Making Learning Mobile: Using Mobile Technologies to Bring GIS into the Geography Classroom
Bibliographic record
Abstract
Physical geography as a discipline is deeply rooted in field-based and technique-based courses where students are expected to learn through lectures and hands-on laboratories (Allen, 2007). Unfortunately, research has shown that just having students âdoâ geography using step-by-step instructions may not be contributing to deep learning or long-term retention of knowledge (Armstrong and Bennett, 2005; Scheyvens et al., 2008). One way in which this learning model can be improved is by adopting the constructivist model in which learning is student-centred and teachers act as expert guides and instructors (Day, 2012; Keengwe et al., 2009; Sheng et al., 2010). One area of physical geography that can be improved with this model of teaching is Geographic Information Systems (GIS). GISs are systems that allow us to visualize, analyze, and interpret spatial data and are a ubiquitous tool in geography (Sanders et al., 2001). Unfortunately, GIS software programs are often expensive, complex, and have steep-learning curves. As a result, students are often provided pre-selected datasets and stepwise instructions for completing assignments. They are rarely given the opportunity to collect their own data, develop their own projects, or link their practical field experiences with the theory learned in lecture. This workshop will introduce participants to using simple mobile GIS technologies, such as Google Earth and Collector for ArcGIS, as an active learning tool for teaching undergraduate Geography students. Specifically, participants will have the opportunity to experience data collection with mobile GIS technology firsthand while also engaging in discussions about technology integration with their peers. By the end of the workshop, participants will be able to integrate mobile GIS-based technologies as an active learning tool into both lectures and laboratories in undergraduate geography courses.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".