Demonstration of Immersive Technologies for Geospatial Learning
Bibliographic record
Abstract
Abstract. Immersive technologies are becoming a powerful tool for educators across multiple disciplines including geospatial science. They offer new ways to engage and educate geospatial students, removing barriers that may exist in traditional teaching methods. Especially because the limitations of 2D screens are often exceeded by the complexity of modern data sets. Examples of challenges that educators often encounter are explaining theoretical concepts in the classroom, providing alternate scenarios, preparing students for physical labs, and limited / restricted access to physical sites. Immersive technologies can be a great resource to support on-site lectures and enhance remote learning, a necessity in today's educational panorama. Immersive technologies typically include virtual, augmented, and mixed reality. Each method offers different pedagogical advantages and poses different challenges. Before any implementation, these advantages and challenges must be examined and understood to maximize the benefit of immersive methods to the students and mitigate potential drawbacks that could hinder learning outcomes. This paper provides a review of the different immersive methods, presenting examples of their application in geospatial education with lessons learned and recommendations for future work. The case examples include a variety of different instruments and tasks such as the simulation of GNSS, differential leveling, total station operations, and airborne LiDAR data collection.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".