Bibliographic record
Abstract
Engagement is a quality of user experience that facilitates more enriching interactions with computer applications. It is defined by a core set of attributes: aesthetic appeal, novelty, involvement, focused attention, perceived usability, and endurability. The ability to engage users influences the products they purchase (e.g. cell phones), the websites they use, and the decisions they make regarding what they will use in future and what they will recommend to others. Engagement is clearly an important component of the user experience, but like other components, it is somewhat intangible, and therefore difficult to measure and evaluate. This workshop paper outlines previous research that has focused on the evaluation of engagement as an outcome of experience. We propose that focusing on measuring the process of engagement is a crucial direction for future research. In order to assess whether or not users are engaged while using an application and what aspects of the system engage them, we must employ mixed methodologies to capture the cognitive, affective, and behavioural components of the experience. But which methods are most appropriate, and how can they be used in concert? Addressing these questions will allow us to understand the nature of engagement and inform design.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".