Keynote Speakers
Prof. Xinguo Yu, National Engineering Research Center for E-Learning, Central China Normal University, China
余新国教授，华中师范大学，华师伍伦贡联合研究院，中国
Research Area: Education Human-Computer Interaction; Intelligent Education System; Educational Robot
研究领域：教育人机交互； 智能教育系统； 教育机器人
Title: Problem Solving in Computer Science and Education
Abstract:
Problem solving is a most demand capacity for computer and human being; of course, the capacity indicators for two subjects are different. This talk first explain the various formulations of problem solving in computer science and education. Next, this talk presents the typical problems in problem solving in computer science. Then it analyzes the status of art in developing auto solvers for the basic education. Last, it predicates the further progress in the near future in strengthening the capacity of problem solving.
Prof. Tong Li, Shenzhen University/ College of Management, Institute of Mobile Internet Things Industrialization, China
李彤教授，深圳大学管理学院、移动互联产业化研究所，中国
Research Area: Intelligent Decision Support System, Business Intelligence, Data Mining etc.
研究领域：智能决策支持系统，商业智能，数据挖掘等
Title：Is Artificial General Intelligence Coming?
Abstract：
AGI, or artificial general intelligence, is mainly focused on the development of machines that think like people and engage in multiple uses like people. This word comes from AI. However, as the mainstream AI research is gradually moving towards the intelligence of a certain field or a certain task (such as machine vision, speech input, etc.), in order to distinguish them, general is added. It is also defined as: the AI system that matches or exceeds humans at almost all (such as at least 95%) economically valuable work.
This talk will briefly describe the landmark events of AI research and development, sort out several AI research and development paths, discuss the possibility of the arrival of AGI, and put forward some views on the future research trend of AI.
A. Prof. Yunwu Wang, Jiangsu Normal University, China
王运武副教授，江苏师范大学，中国
Research Area: Education Informatization, Wisdom Campus, Strategic Planning, Educational Robot, Learning Science and Technology, Creativity Training
研究领域：教育信息化，智慧校园，战略规划，教育机器人，学习科技，创造力训练
Title：Live Teaching in 5G Era: Subverting the Form of Online Education in the Future
Abstract：
During the period of epidemic prevention and control, online education flourished. As a new form of online education, live education received the attention of teachers and students. This paper traces the development of live broadcast and live education, and believes that the narrow sense of live education originated from the 1920s' live broadcast, which can be divided into four stages after a hundred years of development. The 5G era gives the potential of live education, which is an important force to subvert the traditional online education. Live education has significant advantages, and the reduction of live technology threshold has stimulated the rapid development of live education. The 5G era live education system consists of five elements: teachers, students, live education media, Ultra HD live education resources, and ultra-high speed information dissemination channels. There are six major application scenarios of live education in 5G era. Schools with advanced awareness have started demonstration application and achieved good results. In 5G era, live education will subvert the development of online education in the future, and live curriculum will become an important form of online education curriculum. In the future, we should speed up the development of live education, actively explore a new mode of live education, and open up a new way of sharing high-quality education resources.
A. Prof. Ji Zhang, University of Southern Queensland, Australia
张吉副教授，南昆士兰大学，澳大利亚
Research Area: Data Science; Big Data Analytics; Knowledge Discovery and Data Mining (KDD); Health Informatics
研究领域：数据科学 大数据分析； 知识发现和数据挖掘（KDD）； 健康信息学
Title：Smart-education: Personalized Learning Optimization for Students
Abstract：
Smart-education aims at providing the most proper education, in terms of both style and contents, to students. In this talk, we would like to present some of our recent research outcomes and findings in the area of personalized learning optimization for university students in smart-education. More specifically, we target on the real-life educational scenarios for university students and utilize the techniques in data mining and AI to study the issues around the personalized learning process optimization in order to improve the learning efficiency for university students.
