Use of Interactive Online Histopathology Modules at Different Stages of a Veterinary Program
Bibliographic record
Abstract
A mixed-method educational research study was conducted to evaluate the use of a series of online histopathology modules by students at two levels in a professional veterinary medical program. The materials were hypothesized to support student learning of histopathology concepts by facilitating "dual coding" of the content, since they provide both visual and verbal information. Second- and fourth-year professional veterinary students were surveyed about their perceptions of the effectiveness, efficiency, and appeal of the online modules. A historical comparison of final grades in the second-year Pathology course was conducted to evaluate the impact of the materials on academic outcomes. Think-aloud interviews were conducted with the fourth-year students in which the researcher observed the students interacting with the materials and asked them to "tell me what you're thinking as you use the modules." Survey results show that the only area in which the two student groups quantitatively rated the materials differently was their perception of how the materials complemented their learning. In contrast, the qualitative data indicate that advanced students used and perceived the materials differently, perhaps because of their greater metacognitive skills and pre-existing knowledge. We concluded that these findings can be attributed to the manner in which students approached the materials: the fourth-year students felt the materials were less relevant because they did not directly correspond to cases they were seeing, while the second-year students appreciated their relevance to topics in the Pathology course. There was no significant difference in final exam performance for the group of students that used the supplemental online modules and the prior four years' classes, who did not have access to the materials. Overall, this study demonstrated that it is possible to create a set of materials to be used by multiple audiences, provided the needs of each group are taken into consideration during the design process.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".