- xAPI 案例研讨 – Medical Education & Healthcare with xAPI
- xAPI + 机器学习智慧助理构思
資策會教研所 鄭淵澤, 賴弘毅, 胡士鑫
Classroom Aid Inc. Jessie Chuang , Henry Chen
Individual Contributor: Yao Shih
宏鼎(HDT) : 小白
育睿科技ABCtech : 張大明
Virtual Reality (虛擬實境) 化學實驗 use case:
Rehabilitation (復健) 與病人食療 use case:
Visca new wrappers:
Online forum discussion:
2. xAPI 案例研讨 – Medical Education & Healthcare with xAPI
From Medbiq workshop, 实时示範介接来自不同工具与sensors之资料
Medbiq xAPI Workshop Report
Authors: David Topps, Corey Albersworth, Ellen Meiselman, Maureen Topps, Sarah Topps, Sandra Morrison, from University of Calgary, Athabasca University , University of Michigan, Simon Fraser University
Enhancing Code Blue Education Performance with xAPI
- Improving Code Blue performance a top MedStar priority
- Performance Issues:
–Delays in defibrillation. Lack of device knowledge
–Lack of process/algorithm knowledge
–Team coordination issues
- communication, sharing of knowledge.
- Team members afraid to ask for help.
In the case of our mobile app for training defibrillator use, xAPI allowed us to be more granular. We are analyzing on which step the learner failed.
Information provided by Dave Bauer, Director of Learning Technologies at the MedStar Simulation Training and Education Lab (SiTEL).
MedStar Health is a large regional health system in Washington, DC and Maryland, and MedStar SiTEL provides learning solutions and infrastructure to the organization. Our mission is to create an adaptable learning infrastructure and provide resources to meet MedStar’s current and emergent learning needs. This includes supporting a custom LMS along with providing mobile performance support tools and integrating new technologies for delivery and measurement of learning.
Physical Therapy tracked with xAPI
这是一个应用在复健的案例，提供该服务的客户要求以 xAPI 为资料收集标準：
Information provided by Nick Washburn, Director of Learning Division, Riptide Learning
xAPI and Machine Learning for Patients/ Learners
xAPI and Machine Learning can help us build “intelligent assistance” for patients and learners, but human-in-the-loop machine learning is important. We need good learning design from the beginning and as we return data to instructors and learners immediately, humans can give great inputs to this human-machine collaboration.
jia-Ru Ho, Yun Yen Chuang, Ray-I Chang, “SmartChair APP – Mobile Technologies for Supporting Patients with Spinal Cord Injury,” The 11th E-Learning and Information Technology Symposium, 2016.