ATLes: The Strategic Application of Web-Based Technology to Address Learning Objectives and Enhance Classroom Discussion in a Veterinary Pathology Course
Bibliographic record
Abstract
A case-based program called ATLes (Adaptive Teaching and Learning Environments) was designed for use in a systemic pathology course and implemented over a four-year period. Second-year veterinary students working in small collaborative learning groups used the program prior to their weekly pathology laboratory. The goals of ATLes were to better address specific learning objectives in the course (notably the appreciation of pathophysiology), to solve previously identified problems associated with information overload and information sorting that commonly occur as part of discovery-based processes, and to enhance classroom discussion. The program was also designed to model and allow students to practice the problem-oriented approach to clinical cases, thereby enabling them to study pathology in a relevant clinical context. Features included opportunities for students to obtain additional information on the case by requesting specific laboratory tests and/or diagnostic procedures. However, students were also required to justify their diagnostic plans and to provide mechanistic analyses. The use of ATLes met most of these objectives. Student acceptance was high, and students favorably reviewed the online ''Content Links'' that made useful information more readily accessible and level appropriate. Students came to the lab better prepared to engage in an in-depth and high-quality discussion and were better able to connect clinical problems to underlying changes in tissue (lesions). However, many students indicated that the required time on task prior to lab might have been excessive relative to what they thought they learned. The classroom discussion, although improved, was not elevated to the expected level-most likely reflecting other missing elements of the learning environment, including the existing student culture and the students' current discussion skills. This article briefly discusses the lessons learned from ATLes and how similar case-based exercises might be combined with other approaches to enhance and enliven classroom discussions in the veterinary curriculum.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".