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Record W4224990477 · doi:10.1057/s41304-022-00386-6

Can active learning be asynchronous? Implementing online peer review assignments in undergraduate political science and international relations courses

2022· article· en· W4224990477 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

VenueEuropean Political Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsQueen's UniversityUniversity of Ottawa
Fundersnot available
KeywordsActive learning (machine learning)Asynchronous learningAsynchronous communicationComparative politicsPedagogyPeer learningPoliticsPolitical scienceHigher educationPeer instructionSynchronous learningPublic relationsCooperative learningMathematics educationComputer scienceSociologyTeaching methodPsychology

Abstract

fetched live from OpenAlex

Abstract The phenomenon known as emergency eLearning saw many institutions of higher education switch from face-to-face learning to virtual or online course delivery in response to the COVID-19 pandemic. The transition posed a unique suite of challenges to instructors and students alike, especially in the case of active learning pedagogy. This article reflects on the experiences of a multi-institutional, multi-term pedagogical project that implemented peer review assignments as opportunities for asynchronous but nevertheless active learning. We shared instructor experiences through the course design and application stages of courses in International Relations and political economy, discuss the ability of peer review assignments to create active learning opportunities in online courses, and reflect on our own pedagogical development benefited from the community of practice.

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)0.043
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0430.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0060.010
Scholarly communication0.0000.001
Open science0.0020.002
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 head0.151
GPT teacher head0.469
Teacher spread0.318 · 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