Intersections of Student Engagement and Academic Integrity in the Emergency Remote ‘English for Academic Purposes’ Assemblage
Bibliographic record
Abstract
This paper explores the disruption of space, place, and material conditions brought on by the migration of traditional on-site language teaching to emergency remote teaching (ERT) in an English for Academic Purposes (EAP) program designed to bridge international students into higher education. We focus on two aspects of language teaching considered essential to academic success: student engagement and academic integrity. Through the Deleuzian concept of assemblage and post-qualitative inquiry, data vignettes from interviews with 12 teacher participants are presented to examine the contingency and relationality between the affordances of technological tools and the absence of embodied connection brought on by the move to ERT. Data vignettes are linked to map how instructors’ perceptions of student engagement mediated through space, place, and materials, inadvertently shape/are shaped by perceptions of academic dishonesty.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".