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Record W1600801848 · doi:10.19173/irrodl.v15i6.1928

The influence of personality and chronotype on distance learning willingness and anxiety among vocational high school students in Turkey

2014· article· en· W1600801848 on OpenAlexvenueno aff
Christoph Randler, Mehmet Barış Horzum, Christian Vollmer

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsChronotypePsychologyAgreeablenessExtraversion and introversionAnxietyNeuroticismPersonalityConscientiousnessBig Five personality traitsEveningOpenness to experienceDistance educationSocial psychologyMathematics education

Abstract

fetched live from OpenAlex

<p>There are many studies related to distance learning. Willingness and anxiety are important variables for distance learning. Recent research has shown that anxiety and willingness towards distance learning are moderated by personality. This study sought to investigate whether distance learning willingness and distance learning anxiety are associated with age, gender, occupation, chronotype and personality in a Turkish vocational high school students sample. Two measures of individual differences were implemented: chronotype (morningness/eveningness preference) and BIG-5 dimensions (agreeableness, conscientiousness, extraversion, neuroticism, and openness). Seven hundred and sixty-nine vocational high school students from Turkey filled out a self-administered questionnaire. Evening types, older, and female students had higher distance learning willingness scores than morning types, younger, and male students. No significant difference was found between chronotype groups with respect to distance learning anxiety. Furthermore, extraverted students reported a lower distance learning anxiety. Openness to experience was associated with high distance learning willingness. We conclude that evening types may benefit from distance learning more than other types.</p>

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)0.010
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.426
Teacher spread0.396 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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