Impact of Washback on ESL Students’ Performance at Secondary Level
Bibliographic record
Abstract
This descriptive study explores the impact of washback on ESL students’ performance at secondary level. In this study, the term “washback” refers to the test effect on content of curriculum, learning English, teaching and the activities conducted in classroom. The factors other than the test itself may affect positive washback; lack of positive washback does not make test invalid whereas the negative washback effect occurs when there is lack of construct validity of test. Test design and validity plays vital role in achieving positive washback (Messick, 1996). The study aims to investigate the effects of positive washback and benefits in learning and teaching processes in ESL classrooms, while negative washback effects are destructive and can be a hindrance in achieving the goals in ESL classrooms. Recent research is descriptive in nature and survey based method was adopted for this study. 50 teachers were selected by using purposive sampling technique and 100 students were selected by using simple random sampling technique. Three tools were used for this study including: Questionnaire, Test and Observation checklist. The findings of the study exhibit that negative washback effect has its influence on tests, learning and teaching. The study concludes with a realization of the fact that language pedagogy is affected by washback. However, it is claimed by majority of the teachers that washback affects the selection of teaching methods because exams stress brings pressure and it becomes necessary for English teachers to develop linguistic competence in their students. For future researches it is recommended that other studies should be made in order to find out the impact of washback on the strategies adopted by learners while learning second language.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".