Implementing the process oriented guided-inquiry learning (POGIL) pedagogy of group scenario exercises in fundamentals and Medical Surgical II nursing courses
Bibliographic record
Abstract
Background and objective: Research with Process Oriented Guided Inquiry Learning (POGIL), an interactive learning pedagogy, has shown improvement in grades and student satisfaction in science and nursing courses. POGIL is an active teaching strategy which utilizes small groups of students to analyze case studies. The student teams participate in groups of four to problem solve topics based on the material taught. POGIL can be additional to lecture and didactic teaching methods to help with the synthesis and analysis of content taught. The object of this study was to compare final course and national standardized exam grades between POGIL and comparison groups in both Fundamentals and Medical-Surgical II nursing courses.Methods: A quantitative, comparative design was used.Results: The Fundamentals POGIL group had significantly higher scores on a standardized national exam (p = .001) than a comparison group; no significant difference in final course grades was found. The Medical-Surgical Nursing II POGIL and comparison groups had no significant differences in standardized national exam or final course grades. Students in POGIL groups were given a satisfaction survey and indicated the experience was helpful to improving grades and understanding course content.Conclusions: In classes that used POGIL, there were higher scores on a standardized national exam scores but not final course grades for students in the Fundamentals course. Using POGIL in Medical Surgical Nursing II courses revealed no difference in final course grades or on national standardized exam scores. The use of POGIL for beginning nursing students may be more helpful as these students are in the process of determining which learning strategies are most helpful as they progress through the nursing curriculum. Introducing a new pedagogy to students in their last semester of the nursing program was not as helpful possibly because students have established successful strategies for learning prior to this last semester. Future research to further explore the impact of POGIL on grades and standardized tests scores in other nursing curriculum courses such as mental health or care of the emerging family is recommended. Exploring POGIL and the impact on the development of clinical thinking and clinical practice is another line of inquiry that could be explored.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".