Abstract: The educational robot is becoming the indicator product of AI education. It will be the representative technology of the future AI education. This talk discusses the concept and the properties of educational robot. It further explores the foundation of theory and technology of educational robot. Since the various educational robots have a wide spectrum of applications, so we can identify and explain the usage scenarios of educational robots from the usage experience. Educational robot is in its early stage and it must have a long development. Hence, it deserves to discuss the potential of educational robot and challenges of developing educational robot.
Biography: Xinguo YU is the dean and Professor of CCNU Wollongong Joint Institute, faculty of Artificial Intelligence in Education at Central China Normal University, Wuhan, China, senior member of both IEEE and ACM, and an adjunct professor of University of Wollongong, Australia. He is a member of steering board of PSIVT conference and a member of steering board of Smart Educational Technology Branch Society under Automation Society, China. He received Ph.D. degree in Computer Science from National University of Singapore. His current research mainly focuses on intelligent educational technology, educational robotics, multimedia analysis, computer vision, artificial intelligence, and virtual reality. He has published over 100 research papers. He was general chairs and program chairs of more than 10 international conferences in the past 10 years. He is general chair of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE) 2021.
Abstract: As technology has improved over the years, we have seen a significant change in learning environments and ways how students learn. The concept of adaptive learning and personalized learning has gained considerable traction among educators. To make it possible, learning oriented diagnostic assessment is expected to play an important role. Firstly, this address will deal with cognitive diagnostic assessment (CDA), which is designed to measure specific knowledge structures and processing skills in students, allowing fine-grained feedback for students to self-regulate their learning, and for teachers to develop individualized intervention plans based on the students’ diagnostic profiles. Following that, data-driven Q-matrix learning and dynamic knowledge tracing (KT) technologies will be introduced from data science and online learning perspectives. In recent years, how to apply deep neural network techniques to learning logs in online learning systems to dynamically assess student performance have been attracting more attentions in learning analytics and educational data mining areas. In this talk, a Boolean Matrix Factorization technique on Q-matrix learning and KT models we proposed will also be shown.
Biography: Yuan Sun is an associate professor in the Information and Society Research Division at National Institute of Informatics and the Graduate University for Advanced Studies, Japan. Her academic background is psychometrics and measurement in education, especially test theory including cognitive diagnostic modeling and item response theory, application of theoretical and data-driven computational methods and statistical modeling of assessment data. She serves as a project leader for data-centric human and social Informatics in Research Organization of Information and Systems Japan, focusing on data-driven personalization of student learning support. She has been a guest professor at Beijing Normal University China; a guest research fellow at National Institute of Science and Technology Policy Japan; an executive director of the Japan Society of Information and Knowledge, a standing editorial board member of the Japanese Journal of Educational Psychology. She has also served as editorial board members of a number of national and international journals, and regularly as program committees of numerous Japanese and international conferences. Her current research interests involve developing and adapting methodologies in support of personalized learning using techniques incorporating machine learning, data mining, as well as educational assessment.