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Record W2530921046

Researching and designing for the orchestration of learning in the CSCL classroom.

2015· article· en· W2530921046 on OpenAlex
Emma Mercier, Cresencia Fong, Rebecca Cober, James D. Slotta, Karin Forssell, M. Isreal, Andrew Joyce‐Gibbons, Nikol Rummel

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

VenueDurham Research Online (Durham University) · 2015
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOrchestrationComputer scienceProcess (computing)Collaborative learningComputer-supported collaborative learningHuman–computer interactionMultimediaKnowledge management
DOInot available

Abstract

fetched live from OpenAlex

Designing tools for teachers to orchestrate computer supported collaborative
\nlearning activities in their classrooms requires that attention be paid to the range of roles and
\nactivities a teacher must take throughout the process. Drawing on the Implementing
\nCollaborative Learning in the Classroom framework proposed by Kaendler, Wiedmann,
\nRummel and Spada (2014), the contributors to this symposium will speak to the way their
\ndesigns address the various parts of this framework, allowing us to draw conclusions about
\nwhat has been successful for different parts of this process, and identifying future directions
\nfor development and research.

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)0.023
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.404
GPT teacher head0.499
Teacher spread0.094 · 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