An environmental scan of virtual and augmented reality services in academic libraries
Bibliographic record
Abstract
Purpose The growing popularity of virtual and augmented reality (VR and AR) technologies, and increased research into their educational uses, has seen them appearing in a significant number of academic libraries. Little is known, however, about how many libraries have actually adopted these technologies or how they have structured library services around them. The purpose of this paper is to answer these questions. Design/methodology/approach The authors surveyed the websites of the Association of Research Libraries (ARL) member libraries to gather information about the availability of VR and AR equipment as well as information about how access is provided. Recorded details about these services included information about staffing, dedicated space, software, what type of technology was offered and whether or not the technology was lent out or only made available for in-library use. Findings Results of the research project showed that a significant number of ARL-member libraries do offer access to VR technology. AR technology was much less widespread. The most common technologies offered were the Oculus Rift and HTC Vive. The technology was most typically offered for in-library use only. There were few details about staff or what software was offered to be used with the technology. Originality/value While there is growing research around how VR and AR is being used in education, little research has been undertaken into how libraries are adopting these technologies. This paper summarizes the research that has been done so far and also takes the next step of providing a larger picture of how widespread the adoption of VR and AR technologies has been within academic libraries, as well as how access to these technologies is being provided.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".