The Use of Scaffolding and Interactive Learning Strategies in Online Courses for Working Nurses: Implications for Adult and Online Education
Bibliographic record
Abstract
This paper reviews the foundational litera- ture of contemporary e-learning, with a focus on scaffolding, instructional design, and engagement. These concepts are then considered in two limited case studies, each involving e-learning and adult learners—in particular, nurse-learners. The first case study describes the use of a scaffolding model called Introduction, Connect, Apply, Reflect, and Extend (ICARE) in e-learning for nursing education. The second is a reflection on the use of engagement strategies for the purposes of discourse and learning in a different online nursing context.Because nursing educators were among the early adopters of e-learning, they are important mentors to others who are adopting e-learning strategies at this time. Additionally, the paper is a crossroads publication: it reminds the reader of the imperative to review theory and emerging evidence related to e-learning and to bring key findings to the actual practice of e-learning in order to benefit the adult student. This commitment to theory and practice will enable the evolution of e-learning for all learners, including returning adult learners and working professionals.Keywords: scaffolding, instructional design, interaction, best practices, engage- ment, adult education, working profes- sionals, e-learning.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".