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Record W2156111882 · doi:10.1109/icalt.2007.95

Cognitive Trait Model and Divergent Associative Learning

2007· article· en· W2156111882 on OpenAlexaff
Tai‐Yu Lin, Kinshuk Kinshuk, Sabine Graf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsAthabasca University
Fundersnot available
KeywordsTraitAssociative propertyCognitionAssociative learningPsychologyCognitive psychologyComputer scienceSign (mathematics)Divergent thinkingArtificial intelligenceCognitive scienceMathematics

Abstract

fetched live from OpenAlex

Cognitive trait model (CTM) is a student model that aims to create profiles of learners' cognitive traits. Divergent associative learning (DAL) denotes the characteristic of learning that develops links between new and existing concepts. Relationships of DAL to divergent thinking and associative learning are examined in this paper. Manifestations of DAL are extracted from literature and used in CTM to create approximations of learners' DAL. A Web-based tool called Web-DAL was developed to measure DAL psychometrically in an empirical study. In this study, the comparison of the approximations of DAL and the psychometric data shows a promising sign of using the proposed CTM to profile learners' DAL.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.401
Teacher spread0.336 · 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.

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

Citations4
Published2007
Admission routes1
Has abstractyes

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