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Record W3198234616 · doi:10.5430/ijhe.v11n1p175

Can Laughter Lead to Learning?: Humor as a Pedagogical Tool

2021· article· en· W3198234616 on OpenAlexvenueno aff
Faieza Chowdhury

Bibliographic record

VenueInternational Journal of Higher Education · 2021
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLaughterPsychologyPerceptionBeautyQualitative researchQualitative propertyInstitutionQualitative analysisPedagogyMathematics educationSocial psychologyAestheticsSociologyArtComputer scienceSocial science

Abstract

fetched live from OpenAlex

The word humor can be defined as something which is perceived to be funny, comical, or amusing. However, in the case of humor perception plays a key role. This is mainly because what is regarded as humorous by one person may not be funny to another person. Hence, humor like beauty lies in the eyes (ears) of the beholder. The potential of humor as a pedagogical tool is not a new concept in education. Teachers around the world have a mixed attitude towards the use of humor in the classroom as a pedagogical tool. Thus, in this study we sought to investigate the perceptions of students towards the use of humor as a teaching tool at higher education institutions in Bangladesh. For this purpose, we have performed both quantitative and qualitative analysis. In the quantitative part of the study, we collected data from 300 students and performed a binary logistic regression. On the other hand, for the qualitative analysis we have undertaken interviews of 30 selected students at a higher academic institution in Bangladesh. Overall, the results of this study indicate that most of the students considered humor as a positive and beneficial teaching tool in the classroom.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.058
GPT teacher head0.456
Teacher spread0.398 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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