Immersive virtual reality for supporting complex scientific knowledge: Augmenting our understanding with physiological monitoring
Bibliographic record
Abstract
Abstract Educators are recognizing the potential power of immersive virtual reality (IVR) to allow learners to experience previously intangible firsthand phenomena, such as atoms and molecules. In this study, an IVR simulation of a complex gene regulation system was co‐designed with an undergraduate microbiology course instructor. The course, with 234 students, was taught using active learning strategies, including peer instruction and exposure to a two‐dimensional computer simulation. Thirty‐four students from the course participated in an interactive IVR experience using head‐mounted displays. We assess students' conceptual understanding using tests, multimodal data collected during the IVR sessions (including video analysis in combination with physiological sensor data and eye‐tracking data) as well as semi‐structured interviews. We found that students who were seated while in IVR demonstrated significantly higher conceptual understanding of gene regulation at the end of the course and higher overall course outcomes, as compared to students who experienced the course as originally designed (control). However, students who experienced IVR in a standing position performed similarly to the control group. In addition, learning gain appears to be influenced by a combination of prior knowledge and how IVR is experienced (ie, sitting vs. standing). Learning implications for the connections between sensorimotor systems and cognition in IVR are discussed. Practitioner Notes What is already known about this topic Research on the educational applications of IVR for K‐12 and higher education emerged in the nineties, which can be summarized by several key reviews and meta‐reviews surveying the field but the answer to questions about the “added‐value” of IVR is often mixed (Dede, Jacobson, & Richards, 2017; Merchant, Goetz, Cifuentes, Keeney‐Kennicutt, & Davis, 2014)—we turn to the question of when IVR is effective for student learning. A common issue reported by researchers is that cognitive overload can hinder learning in IVR (Makransky & Lilleholt, 2018; Moreno & Mayer, 2004). Research considering the contribution of body positioning and sensorimotor perception on cognitive load is just emerging (Funk et al ., 2012; Nerhood & Thompson, 2016). What this paper adds Our finding that learning outcome is influenced by a combination of how IVR is experienced (ie, sitting vs. standing position) and students’ level of prior domain knowledge, builds on earlier findings that suggest IVR experiences to be taxing on cognitive resources and further suggests that body position and prior knowledge are related mitigating factors for learning outcomes in an IVR experience—thus a more nuanced relationship exists between cognitive resources, prior knowledge and learning outcomes in IVR. We offer a new approach for using multimodal physiological measures to gain insight into the conditions under which IVR impacts the learning experience. Implications for practice and/or policy Implications of our preliminary study suggest for a seated IVR learning experience for supporting students with lower levels of prior knowledge of complex concepts, while students with higher levels of prior knowledge could choose between either sitting or standing, full‐body experience.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".