Applications of Communicative Language Teaching Techniques in Reading Classes: A Special Focus on Junior Colleges in Telangana Region (India)
Bibliographic record
Abstract
Having worked in several Junior Colleges for many years and taught the students of both First and Second Year of Intermediate, the researcher was motivated to investigate into the perilous problems of both pedagogues and learners in teaching and learning, in order to discover judicious remedies for their irradiation. A prime focus on learners and teaching methods have provided an understanding to the researcher that too much interference of regional language, lack of reading habits and total absence of learning activities are the major causes for the poor communicative ability of students. The researcher’s experiments with students of different learning capacities and the presentation of data analysis in the paper confirm the adaptability of communicative approach in reading classes. The paper accentuates the need for promoting learning ambience in English classrooms through compulsory participation of every student in various modes of interactions. It also offers an explanation regarding the role of teachers and students in communicative classes. Focusing on language acquisition through communicative approach the author proceeds to highlight the effectiveness of the approach and suggests reforms in reading activities in English class. In conclusion, the paper asserts that communicative ability is strengthened through reading and judicious implementation of teaching strategies will be successful in training and guiding the students to acquire sound communicative ability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".