Bibliographic record
Abstract
User engagement (UE) is a quality of user experience characterized by the depth of an actor's cognitive, temporal, and/or emotional investment in an interaction with a digital system. Currently more art than science, UE has gained theoretical and methodological traction over the past decade, yet there is still a need to establish empirical links between UE and desired outcomes (e.g., learning, behavior change), and to understand the myriad user, system, contextual, and so on, factors that predict successful digital engagement. This paper focuses on the relationship between UE and media format as a potential antecedent, and the outcome of learning, operationalized as short‐term knowledge retention. Participants interacted with two human‐interest stories in one of four media formats: video, audio, narrative text, or transcript‐style text; short‐term knowledge retention was measured using post‐task multiple choice and short‐answer questions. It was anticipated that format would have a strong effect on UE, and that more engaged users would recall more information about the stories. However, these hypotheses were not fully supported, and the nature of the relationship between UE and learning was more nuanced than expected. This research has implications for the design of information systems and, more fundamentally, the impetus to make digital environments engaging.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".