The Effect of Inductive and Deductive Teaching on EFL Undergraduates’Achievement in Grammar at the Hashemite University in Jordan
Bibliographic record
Abstract
This current study aims at investigating the impact of using inductive and deductive teaching upon EFL undergraduate students’ achievement at the Hashemite University. More specifically, the study attempts to explore the effect of using inductive and deductive approach on students’ achievement in some grammatical issues included a book adopted for teaching Grammar 2 in the Department of English Language and Literature. The research instrument used is a pre-post-test developed by the researchers. Two groups of students are chosen for the purpose of the study. Whereas the experimental group was taught through inductive approach, the controlled group was taught through the deductive approach. Results show significant differences between the means of students’ scores in the two groups on the post-test, in favor of the experimental group. Results also reveal no significant differences according to study-year, the type of school they graduated from, and gender. In light of these results, the researchers suggest some recommendations for TEFL researchers and EFL instructors.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".