Bibliographic record
Abstract
The purpose of this dissertation is to investigate the effect of using Virtual Reality (VR) technology on the students' experience in science education, particularly for interaction, learning, and accessibility. Science, Technology, Engineering, and Math (STEM) education has special requirements such as lab-based activities and abstract concepts that complicate setting up the environment and learning process. These complications are increased due to the pandemic and other remote access requirements. VR has unique affordances that make it a promising solution but its use in this regard has not been properly investigated and there are many open research questions related to its effect on interaction, learning, and accessibility in STEM education. Focusing on these three aspects, we ran a series of quantitative and qualitative studies to find out if the use of VR in science labs leads to an increased level of learning, efficiency, and accuracy of the tasks (measured by pre-post knowledge tests and the in-app data collection system). The Immersive/head-mounted VR (IVR) was compared to Desktop VR (DVR) and 2D/text-based conditions. Results indicated a significant difference in some areas particularly related to post-knowledge score, spatial skills, and learnability between 2D and VR conditions. Task completion rate, efficiency, and accuracy also indicated a significant difference between IVR and DVR groups, showing IVR performing better. The qualitative evaluation included an analysis of students' experiences with the use of VR and their perspective on its application for education. The result indicated that in areas such as ease of use, learnability, engagement, and overall satisfaction they preferred VR treatment better compared to 2D/ Text as they found the use of VR to be beneficial to their learning. Our studies also showed the efficacy of software-based accessibility features that improved interaction and learning for wheelchair users. We concluded that the implementation of a virtual environment for STEM education requires careful considerations in the design and implementation to make it technically practical to run on mobile Head Mounted Displays (HMD), be relevant based on established learning theories, minimize the effect of cybersickness, and be accessible for a wider range of audience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".