Explicit and Implicit Grammar Instructions in Higher Learning Institutions
Bibliographic record
Abstract
Two universally accepted approaches to grammar instruction are explicit and implicit teaching of the grammar. Both approaches have their own strengths and limitations. Educators may face a dilemma whether to teach grammar explicitly or implicitly. This paper aims to provide insights into the educators’ beliefs towards grammar teaching in Malaysian Higher Learning Institutions, and the sources of the held beliefs. Data were generated through semi-structured interviews with five educators from several private colleges located in peninsular Malaysia. Data analysis reveals that a majority of the respondents preferred grammar to be taught explicitly in their ESL classrooms; nevertheless they viewed implicit instruction as necessary when conforming to students’ needs. The sources of the held beliefs are educators’ experience as well as the institutional requirement. This paper draws our attention to the role of educators as an eclectic teaching practitioner who are able to apply a suitable grammar instruction according to learners’ needs. It is hoped that this study will contribute to the growth of literature on grammar teaching and learning especially in Malaysian ESL classrooms.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".