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Record W3082661888 · doi:10.1108/intr-04-2019-0155

The determinants of learner satisfaction with the online video presentation method

2020· article· en· W3082661888 on OpenAlexaff
Sameh Al‐Natour, Carson Woo

Bibliographic record

VenueInternet Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of British ColumbiaToronto Metropolitan University
Fundersnot available
KeywordsOriginalityPerspective (graphical)Presentation (obstetrics)Blended learningTest (biology)Value (mathematics)PsychologyMultimediaComputer scienceHigher educationMathematics educationEducational technologySocial psychologyCreativityArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the determinants of learners' satisfaction with a new blended learning method, namely online video presentations. Design/methodology/approach The study tests the proposed model using responses from 353 students who were exposed to the new method. Regression analysis was used to test the hypotheses. Findings The results show that both the perceived social (e.g. reduction in comparison bias) and utilitarian (e.g. presentation originality) benefits increase satisfaction with the online video presentation method, from both the creator's and the learner's perspectives. Practical implications This study provides several guidelines to instructors employing blended learning methods, as well as designers of platforms that enable blended learning. Originality/value This study provides a model to understand the determinants of learners' satisfaction with a new blended learning method. It looks at these determinants from both the content creators' perspective and the content viewer's perspective.

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)0.003
metaresearch head score (Gemma)0.031
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.127
GPT teacher head0.494
Teacher spread0.367 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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