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Record W4383292983 · doi:10.24818/basiq/2023/09/004

The Impact of Using Enhanced Teaching Materials on Core Skills When Teaching English as a Foreign Language

2023· article· en· W4383292983 on OpenAlexaff
Maria Ana Cumpăt, Nicoleta Zouri

Bibliographic record

VenueNew Trends in Sustainable Business and Consumption · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsCentennial College
Fundersnot available
KeywordsVocabularyActive listeningMathematics educationReading (process)Reading comprehensionForeign languagePsychologyEnglish languageComputer scienceEnglish as a foreign languageListening comprehensionPedagogyLinguistics

Abstract

fetched live from OpenAlex

Our study evaluated the effectiveness of several types of study materials used in addition to the textbook in teaching English as a second language to middle school-level students in Iasi, Romania. The additional classroom materials used were a combination of workbooks used for the students to practice their writing and reading skills and online video materials to practice their listening and comprehension skills. The same materials contributed to the English vocabulary enrichment of the students. The study participants were students from grades 5, 6, and 7 at Dimitrie A. Sturzda School from Iasi, Romania. The study showed that the additional study materials were effective at improving the reading, writing, and listening skills of students in grades 6 and 7 while decreasing the skill levels of students in grades 5. The study did not take into consideration other factors that may contribute to a decrease in skill levels for students in grade 5.

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)0.006
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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