A Theoretically-Informed Approach to Reflective Teaching Practices in an Online EAP Context: A Mixed Methods Action Research Study
Bibliographic record
Abstract
The sudden transition to online instruction during the COVID-19 pandemic presented novel challenges for English for Academic Purposes (EAP) instructors employing collaborative pedagogies to provide important scaffolding to support academic writing instruction. Using a facilitating framework, such as the Community of Inquiry (CoI) framework, a model of online learning, can assist EAP instructors in course development by supporting EAP instructors' reflective teaching practices. The CoI informs decision-making when designing collaborative writing tasks and pedagogical approaches by providing useful heuristics that measure student learning experiences. This study explores an instructor's experience in implementing a Mixed Methods Action Research approach to reflective practice using the CoI framework and survey instrument to guide course development and collaborative writing task design in a year-long online EAP course. This study aims to understand to what extent the CoI supports reflective practice and to examine pedagogical issues that emerge that inform the use of the CoI for these purposes. Findings reveal that the CoI framework effectively guides reflective teaching practices and fosters meaningful learning experiences by breaking down the three constructs into actionable items, facilitating interactivity, discussion, and collaboration, and providing scaffolding for learners. However, findings also emphasize the need to use the CoI framework with caution and to develop a deeper understanding of pedagogical theory and practice to better address challenges in collaborative writing tasks. Overall, this study contributes to a deeper understanding of effective EAP pedagogy in online settings and has the potential to inform the design and delivery of future online EAP courses using the CoI as a facilitating framework.
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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 | 0.043 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".