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Record W2997403840 · doi:10.5539/elt.v13n1p180

The Washback Effect of WAEC/SSCE English Test of Orals on Teachers Methodology in Senior Secondary Schools in Sokoto Metropolis

2019· article· en· W2997403840 on OpenAlexvenueno aff
IURMANOVA S.A., Umar Muhammad Bello

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Mathematics educationCurriculumRanking (information retrieval)Simple random samplePopulationMedical educationPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.363
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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