<i>Creating a computer-based language learning environment</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it