Preparing for the exam vs. developing the language proficiency: A washback study on the learners
Bibliographic record
Abstract
This study reported the results of a washback study conducted into the test candidates of the secondary school leaving English public examination in Bangladesh. The examination is known as the Secondary School Certificate (SSC) examination, which is the biggest school leaving examination in the country. Pellegrino, DiBello, and Goldman’s (2016) argument and evidence-based validity framework was employed to guide this qualitative research design. As research sites, two schools were selected based on their previous five years results at the SSC examination. School A was one of the high-performing schools and School B was one of the low-performing schools. Eight focus groups were conducted with the SSC test candidates- four from each of the schools. Selecting two schools added an extra layer of complexity in understanding the nature of washback if students from two different schools perceive the effects of examinations in diverse ways. The findings suggested how students did not perceive that studying for the examination could develop their English proficiency. The paper will discuss how there was a presence of negative washback in students’ learning and how students from two schools perceived washback in diverse ways because of many socio-financial backgrounds of the schools, teachers, and students.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".