USING AUGMENTED REALITY AND HOLOGRAPHIC TECHNOLOGY IN AN INTRODUCTORY COURSE ON DATA STRUCTURES AND ALGORITHMS
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
Abstract – Augmented reality (AR) technology facilitates augmentation of current views with digital artifacts, such as information, three-dimensional objects, audio, and video. Mixed reality (MR) represents an enhanced version of AR, where advanced spatial mapping is used to anchor digital artifacts in physical space. Using MR technology, digital artifacts can be more closely integrated into the natural environment, thereby transcending physical limitations and creating enhanced blended learning environments. In this paper, we propose an approach for integration of MR technology into engineering education. Specifically, we propose to integrate Microsoft HoloLens into a first-year course on data structures and algorithms to improve student engagement and learning outcomes. In the pilot study, students were assigned to implement A* algorithm and then given a chance to visualize their implementation using Microsoft HoloLens. The feedback provided by students indicated increased engagement and interest in graph-based path-finding algorithms as well as MR technology.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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