1. Prof. Stephen J.H. Yang (Stephen J.H. Yang - Google 学术搜索)
National Central University, Taiwan
Professor Stephen Yang graduated from the University of Illinois at Chicago in 1995 with a Ph.D. in computer science. He is currently the Vice President for R&D and Chair Professor of Computer Science and Information Engineering at National Central University, Taiwan. Professor Yang was the first Director of Department of Information and Technology Education, Ministry of Education, Taiwan. Professor Yang also serves as the convener of the Future Earth Taipei, Sustainable in the Digital Age WG, promoting digital technology to explore sustainable development.
Professor Yang is a top computer scientist and educational scholar. Professor Yang ranks in the top 2% (actually ranks in the top 1.2%) of the world based on the “World’s Top 2% Scientists 2020” released by Stanford University. Which selected the top 2% of scientists from nearly 8 million scientists in the world; there are only 1,400 scientists in education, of which only 20 scholars are Taiwanese, and Professor Yang ranks sixth in Taiwan. Research.com selected the top 6,300 computer scientists of 2021, including only 42 scholars from Taiwan, and Professor Yang is one of them.
Professor Yang has published more than 100 journal papers, of which more than 80 are SSCI and SCI journal papers. According to 2016 to 2021 Elsevier SciVal, 26.8% of Prof. Yang’s publications are in the top 10% of most-cited publications worldwide; 54.5% are in the top 10% journals CiteScore worldwide, with an FWCI of 2.23. Professor Yang’s paper citations have exceeded 9,400 with 42 h-index in Google Scholar.
Professor Yang won the Ministry of Science and Technology Outstanding Research Award twice in 2010 and 2017. His research areas include learning analytics, artificial intelligence in education, human-centered AI, sustainable education. Research projects include ESG analytics, Precision education, Human-center AI in education, and the BookRoll Partnership with Kyoto University.
Professor Yang is currently counseling a multinational company in ESG analytics, investigating how to use natural language processing (NLP) technology to analyze corporate ESG reports, and developing more accurate ESG rating prediction algorithms through the training data of Fortune 500 corporate reports and media news. Help enterprises identify ESG key issues that need improvement, formulate improvement strategies, and improve ESG ratings.
Professor Yang and Kyoto University jointly established the BookRoll Partnership Taiwan (BRPT), whose members include more than 32 universities, 84 teachers, 383 courses, and 22,527 students. It is the largest international digital learning initiative in Taiwan and Japan.
Title : "Sustainable learning analytics in the digital age"
As we strive to develop techniques in the digital age, we also need to reflect appropriately on the impact of ethical and social change on learning analytics. How to guide stakeholders to meet the challenges of sustainable learning analytics from the technical, social and educational aspects? How to incorporate ethical considerations such as equality, fairness, trust, transparency, and safety in the design stage?
Fairness means that learning analytics should produce fair results. Transparency refers to explaining learning analytics and adjusting data transparency according to the differences of stakeholders. Trust goes hand in hand with transparency in decision-making. Still, whether the decisions for learning analytics are fair and trustworthy remains an ethical issue. We still need to be concerned about human behavior and social values, such as respecting privacy, providing equal opportunities, and accountability.
2. Prof. Gwo-Jen Hwang (Gwo-Jen Hwang - Google 学术搜索) (www.idlslab.net)
National Taiwan University of Science and Technology, Taiwan
Dr. Hwang serves as an editorial board member and a reviewer for more than 50 academic journals of educational technology and e-learning. He has also been the principal investigator of more than 150 research projects funded by Ministry of Science and Technology as well as Ministry of Education in Taiwan. His research interests include mobile and ubiquitous learning, flipped learning, digital game-based learning, and artificial intelligence in education. He is the scholar who defined the term “seamless flipped learning” as “mobile technology-enhanced flipped classroom with effective learning strategies.” Dr. Hwang is currently the editor of several well recognized journals, such as
l Lead editor of Australasian Journal of Educational Technology (SSCI)
l Editor-in-chief of Computers & Education: Artificial Intelligence (SCOPUS)
l Editor-in-chief of International Journal of Mobile Learning and Organisation (Scopus, Q1)
l Editor-in-chief of Journal of Computers in Education (Scopus, ESCI)
l Associate editor of IEEE Transactions on Education (SCI)
Dr. Hwang has published nearly 800 academic articles, including more than 350 papers published in SSCI journals. Owing to the reputation in academic research and innovative inventions in e-learning, he received the annual most Outstanding Researcher Award from the National Science Council of Taiwan in the years of 2007, 2010 and 2013. Moreover, in 2016, he was announced by Times Higher Education as being the most prolific and cited researcher in the world in the field of social sciences (https://www.timeshighereducation.com/news/ten-most-prolific-and-most-cited-researchers).
Title : " Strategies of publishing AI in education research in SSCI journals"
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3. Prof. Xiaoqing Gu Xiaoqing GU/顾小清 - Google 学术搜索
East China Normal University, China
Dr. Xiaoqing Gu is a professor and head of Department of Educational Information Technology, Faculty of Education, East China Normal University, China. She is the head of Shanghai Engineering Research Center of Digital Education Equipment, and now lead the AIED research community with scholars and practitioners. Professor Gu has long been engaged in the research and practice of educational informationization. Her research interests include the areas of learning science and learning technology, computer-supported collaborative learning (CSCL), learning analytics and leaner profiling, ICT-integrated pedagogical innovation in which she has published more than 150 publications in internationally journals and conferences, more than 10 books and teaching materials. Professor Gu is the chief editor of IJSmartTL (International Journal of Smart Technology and Learning), board member of Journal of Computer Assisted Learning, Education Technology research and development, Internet and higher education, Journal of Computer in Education, International Journal of Computer-Supported Collaborative Learning, and Journal of East China Normal University (Educational Science).
Title : "Approaching adaptive learning by enriching students' digital learning footprint"
With the rapid development of information technology, adaptive learning becomes imperative in the field of education to provide efficient, effective, and customized learning paths to engage students. However, the existing adaptive learning systems lack a comprehensive digital portrait of students, making it hard to pinpoint students' Zone of Proximal Development (ZPD). It is a critical research mission in applying Artificial Intelligence (AI) to build an accurate and robust digital portrait of students and provide real-time learning diagnoses and personalized learning interventions for students.
To extract students’ digital portraits, collecting multidimensional data, including learning interactions, results, and even facial expressions, is needed. By collecting, filtering, and analyzing students learning data, a multidimensional digital portrait of students could be formed to enrich and visualize ZPD. In this talk, I’m going to introduce the efforts of our studies dedicated to this regard: 1) depict and diagnose students’ learning performance in each dimension through tracking a dynamic digital learning footprint of students, 2) lay out a more accurate ZPD for the adaptive learning under developing based on students’ comprehensive digital portrait.