User Engagement Using an Etextbook
Bibliographic record
Abstract
Engagement is an integral pedagogical component underpinning effective educational activities and is of importance for educators using online platforms. Carefully designed, technology-enabled learning resources can increase student engagement. We developed an open educational resource etextbook on vital sign measurement using an interactive and multimodal platform to facilitate student learning. The etextbook design was informed by experiential teaching-learning theory. Students progressed through the etextbook at their own pace, following pedagogy informed by the iterative process of read, observe, practice, and test, commonly used in nursing education. The etextbook was introduced as a required reading in a first-year health assessment course at one university and two colleges. In this project, we explored the level of engagement experienced by users of the etextbook. We conducted a descriptive study using the User Engagement Scale to measure students' degree of engagement using the etextbook. Results from participants (N = 455) who used the etextbook in the study indicated a high level of engagement. The responses to an open-ended item on the survey provided context to the results and shed light on effective design practices. Several recommendations for best practices in developing etextbooks are identified for educators to consider.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".