<i>Creating a computer-based language learning environment</i>
Bibliographic record
Abstract
This paper considers key questions concerning computer-based language-learning environments. Using evidence from current literature, it discusses the main characteristics of such environments including human, technical and physical resources, communicative structures, information management, and cultural contexts. It then uses data from an investigation of the universities of Cambridge, Toronto and Ulster to assess the pedagogical effectiveness of the computer-based environments currently in operation in these three institutions. It considers, in particular, the integrative role that computer-based language learning environments seem to provide. Although each institution has integrated computer technology into language teaching and learning in different ways, a key element of each environment has been the establishment of a common computer-mediated infrastructure, enabling effective information dissemination, resource distribution, communication and teaching and learning. No single common infrastructure would be suitable in all three, however, in each case, it was found that the environments created were valuable, especially in integrating elements of the teaching and learning process that would normally have remained apart. In concluding that the creation of a computer-based language learning environment in the present climate is beneficial, it was noted that adequate technical resources and a management that is keen to integrate computer technology into all aspects of university life is a key factor in their success.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".