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Record W2019383650 · doi:10.1145/2658537.2658691

Interaction design and cognitive gameplay

2014· article· en· W2019383650 on OpenAlexafffund
Kamran Sedig, Robert Haworth

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCognitionHuman–computer interactionComputer scienceAction (physics)Cognitive loadDesign elements and principlesInteraction designCognitive psychologyMultimediaPsychology

Abstract

fetched live from OpenAlex

Currently, there are no frameworks or methods for the systematic design of cognitive gameplay, the cognitive processes that emerge from the gameplay experience. In this paper, our aim is to contribute to the understanding of how to systematically design interaction for cognitive gameplay. The quality of the essential interactions between the player and the game--the sum of the operational forms of several structural elements of interaction--is the heart of cognitive gameplay. One such element is activation time, the timing of the action response of an interaction. We conducted a study to investigate the effect of different operational forms of activation time on cognitive gameplay. Two puzzle games were developed, each with one version for immediate activation time and another for on-demand activation time. The on-demand version of both games engaged participants in more effortful and reflective cognitive gameplay, while the immediate version was not conducive to such engagement.

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)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.010
Scholarly communication0.0070.004
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.043
GPT teacher head0.363
Teacher spread0.319 · 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 designNot applicable
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

Citations10
Published2014
Admission routes2
Has abstractyes

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