aff
Noha Halwani

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyReading (process)Class (philosophy)Action researchMathematics educationTest (biology)ShynessFocus (optics)Action (physics)PedagogyLinguisticsComputer scienceAnxiety

Abstract

fetched live from OpenAlex

Education involves more than simply passing the final test. Rather, it is the process of educating an entire generation. This research project focused on language learners of English as a Second Language. This action research was conducted in an ESL classroom in H. Frank Carey High School, one of five high schools in the Sewanhaka Central District of Nassau County. The research project explored the question: “Can visual aids improve English language acquisition in reading and writing for a beginner ESL?” The data analyzed were log observation sheets, pull-out focus groups, checklists, and surveys of students. The basic findings were that reading and writing improved when teachers used visual aids, especially when teachers pulled students out of the classroom for individualized instruction. Therefore, the study concluded that the use of visual aids and multimedia can help the students to absorb the content and become interactive in the classroom with no fear of giving wrong answers or, of having trouble being a participant in the class because of shyness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.019
GPT teacher head0.386
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations41
Published2017
Admission routes1
Has abstractyes

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