Use of Nutrition-Related Case-Based Learning Modules to Facilitate Learning in a 2nd Year Biochemistry Course at the University of Guelph
Bibliographic record
Abstract
Studies indicate that the majority of students in undergraduate biochemistry take a surface approach to learning, associated with rote memorization of material, rather than a deep approach, which implies higher cognitive processing. This behavior is associated with poorer outcomes, including impaired course performance and reduced knowledge retention. The use of case-based-learning (CBL), a sub-type of problem-based-learning (PBL), and the incorporation of nutrition content into biochemistry teaching may facilitate deep learning by increasing student engagement and interest. This long-term project, aims to modifying the curriculum in an undergraduate Biochemistry course to be focused around nutrition-related cases. The goal is to determine if nutrition-related CBL modules encourage deep learning (measured by R-SPQ-2F), improves student performance (measured by the accuracy of student performance across exam questions using Bloom’s taxonomy), long-term retention (measured by a retention test targeting key Biochemistry concepts) and the student perception of the course experience (measured by the Course Experience Questionnaire). This poster will describe the process of development of the modules, including: (1) types of CBL and how these can be incorporated into undergraduate education, (2) the specific CBL framework adopted for this project and justification for its selection, and (3) examples of developed case studies highlighting the elements designed to facilitate deep learning. While this project is focused specifically on undergraduate biochemistry education, our findings can facilitate adopting CBL with nutrition content to other courses. Consequently, the information presented herein will be of value to undergraduate science educators with an interest in active learning curriculum development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".