Comparison of Instructor-Provided Versus Student-Generated Graphic Organizers in an Elective Veterinary Cardiology Course
Bibliographic record
Abstract
Graphic organizers (GOs) are visual and spatial displays that facilitate learning by making conceptual relationships between content more apparent. It remains unknown whether GOs are more effective when completed by the teacher (instructor-provided [IP]) versus the learner (student-generated [SG]). A mixed-methods prospective randomized crossover trial was undertaken with veterinary students ( n = 60) in an elective cardiology course. All students received identical content presented via weekly in-class lectures and were subsequently given study aids in either IP or SG format. One week later, students completed quizzes of content knowledge for each lesson and indicated amount of time spent studying. Crossover occurred such that groups of students alternated between receiving IP and SG. Quantitative and qualitative data were collected in the form of in-depth pre- and post-course surveys. Overall, there was no significant difference in quiz scores based on study aid type ( p = .06). Students spent an average of 25% less time studying per lesson when using IP GOs compared with SG GOs ( p < .001). Time spent studying for each quiz, as well as time period between date of studying and date of quiz, decreased significantly throughout the semester. Overall, students strongly preferred IP to SG format ( p < .001); reasons listed included confidence in accuracy and completeness of information, as well as increased study efficiency. In an elective veterinary cardiology course, use of IP compared to SG format study aids resulted in higher study efficiency and student satisfaction with equivalent short-term learning outcomes.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".