Corpus Use in Language Learning: A Meta‐Analysis
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Meta-epidemiology (narrow), Scholarly communication
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.531
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.281 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
Abstract This study applied systematic meta‐analytic procedures to summarize findings from experimental and quasi‐experimental investigations into the effectiveness of using the tools and techniques of corpus linguistics for second language learning or use, here referred to as data‐driven learning (DDL). Analysis of 64 separate studies representing 88 unique samples reporting sufficient data indicated that DDL approaches result in large overall effects for both control/experimental group comparisons ( d = 0.95) and for pre/posttest designs ( d = 1.50). Further investigation of moderator variables revealed that small effect sizes were generally tied to small sample sizes. Research has barely begun in some key areas, and durability/transfer of learning through delayed posttesting remains an area in need of further investigation. Although DDL research demonstrably improved over the period investigated, further changes in practice and reporting are recommended. Open Practices This article has been awarded Open Materials and Open Data badges. All materials and data are publicly accessible via the Open Science Framework at https://osf.io/jkktw . Learn more about the Open Practices badges from the Center for Open Science: https://osf.io/tvyxz/wiki .
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.
The record
- Venue
- Language Learning
- Topic
- Natural Language Processing Techniques
- Field
- Computer Science
- Canadian institutions
- Université du Québec à Montréal
- Funders
- not available
- Keywords
- ModerationMeta-analysisOpen dataComputer scienceTransfer of learningPsychologySample (material)Language acquisitionNatural language processingOpen scienceApplied linguisticsArtificial intelligenceMathematics educationLinguisticsWorld Wide WebStatisticsMachine learningMathematics
- Has abstract in OpenAlex
- yes