Invited Session



Workshop Topic

Smart Mechatronics and Robotics Systems

Session Chair(s)

Dr. Ying Wang, Kennesaw State University, USA, ying.wang@kennesaw.edu; Dr. Clarence W. de Silva, University of British Columbia, Canada, desilva@mech.ubc.ca

Date

August 20, 2019



Workshop Topic

VR, AR, and MR technologies in Higher Education

Session Chair(s)

Carsten Lecon c.lecon@web.de



Workshop Topic

Technology-Inspired Smart Learning

Session Chair(s)

Prof. ZHENG Li, Tsinghua University, Beijing, P.R.China, zhengli@tsinghua.edu.cn

Session Co-Chair(s)

Associate Prof. LI Chao, Tsinghua University, Beijing, P.R.China, li-chao@tsinghua.edu.cn

Introduction:

Smart learning environments are very diverse: massive open online courses with AI assistants, intelligent tutoring systems, interactive learning courseware, learning games, collaborative programming communities, community tutorial systems, personalized exercise programs, tutoring robotics are all examples, especially in off-campus scenarios. A growing number of current campus-based courses in popular fields are also involved. All share a common purpose to increase human potential, leveraging data collection, data analysis, human interaction, and varying forms of computational assessment, adaptation and guidance.

Information technologies have been proposed to the modern smart learning space, but may instead increase the digital divide if applied without reflection. So, further work is needed to understand how to leverage technologies to provide equitable education for all, and many questions remain to be answered. What are the main barriers to providing educational opportunities to underserved teachers and learners? How can AI and advanced technologies help overcome these difficulties? How can this work be done ethically? etc. In this session we gather the collective intelligence of the community to provide innovative and creative solutions, thus try to investigate “Technology-Inspired Smart Learning”.

This session solicits original research paper submissions on methodologies, case studies, analyses, tools, or technologies for smart learning, broadly construed. Four kinds of contributions will be accepted: Research Papers, Synthesis/Review Papers, Work-in-Progress Short Papers, and Demonstrations. Topics are included but not limited to: