Direct versus Indirect Explicit Methods of Enhancing EFL Students’ English Grammatical Competence: A Concept Checking-based Consciousness-raising Tasks Model
Bibliographic record
Abstract
Two approaches to grammar instruction are often discussed in the ESL literature: direct explicit grammar instruction (DEGI) (deduction) and indirect explicit grammar instruction (IEGI) (induction). This study aims to explore the effects of indirect explicit grammar instruction on EFL learners’ mastery of English tenses. Ninety-four eleventh-graders were conveniently selected and randomly assigned into either the experimental group (EG) or the control group (CG). A pre-post tests design was used to collect the data. Before and after the treatment, the following tests were administered: rule analysis, grammar, and speaking. A delayed written test was given to both groups to assess students’ retention of structure acquired; in addition, a questionnaire was provided to the EG to investigate their perception on the treatment. The results indicated that the EG significantly outperformed the CG in the analysis of grammar rules and the oral proficiency, except for the use of grammar structures in a pre-defined context. Convincingly, there was a positive correlation between the grammar rules and their subsequent use. This validates the cause and effect of grammar rules’ acquisition and the use of them in receptive and productive stages. Also, the EG had favorable attitudes towards the instruction. This study may provide practical implications and techniques for improving EFL students’ grammar performance in high schools in Vietnam.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".