The Bloomsbury Handbook of Language Learning and Technology
Bibliographic record
Abstract
This handbook draws together international perspectives on technology and its application to language teaching and learning, written and edited by leading scholars in the field. It meets the increasing demand for pedagogically-informed online language instruction, which is particularly important in the context of the effects that the Covid-19 pandemic has had on the education sector on a global scale, as well as exploring language learning in informal and non-formal contexts. With contributions from5 continents and over 20 countries, including Australia, Canada, Cyprus, Denmark, Finland, France, Greece, Ireland, Japan, Spain, Sweden, the Netherlands, the UK and the USA, the book offers a thorough overview of the main influential theories and explores technology tools, approaches to research, and applications to practice. Carefully curated, this is an innovative and exciting volume for students, teachers, researchers and lecturers in language education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".