Keynote Speakers
Prof. Emeritus Bob Fox, University of New South Wales Sydney, Australia
名誉教授Bob Fox，悉尼新南威尔士大学，澳大利亚
Research Area: learning and teaching innovation and change in higher education; technological practice and curriculum change; blended, mobile and open learning
研究领域：教学的创新和高等教育的变革； 技术实践和课程变更； 混合、移动和开放式学习
Title: Transforming Higher Education Through Digital Learning to Improve Student Learning Experiences in the COVID Era
Abstract:
Higher education has gone through major challenges and changes since the outbreak of the COVID-19 pandemic. In fact, universities are unlikely to entirely go back to their old practices, post COVID. The required online focus has raised the need for multiple adjustments to the delivery of teaching and learning. This keynote reviews case initiatives in universities that focus on improving the quality of online student learning experiences and will focus on curriculum, course and program design and assessment and associated capacity building for staff in teaching online. In particular, the paper will review digital uplift initiatives; institution-wide curriculum frameworks and course design models and tools; capacity building initiatives and new education focused contracts for teachers; students centred initiatives; experiments in online assessments to replace traditional face-to-face assessments; and finally lessons learnt to-date.
Prof. Zehui Zhan, South China Normal University, China
詹泽慧教授，华南师范大学，中国
Research Area: Learning science, STEAM, innovation and entrepreneurship education
研究领域：学习科学，STEAM，创新和创业精神教育
Title: Investigating the effect of reverse engineering pedagogy in K‐12 robotics education
Abstract:
The purpose of the study is to explore the effectiveness of reverse engineering pedagogy (REP) and forward project‐based pedagogy (FPP) in K‐12 robotics education. A two‐stage study was conducted in two secondary schools, involving a total of 169 students. Based on the experience of the pilot study (Study 1), we refined the REP and examined its effectiveness through a quasi‐experimental design in the formal study (Study 2), which included four teaching models: the Deconstruction Recovery Model, Troubleshooting Model, Element Minitrim Model, and Structural Innovation Model. Results indicated that students in the REP group performed significantly better and perceived a higher degree of compatibility and creative self‐efficacy than those in the FPP group, which is consistent with the final robotics product evaluation. However, no significant difference was found in the learning attitude towards the course. The research findings highlight the value of REP in robotics education, especially in cultivating creativity and enhance learning performance, however, it needs reasonable planning and design.
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：Learning theory and machine learning
Abstract:
Learning theory is a variety of theories that explain the nature, process and influencing factors of human and animal learning. Psychologists from different points of view, using different methods, according to different experimental data, put forward a lot of learning theory. It is generally divided into two theoretical systems: stimulus response (S-R), also known as connectionism (or behaviorism) theory and cognitive theory.
Machine learning is a multi-disciplinary interdisciplinary, specialized in how to simulate or realize human learning behavior, in order to obtain new knowledge or skills, reorganize the existing knowledge structure, and constantly improve their own performance. It is the core of artificial intelligence and the fundamental way to make computer have intelligence.
In this paper, they are briefly introduced for some schools of connective learning theory and cognitive learning theory. It is described for the relationship between the research and development of artificial intelligence and machine learning and learning theory. Finally, It is discussed for the bottleneck and future development trend of artificial intelligence machine learning.
A. Prof. Han-Teng Liao (DPhil. Oxon), Director at Higher Education Impact Assessment Center, Guangzhou Nanfang College, China
廖汉腾（牛津大学博士）副教授，高校影响力研究中心主任，互联网新兴设计学科带头人，广州南方学院，中国
Research Area: Smart and Sustainable Design；Design Innovations； Intelligent Human Computer Interaction; Internet Emerging Design Education；Eco-Design of Internet；Technology for Good
研究领域：智能和可持续设计；设计创新；智能人机交互；互联网新兴设计教育；互联网生态设计；科技向善
Title：Low-Code Development as Enabler of Green and Digital Transformation in Design Education: A Case Study of the integration of Sustainable Production and Consumption Design Education into an API-driven Development Curriculum
Abstract:
As demand for professionals with digital technical and management skills grows, the low-code development and application platforms provide more open and diverse participation by non-technical or citizen developers to contribute to the digital or data-driven solutions. Yet such latest practices and outcomes have not yet been systematically integrated or even introduced into the higher education curriculum, and thus little educational research has been conducted to summarize the opportunities and challenges on learning low-code development in higher education settings. Based on a three-year reform of an undergraduate program of Internet and New Media in China, including Enterprise-Higher Education collaborative design workshops, the case study demonstrates the desirability, feasibility, and viability of an API-driven Development Curriculum (including the Guangdong first-class course called "API, Machine Learning, and Artificial Intelligence"). The overall findings reveal the opportunities and challenges in empowering students for careers in a data-intensive or data-driven world, especially under the China's national policy of "empowering organizational intelligence with digital and cloud services" and the international research fronts of "green digital transformation".