Discovery Quest 2.0 Reinvented: A Canadian Approach to Enhancing Mobile-Driven Experiential Education with Presentria GO
Bibliographic record
Abstract
Growing concerns about digital fatigue, declining engagement, and the limitations of traditional online and classroom-based instruction have renewed interest in experiential and location-based learning. This study examines the feasibility and perceived value of mobile-driven, location-based experiential learning using Presentria GO, a Canadian educational technology platform. An exploratory mixed-methods design was employed, combining survey responses from 74 educators, interviews with 11 award-winning professors, a focus group with five college instructors, and interviews with three students who had used the platform in applied coursework. Findings indicate widespread perceptions of online-learning fatigue, strong interest in instructional approaches that extend beyond classroom and home-based environments, and cautious openness to integrating mobile, location-based tools for academic purposes. Educators identified several suitable disciplinary contexts, along with practical challenges related to safety, accessibility, and implementation. Student feedback suggested that mobile, location-based tasks can support engagement and real-world application of course concepts. The study introduces the concept of Mobile-Driven Location-Based Experiential Learning and outlines implications for instructional design, accessibility, and future integration of emerging technologies such as artificial intelligence and augmented reality.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".