Self-Assessment of Gram Staining Skill Using Video Recording in a Classroom Environment for Veterinary Nursing Students
Bibliographic record
Abstract
Practical skills are essential in the veterinary nursing curriculum. Given the increasing implementation of video recording in higher education, this study explored the feasibility and benefits of video recording as a classroom tool in professional education. Concerns regarding the inability to monitor individual students' performance during their laboratory course promoted the implementation of video recording-a blended learning method-in a veterinary nursing course. The approach was personalized for this study, particularly for the Gram staining skill. Students submitted video recordings demonstrating the progression of their skills development, and the instructor reviewed the recordings for assessment. The Participant Perception Indicator, a self-assessment, was used to determine students' experience, knowledge, and confidence gained after performing the skill. Video recording helped students to identify areas for self-improvement. It is also a helpful tool for instructors to ensure that students are meeting the learning standards. The results suggest that the use of video recording in learning Gram staining skills was effective. The evidence-based approach maximized students' learning and engagement, and it improved individualized assessment by the instructor and enabled the instructor to provide feedback on students' performance. During this period of increasing reliance on online teaching and learning, video recording in a classroom environment could be more widely used by instructors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".