Engaging with Local Spaces: Student-created digital field tours to facilitate community learning
Bibliographic record
Abstract
The disciplines of geology and physical geography often rely on experiential learning and real-world observations, like those offered on field trips, to share knowledge and engage students. During the shift to online teaching during the COVID-19 pandemic, those in higher education had to quickly embrace innovative technologies (e.g., handheld LiDAR scanners, 3D scanner apps, affordable drones, and 360-cameras) and online applications such as ArcGIS StoryMaps to simulate these field investigations. Here, we are applying what we learned in higher education teaching to share knowledge and engage the general public with the geology and geomorphology of their region. Furthermore, we are employing a user-created content approach, whereby university students create educational content aimed at other students and the general public, to enhance their learning and professional development. Since 2020, undergraduate and graduate university students have collected photos, synthesized literature, and created digital content of outdoor spaces that can be explored freely online. This content includes digital tours of urban and natural spaces highlighting local points of interest, with a focus on geology and geomorphology (e.g., tour of the University Campus, regional geology of Southern Ontario), presented with ArcGIS StoryMaps.Our goal is to equip all users with fundamental scientific knowledge, along with real-world observations and examples, so that they can recognize natural landforms and processes (like weathering and erosion) while deepening their understanding of the role and impact of human activities (e.g., erosion control) on the environment. To engage users and have them reflect on their learning, we will be incorporating interactive components such as knowledge check questions and citizen science contributions (e.g., photo submissions, and observational surveys) in the StoryMaps. To monitor professional development and learning progress of our student creators, we will include goal-setting and self-evaluation components throughout the project. Student creators will also be asked to evaluate whether participating in these projects enhanced their connection with their environment, provided opportunities to apply knowledge from their classes, and helped develop a sense of accomplishment given the finished products, their ability to share knowledge with others, and their ability to learn new skills and technologies.Beyond regional geology and University campus tours, we are now expanding the network of sites into popular recreational spaces like parks and walking trails alongside interesting natural and designed landscapes, like urban rivers. These projects consider regional geology alongside surface processes, natural hazards, and environmental change, as well as the connections between historical and cultural context with the landscape.
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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 | 0.001 | 0.000 |
| 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 | 0.002 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.005 |
| 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".