Structural Equation Modelling of EFL Learners’ Perceived Preferences for Data-driven Learning and Learners’ Agency
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
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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