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Feasibility of RPG for Learning about Empathy, Creativity, and Self-efficacy

2024· article· en· W4392751880 on OpenAlexaff
Yiyang Shi

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmpathyCreativityPsychologySocial psychologyApplied psychology

Abstract

fetched live from OpenAlex

The field of psychology has a growing interest in role-playing games (RPGs) and is committed to exploring the processes of learning and development involved in RPG gaming. However, there is currently limited research on RPGs in the discipline of psychological science, as RPG was once considered a form of leisure that did not consider the potential learning and development involved. Therefore, the topic of the present review is the relationship between RPG gaming and the development of personal competencies involving empathy, creativity, and self-efficacy. The paper reviews various past related studies, all of which used a self-report format in a laboratory setting to collect data from RPG and non-RPG players on scales related to empathy, creativity, and self-efficacy to measure their levels of these competencies. The results show that RPG players demonstrate higher levels of empathy and creativity than non-RPG players, and that there is no correlation between RPG gaming experience and self-efficacy. Creativity and self-efficacy are positively correlated for both RPG and non-RPG players. Behaviorism, constructivism, and sociocultural learning explain the mechanisms underlying the improved competencies. Future research could establish more accurate procedures for measuring the abilities discussed in this review and test whether current results can be replicated. Also, future research could explore what in-game mechanisms or settings are more conducive to stimulating the acquisition and cultivation of various competencies in RPG players.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.042
GPT teacher head0.402
Teacher spread0.360 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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