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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 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)0.006
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.114
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 teacher head, not a consensus.

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

Citations66
Published2021
Admission routes1
Has abstractyes

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