Developing a Flipped Learning model for Teaching EAP Students Struggling with Multi-Level Challenges in a College Context
Bibliographic record
Abstract
This article aims to understand students’ experiences regarding the implementation of flipped learning (FL) as a modern blended learning technique in teaching English for academic purposes (EAP) in a community college context in Toronto. Based on students’ views, blended learning theories, and several previous studies, the study also aims to develop a holistic contextualized flipped learning model that helps both students and teachers in the context of EAP to cope with the challenges of a multilevel EAP classroom. The study is guided by the epistemology and philosophy of the interpretive paradigm as an underpinning stance. Accordingly, the qualitative approach has been selected for determining the strategy and methods of sampling, and data collection and data analysis. Results revealed that students’ views are compatible with the theoretical views in validating the utilization of flipped learning as a modern technique in the context of EAP. However, results revealed that the development of a holistic model includes a further component-online engagement as an extension component to the model. The study offers a set of recommendations and implications for EAP teachers and instructors within the area of ELT for classroom practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".