Observing the Effectiveness of Task Based Approach in Teaching Narrative Essay at a Private University
Bibliographic record
Abstract
Writing is one of the most challenging skills of English language. Learners in Pakistan seem unable to master this skill even after years of using English as an official/second language. The focus of this research was to prove that within task-based learning (TBL) framework, language learners engage in purposeful, problem-oriented, and outcome-driven tasks that yield much better results as compared to traditional teaching methods which often fail to generate the desired output. The aim of this research was to prove that Task Based Approach is quite effective and successful in teaching narrative essay writing with an only disadvantage of time consumption. This study resorted to semi-structured interviews and post-test for data collection targeting the undergraduate students in Pakistan. This action research used purposive sampling and employed qualitative research design since the data comprised of both; final drafts of narrative essays and open-ended interviews. The data collected in the post-task phase i.e. the narrative essays were assessed via writing assessment rubrics presented in the IELTS guide for the teachers (2015). The bands were awarded on the basis of four parameters: task achievement, cohesion and coherence, lexical resource, and grammatical range and accuracy. The results delineated that majority of students achieved 5 bands and an overall improvement was observed in the narrative writing skills of students. In the same stead, the students in interview presented the view that Task Based Approach was much more successful in teaching them narrative essay writing.
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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 | 0.004 | 0.015 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".