Review of Contemporary Computer-Assisted Language Learning
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
the parameters of contemporary computer-assisted language learning (CALL) and illustrates how a range of different areas, as well as research approaches, are shaping this field.The introductory chapter states four objectives fulfilled by the volume.Firstly, it offers an overview of the historical perspectives of CALL and identifies relationships with other disciplines.Secondly, it provides a critical synopsis of CALL research and ways in which this empirical base is leading to richer theoretical understandings.Thirdly, the introduction documents research that explores relationships between theory and practice in a range of educational settings, and finally, it outlines new research directions and approaches.The edited volume offers a balanced representation of voices through its 18 chapters contributed by researchers in Canada, Germany, Hong Kong, Japan, Spain, the United Kingdom, the United States, and Venezuela.A wide range of topics are covered, which supports the editors' observation that CALL has developed from a narrow area of specialist interest to an area of scholarship in its own right, with independent subfields, including teacher education, evaluation, language testing, social media, and task design.The contributions are organized in a three-part architecture: CALL context, CALL learning environments, and CALL in language education.The volume features an insightful foreword by Mike Levy, who examines reasons to employ the label CALL.He describes the book as essential reading for postgraduate students of language teaching, for CALL researchers-in short, for "old hands and newcomers alike" (p.xviii).In Part I, The CALL Context, five chapters examine the historical development of the field and its research trends, in view of theories of language education and the process of technological integration and normalization.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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 it