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Record W2611978021 · doi:10.19173/irrodl.v18i3.2484

The Online Student Connectedness Survey: Evidence of Initial Construct Validity

2017· article· en· W2611978021 on OpenAlexvenueno aff
Tekeisha Denise Zimmerman, Kim Nimon

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessNomological networkPsychologyFeelingReliability (semiconductor)Construct validityLearning analyticsMedical educationSocial psychologyPsychometricsComputer scienceClinical psychologyMedicineData scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

The Online Student Connectedness Survey (OSCS) was introduced to the academic community in 2012 as an instrument designed to measure feelings of connectedness between students participating in online degree and certification programs. The purpose of this study was to examine data from the instrument for initial evidence of validity and reliability and to establish a nomological network between the OSCS, the Classroom Connectedness Survey (CCS), and the Community of Inquiry Survey (COI), which are similar instruments in the field. Results provided evidence of factor validity and reliability. Additionally, statistically and practically significant correlations were demonstrated between factors contained in the OSCS and established instruments measuring factors related to student connectedness. These results indicate that for the sample used in this study, the OSCS provides data that are valid and reliable for assessing feelings of connection between participants in online courses at institutions of higher 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)0.013
metaresearch head score (Gemma)0.035
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 score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.351
GPT teacher head0.600
Teacher spread0.250 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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