MétaCan
Menu
Back to cohort
Record W1496853860 · doi:10.24059/olj.v11i1.1737

ONLINE COMMUNITY OF INQUIRY REVIEW: SOCIAL, COGNITIVE, AND TEACHING PRESENCE ISSUES

2019· article· en· W1496853860 on OpenAlexaff
D. Randy Garrison

Bibliographic record

VenueOnline Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCommunity of inquiryCognitionFocus groupOnline discussionCohesion (chemistry)PsychologyCoding (social sciences)PedagogySociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

This paper explores four issues that have emerged from the research on social, cognitive and teaching presence in an online community of inquiry. The early research in the area of online communities of inquiry has raised several issues with regard to the creation and maintenance of social, cognitive and teaching presence that require further research and analysis. The other overarching issue is the methodological validity associated with the community of inquiry framework.The first issue is about shifting social presence from socio-emotional support to a focus on group cohesion (from personal to purposeful relationships). The second issue concerns the progressive development of cognitive presence (inquiry) from exploration to resolution. That is, moving discussion beyond the exploration phase. The third issue has to do with how we conceive of teaching presence (design, facilitation, direct instruction). More specifically, is there an important distinction betweenfacilitation and direct instruction? Finally, the methodological issue concerns qualitative transcript analysis and the validity of the coding protocol.

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)0.099
metaresearch head score (Gemma)0.300
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: Review · Consensus signal: Review
Teacher disagreement score0.099
Threshold uncertainty score0.525

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0990.300
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0170.015
Science and technology studies0.0040.008
Scholarly communication0.0130.014
Open science0.0020.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.412
Teacher spread0.362 · 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
GenreReview

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

Citations903
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOnline LearningSame topicOnline and Blended LearningFrench-language works237,207