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Record W2908298754

The Effective Affect: A Scoping Review of Social Presence

2018· review· en· W2908298754 on OpenAlexaff
David Mykota

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typereview
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAffect (linguistics)PsychologyCommunication
DOInot available

Abstract

fetched live from OpenAlex

This paper reports the findings of a scoping review of the construct social presence. The methodology follows the design for scoping reviews as advocated by Arksey and O’Malley (2005). A scoping study is desirable because by synthesizing the research literature the opportunity to identify practical guidelines for the development of social presence is facilitated. A two-stage screening process resulted in 105 studies identified for inclusion with data extracted using a standardized form. A descriptive numerical analysis and qualitative content analysis for those studies included was undertaken. Results from the manuscripts screened for inclusion and synthesized from the data extracted in the scoping review, provide strategies for the structuring of social presence; the potential benefits of effective affective communication in an online environ; and an overview of the evolution of the construct social presence. Future research that aggregates research findings on social presence is desirable so as to ascertain how the development, design, and instruction of online learning moderates the effects of social presence on student outcomes. As well, future comparative research that considers course enrollment, length of course, course level and discipline is also recommended so as to determine what social presence practices are situation specific and what social presence practices can be generalized to all online learning environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0060.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.327
GPT teacher head0.657
Teacher spread0.330 · 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 teacher head, not a consensus.

Study designOther design
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

Citations22
Published2018
Admission routes1
Has abstractyes

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