Peer assisted learning model to support students’ success in a complex science course
Bibliographic record
Abstract
The application of peer learning models, including Peer Assisted Learning (PAL), has been primarily in the clinical and simulation settings with the focus on readiness for clinical and professional practice. We present a single center experience with the design, structure and implementation of an in-house PAL tutorial in a Pathophysiology; a complex mandatory 12 weeks science course taken by 2nd-year students. This experience represents a phase and progression towards a more coherent model. In phase I, a short survey was conducted to gauge the students’ interest and assess the feasibility of a tutorial model. In Phase II and III, PAL tutorials were introduced and implemented over the course of two semesters for two cohorts of students. Phase I provided sufficient evidence to proceed with tutorial development, and provided guidance for tutorial planning and implementation. Phase II and III showed tutorial participation gradually increased over time. We have integrated complexity science as a theoretical basis that guided the study and unified the findings throughout the study. The tutorials helped students to integrate concepts from related courses, encouraged them to find similarities, and enhanced overall understanding of course content, while providing a support system to reduce anxiety and stress. The use of online material and a concept mapping approach received the most significant positive feedback as learning tools. We believe the PAL approach is important to tutorial development and, when implemented within the theoretical model of complexity science, may carry potential in developing nursing or other curricula. Further research into the application of tutorial models more broadly in nursing education curriculum is necessary to determine more coherently the unique design characteristics required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".