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Record W625602122 · doi:10.24059/olj.v20i1.563

Emotional Presence in a Relationship of Inquiry: The Case of One-to-One Online Math Coaching

2016· article· en· W625602122 on OpenAlexaff
Stefan Stenbom, Martha Cleveland‐Innes, Stefan Hrastinski

Bibliographic record

VenueOnline Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCoachingPsychologyCoding (social sciences)Online discussionOnline learningCommunity of inquiryMathematics educationCognitionComputer scienceMultimediaMathematicsWorld Wide Web

Abstract

fetched live from OpenAlex

Emotions have been confirmed to be a critical component of the process of learning. In the online Community of Inquiry theoretical framework, and the recently suggested online Relationship of Inquiry framework, emotions are considered a subsection of social presence. In this study, the concept of emotional presence is examined. This examination occurs within the Relationship of Inquiry framework, developed to analyze one-to-one online coaching. A survey of online coaches and a transcript coding procedure from the online coaching service Math Coach provide the data for the study. The results indicate that a Relationship of Inquiry framework consisting of cognitive, social, teaching, and emotional presence enhances the exploration of one-to-one online coaching settings. The interpretation of these results identifies emotional presence as an essential and distinct part of one-to-one online math coaching.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.016
Scholarly communication0.0080.008
Open science0.0020.012
Research integrity0.0040.005
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 head0.070
GPT teacher head0.371
Teacher spread0.301 · 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 designQualitative
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

Citations47
Published2016
Admission routes1
Has abstractyes

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