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An Analysis of Synchronous and Asynchronous Online Undergraduate Learning During the COVID Pandemic

2022· article· en· W4229004692 on OpenAlexaffvenue
Iman Al-Areibi, Brandon Dickson, Donna Kotsopoulos

Bibliographic record

VenueInternational journal of e-learning & distance education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsAsynchronous communicationCoronavirus disease 2019 (COVID-19)Online learningAsynchronous learningSynchronous learningPandemicStudent engagementMathematics educationHigher educationComputer scienceMetadataPsychologyTeaching methodMultimediaCooperative learningWorld Wide WebPolitical scienceMedicine

Abstract

fetched live from OpenAlex

In response to the COVID pandemic, university classes across the world were forced online. In this research, we explored students’ experiences in emergency online learning in two undergraduate business classes. Where the literature on online learning has traditionally focused on those students who chose online learning, in light of recent shifts in education, online learning continues to increase its prevalent in the education of all students, even those who would not have traditionally chosen this medium. In this research, through the use of learning platform metadata and students survey responses, we examined the impact of the various pedagogical techniques used in the online classroom and their ability to maintain high student motivation. Ultimately, we concluded that providing multiple opportunities for engagement through the use of both synchronous and asynchronous tools in is crucial to promoting student motivation, learning and course success. Implications for classroom instruction and further research will be discussed.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.353
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.336
Teacher spread0.326 · 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.

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

Citations8
Published2022
Admission routes2
Has abstractyes

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