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Record W1524529233

Using Role-Playing Games for Teaching History and Literacy

2011· article· en· W1524529233 on OpenAlexaff
Richard Levy

Bibliographic record

VenueE-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education · 2011
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWorkbookGame mechanicsSet (abstract data type)NarrativeMathematics educationLeverage (statistics)MindsetClass (philosophy)Video game designFantasyComputer scienceVideo gameMultimediaLiteracyGame DeveloperGame designPedagogyPsychologyArtificial intelligenceLinguistics
DOInot available

Abstract

fetched live from OpenAlex

Role-playing games set in immersive virtual environments can leverage student interest in narrative and fantasy to motivate learning in literacy and history. This paper describes an educational role-playing game designed to run on a typical PC. The game is set in a virtual medieval village, and incorporates a mystery quest that can only be solved by successfully unraveling a series of challenging written and verbal clues. Game-related workbook activities which are integral to game play are also described. The full paper presents the results from an inclass pilot test of the game with 5 grade students. Though it is difficult to evaluate the long-term benefits of game play in the classroom from a single class experience, these preliminary findings suggest that games can be used to motivate students, reinforce recall and provide an opportunity to apply language skills acquired in class.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.152
GPT teacher head0.356
Teacher spread0.204 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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