The Washback Effect of WAEC/SSCE English Test of Orals on Teachers Methodology in Senior Secondary Schools in Sokoto Metropolis
Bibliographic record
Abstract
The study investigates the washback effect of WAEC/SSCE English Test of Orals on Teachers Methodology. The method used in this research is a mixed method employing survey and case study strategies. Questionnaire and semi- structured interview were used to collect data. 32 out of the 41 teachers of English taking senior secondary school classes in Sokoto metropolis were sampled to respond to the questionnaire and the selection of participants was done using random sampling method. 5 teachers of English outside the sampled population were purposively selected to participate in the interview. The data from the questionnaire was analyzed quantitatively using frequencies, simple percentages and mean ranking while the data from the interview was analyzed qualitatively. The findings were presented sequentially, quantitative followed by qualitative. The result obtained from the study revealed that examination related factors affect the teachers in their choice and use of methodology the more. Teachers follow the format of the test and skip other content in the curriculum that did not feature in the test. The research concludes that the practice constitutes negative washback on teaching methodology and that examination bodies must improve on their testing system for the attainment of the envisaged positive washback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".