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Record W3093116198 · doi:10.21432/cjlt27883

An Analysis of Discipline and Personality in Blended Environments

2020· article· en· W3093116198 on OpenAlexvenueno aff
Chan Chang-Tik

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsConscientiousnessPersonalityBig Five personality traitsPsychologyTraitSignificant differenceBig Five personality traits and cultureSocial psychologyComputer scienceExtraversion and introversionMathematicsStatistics

Abstract

fetched live from OpenAlex

The purpose of the study is to investigate the interaction between discipline and personality in a blended classroom using the community of inquiry model. To this end, a factorial ANOVA is used to determine the main effects of the high and low of each personality trait as well as the four different clusters of discipline on the presences. The study used a non-experimental design to gather data. A total of 12 lecturers and 408 students from three institutions were involved. The results indicate that there is a significant difference in teaching presence between the hard-applied and hard-pure as well as the hard-applied and soft-pure disciplines only for the conscientiousness personality. Correspondingly, there is a significant difference in social presence between the hard-applied and soft-pure disciplines across all the five personality traits. However, there is no significant difference in cognitive presence for all the discipline clusters across all the personality traits.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.289
Teacher spread0.276 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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