A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form
Bibliographic record
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.
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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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".