MétaCan
Menu
Back to cohort
Record W4362698257 · doi:10.1177/20427530231167644

Exploring students’ Twitter use in the online classroom across 4 years

2023· article· en· W4362698257 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

VenueE-Learning and Digital Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of WindsorMemorial University of Newfoundland
Fundersnot available
KeywordsAsynchronous communicationSocial mediaStudent engagementPsychologyComputer-mediated communicationComputer scienceOnline learningMathematics educationMultimediaWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

Online asynchronous courses require close attention to course design to ensure there are strategies in place to foster social presence to build stronger senses of community and to motivate students to engage (content, peers and instructors). Judicious use of social media may serve this purpose. Since its inception, social media, Twitter in particular, has been employed in higher education courses for teaching and learning experiences with a notable impact on student engagement and social presence. This research examines students’ use of Twitter for assessment and interaction in the online asynchronous classroom from 2014 to 2018, to determine if there has been an increase in the length, amount or content within Tweets, and if students report stronger engagement and interaction following the use of Twitter for assessment. While results indicate such a connection exists, students were more focused on completing course requirements than creating connections or interacting with others, and were bothered by the constraints of the Tweet length.

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)0.002
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 score0.352
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.137
GPT teacher head0.372
Teacher spread0.235 · 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