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Record W3001323673 · doi:10.24908/pceea.vi0.13806

USING MICROSOFT TEAMS TO SUPPORT COLLABORATIVE KNOWLEDGE BUILDING IN THE CONTEXT OF SUSTAINABILITY ASSESSMENT

2019· article· en· W3001323673 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsWilfrid Laurier UniversityWestern University
Fundersnot available
KeywordsSustainabilityContext (archaeology)Knowledge managementComputer scienceKnowledge sharingCollaborative learningMedical educationMedicine

Abstract

fetched live from OpenAlex

The current study investigates students’ use of Microsoft Teams as a collaborative knowledge building platform for a group sustainability assessment project. Ashby’s sustainability assessment method was used to provide scaffolding. Surveys (n=16) were administered to assess the nature of student collaboration, including students’ experiences using collaboration tools in the past, the activities students engaged in while working on the group project in MS Teams, self-assessment of collaborative abilities, comfort with giving, receiving and sharing comments and feedback, assessment of the effectiveness of Ashby’s sustainability assessment method in developing these abilities, and their overall assessment of MS Teams as a collaborative knowledge building tool. Students rated their collaborative abilities to be good to excellent and felt that the project was effective in developing those abilities. They are comfortable providing and receiving feedback and sharing their contributions openly. They found MS Teams to be extremely useful, and better than alternative platforms for key tasks including messaging, file sharing and collaborative authoring.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.365
Teacher spread0.351 · 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