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Record W2624967628

The effectiveness of pairing analog and digital games to teach computer science principles to female youth

2017· article· en· W2624967628 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

VenueJournal of computing sciences in colleges · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer gameEconomic shortageComputer scienceMathematics educationRepresentation (politics)Analogue electronicsMultimediaPsychologyEngineeringElectrical engineeringPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Computer science and its related fields are rapidly growing. However, there is a significant and rising shortage of women in this career domain. To combat this shortage, we explored the potential of coupling analog and digital games teaching computer science concepts to educate, interest, and engage female youth. For our purposes we created and used an analog and digital game both teaching the same computer science principle, image representation, in an after school program setting with middle school-aged females. Assessments were completed by the girls before and after the analog game version to compare what participants already knew to what they had learned through the analog game play. Vital game play statistics were also recorded from the digital game to assess if further engagement or applied learning took place. The observations, assessments, and statistics gathered were evaluated to determine whether or not analog and digital games teaching computer science principles combined together can provide an engaging and enriching educational experience.

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)0.006
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 score0.096
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0010.000
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 head0.048
GPT teacher head0.374
Teacher spread0.326 · 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