Game on: immersive virtual laboratory simulation improves student learning outcomes & motivation
Bibliographic record
Abstract
The use of gamified learning interventions is expanding in postsecondary education as a means to improve students' motivation and learning outcomes. Virtual laboratory simulations have been used in science education to supplement students' learning, as well as to increase engagement with course material. Due to COVID-19, many instructors sought to replace or supplement hands-on 'wet-lab' work in an online environment. In this paper, we explored how the use of head-mounted display technology in two laboratory simulations impacts learner motivation and learning outcomes. We used a mixed-methods approach to analyze the experience of 39 undergraduate participants, examining test scores pre- and postsimulation, qualitative feedback, and quantitative experience ratings. The head-mounted display technology was described as easy to use, with eye strain identified as a common occurrence. Participants had increased test scores following the laboratory simulations, with no significant difference between simulation groups. Very positive self-reported measures of motivation and learner engagement were documented. Ninety-one percent of participants agreed that virtual reality laboratory simulation would be a good supplement to regular teaching modalities. Overall, our results suggest that immersive virtual reality laboratory simulations experienced through head-mounted display technology can be used to enhance learning outcomes and increase learner motivation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.011 | 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".