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Record W2214408454

The Extent of Both Instructor and Student Discourse in Online Courses

2008· article· en· W2214408454 on OpenAlexaff
Peter Kiriakidis

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsVale (Canada)
Fundersnot available
KeywordsVitalityDiscussion boardOnline courseQuality (philosophy)InstitutionGrounded theoryMedical educationPsychologyQualitative analysisQualitative researchHigher educationMathematics educationQualitative propertyComputer sciencePedagogyMultimediaSociologyPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

This study was grounded on the assumption that there is a correlation between the extent of both instructor and student discourse (ISD) in Threaded Discussions (TDs) in online courses.It was also grounded on the assumption that ISD is a factor of importance to both students and the vitality of the online institution.This study empirically examined the extent of ISD in TDs in online courses.The quantitative data analysis indicated that students participate more in TDs when instructors post timely and frequently to the discussion board.The qualitative data analysis indicated that TDs should be detailed, interesting, enjoyable, and valuable, and during TDs, instructors should provide students with continuous encouragement, guidance, assistance, quality and timely feedback, motivation, and support.Policy makers and online course administrators may achieve greater enrollment and retention rates in online courses with ISD support and a policy on clear expectations in ISD in TDs.

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)0.011
metaresearch head score (Gemma)0.074
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 score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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".

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Citations0
Published2008
Admission routes1
Has abstractyes

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