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 ‘do’ 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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".