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Record W3000822704 · doi:10.34105/j.kmel.2019.11.025

Self-determination, loneliness, fear of missing out, and academic performance

2019· article· en· W3000822704 on OpenAlexaff
David John Lemay, Tenzin Doleck, Paul Bazelais

Bibliographic record

VenueKnowledge Management & E-Learning An International Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcGill University
Fundersnot available
KeywordsLonelinessPsychologyFeelingAutonomyAnxietyCompetence (human resources)Social psychologySelf-determination theoryDevelopmental psychology

Abstract

fetched live from OpenAlex

Instances of anxiety, depression, and loneliness are attaining epidemic-levels among college-age students. Self-determination theory suggests that such feelings are attributable to antagonistic situations hindering the satisfaction of an individual’s basic needs for autonomy, competence, and relatedness. Fear of Missing Out (FOMO) is a phenomenon that arose in the context of social media use and refers to the need to stay continually connected. Studies have shown that problematic social media and mobile technology use are related to feelings of anxiety, depression, and loneliness, and FOMO. Few studies have examined the relationships between these factors and academic performance. This study examines how Loneliness, FOMO, and the basic needs Autonomy, Competence, and Relatedness, are related to Academic Performance. We find a positive influence of FOMO and a negative influence of Autonomy on Academic Performance. We discuss these and other findings.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.345
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations41
Published2019
Admission routes1
Has abstractyes

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Same venueKnowledge Management & E-Learning An International JournalSame topicImpact of Technology on AdolescentsFrench-language works237,207