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Record W3202627678 · doi:10.1080/15228053.2021.1980848

Online education next wave: peer to peer learning

2021· article· en· W3202627678 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

VenueJournal of Information Technology Case and Application Research · 2021
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsMinnow Environmental (Canada)
Fundersnot available
KeywordsAsynchronous communicationHigher educationOnline learningMathematics educationPhenomenonPeer feedbackComputer scienceSynchronous learningAsynchronous learningCoronavirus disease 2019 (COVID-19)Mode (computer interface)Peer tutorPedagogyKnowledge managementPsychologyCooperative learningMultimediaPolitical scienceTeaching methodTelecommunications

Abstract

fetched live from OpenAlex

Online education is no longer a trend; it is slowly but surely becoming a norm. It has become a global phenomenon driven by the onslaught of coronavirus pandemic, emergence of new learning platforms, and wide acceptance of teaching and learning in online synchronous and asynchronous modes by diverse stakeholders. Current online education technologies and platforms emphasize interactions between professors and students. Through the holistic model of online education, we emphasize in this article student-to-student (peer-to-peer) learning in the online mode similar to what exists in the traditional F2F mode. The evolving student-to-student interactional SolveitNow model at present covers tertiary education students. With requisite changes, it can be easily applicable to secondary and primary education students. SolveItNow is currently in beta testing on a large scale at multiple levels of Mathematics education.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.383
Teacher spread0.351 · 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