MétaCan
Menu
Back to cohort
Record W2122495635 · doi:10.5334/2002-1

Using Peer Teams to Lead Online Discussions

2002· article· en· W2122495635 on OpenAlexaffabout
Liam Rourke, Terry Anderson

Bibliographic record

VenueJournal of Interactive Media in Education · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca UniversityUniversity of Alberta
Fundersnot available
KeywordsClass (philosophy)PsychologyLibrary scienceSociologyMedical educationMathematics educationPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Abstract: This study investigated an online course in which groups of four students were used to lead online discussions. The teams were examined for their ability to bring instructional design, discourse facilitation, and direct instruction to the discussions. The setting was a graduate-level communications networks course delivered asynchronously to a cohort group of 17 adults enrolled for professional development education. Interviews, questionnaires, and content analyses of the discussion transcripts indicate that the peer teams fulfilled each of the three roles and valued the experience. Students preferred the peer teams to the instructor as discussion leaders and reported that the discussions were helpful in achieving higher order learning objectives but could have been more challenging and critical. Editors: Xiufeng Liu (U. Prince Edward Island, CA) Reviewers: Martha Gabriel (U. Prince Edward Island, Canada), William Hunter (U. Calgary, Canada), Gilly Salmon (Open U., UK)

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)0.052
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.052
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.002
Scholarly communication0.0050.005
Open science0.0030.010
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.058
GPT teacher head0.426
Teacher spread0.368 · 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
GenreEmpirical

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".

Quick stats

Citations214
Published2002
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Interactive Media in EducationSame topicOnline and Blended LearningFrench-language works237,207