Engagement as process in human‐computer interactions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".