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Record W3039549044 · doi:10.5430/wje.v10n3p170

Student Awareness of Space Science: Rasch Model Analysis for Validity and Reliability

2020· article· en· W3039549044 on OpenAlexvenueno aff
Roslinda Rosli, Mardina Abdullah, Nur Choiro Siregar, Nurul Shazana Abdul Hamid, Sabirin Abdullah, Lilia Halim, Noridawaty Mat Daud, Siti Aminah Bahari, Rosadah Abd Majid, Badariah Bais

Bibliographic record

VenueWorld Journal of Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
Fundersnot available
KeywordsRasch modelReliability (semiconductor)PsychologyOutreachPolytomous Rasch modelMeasure (data warehouse)Space (punctuation)Item response theoryPsychometricsItem analysisTest validityDifferential item functioningContent validityValidityApplied psychologyComputer scienceDevelopmental psychologyData mining

Abstract

fetched live from OpenAlex

Validity and reliability are crucial when conducting research to ensure the truthfulness of an instrument. This study investigated the measurement functioning of an instrument on students' awareness of space science. The instrument was administered to 206 secondary school students involved in the Sudden Ionospheric Disturbance π outreach program. Two experts evaluated the content validity of the instrument. Data were analyzed using the Winsteps 3.71.0.1 software to obtain the Rasch model analysis (RMA) on item reliability and persons' separation, item measure, item fit based on PTMEA CORR, polarity items, misfit items, unidimensionality, and a person-item map. The findings revealed that the items are valid, reliable, and appropriate to measure awareness of space science.

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)0.069
metaresearch head score (Gemma)0.176
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.176
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0050.007
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.383
Teacher spread0.308 · 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 designObservational
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
Published2020
Admission routes1
Has abstractyes

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