Not <i>More</i> Technology but <i>More</i> Effective Technology: Examining the State of Technology Integration in EAP Programmes
Bibliographic record
Abstract
Technology use in English for Academic Purposes (EAP) programmes is seen as a strategy to support pedagogical innovation and intensifying growth in post-secondary international student enrolments. This article discusses government-funded research documenting the largely undefined use of technologies in post-secondary North American EAP programmes. This study surveyed EAP teachers and administrators in over 40 universities and colleges across North America using qualitative and quantitative approaches. Site visits involving classroom observations, interviews with teachers and administrators, student focus groups and student surveys were then conducted to deepen understanding of the affordances of technology-mediated EAP approaches from stakeholder perspectives in situated post-secondary contexts. Findings reveal widespread enthusiasm about emerging technologies to engage learners, develop autonomous learning, instructional pathways and transferable 21st century skills. However, despite this enthusiasm, many participating teachers, administrators and students also expressed critical views towards technology integration. Instructors noted time, lack of pedagogical guidance and vision, inadequate support, and training impacting their actual use and visions of technology use. Participants also revealed a ‘visioning’ dilemma where they had difficulty identifying the potential of emerging technologies that they had no concrete experience with. Findings suggest the need for sound theoretically informed techno-pedagogy in order to support technology integration in EAP. Implications for teacher education, further research and EAP teaching and curriculum design in today’s digital era conclude the article.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".