Blended learning versus face-to-face learning in an undergraduate nursing health assessment course: A quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: Blended learning, which integrates face-to-face and online instruction, is increasingly being adopted. A gap remains in the literature related to blended learning, self-efficacy, knowledge and perceptions in undergraduate nursing. OBJECTIVES: To investigate outcomes of self-efficacy, knowledge and perceptions related to the implementation of a newly blended course. DESIGN: This was a quasi-experimental pre-post test design. SETTING: This study was conducted at an undergraduate university in Alberta, Canada. PARTICIPANTS: A total of 217 second-year undergraduate nursing students participated and 187 participants completed all study components. METHODS: A convenience sampling method was used. Data were collected at the start and end of the semesters. Data were analyzed using descriptive and inferential statistics using R(3.4.3) and R-Studio(1.1.423). RESULTS: There were no significant differences in self-efficacy scores between groups or in the pre-post surveys (p > 0.100) over time. There was no significant difference in knowledge between the blended online and face-to-face groups (p > 0.100). For students in the blended course, perceptions of the online learning environment were positive. CONCLUSION: Blended learning has the potential to foster innovative and flexible learning opportunities. This study supports continued use and evaluation of blended learning as a pedagogical approach.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".