Outcomes Assessment of Case-Based Writing Exercises in a Veterinary Clinical Pathology Course
Bibliographic record
Abstract
Our second-year core clinical pathology course uses free-response case-based learning exercises in an otherwise traditional lecture or laboratory course format to augment the development of skills in application of knowledge and critical thinking and clinical reasoning. We previously reported increased learner confidence accompanied by perceived improvements in understanding and ability to apply information, along with enhanced feelings of preparedness for examinations that students attributed to the case-based exercises. The current study prospectively follows a cohort of students to determine the ability of traditional multiple-choice versus free-response case-based assessments to predict future academic performance and to determine if the perceived value of the case-based exercises persists through the curriculum. Our data show that after holding multiple-choice scores constant, better performance on case-based free-response exercises led to higher GPA and better class rank in the second and third years and better class rank in the fourth year. Students in clinical rotations reported that the case-based approach was superior to traditional lecture or multiple-choice exam format for learning clinical reasoning, retaining factual information, organizing information, communicating medical information clearly to colleagues in clinical situations, and preparing high quality medical records. In summary, this longitudinal study shows that case-based free-response writing assignments are efficacious above and beyond standard measures in determining students' GPAs and class rank and in students' acquisition of knowledge, skills, and clinical reasoning. Students value these assignments and overwhelmingly find them an efficient use of their time, and these opinions are maintained even two years following the course.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".