Exploring the motivation and classroom engagement of college learners of English for academic purposes: A participatory action research intervention
Bibliographic record
Abstract
Learners of English for academic purposes (EAP) encounter various academic and social challenges in their learning pursuits. To explore collaborative ways of energizing their learning behaviour, an intervention was designed and implemented through the participation of 18 EAP learners and their teacher in a Canadian college classroom for one semester. Adopting a participatory action research framework, the group formed a community in which power was gradually shared and collaborative changes were made to lesson design and delivery. Moreover, the learners participated in the design and implementation of social activities in which they interacted with other members of the public and investigated their mini-research projects. Data were collected through recurrent experience sampling surveys ( n = 449), semi-structured interviews, and the researcher’s journal and field notes. Results indicated an overall increase in classroom engagement and motivation after implementing the intervention despite the fluctuations of the former and the occasional stagnation of the latter. Furthermore, a linear mixed-effects analysis of repeated measures data revealed significant differences between the learners’ engagement levels before and after implementing the intervention. Finally, the concepts of motivation and engagement and their timescales were complementary, revealing noteworthy information about the learners’ desire to participate in classroom activities, as well as their actual participation in activities.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".