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Record W4406815933 · doi:10.14236/ewic/bcshci2024.12

Comparing Fun and Performance: A User Study on Children’s Gaming Experiences with Mid-Air Hand Gestures

2024· article· en· W4406815933 on OpenAlex
Saba Fallah, I. Scott MacKenzie

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

VenueElectronic workshops in computing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork University
Fundersnot available
KeywordsGestureComputer scienceHuman–computer interactionMultimediaArtificial intelligence

Abstract

fetched live from OpenAlex

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d5393395e64">This study investigates the effects of two types of mid-air hand-gesture input methods on children’s performance, fun, and preference and compares them toatraditionalmouseinputmethod.Theevaluation was doneusingacard-matchinggameonalaptopwith18childrenbetweenfiveandseven.Thetrial completion time(s),numberofselectedcards,andchildren’sperceptionregardingeaseofuse,likability,and willingness toplaythegameusingeachinputmethodagainwererecordedandanalyzed.Theperformance between thetwotypesofmid-airhandgestureinputsshowednosignificantdifference,asevidencedby trial completiontimeandthenumberofselectedcards. Similarly, regarding fun, there was no significant differencebetweenthetwogesturemethods,asindicatedbychildren’sperceptionsregardingease of use, likability,andwillingnesstoplaythegameagainusingeachinputmethod.Contrarily,themouse,serving as thebaselineconditionratherthanaviablemethod,exhibitedbetterperformanceandmorefun. This highlights the importance of considering user familiarity and usability challenges associated with mid-air gesture-based input methods.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.280
Teacher spread0.267 · 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