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Record W3090230960 · doi:10.21315/eimj2020.12.3.6

Social Media Usage among University Students during Exams: Distraction or Academic Support?

2020· article· en· W3090230960 on OpenAlexaff
Camille Le Gall Le Gall, Isabelle Le Gall, Alireza Jalali

Bibliographic record

VenueEducation in Medicine Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSocial mediaDistractionPsychologyMedical educationMathematics educationComputer scienceMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

Social media are known as facilitation methods when it comes to the way students learn in university. The purpose of this study is to identify the nature of social media usage and their influence on stress levels during exams. A survey directed towards students (n = 179) was shared on social media, evaluating the use of social media during exams. About 84.8% of the participants used social media for educational purposes. The social media employed the most for educational purposes were YouTube (71.5%) and Facebook (68.2%). Generally, the use of these platforms decreased student stress, particularly Skype, which was mostly used for personal communication. This study validates that social media has a great educational potential and is used by the majority of students. Students have similar yet different needs when using technology during exams and their variability should be considered when creating educational initiatives.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.049
GPT teacher head0.402
Teacher spread0.352 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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