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Record W4366525825 · doi:10.33063/ijrp.vi12.291

A Scholarly Character Sheet to Frame Learning Activities and Improve Engagement

2022· article· en· W4366525825 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Role-Playing · 2022
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversité de Montréal
FundersYork University
KeywordsMindsetCurriculumSession (web analytics)RubricCharacter (mathematics)PsychologyComprehensionSyllabusMathematics educationComputer scienceMultimediaPedagogyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

Character sheets are an essential element of game design for tabletop role-playing games. They can be adapted to frame a series of learning activities and to improve participants' engagement. In this study, they are used at the beginning of a five-session graduate seminar on library instruction to assess the participants' knowledge and present the curriculum. They are also used as wrap-up at the end of the final session to measure the participants' progress. Besides providing a better assessment of the group by the instructor, the character sheet activity improves comprehension of the content and offers an engaging opportunity to start the seminar and create a personal connection with the students. Through objectivation, a theoretical framework, I argue that character sheets can support metacognitive abilities like self-authorship, self-determination, and a growth mindset. By framing and matching the content with the participants' personal journey, character sheets provide gamified syllabi to improve motivation, engagement, and learning outcomes in a series of workshop activities.

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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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