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Record W4404936250 · doi:10.24059/olj.v28i4.4133

The Effect of Video Camera, Microphone and Chat Box Use on Social Presence and Engagement in an Online Group Activity

2024· article· en· W4404936250 on OpenAlexafffund
Shayna A Minosky, Nurul Aini, Brandon J. Justus, Tanisha Bali

Bibliographic record

VenueOnline Learning · 2024
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsKwantlen Polytechnic University
FundersKwantlen Polytechnic University
KeywordsPsychologyMicrophoneGroup (periodic table)MultimediaOnline videoComputer scienceComputer graphics (images)AdvertisingInternet privacyTelecommunicationsBusinessPhysics

Abstract

fetched live from OpenAlex

With the rapidly expanding availability of online courses, concerns have been raised about student engagement and connection within the online environment. Using an experimental design, we examined the effects of video camera, microphone, and chat box communication mediums on students’ experiences of social presence, peer rapport, motivation, satisfaction, and anxiety. A total of 133 undergraduate students were randomly assigned to a video, audio, or chat box condition and asked to complete an online interactive group task and a post-task survey. One-ways ANOVAs indicated that participants in the chat box condition reported lower levels of social presence, peer rapport, motivation and satisfaction compared to both the video and audio conditions, with no differences between the video and audio conditions. Those in the video condition reported higher anxiety levels than those in the chat box condition. We recommend that students participate in their online classes using video cameras and/or microphones to increase engagement and interpersonal connections with peers.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.077
GPT teacher head0.436
Teacher spread0.358 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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