Grammar Learning Methods and Grammar Learning Strategies: Are They Related?
Bibliographic record
Abstract
This study responds to numerous calls for research on Grammar Learning Strategies (GLS), an area that has been neglected for years. The research sheds light on the relationship between grammar learning or instructional approaches and students' utilization of GLS assigned to develop explicit and implicit knowledge of grammar. Two sub-categories of Pawlak's (2018) Grammar Learning Strategy Inventory (GLSI) were employed to assess GLS use in these two sub-categories by students who prefer explicit grammar learning and those who prefer implicit grammar learning. A Pearson correlation coefficient test was conducted to examine this relationship. The study revealed a moderate use of GLS for developing explicit and implicit grammar knowledge. Furthermore, it found an insignificant, fragile, and negative correlation between grammar learning or instructional approaches and GLS used to develop explicit knowledge of grammar. Similarly, an insignificant, very weak, and negative correlation was reported between grammar learning or instructional approaches, and GLS used to develop implicit knowledge of grammar. The study discusses factors influencing language learning and GLS use and highlights the limitations.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".