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Modeling for Learning

2010· book-chapter· en· W24902332 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

VenueIGI Global eBooks · 2010
Typebook-chapter
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsUniversité TÉLUQ
Fundersnot available
KeywordsDocumentationCurriculumComputer scienceProcess (computing)Distance educationVocational educationKnowledge managementMathematics educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

Some years ago, we have introduced in the pedagogical scenario of a distance university course a learning activity consisting at having students create their own knowledge models with the knowledge modeling software MOT developed by Paquette (2002). At the same time, was initiated a series of studies1 aiming at evaluating the learning benefits and exploring the meditating effect of the use of this tool in the learning process, both in individual or collaborative conditions, as well as in face-to-face or distance educational settings. In our research, MOT is mainly used as a mean to support students’ text comprehension, but we also proposed this tool to professionals engaged in a vocational university program to help them reflect on how the curriculum knowledge domain is instantiated in their own professional practice. In addition, we provided training sessions to faculties and produced some documentation (available on the web) on the educational uses of knowledge model- ing software in higher education (Pudelko & Basque, 2005).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.431
Threshold uncertainty score1.000

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 science0.0010.001
Research integrity0.0000.001
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 head0.024
GPT teacher head0.251
Teacher spread0.226 · 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