MétaCan
Menu
Back to cohort
Record W3173703492 · doi:10.5539/ies.v14n7p27

An Investigation into the Influence of Blended Learning on Oral English Proficiency of Senior High School Students

2021· article· en· W3173703492 on OpenAlexvenueno aff
Xin Li, Zhongbao Zhao

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningMathematics educationPsychologyPronunciationFluencyClass (philosophy)Language proficiencyVocabularyTeaching methodEmpirical researchMultimethodologyEducational technologyPedagogyComputer scienceMathematicsLinguistics

Abstract

fetched live from OpenAlex

We advocate the in-depth integration of information technology and education in the digital age, and we also encourage teachers of all disciplines to actively carry out online and offline blended learning. This study attempts to use an empirical research to apply the Blended Learning to the oral English teaching in the first year of senior high school. A one-semester teaching experiment is conducted to explore whether there is a significant difference in the students’ oral English proficiency between the experimental class and the controlled class. The major findings of the study are as follows: (1) There are significant differences of students’ oral English proficiency before and after the experiment in the experimental class and the controlled class; (2) Blended learning can improve students’ oral English proficiency, among which pronunciation and intonation, range and accuracy of vocabulary and fluency of language are the most significant ones, while the accuracy and complexity of grammatical structure are insignificant.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.024
GPT teacher head0.391
Teacher spread0.367 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Education StudiesSame topicEducational Technology and AssessmentFrench-language works237,207