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Record W4390536621 · doi:10.51357/jei.v4i2.225

Examining the Benefits and Challenges of Using Discord in Online Higher Education Classrooms

2024· article· en· W4390536621 on OpenAlex
Sharon Lauricella, Chris D. Craig, Robin Kay

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 Educational Informatics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCasualPerceptionKey (lock)DisseminationHigher educationSocial mediaPsychologyPublic relationsComputer scienceWorld Wide WebPolitical science

Abstract

fetched live from OpenAlex

Building community and connection in online courses can be challenging. Discord, a mobile and desktop app popular with gamers, is explicitly designed to stimulate discussion, conversations and community. This paper explored student perceptions of the benefits and challenges of using Discord in two upper-year, undergraduate, online social science courses (n = 45). Key benefits of using Discord included connecting to students or the professor, building community, disseminating course information, increasing engagement, and establishing a casual, informal learning environment. Challenges were reported less often than benefits and included wanting a tutorial to use Discord, needing to check for notifications, and occasional technical issues. Students suggested that a more structured use of Discord might further benefit their 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)0.000
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: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.381
Teacher spread0.267 · 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