Learning Geospatial Concepts as Part of a Non-Formal Education Robotics Experience
Bibliographic record
Abstract
In the increasingly modern and technological world, it has become common to use global navigation satellite system (GNSS), such as Global Positioning System (GPS), receivers, and Geographic Information Systems (GIS) in everyday life. GPS-equipped mobile devices and various Web services help users worldwide to determine their locations in real-time and to explore unfamiliar land areas using virtual tools. From the beginning, geospatial technologies have been driven by the need to make efficient use of natural resources. More recently, GPS-equipped autonomous vehicles and aircraft have been under development to facilitate technological processes, such as agricultural operations, transportation, or scouting, with limited or virtual human control. As outdoor robotics relies upon a number of principles related to science, technology, engineering, and mathematics (STEM), using such an instructional context for non-formal education has been promising. As a result, the Geospatial and Robotics Technologies for the 21st Century program discussed in this chapter integrates educational robotics and GPS/GIS technologies to provide educational experiences through summer camps, 4-H clubs, and afterschool programs. The project’s impact was assessed in terms of: a) youth learning of computer programming, mathematics, geospatial and engineering/robotics concepts as well as b) youth attitudes and motivation towards STEM-related disciplines. An increase in robotics, GPS, and GIS learning questionnaire scores and a stronger self-efficacy in relevant STEM areas have been found through a set of project-related assessment instruments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".