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Record W2588899142 · doi:10.5539/jel.v6n2p254

Developing Achievement Test: A Research for Assessment of 5th Grade Biology Subject

2017· article· en· W2588899142 on OpenAlexvenueno aff
Nilay Şener, Erol Taş

Bibliographic record

VenueJournal of Education and Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, Safety, and Science Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOptimal distinctiveness theoryTest (biology)PsychologyAchievement testReliability (semiconductor)Item analysisMathematics educationStatisticsContent validityTest validityMultiple choiceMathematicsPsychometricsStandardized testSocial psychologySignificant difference

Abstract

fetched live from OpenAlex

The purpose of this study is to prepare a multiple-choice achievement test with high reliability and validity for the “Let’s Solve the Puzzle of Our Body” unit. For this purpose, a multiple choice achievement test consisting of 46 items was applied to 178 fifth grade students in total. As a result of the test and material analysis performed during the test development process, difficulty, distinctiveness, and item-total correlation coefficients of the materials were calculated. For the validity study, a table of specifications was prepared and the Content Validity Index (CVI) was found to be 0.95 by taking an expert opinion. As a result of the analysis, 8 items were removed from the test and the KR-20 reliability coefficient of the final test consisting of 38 items was calculated as 0.87. As a result of the item analyses, while item difficulty indices were valued between 0.30 and 0.74, item distinctiveness indeces were valued between 0.31 and 0.71. The average difficulty of the test was calculated as moderate (0.56) and its distinctiveness was calculated as very good (0.49).

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)0.006
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.332
GPT teacher head0.591
Teacher spread0.259 · 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

Citations38
Published2017
Admission routes1
Has abstractyes

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