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Record W3006770466 · doi:10.5539/ies.v13n4p17

Perspectives of Students in Thailand on the Use of Humor in University Classroom

2020· article· en· W3006770466 on OpenAlexvenueno aff
Henzel Tagalog Embalzado, Pair Sajampun

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyDecorumPedagogyAtmosphere (unit)PerceptionStress managementMathematics education

Abstract

fetched live from OpenAlex

Humor is a serious business. This study explores the perception of students in Thailand on the use of humor in business classrooms indicating that humorous classroom brings about physiological and emotional benefits to students as it relieves tension and stress, makes the classroom atmosphere more comfortable and less intimidating, and facilitates better teacher-student relationship. Around 358 students from a business school in Thailand were surveyed and the results found that most of the students prefer to have certain degree of humor in classroom as opposed to the general contention that Thai classrooms should command decorum and order. This instigates a welcome shift as Thailand prepares for the full implementation of Thailand 4.0 and the education sector is looking for ways into which classrooms could facilitate a more participatory, conducive and creative learning settings. Further discussion of the results and recommendations for future study are presented in the paper.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.003
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 head0.163
GPT teacher head0.433
Teacher spread0.270 · 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 designQualitative
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

Citations29
Published2020
Admission routes1
Has abstractyes

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