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THE CONCEPT OF WORKSPACES— REDEFINED FOR E-LEARNING

2007· article· en· W2077642593 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAdvanced Technology for Learning · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsnot available
Fundersnot available
KeywordsWorkspaceComputer scienceLearning environmentHuman–computer interactionTask (project management)Synchronous learningCollaborative learningTUTORVirtual learning environmentContrast (vision)Cooperative learningMultimediaArtificial intelligenceKnowledge managementEngineeringTeaching methodPsychologyMathematics education

Abstract

fetched live from OpenAlex

E-learning environments are mostly associated with systems for tutor-assisted learning, blended learning, or collaborative learning. Static roles and functional structures are the rule. Thereby, the learning environment takes centre stage and users must adapt to the specific characteristics of this learning environment. In contrast to this, we present the concepts of a learning environment that, in its structures, largely adapts to the real world: We assume that all users initially act on an equal basis in the environment. Only when a specific task in a shared workspace of the e-learning environment is to be approached do differences, and thus roles and rights, become relevant.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.321
Teacher spread0.313 · 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