Evaluating the effectiveness of virtual laboratory simulations for graduate‐level training in genetic methodologies
Bibliographic record
Abstract
Virtual Labs (vLabs) have been gaining popularity in high school and undergraduate education, but there are few studies looking at their use in graduate-level courses. In this study, we investigated the use of six Labster vLabs assigned as homework in a graduate-level in-person Genomic Methodologies course at the University of Toronto. This course teaches the theory and practice of molecular biology relevant to genetic testing, focusing on computational techniques used to analyze genetic data. The course does not contain a wet-lab component; therefore, we evaluated whether vLabs could complement the dry-lab course components to provide a realistic experience of laboratory techniques and improve content understanding. We evaluated the addition of vLabs with one cohort of 14 students using assessment-informed data, student perception questionnaires, and think-aloud interviews. We found that engaging with vLabs resulted in a knowledge gain for most (89%) graduate students. Students (85%) found vLabs to be useful to understand the theory behind advanced laboratory concepts; however, many students (54%) were critical of vLabs ability to provide a realistic laboratory experience. We also investigated whether the student experience differs when performing Labster vLabs on a laptop versus a virtual reality headset and found that the headset provided no additional benefits to students. We show that vLabs can be effectively used in graduate-level courses to provide students with background relevant to laboratory techniques; however, the level of material could be enhanced to provide a more detailed and advanced understanding of the concepts for students with prior knowledge of the topic.
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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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".