Communicative Approach: An Alternative Method Used in Improving Students’ Academic Reading Achievement
Bibliographic record
Abstract
Academic reading is a difficult subject to be mastered. It is needed because most of books or references are written in English. The emphasis is on academic reading which becomes a compulsory subject that must be taught and understood in Faculty of Letters UAD Yogyakarta. Communicative approach is used and applied as an alternative method in the process of teaching?learning that focuses on language as a medium of communication. Communication ability involves in understanding fully the vocabulary, grammar, comprehension, and all aspects of English skills such as reading, listening, speaking and writing. One main focus of the classroom action research that appears from communicative approach which is applied in the process of teaching?learning academic reading will be discussed. The objectives are (1) How is the application of communicative approach?, (2) How far is the influence of using communicative approach in teaching academic reading? It includes the study intensity of the students, the benefit and the weakness of using the approach. The results showed the application of communicative approach effectively improved the students’ ability in academic reading. It could be seen from the improvement of some aspects, (1) aspects of reading ability, (2) aspects of English: vocabulary, grammar, pronunciation, communication, and ability of cooperation, colaboration, socialization, sharing ideas, opinion, and suggestion. The objective of teaching, the model of syllabus, the form of teaching?learning activity, and the kinds of learning material used by the teachers were consistent with those which were recommended by communicative approach.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".