REVIEW 1 cited by
Modeling novel physics in virtual reality labs: An affective analysis of student learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We report on a study of the effects of laboratory activities that model fictitious laws of physics in a virtual reality environment on (1) students' epistemology about the role of experimental physics in class and in the world; (2) students' self-efficacy; and (3) the quality of student engagement with the lab activities. We create opportunities for students to practice physics as a means of creating and validating new knowledge by simulating real and fictitious physics in virtual reality (VR). This approach seeks to steer students away from a confirmation mindset in labs by eliminating any form of prior or outside models to confirm. We refer to the activities using this approach as Novel Observations in Mixed Reality (NOMR) labs. We examined NOMR's effects in 100-level and 200-level undergraduate courses. Using pre-post measurements we find that after NOMR labs, students in both populations were more expertlike in their epistemology about experimental physics and held stronger self-efficacy about their abilities to do the kinds of things experimental physicists do. Through the lens of the psychological theory of flow, we found that students engage as productively with NOMR labs as with traditional hands-on labs. This engagement persisted after the novelty of VR in the classroom wore off, suggesting that these effects are due to the pedagogical design rather than the medium of the intervention. We conclude that these NOMR labs offer an approach to physics laboratory instruction that centers the development of students' understanding of and comfort with the authentic practice of science.
Forward citations
Cited by 1 Pith paper
-
Injecting Knowledge Graphs into Large Language Models
A frozen LLM answers graph reasoning questions when a learned knowledge-graph embedding vector is prepended to the query, outperforming prompting baselines in the reported experiments.
Discussion (0). Continue with ORCID to comment.