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Record W4404249239 · doi:10.54337/nlc.v5.9505

Symposium 6: Collaborative Learning

2006· article· en· W4404249239 on OpenAlexaff
Kewal S. Dhariwal

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2006
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCollaborative learningComputer scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

What: This paper reports on the results of participative action research by multiple teams of participants who played various roles and fostered the evolution of an integrated research and business simulation environment by sharing data, making decisions visible and discussing solutions in both a competitive and a collaborative environment. Why: Collaborative Networked Learning is needed for the training of effective management and operation of global corporate entities and in understanding the value of integrating information systems between organizations that collaborate and compete with each other in different times and markets. This is necessary since competition, in business today, is between supply chains of competing collectives of organizations, each seeking a larger market share and bigger profits and where changes in partnerships come at an ever increasing pace. Who: Managers, students, tutors & administrators of classroom, online courses, and boardroom based professional development programs. When: During the period July 2003-December 2005 using the simulator located at www.sccori.com Where: In online courses, residential programs, in bricks and mortar classrooms and in the boardrooms of major corporations. How: Using an internet browser-based online business simulator and internet communications tools allowing participants to play the roles of Retailer, Wholesaler, Distributor and Manufacturer in a number of business simulations with variable parameters. Participants experience simulations and learn by doing, build problem solving skills, develop strategies, plan, negotiate, share, build trust and implement solutions. Results: The networked management learning business simulator improves the systems dynamics models from MIT and the Systems Dynamics Society by at least two orders of magnitude. Results indicate that participants move from an individualistic competitive stance to a collaborative team-based solutions focus to threats and problems faced by their supply chain during increasingly challenging business simulations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.171
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.018
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0060.003
Scholarly communication0.0170.013
Open science0.0050.016
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.1710.072

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 head0.029
GPT teacher head0.326
Teacher spread0.297 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2006
Admission routes1
Has abstractyes

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