Structural Equation Modelling of EFL Learners’ Perceived Preferences for Data-driven Learning and Learners’ Agency
Bibliographic record
Abstract
Data-driven learning (DDL) has drawn researchers’ eyes on corpus linguistics and language learning successfully, particularly on English writing. However, the structural relation between the students’ preferences for data-driven learning and the EFL students’ learning agency has not been well examined yet. This study examined the hypothetical model of measurement for EFL learners’ perceived preferences for DDL and their learning agency. Two questionnaires were used for collecting the data. Structural equation modeling (SEM) was assessed using AMOS. The results revealed that the developed model enjoyed an acceptable level of goodness of fit. The results also showed that the students’ perceived preferences for DDL strongly affect their learning agency. Therefore, it could be concluded that exposure to DDL fosters language learners’ self-efficacy and the ability to self-regulate their learning activities. All in all, the results have implications (theoretical and practical) for language teachers, learners and those interested in corpus linguistics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".