Flipped Classroom-Based Corpus for EFL Grammar Instruction: Outcomes and Perceptions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it