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Record W2000364698 · doi:10.1145/381234.381249

MIACE, a human cognitive architecture

2001· article· en· W2000364698 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

VenueACM SIGCUE Outlook · 2001
Typearticle
Languageen
FieldComputer Science
TopicIntelligent Tutoring Systems and Adaptive Learning
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsCognitive architectureArchitectureComputer scienceCognitionOriginalityDomain (mathematical analysis)Point (geometry)Cognitive scienceHuman–computer interactionCognitive modelLIDACognitive systemsArtificial intelligencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Miace as a human cognitive architecture is a computational model that explains how a student acquires, encodes and uses domain knowledge. Because Miace takes into account the cognitive psychological laws and the environment in which the student works, it can be used as a virtual student in help systems dedicated to pedagogical formation, in intelligent tutoring systems, in cooperative learning applications and for the conception of didactic material. This paper describes the implementation of Miace and discusses the Miace theoretical components from three point of view: temporal, their roles in cognitive activity and their generic or functional forms. A comparison is done to show the originality and the contribution of Miace in user modeling.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.669

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.280
Teacher spread0.253 · 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