A Self-Study Action Research Approach to English for Academic Purposes
Bibliographic record
Abstract
This study examines the effectiveness of a course for international Master’s students integrating second language acquisition (SLA) content with English for Academic Purposes (EAP) development, using a two-layered action research approach. The students in the course created self-study action research plans to achieve their EAP goals, and designed instruments to assess their progress resulting from the implementation of their plans (the first layer). The instructor also used an action research lens through which to investigate this pedagogical approach and make adjustments to the course (the second layer). The research aimed to investigate the EAP developments that students achieved through their action research plans, and the aspects of the course that contributed to those improvements. Four focal student participants' electronic portfolios were analyzed using Scarcella's (2003) academic English framework. Findings suggest that the action research model benefited students by helping them develop their linguistic, cognitive and sociocultural/psychological dimensions of academic English, and encouraging learner autonomy. Furthermore, this model benefited the instructor and her future students in terms of insights gained which allowed for improvements to the course curriculum and delivery. Implications for supporting graduate students’ EAP development and cultivating their research skills through action research 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 | 0.059 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".