A Comparative Study of the Effectiveness of Two Methods of Teaching Resumptive Pronouns in Writing: TBLT and Blended Learning
Bibliographic record
Abstract
Resumptive pronouns (RPs) are one of the most challenging grammatical points for EFL learners because this structure is different in their L1. We aimed to examine whether blended learning/TBLT are useful to teach RPs. We examined the extent to which such methods improve performance on the posttest. Forty learnerstook part in the study who were assigned to 2 groups: one group was taught via TBLT, and the other via blended learning. Before piloting the study, the participants were given an OPT to check their homogeneity. Besides,they were given a researcher-made test on RPs to check their knowledge, the result of which indicated that the participants did not have sufficient knowledge about this point. Finally, the participants were given a researcher-made test as the posttest to check the effect of the treatment and the extent to which it was helpful for the correct use of RPs. Findings indicated that TBLT was more fruitful. Findings of the present study have pedagogical as well as practical implications.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".