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Record W3198195778 · doi:10.19173/irrodl.v22i3.5263

A Meta-Analysis on the Effects of Synchronous Online Learning on Cognitive and Affective Educational Outcomes

2021· article· en· W3198195778 on OpenAlexvenueno aff
Florence Martin, Ting Sun, Murat Turk, Albert D. Ritzhaupt

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAsynchronous communicationDistance educationMeta-analysisCognitionAsynchronous learningPsychologyEducational technologyOnline learningEquivalence (formal languages)Community of inquiryMathematics educationInstructional designComputer scienceSynchronous learningTeaching methodMultimediaCooperative learning

Abstract

fetched live from OpenAlex

Synchronous online learning (SOL) provides an opportunity for instructors to connect in real-time with their students though separated by geographical distance. This meta-analysis examines the overall effect of SOL on cognitive and affective educational outcomes, while using asynchronous online learning or face-to-face learning as control groups. The effects are also examined for several moderating methodological, pedagogical, and demographical factors. Following a systematic identification and screening procedure, we identified 19 publications with 27 independent effect sizes published between 2000 and 2019. Overall, there was a statistically significant small effect in favor of synchronous online learning versus asynchronous online learning for cognitive outcomes. However, the other models were not statistically significant in this meta-analysis. The effect size data were normally distributed and significantly moderated by course duration, instructional method, student equivalence, learner level, and discipline. Implications for educational practice and research are included.

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)0.016
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.047
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.029
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.114
GPT teacher head0.489
Teacher spread0.375 · 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 designMeta-analysis
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

Citations66
Published2021
Admission routes1
Has abstractyes

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