Tutorial I
Tianyong HAO
South China Normal University, China
Dr. Tianyong Hao is a full Professor at School of Computer Science. He received his Ph.D. degree at City University of Hong Kong in 2010. He studied at York University, Canada, in 2008 and Emory University, USA, in 2009. After that, he worked at University of New South Wales, Australia, in 2012 and at Columbia University until 2014. Dr. Hao is a senior member of IEEE/CCF and committee member of ISO TC37, SAC TC52 (associate secretary), CCF Chinese Information Technology, CCF YOCSEF GZ (chair), CIPS Health Informatics (associate secretary), etc. He is the lead guest editor of several SCI journals including JMIR Medical Informatics and also serves for IEEE TII, ACM TIST, KAIS, JBI, as well as top-tier conferences such as AAAI, IJCAI, COLING, etc. He has published more than 90 SCI/EI indexed papers including IEEE TKDE, ACM TALIP, JBI, JAIST, KBS, C&E, etc. He is the PI of 2 grants from National Science Foundation of China and the leader of provincial level research team on NLP for big data. He owns 6 best paper awards, 1 ISO issued international standard (DIS), 4 granted patents, 15 national software copyrights. His research interests include natural language processing, health text mining, and question answering.
Large Scale Clinical Trial Text Processing and Mining
Clinical trials generate highly relevant evidences for effective disease treatments. The extraction of necessary information from a large scale clinical trial text through natural language processing for patient characteristic aggregation remains a research problem due to the complex of the investigator-authored free-text. By collaborating with a research group at Columbia University Medical Center, our research group has made some progresses on several research topics. This talk will introduce the recent research on: an extensible approach for automated semantic tag mining, clinical trial clustering by similar eligibility criteria, disease named entity recognition, temporal expression extraction and normalization, transgender identification for enhancing clinical trial recruitment, and measurable quantitative information extraction and normalization.
Tutorial II
Hui-Chun CHU
Soochow University, Taiwan
Dr. Hui-Chun Chu is currently an Professor at the Department of Computer Science and Information Management, Soochow University. Dr Chu serves as an editorial board member and a reviewer for more than 15 academic journals. Her research interests include mobile and ubiquitous learning, game-based learning, information technology-applied instructions, flipped learning and knowledge engineering in education. Dr. Chu has published more than 135 academic papers, including 58 academic journal papers, in which 31 papers are published in well-recognized SSCI journals. Owing to the distinguished academic performance and service in e-learning, she received the Annual Young Scholars Outstanding Researcher Award--Ta-You Wu Memorial Award from the Ministry of Science and Technology in 2014. In addition, she has been invited to serve the Associate Editor of IEEE Transactions on Learning Technologies (SSCI) since 2015. Moreover, Dr. Chu received the reward of “The top 50 Flipped Learning leaders in higher education worldwide” in 2018.
Learning Behavior and Interactive Pattern Analysis- Methodologies and Applications
Learning analytics refers to the analysis and interpretation of data related to learners’ behaviors, interactive content and learning contexts recorded during learning process as well as their profiles and portfolios. The objective of learning analytics is to provide helpful information to optimize or improve learning designs, learning outcomes and learning environments based on the analysis results. In this talk, Prof. Chu would review the current states of learning analytics research and the design considerations. To this end, the methodologies and tools for analyzing students’ online learning behavioral patterns and interactive patterns are introduced. Several relevant applications are presented to show how the methodologies and tools work. It is expected that this talk can inspire researchers to discover potential research issues of e-learning or blended learning and to apply the methodologies and tools to their studies in the future.
Tutorial III
Guanliang CHEN
Monash University
Dr. Guanliang Chen is serving as a Lecturer at the Faculty of Information Technology, Monash University in Melbourne, Australia. Before joining Monash University, Guanliang obtained his Ph.D. degree at the Delft University of Technology in the Netherlands, where he focused on the research on large-scale learning analytics with a particular focus on the setting of Massive Open Online Courses. Currently, Guanliang is mainly working on applying novel language technologies to build intelligent educational applications. His research works have been published in international journals and conferences including AIED, EDM, LAK, L@S, EC-TEL, ICWSM, UMAP, Web Science, Computers & Education, and IEEE Transactions on Learning Technologies. Besides, he co-organized two international workshops and has been invited to serve as the program committee member for international conferences such as LAK, FAT, ICWL, etc.
Towards Building Intelligent Educational Applications with Languages Technologies
Textual data is widespread across a variety of educational settings, which, to name a few, includes the utterances in tutorial dialogues, questions asked by students in the discussion forum, the informative feedback crafted by instructors, and the reflective statements written by students throughout the whole learning process. Undoubtedly, this data plays an essential role in capturing the learning performance of students as well as the quality of educational services. In recent years, the advances in language technologies, especially those driven by deep neural networks, have supported the development of a range of domains including healthcare, e-commerce, and financial analysis. However, the effectiveness of these advanced technologies remains largely unexplored in the setting of learning and education. In this tutorial, I will mainly describe how language technologies can be applied to better support (i) the modeling of students; and (ii) the construction of intelligent educational applications for different learning and teaching practices.
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