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Record W1548326146 · doi:10.19173/irrodl.v14i5.1510

The teacher as leader: Effect of teaching behaviors on class community and agreement

2013· article· en· W1548326146 on OpenAlexvenueno aff
Beth C. Rubin, Ronald Fernandes

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAgreementClass (philosophy)PsychologyCognitionMathematics educationCommunity of inquiryTeaching methodSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This article examines the effects of teaching behaviors in online university classes, focusing on the agreement among class members. Literature on group leaders’ effects on group agreement about workplace climate is reviewed. Hypotheses are generated about the effects that teachers of online courses, as class leaders, have on both the level and agreement about the community of inquiry. They are tested with a sample of 874 students in 126 online courses. The aggregate class level and strength of agreement about the teaching presence have significant effects on the level and agreement about cognitive presence and social presence. Although the aggregate levels and agreement about community of inquiry are related, different patterns emerge. The paper explores the interaction effects of level and agreement, finding that in classes with high levels of teaching presence, the higher the agreement about teaching presence, the higher the agreement about cognitive and social presence especially for classes reporting stronger levels of cognitive and social presence. In classes with lower levels of teaching presence, agreement has a different effect.

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)0.007
metaresearch head score (Gemma)0.047
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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.073
GPT teacher head0.486
Teacher spread0.412 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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