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Record W2930367903

Preparing for the exam vs. developing the language proficiency: A washback study on the learners

2018· article· en· W2930367903 on OpenAlexaff
Nasreen Sultana

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsQueen's University
Fundersnot available
KeywordsMathematics educationCertificatePsychologyTest (biology)School CertificatePedagogyFocus groupArgument (complex analysis)English languageMedical educationSociologyMedicineMathematics
DOInot available

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.069
GPT teacher head0.371
Teacher spread0.301 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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