Peer led post exam review- a paradigm shift in undergraduate nursing education
Bibliographic record
Abstract
Traditional approaches in nursing education for students to review exam answers involve a one-on-one interaction between the student and the educator. Students review exam answers and discuss questions with the educator. This may be daunting for many students who are uncomfortable with the traditional instructor-centered paradigm of a one-on-one interaction with educators and therefore may be reluctant to participate, seek feedback and ask relevant questions pertaining to their knowledge. However, this may not provide a supportive and effective learning environment for the student to problem solve and translate knowledge. Increasing student enrollment and faculty shortages are factors that must be considered when developing meaningful strategies for reviewing exam answers. A peer-led post exam review session, using a constructivist learning approach was implemented with second-year course in an undergraduate nursing program within Canada on two separate occasions. Educators facilitated student-centered 20-minute sessions with 12 to 15 students. Each student was provided with their exam answers as well as a copy of the exam answer key. Students were encouraged to be self-directed and actively collaborate with their peers to reflect on their exam answers; identifying areas of strength and growth. Students were allowed to access additional learning resources relevant to the course content. The objective of the session was to create a social learning environment for students that supported peer learning; building their knowledge and capacity to be self-sustainable in their learning. Overall both students and educators found this experiential learning meaningful and supportive.
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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 | 0.094 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".