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Record W4242115030 · doi:10.4018/9781599048086.ch072

Video-Game Creation as a Learning Experience for Teachers and Students

2011· book-chapter· en· W4242115030 on OpenAlexaff
Leanna Madill, Kathy Sanford

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsGame based learningMathematics educationVideo gamePsychologyMultimediaComputer science

Abstract

fetched live from OpenAlex

This chapter explores changing conceptions of learning brought about by technological changes and opportunities and examines more closely the understanding of video game creation as a learning experience. Based on the first year of a three-year ethnographic research study of the educative value and potential of video games within a school setting, this chapter examines the powerful learning and teaching practises in classes of information technology and programming in which video game creation has been used as entry points into learning programming skills. Observations, interviews, and video recordings coupled with students’ articulation of their process were used to examine the depth of students’ learning and revealed the development of their multi-literacy skills, social skills, and their learning process awareness. Suggestions within this chapter include how a social constructivist classroom involving technology and popular culture can be developed and valued. Request access from your librarian to read this chapter's full text.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.040
GPT teacher head0.361
Teacher spread0.321 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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