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Record W2160062293 · doi:10.1002/meet.14504201233

Engagement as process in human‐computer interactions

2005· article· en· W2160062293 on OpenAlexafffund
Heather L. O’Brien, Elaine G. Toms

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2005
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUsabilityHuman–computer interactionConstruct (python library)Process (computing)Computer scienceInterface (matter)User interfaceUser engagementWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Recently there has been an increased emphasis on holistic user experiences in human‐computer interactions. Interface design is moving beyond usability, aiming to be aesthetically pleasing, emotionally appealing, and engaging. The term engagement is frequently mentioned in the literature as a goal of interface design, yet the construct remains abstract and ill‐defined. The well‐established frameworks of Flow Theory, Play Theory, and Aesthetic Theory provide a foundation in which to ground engagement and to begin to explore the attributes that must be present in engaging design. We conceptualize engagement as a process rather than a single instance. Our proposed model views engaging interactions as being comprised of three distinct stages: the user must become engaged, sustain the engagement, and eventually disengage from the system. Establishing a solid framework for engagement will enable us to operationally define the term and to develop techniques and instruments for measuring it. Without a rich, theoretical understanding of what constitutes engaging interactions between users and computer interfaces, we cannot ensure that design practices are truly engaging; user's experience with computer‐mediated environments must involve the user cognitively, behaviorally, and affectively.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.001
Scholarly communication0.0000.005
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 head0.016
GPT teacher head0.318
Teacher spread0.302 · 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 designTheoretical or conceptual
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

Citations7
Published2005
Admission routes2
Has abstractyes

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