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Record W2905354662 · doi:10.1177/0273475318818864

Exclusively Synchronous Online (VIRI) Learning: The Impact on Student Performance and Engagement Outcomes

2018· article· en· W2905354662 on OpenAlex
Anthony Francescucci, Laila Rohani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Marketing Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStudent engagementOnline learningBlended learningVirtual learning environmentComputer scienceMathematics educationEducational technologyPsychologyMultimedia

Abstract

fetched live from OpenAlex

There are growing trends in postsecondary education that emphasize the importance of online and technology-enabled learning. This study aims to investigate whether the use of virtual, interactive, real-time, instructor-led (VIRI) online learning can deliver the same student performance and engagement outcomes as a face-to-face (F2F) course. The data consist of 698 participants taught in eight sections, over two semesters, with two different instructors. An analysis of variance was used to compare the differences for both student performance and engagement outcomes. The findings show that a synchronous course delivered using VIRI classroom technology has the same level of student performance outcomes as F2F learning. This study suggests that VIRI technology is an effective synchronous learning environment.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.463
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.387
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