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Record W4389369445 · doi:10.19173/irrodl.v24i4.7379

Development and Validation of the Sense of Online Community Scale

2023· article· en· W4389369445 on OpenAlexvenueno aff
Craig E. Shepherd, Doris U. Bolliger, Courtney McKim

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
FundersTouro University CaliforniaTexas Tech UniversityUniversity of WyomingGeorge Mason University
KeywordsLikert scaleScale (ratio)Sense of communityPsychologyOnline communityMedical educationComputer scienceHigher educationInstitutionApplied psychologyMathematics educationSocial psychologyWorld Wide WebSociologyDevelopmental psychologyMedicinePolitical scienceSocial scienceGeography

Abstract

fetched live from OpenAlex

This study focused on the development and validation of the Sense of Online Community Scale (SOCS), which includes 28 Likert-type scale items across six subscales: (a) program community, (b) program academic activities, (c) program social activities, (d) institutional academic activities, (e) institutional social activities, and (f) affiliation. The validation process included an implementation with 293 learners enrolled in online programs at a higher education institution in the eastern United States. The model was evaluated with and without outliers, and results show that the model aligned well with the SOCS. The means of all items except one exceeded 3.5 on a 5.0 scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Time in an online program was not a statistically significant predictor of the validation model, though most of our participants were in the first or second year of their degree programs. Findings demonstrate that the SOCS is a reliable and valid instrument that other researchers may use to investigate community in online environments on both the program and the institutional level.

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)0.022
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.004
Research integrity0.0010.001
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 head0.158
GPT teacher head0.490
Teacher spread0.332 · 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 designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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