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Record W2189688783 · doi:10.5539/elt.v9n1p107

Investigating Content and Face Validity of English Language Placement Test Designed by Colleges of Applied Sciences

2015· article· en· W2189688783 on OpenAlexvenueno aff
Sharifa Said Ali Al’Adawi, Aaisha A. K. Al-Balushi

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Mathematics educationCLARITYActive listeningContent validityCertificationReading (process)Face validityPedagogyMedical educationPsychometricsLinguistics

Abstract

fetched live from OpenAlex

<p>An English placement test (PT) is an essential component of any foundation program. It helps place students into their suitable language proficiency level so that they do not spend time learning materials below or above their levels. It also helps teachers to prepare teaching materials to students of similar levels (Brown, 2004; Illinois, 2012). This paper aims to investigate the extent to which the PT used at Colleges of Applied Sciences (CAS) is achieving this goal by exploring teachers’ and students’ perceptions of the current exam via questionnaires and interviews. Furthermore, it examines the format and content of the PT and students’ PT score against their mid-term score. It was found that face validity of CAS PT ranged from low (teachers) to moderate (students). The majority of teachers and students emphasized the importance of including the listening and speaking components in the test. Moreover, a modified version of the reading section of the test needs to be incorporated into the test. Another suggested using a valid certified computerized test. For future research, it is recommended to design a new test, taking into consideration the findings of this research and pilot it to test its effectiveness. Furthermore, an analytical test of the current marking criteria is essential to check its clarity and consistency.</p>

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)0.006
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.302
Teacher spread0.248 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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