Prof. Chin-Chung Tsai
National Taiwan Normal University, Taiwan, China
Title: The role of conceptions of learning in technology-enhanced learning environments (09:15-10.00 AM 22 October 2020)
How students conceptualize learning plays an important role in their learning processes and outcomes. Previous research has indicated that the students' conceptions of learning guide their learning in traditional schooling context. It is generally recognized that if the students have more sophisticated conceptions of learning, they are likely to have more meaningful approaches to learning as well as favorable learning outcomes. In recent years, various applications of technologies have been widely utilized in educational settings and students are more likely to engage in some learning opportunities enhanced by technology (such as Internet, mobile computing technologies, augmented reality, and video). This talk will first review a series of studies from my research team regarding students’ conceptions of learning for different subject matters and various types of technology-enhanced instructional activities. It is found that the students possess quite different conceptions of learning by technology-enhanced learning environments than those in traditional school settings. The interplay among conceptions of learning, approaches to learning and learning outcomes for certain technology-supported environments will be discussed. How the technology may play a role in fostering students’ conceptualization of learning will also be addressed.
bio: Prof. Chin-Chung Tsai holds a B.Sc. in physics from National Taiwan Normal University. He received a Master of Education degree from Harvard University and completed his doctoral study at Teachers College, Columbia University in 1996. From 1996 to 2006, he joined the faculty of Center for Teacher Education and Institute of Education, National Chiao Tung University, Hsinchu, Taiwan. He was a Chair Professor at the Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan from 2006 to 2017. He is currently a Chair Professor and Dean for School of Learning Informatics, National Taiwan Normal University, Taipei, Taiwan. He is also the Director of the Institute for Research Excellence in Learning Sciences, National Taiwan Normal University. Since July 2009, he has been appointed as the Co-Editor of Computers & Education (SSCI, IF= 5.296, rank 4/263). He is also currently served as the Editor of International Journal of Science Education (indexed in SSCI, one among the three core journals in science education). His research interests deal largely with constructivism, epistemic beliefs, and various types of technology-enhanced (such as VR, AR, game) instruction. He has a publication record of more than 300 SSCI papers in recent 20 years.
Prof. Dragan Gasevic
Faculty of Information Technology
Title: The Promise of Learning Analytics in the Age of AI (09.15-10:00 AM 23 October 2020)
The unprecedented opportunities to collect data about learning and contexts in which learning occurs has attracted much attention in education. The use of techniques from artificial intelligence have offered much promise to address many relevant questions in education. The talk initially first outlines the major achievements in learning analytics and open research challenges. The talk will then discuss how learning analytics in the future can benefit from the developments in related fields that have strong roots in recent developments in artificial intelligence. The talk will use examples from numerous empirical studies that looked at self-regulated learning, learning strategy, problem solving in solo and group activities, and second language learning.
Dragan Gašević is Professor of Learning Analytics in the Faculty of Information Technology and Director of the Centre for Learning Analytics at Monash University. He is a founder and served as the President (2015-2017) of the Society for Learning Analytics Research (SoLAR). He has also held several honorary appointments in Asia, Australia, Europe, and North America. In 2019 and 2020, he was recognized as the national field leader in educational technology in The Australian’s Research Magazine. He led the EU-funded SHEILA project that received the Best Research Project of the Year Award (2019) from the Association for Learning Technology. Dragan’s research interests center around the development of computational methods that advance understanding of self-regulated and collaborative learning.
Prof. Kaizhu Huang
Department of Electrical and Electronical Engineering
Xi’an Jiaotong-Liverpool University
Title:Adversarial Machine Learning and Its applications for Robust Pattern Recognition (09.15-10:00 AM 24 October 2020)
Adversarial machine learning aims to learn a robust classifier in an adversarial way. In this talk, we start from adversarial examples that are augmented data points generated by imperceptible perturbation of input samples. Being difficult to distinguish from real data, adversarial examples could change the prediction of many state-of-the-art deep learning models. Recent attempts have been made to build robust and safe models that consider adversarial examples. However, these methods can either lead to performance drops, or are ad-hoc in nature and lack mathematic motivations. In this talk, by harnessing adversarial examples, we introduce the fundamental, present the interpretations, and propose a unified adversarial framework to build robust machine learning models in a systematic way. Finally, we demonstrate a series of successful applications with adversarial machine learning.
bio: Kaizhu Huang is currently a Professor at Xi’an Jiaotong-Liverpool University (XJTLU), China. He acts as associate dean of research in School of Advanced Technology, XJTLU and is also the founding director of Suzhou Municipal Key Laboratory of Cognitive Computation and Applied Technology. Prof. Huang obtained his PhD degree from Chinese University of Hong Kong (CUHK) in 2004. He worked in Fujitsu Research Centre, CUHK, University of Bristol, National Laboratory of Pattern Recognition, Chinese Academy of Sciences from 2004 to 2012. Prof. Huang has been working in machine learning, data mining, neural information processing, and pattern recognition. He was the recipient of 2011 Asia Pacific Neural Network Society Young Researcher Award. He received best paper or book award five times. He has published 9 books and over 190 international research papers (70+ international journals) e.g., in journals (JMLR, Neural Computation, IEEE T-PAMI, IEEE T-NNLS, IEEE T-BME, IEEE T-Cybernetics) and conferences (NeurIPS, IJCAI, SIGIR, UAI, CIKM, ICDM, ICML, ECML, CVPR) He serves as associated editors/advisory board members in a number of journals and book series. He was invited as keynote speaker in more than 20 international conferences or workshops.