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Gleaning Strategies for Knowledge Sharing and Collective Assessment in the Art Classroom from the Videogame, “Little Big Planet’s Creator Spotlights”

2014· book-chapter· en· W2484187178 on OpenAlex
Renee Jackson, William A. Robinson, Bart Simon

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.

Bibliographic record

VenueAdvances in social networking and online communities book series · 2014
Typebook-chapter
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsConcordia University
Fundersnot available
KeywordsConstructivist teaching methodsPremiseResource (disambiguation)PsychologySociologyPedagogyComputer scienceTeaching methodEpistemology

Abstract

fetched live from OpenAlex

This chapter examines the notion of videogames as a resource for teaching practice. Games are often used as teaching tools, but not often used as resources for informing pedagogical practice. Media Molecule’s game, Little Big Planet (LBP) for the Playstation 3, is a constructivist game with a niche online community of practice known as LBP Central. The game, along with the community, exemplifies multiple learning strategies in a constructivist environment, lending itself as a potentially powerful resource for studying constructivist teaching/learning strategies. In this chapter, the authors look closely at a community assessment and knowledge sharing strategy known as the “creator spotlight” and, based on the premise that art classrooms tend to be more constructivist by nature than other subject areas and because LBP has strong links to visual art, they suggest ways in which this process could be explored and applied with secondary visual arts students within a constructivist learning environment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.998

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.347
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