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Record W3034872091 · doi:10.1080/02697459.2020.1778859

Game On: Exploring the Effectiveness of Game-based Learning

2020· article· en· W3034872091 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.

Bibliographic record

VenuePlanning Practice and Research · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of WaterlooQueen's University
Fundersnot available
KeywordsGame based learningTeamworkPerceptionGame designPsychologyStyle (visual arts)Mathematics educationExperiential learningGame mechanicsComputer scienceMultimediaPolitical science

Abstract

fetched live from OpenAlex

Game-based learning has emerged as an innovative learning technique that can increase student motivation, emotional involvement and enjoyment. Our study examines the effectiveness of game-based learning in planning education. Specifically, we explore the impact of gamification on planning students’ perception of learning, engagement and teamwork. Two lectures in an undergraduate planning course were delivered using two different methods of teaching (one traditional lecture-style, one game-based). Feedback was gathered through an online questionnaire and semi-structured interviews. Results show that students favored and were more engaged in the game-based lecture. Finally, we contend that gamification is particularly well suited for planning education.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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)0.0000.000

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 head0.315
GPT teacher head0.496
Teacher spread0.181 · 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