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Record W1512951335 · doi:10.24059/olj.v8i2.1828

STUDENT ROLE ADJUSTMENT IN ONLINE COMMUNITIES OF INQUIRY: MODEL AND INSTRUMENT VALIDATION

2019· article· en· W1512951335 on OpenAlexaff
D. Randy Garrison, Martha Cleveland‐Innes, Tak Fung

Bibliographic record

VenueOnline Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsCommunity of inquiryPsychologyProcess (computing)Online communityOnline learningMathematics educationConceptual modelComputer scienceMultimediaWorld Wide WebCognition

Abstract

fetched live from OpenAlex

The purpose of this study is to validate an instrument to study role adjustment of students new to an online community of inquiry. The community of inquiry conceptual model for online learning was used to shape this research and identify the core elements and conditions associated with role adjustment to online learning (Garrison, Anderson and Archer, 2000). Through a factor analytic process it is shown that the instrument did reflect the theoretical model. It was also useful in refining the items for the questionnaire. The instrument is for use in future research designed to measure and understand student role adjustment in online learning.

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)0.055
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.095
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.003
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.350
Teacher spread0.313 · 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 designSimulation or modeling
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

Citations248
Published2019
Admission routes1
Has abstractyes

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