Technology and teaching: Avoiding the pitfalls, increasing student engagement, and improving outcomes
Bibliographic record
Abstract
In an ever-changing educational landscape challenged by rapidly evolving technological advances, nursing educators are challenged to incorporate their best teaching approaches in the classroom and beyond to ensure student engagement and best learning outcomes. Innovation is then balanced with student needs and learning styles. As we respond to the demands of an ever changing health care environment and a new generation of nursing students with a variety of learning styles, we focused our efforts to help these students incorporate challenging material and use their critical thinking skills. We also focused on developing their roles as nurse practitioners who utilize the latest evidence based practice. At the same time, we are trying daily to avoid the educational pitfalls of the past, and to transform curriculum to meet the needs of the students and the pediatric population they will serve. Adapting new technologies should be carefully weighed against the traditional methods of lecturing. Increasingly, hybrid courses, a combination of teaching in the digital environment (online) and face-to-face interaction between students and faculty, are proving to be very effective, and the student feedback regarding this teaching method is overwhelmingly positive. In this article, we share some of our best practices to teaching in this hybrid, digital environment.
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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 | 0.034 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".