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Record W2770206301 · doi:10.1121/1.5014590

Online vs. in-person: Exploring the outcomes and impacts

2017· article· en· W2770206301 on OpenAlexaffabout
Benjamin V. Tucker, Timothy Mills

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPopularityPresentation (obstetrics)Massive open online coursePerspective (graphical)Online learningOnline courseComputer scienceCourse (navigation)Mathematics educationMedical educationMultimediaPsychologyWorld Wide WebArtificial intelligenceEngineeringMedicine

Abstract

fetched live from OpenAlex

Over the last few decades, the teaching of online courses has grown tremendously, especially with the rise in popularity of massive open online courses. In phonetics, the offerings of online courses has also grown. The University of Alberta has offered an online phonetics course for four years, which is academically equivalent to the in-person course. The University of Alberta online course runs as a standard semester course offered twice a year with the similar deadlines and structure as the in-person course. In this presentation we discuss and explore the advantages and challenges of online versus in-person courses from the instructor's perspective. We report the differences and similarities in student learning outcomes and student success. We discuss methods of student engagement, implementation of laboratory activities (active-learning), and exam invigilation. We also comment on the advantages of combining materials from online and in-person courses in blended 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 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)0.038
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 score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.053
GPT teacher head0.343
Teacher spread0.290 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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