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An NCME Instructional Module on Exploring the Logic of Tatsuoka's Rule‐Space Model for Test Development and Analysis

2000· article· en· W2006618588 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducational Measurement Issues and Practice · 2000
Typearticle
Languageen
FieldComputer Science
TopicEducational Technology and Assessment
Canadian institutionsSocial Sciences and Humanities Research CouncilAlberta Advanced EducationUniversity of Alberta
Fundersnot available
KeywordsTest (biology)Set (abstract data type)Space (punctuation)BlueprintComputer scienceCognitionArtificial intelligenceRule-based systemMachine learningPsychologyProgramming languageEngineering

Abstract

fetched live from OpenAlex

K. Tatsuoka's rule‐space model is a statistical method for classifying examinees' test item responses into a set of attribute‐mastery patterns associated with different cognitive skills. A fundamental assumption in the model resides in the idea that test items may be described by specific cognitive skills called attributes which can include distinct procedures, skills, or processes possessed by an examinee. The rule‐space model functions by collecting and ordering information about the attributes required to solve test items and then statistically classifying examinees' test item responses into a set of attribute‐mastery patterns, each one associated with a unique cognitive blueprint. The logic of Tatsuoka's rule‐space model, as it applies to test development and analysis, is examined an this module. Controversies and unresolved issues are also presented and discussed.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.149
GPT teacher head0.366
Teacher spread0.216 · 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