Name
Abstract | ||
|---|---|---|
American college students are increasingly put in situations involving unarmed or armed attacks. Recent incidents demonstrate that college campuses are targets for mass shootings. As such tragic incidents become ever more frequent, it is critical for students to know how to react. However, current self-defense courses in higher education offer experience-based learning without the benefit of much research to measure how well instructors make corrections that actually increase the students ability to defend and escape attacks. Thus, in this paper, we propose to design a revolutionary self-defense education system that transfers self-defense education from an instructor experience-based model into a science-based model assisted with instructor experience. The system is based on modern technology with a combination of sensors, big data, and mobile applications that will target functions and strictly control all of the learning, lab applications assessment, and real-time feedback. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BigDataService.2017.52 | 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService) |
Keywords | Field | DocType |
intelligent learning system design,self-defense education,American college students,armed attacks,unarmed attacks,current self-defense courses,higher education,escape attacks,defend attacks,revolutionary self-defense education system,mobile applications,Big data | Data mining,Know-how,Computer science,Systems design,Feature extraction,Big data,Multimedia,Higher education | Conference |
ISBN | Citations | PageRank |
978-1-5090-6319-2 | 0 | 0.34 |
References | Authors | |
18 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hyeran Jeon | 1 | 70 | 5.00 |
| Kaikai Liu | 2 | 190 | 20.37 |
| Younghee Park | 3 | 123 | 16.10 |
| Jerry Gao | 4 | 20 | 4.53 |
| Gong Chen | 5 | 0 | 0.34 |
| Jim Kao | 6 | 0 | 0.34 |