Language Teachers’ Beliefs About Teaching the Present Perfect Tense
Bibliographic record
Abstract
The growing body of teachers’ cognition research suggests that language teachers’ decisions about grammar teaching are influenced by what they know, think, and believe. While previous research highlights that learning the present perfect tense is challenging for foreign language learners, little research discussed teachers’ beliefs about how these challenges are addressed in language classrooms. To bridge this gap and contribute more broadly to teacher cognition research, this study sought to explore teachers’ beliefs about teaching the simple present perfect tense to foundation year students at an English language center at a University in Saudi Arabia. The basic qualitative research design was adopted, and semi-structured interviews were conducted with 13 teachers of English as a foreign language. The findings suggest that teachers had positive views about the value of teaching present perfect tense which were rooted in their apprentice of observation, pedagogical content knowledge and the textbooks. They viewed grammar as an integral component of language learning and perceived teaching grammar implicitly as an ideal approach to enhance language proficiency. However, their reported practices reflected conflicting explicit grammar teaching approach. The reported challenges to implicit grammar teaching were the learners’ proficiency levels, lack of an equivalent grammatical structure in learners’ native language, contrastive analysis, and translation. It was suggested that teachers’ decisions about teaching the present perfect tense were driven by focus-on-form rather than focus on forms approach. The implications for teaching the present perfect tense are discussed and recommendations for future grammar teaching research are highlighted.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".