Flipped Classroom-Based Corpus for EFL Grammar Instruction: Outcomes and Perceptions
Bibliographic record
Abstract
Although a wide array of studies has sufficiently documented the use of Flipped and Corpus learning methods in ELT context respectively, marrying both in EFL grammar classes remains scanty. To fill this gap, this collaborative action research (CAR) jointly designed and implemented flipped classroom-based corpus instruction involving an English grammar instructor to promote the EFL students’ grammatical knowledge and documented their perceptions on how the learning model promoted their grammatical knowledge and the challenges they encountered at an Indonesian state Islamic University. Pre-, mid- and post-tests measuring the students’ grammatical knowledge were administered, and an open-ended questionnaire and focus group discussion were respectively distributed and conducted to garner the qualitative evidence. The statistical evidence showed that there were statistically and practically significant grammatical knowledge gains at the end of the term. The qualitative evidence suggested that it was due to the adequacy of English input and feedbacks from their peers and the grammar instructor. The students also perceived that low internet bandwidth and a lack of understanding on the use of the Coca database were their primary learning barriers, while the grammar instructor found it more daunting to cater the instruction. This is the first study marrying both the pedagogical methods and provides the empirical evidence of its efficacy and feasibility for EFL grammar instruction. Limitations and recommendations for further studies are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".