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Record W2910841321 · doi:10.24908/pceea.v0i0.13054

Leveraging Escape Room Popularity to Provide First-Year Students with an Introduction to Engineering Information

2018· article· en· W2910841321 on OpenAlexafffundvenueabout
Benjamin Walsh, Michelle Spence

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsPopularityScience and engineeringCurriculumEngineering educationOrientation (vector space)Computer scienceComputer Science and EngineeringMathematics educationMultimediaEngineering managementEngineering ethicsEngineeringPsychologyPedagogySoftware engineering

Abstract

fetched live from OpenAlex

Incoming first-year engineering students at the University of Toronto often have difficulty navigating the library and its resources. Orientation activities at the Engineering & Computer Science Library are designed to introduce students to the library in an informal and entertaining way. In 2017, as a result of dropping interest in previous years' orientation activities, librarians at the Engineering & Computer Science Library collaborated with instructors and staff in the Faculty of Applied Science and Engineering to develop an orientation activity grounded in curriculum and based on the popular escape room game. Core library services and engineering resources were used to build a challenging program that introduced students to basic, but essential, research skills. Voluntary student participation in the game exceeded previous years' participation and all expectations of the game designers.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.243
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations24
Published2018
Admission routes4
Has abstractyes

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