Teaching Nutrition to the Left and Right Brain: An Overview of Learning Styles
Bibliographic record
Abstract
Functioning effectively as a veterinarian requires proficiency in multiple learning styles. Whether the goal is to design a nutrition course, plan a veterinary curriculum, or help students develop interpersonal, communication, and leadership skills, students benefit when content, design, and delivery are balanced to meet their learning-style preferences. An overview of four different learning style models is presented: the Myers-Briggs Type Indicator (MBTI), Kolb's Learning Style Model, the Felder-Silverman Learning Style Model, and the Herrmann Brain Dominance Instrument (HBDI). A whole-brain approach (HBDI) was used in the development and implementation of the small-animal clinical nutrition course at the University of Minnesota College of Veterinary Medicine. One educational objective of this course is to help students develop mental dexterity, increasing their proficiency in both their preferred and their less preferred modes of learning. The instructional goals are to deliver the content of the small-animal clinical nutrition course through exercises that meet the needs of learners in each thinking quadrant (left and right, cerebral and limbic) at least part of the time. Examples of exercises are presented to portray a balanced or whole-brain approach to teaching clinical nutrition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".