<i>Resistance, reluctance and radicalism: A study of staff reaction to the adoption of CALL/C&IT in modern languages departments</i>
Bibliographic record
Abstract
This paper examines staff reaction towards the use of Computer Assisted Language Learning (CALL) and Communications and Information Technology (C&IT) in language learning and teaching. It considers the attitudes of colleagues in three different universities, two in the UK and one in Canada. Our findings suggest that staff in these three locations are not resistant to the use of computer technology in learning and teaching but rather that any hesitations they have are due to a range of different factors of a practical kind, ranging from time pressures to course relevance. We found that staff in one institution are clearly more enthusiastic about using CALL and C&IT than colleagues in the other two, but that they were also widely welcomed in the latter. One of the main reasons for this has been the creation of common learning environments on the Web. In addition, findings show that staff already convinced of the benefits that CALL and C&IT bring to the teaching and learning experience (radicals) have a role in encouraging their less enthusiastic colleagues to begin using this form of technology. However, we found that the majority of colleagues are not radicals, but pragmatists, and are willing to make use of CALL and C&IT provided that the benefits are clearly guaranteed. There remains a small minority of conservatives. No suggestions are made as to how to deal with them.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".