Should We Use It in Our Classrooms: An Analysis of Data-Driven Learning Research
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
Corpus linguistics has become increasingly important to both language researchers and teachers over the past three decades. As a popular practice of corpus linguistics, Data-Driven Learning (DDL) sees a rapidly growing body of research as well as instruction in the field. There is, however, a lack of comprehensive literature reviews that summarize the effectiveness, learners’ perception, as well as factors affecting the success of DDL to guide its practices. In response, this study analyzes previous DDL research to show the feasibility of the activities in EFL classrooms. For the purpose, we collected and analyzed relevant research articles from 19 journals in the discipline of applied linguistics. Our analysis revealed that while DDL has been proved generally effective in improving learners’ target language proficiency with respect to a variety of linguistic aspects, a set of its drawbacks have been elicited from the learners. The results indicate the instructors’ need to take into account the learner as well as technique background before the introduction of DDL into their classrooms.
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 | 0.011 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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