A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Not applicableConsensus signal: Not applicable
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.301
- Threshold uncertainty score
- 0.608
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.248 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
User engagement (UE) and its measurement have been of increasing interest in human-computer interaction (HCI). The User Engagement Scale (UES) is one tool developed to measure UE, and has been used in a variety of digital domains. The original UES consisted of 31-items and purported to measure six dimensions of engagement: aesthetic appeal, focused attention, novelty, perceived usability, felt involvement, and endurability. A recent synthesis of the literature questioned the original six-factors. Further, the ways in which the UES has been implemented in studies suggests there may be a need for a briefer version of the questionnaire and more effective documentation to guide its use and analysis. This research investigated and verified a four-factor structure of the UES and proposed a Short Form (SF). We employed contemporary statistical tools that were unavailable during the UES ’development to re-analyze the original data, consisting of 427 and 779 valid responses across two studies, and examined new data (N = 344) gathered as part of a three-year digital library project. In this paper we detail our analyses, present a revised long and short form (SF) version of the UES, and offer guidance for researchers interested in adopting the UES and UES-SF in their own studies.
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.
The record
- Venue
- International Journal of Human-Computer Studies
- Topic
- Impact of Technology on Adolescents
- Field
- Social Sciences
- Canadian institutions
- University of British Columbia
- Funders
- not available
- Keywords
- UsabilityVariety (cybernetics)NoveltyScale (ratio)Computer scienceMeasure (data warehouse)DocumentationHuman–computer interactionUser engagementWorld Wide WebPsychologySocial psychologyDatabase
- Has abstract in OpenAlex
- yes