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Record W3131816710 · doi:10.29173/isotl520

Role-Playing Gamification Technologies with Adult Learners

2021· article· en· W3131816710 on OpenAlexaffvenueabout
Kirsten Fantazir, Murray Bartley

Bibliographic record

VenueImagining SoTL · 2021
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsLethbridge College
Fundersnot available
KeywordsScholarship of Teaching and LearningContext (archaeology)PsychologyTeamworkLikert scaleScholarshipInstitutionPedagogyStyle (visual arts)Mathematics educationTeaching methodSociologyTeaching and learning centerManagementPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The purpose of this quantitative scholarship of teaching and learning (SoTL) research study was to examine the impact Classcraft had on adult criminal justice students in a face-to-face context in a western-Canadian institution. Specifically, the role-playing digital game was integrated into a first-year applied English and investigative writing course; learners earned points, received “real world” prizes, and completed random, content-related challenges with their teams. Using a survey with Likert-style and open-ended questions, it was determined that most elements of Classcraft motivated and engaged participants. The most impactful finding was that Classcraft promoted teamwork and problem-solving abilities. While little research has been conducted in adult post-secondary settings related to the implementation of Classcraft, it is evident more research is required in other post-secondary learning contexts.

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)0.006
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.305
Teacher spread0.289 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueImagining SoTLSame topicEducational Games and GamificationFrench-language works237,207