“Interacting with the material differently”: A mixed methods study exploring nursing student engagement and satisfaction with a flipped classroom approach
Bibliographic record
Abstract
Engagement is critical for students to meet learning outcomes, yet modern classrooms face challenges with engagement when using a traditional didactic approach. Alternative options include using a flipped classroom where students review content prior to class, and teachers use in-class activities to increase application, analysis, and synthesis of information. This study aimed to (1) explore for changes in student engagement, and satisfaction, and achievement among students in a flipped classroom learning environment between mid and end of semester; and (2) to further explain quantitative results through qualitative student feedback. A convergent mixed methods design was used to recruit sixty-four nursing students in a Midwest U.S. College of Nursing, who completed a survey at mid and end of semester in the Fall of 2015. Students were also given the opportunity to participate in a focus group (n = 36) after the final survey. Most students were satisfied with the flipped classroom approach including before class materials, knowledge check quizzes using real-time response software, and the group test activity. However, participants were unsatisfied with reflective journal writing and group-based in class learning activities. Qualitative focus group data provided an explanation of quantitative results by identifying aspects of the flipped classroom viewed as helpful or not. Themes included small groups, enthusiastic teacher, repetition and application, doubling up on work, engaging, and dislike for course topic. More research is needed on pedogeological approaches to engage modern students, so they are prepared for nursing practice after graduation. The flipped classroom approach, which leverages multiple learning strategies, may be valuable to engage students and promote application of content.
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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 | 0.025 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".