VR-Technology in Teaching: Opportunities and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Use of virtual reality (VR) to teach in upper-secondary schools has become more common during recent years. This article discusses the implementation and testing of VR to teach Swedish in upper-secondary school, a pilot study carried out during the 2020/2021. The purpose of this study is to investigate how VR can be used to teach Swedish, what possibilities and challenges arise from using VR as a learning resource. The method used was inspired by action-based research, where teachers and researchers together, in a symmetrical and complementary approach, explore and evaluate an action. Central theoretical perspectives were TPACK-competences and design principles for gamified learning. The results indicate that students’ motivation increases by possibilities to co-create, co-design and customize their own learning, where the students solve problems and consider and reflect on their own learning. Both students and teachers point out didactical potentials and explain that VR technology offers many opportunities, but cannot exist on its own. It must function in accordance with the curriculum and regulatory documents of the educational institution.
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.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 it