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Record W2208611758 · doi:10.5539/ies.v9n1p161

Developing a Numerical Ability Test for Students of Education in Jordan: An Application of Item Response Theory

2015· article· en· W2208611758 on OpenAlexvenueno aff
Eman Rasmi Abed, Mohammad M. Al-Absi, Yousef Abdelqader Abu Shindi

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Statistical Modeling Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaItem response theoryTest (biology)PsychologyItem analysisTest validityMathematics educationClass (philosophy)Reliability (semiconductor)PsychometricsMeasure (data warehouse)MathematicsStatisticsDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of the present study is developing a test to measure the numerical ability for students of education. The sample of the study consisted of (504) students from 8 universities in Jordan. The final draft of the test contains 45 items distributed among 5 dimensions. The results revealed that acceptable psychometric properties of the test; items parameters (difficulty, discrimination) were estimated by item response theory IRT, the reliability of the test was assessed by: Cronbach’s Alpha, average of inter-item correlation, and test information function (IRT), and the validity of the test was assessed by: arbitrator's views, factor analysis, RMSR, and Tanaka Index. The numerical ability test can be used to measure the strength and weaknesses in numerical ability for educational faculty students, and the test can be used to classify students on levels of numerical ability.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.096
GPT teacher head0.503
Teacher spread0.407 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations7
Published2015
Admission routes1
Has abstractyes

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