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Asynchronous and Synchronous Online TESOL Education

2022· other· en· W4220851790 on OpenAlex

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

VenueThe TESOL Encyclopedia of English Language Teaching · 2022
Typeother
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAsynchronous communicationComputer scienceSynchronous learningFlipped classroomAsynchronous learningClass (philosophy)MultimediaOnline learningModalitiesMathematics educationTeaching methodCooperative learningArtificial intelligencePsychologyTelecommunicationsSociology

Abstract

fetched live from OpenAlex

Online TESOL courses can be categorized into asynchronous, synchronous, and a combination of these two modalities. Despite the two levels of separation that characterize asynchronous online TESOL courses, time and space, they can be as effective or even more effective than synchronous courses, which have to compensate only for one level of separation, space. Asynchronous online learning is more flexible and inclusive than synchronous online learning. Synchronous courses are more similar to on‐campus courses in terms of course materials and communication. Effective online TESOL programs are mainly asynchronous with a limited number of synchronous classes. This blended model of online instruction resembles flipped classrooms, in which learning is front‐loaded through asynchronous materials and communication and the synchronous class time is reserved for active learning activities. Effective online TESOL programs foster facilitating teaching presence, situated learning, social and cognitive presence, and deep learning.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.798
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.004
GPT teacher head0.245
Teacher spread0.241 · 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