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Record W3032645083 · doi:10.5539/elt.v13n6p144

Is it Worth Flipping? The Impact of Flipped Classroom on EFL Students’ Grammar

2020· article· en· W3032645083 on OpenAlexvenueno aff
Ishaq Al-Naabi

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersUniversity of Adelaide
KeywordsFlipped classroomGrammarPsychologyBlended learningMathematics educationClass (philosophy)Test (biology)Teaching methodPedagogyEducational technologyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

In light of contemporary pedagogical methods, the flipped classroom has been recognised as an effective pedagogy in English as a Foreign Language (EFL). This study employed a quasi-experimental one-group research design to investigate the impact of flipped learning on Omani EFL learners’ grammar and to examine students’ perceptions on the flipped classroom. An intact group of students (n=28) enrolled at the foundation programme in Arab Open University-Oman was randomly selected. Seven videos on English grammar were developed and shared with the students prior to the class. A varaiety of activities were conducted in the class following task-based language teaching. Students met for 8 lessons over the period of 8 weeks. Pre-test, post-test and semi-structured interviews were used in the study. The findings indicated that flipped learning had a positive impact on students’ understanding and usage of English grammar. Students’ perceptions on the flipped approach were positive. The study also provided pedagogical insights for the flipped classroom and recommendations for future research. 

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)0.007
metaresearch head score (Gemma)0.026
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 score0.007
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.033
GPT teacher head0.302
Teacher spread0.269 · 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

Citations32
Published2020
Admission routes1
Has abstractyes

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