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Record W2159009731 · doi:10.1145/2460296.2460352

Orchestrating of complex inquiry

2013· article· en· W2159009731 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

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrchestrationLearning analyticsScripting languageComputer scienceAnalyticsImplementationData scienceVisual analyticsCurriculumHuman–computer interactionSoftware engineeringKnowledge managementVisualizationProgramming languageArtificial intelligenceSociology

Abstract

fetched live from OpenAlex

This paper presents our research of a pedagogical model known as Knowledge Community and Inquiry (KCI), focusing on our design of a technological infrastructure for the orchestration of the complex CSCL scripts that characterize KCI curricula. We first introduce the KCI model including some basic design principles, and describe its dependency on real time learning analytics. Next, we describe our technology, known as SAIL Smart Space (S3), which provides scaffolding and analytic support of sequenced interactions amongst people, materials, tools and environments. We outline the critical role of the teacher in our designs and describe how S3 supports their active role in orchestration. Finally we outline two implementations of KCI/S3 and the role of learning analytics, in supporting dynamic collective visualizations, real time orchestrational logic, and ambient displays.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.986

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

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 head0.240
GPT teacher head0.459
Teacher spread0.220 · 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

Quick stats

Citations53
Published2013
Admission routes1
Has abstractyes

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