Invited Speakers

Invited Speakers

We are pleased to have the  following four internationally renowned scholars giving keynotes at WESST 2017:

Keynote: Critical directions for learning analytics research and practice

Prof. Dragan Gašević
University of Edinburgh

Bio: Dragan Gašević is a Professor and the Chair in Learning Analytics and Informatics in the Moray House School of Education and the School of Informatics at the University of Edinburgh. He served as the president (2015-2017) of the Society for Learning Analytics Research (SoLAR) and holds several honorary appointments in Australia, Canada, Hong Kong, and USA. A computer scientist by training and skills, Dragan considers himself a learning analyst who develops computational methods that can shape next-generation learning technologies and advance our understanding of self-regulated and social learning. Funded by granting agencies and industry in Canada, Australia, Europe, and USA, Dragan is a recipient of several best paper awards at the major international conferences in learning and software technology. Committed to the development of international research community, Dragan had the pleasure to serve as a founding program co-chair of the International Conference on Learning Analytics & Knowledge (LAK) in 2011 and 2012 and the Learning Analytics Summer Institute in 2013 and 2014, general chair of LAK’16, and a founding editor of the Journal of Learning Analytics (2012-2017). Dragan is a (co-)author of numerous research papers and books and a frequent keynote speaker.

Abstract: The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach for advancing our understanding of the learning process. This promise motivated the emergence of the new field learning analytics and mobilized the education sector to embrace the use of data for decision-making. This talk will first introduce the field of learning analytics and touch on lessons learned from some well-known case studies. The talk will then identify critical challenges that require immediate attention for learning analytics to make a sustainable impact on learning and teaching practice. The talk will conclude by discussing promising directions that can be followed to address the existing challenges in research and practice of learning analytics.

Keynote: AutoTutor and the Experience API

Prof. Xiangen Hu
University of Memphis

Bio: Dr. Xiangen Hu is a professor in the Department of Psychology and Department of Electrical and Computer Engineering at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and visiting professor at Central China Normal University (CCNU). Dr. Hu received his MS in applied mathematics from Huazhong University of Science and Technology, MA in social sciences and Ph.D. in Cognitive Sciences from the University of California, Irvine. Dr. Hu is the Director of Advanced Distributed Learning (ADL) center for Intelligent Tutoring Systems (ITS) Research & Development, and senior researcher in the Chinese Ministry of Education’s Key Laboratory of Adolescent Cyberpsychology and Behavior.

Dr. Hu’s primary research areas include Mathematical Psychology, Research Design and Statistics, and Cognitive Psychology. More specific research interests include General Processing Tree (GPT) models, categorical data analysis, knowledge representation, computerized tutoring, and advanced distributed learning. Dr. Hu receives funding for the above research from the US National Science Foundation (NSF), US Institute for Education Sciences (IES), ADL of the US Department of Defense (DoD), US Army Medical Research Acquisition Activity (USAMRAA), US Army Research Laboratories (ARL), US Office of Naval Research (ONR), UofM, and CCNU.

Abstract: AutoTutor started as a flagship application at the Institute of Intelligent Systems (IIS) of the University of Memphis 20 years ago. In the past 20 years, there have been major research funding on AutoTutor Research and Development (over $35 Million US Federal Funding). In this talk, I will talk about three aspects of AutoTutor: 1) selected cognitive theories of learning that served as theoretical foundations of AutoTutor, 2) Enabling technologies that make AutoTutor work, 3) Example applications of AutoTutor in different domains. 

In addition to AutoTutor, I will talk about Experience API (xAPI). xAPI is an example of learning technology standards that US DoD developed to serve the needs of advanced distributed learning. In this talk, I will 1) talk about the needs for data standard in advanced learning environments. 2) introduce xAPI and xAPI based Learning Record Store (LRS), 3) demonstrate the use of xAPI and LRS in some of the applications (such as AutoTutor).

Keynote: Technology Support for Effective Team-Based Learning in Online Education

Prof. Carolyn Rosé
Carnegie-Mellon University

Bio: Dr. Carolyn Rosé is a Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University.  Her research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. In order to pursue these goals, she invokes approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning.  Her research group’s highly interdisciplinary work, published in over 200 peer reviewed publications, is represented in the top venues in 5 fields: namely, Language Technologies, Learning Sciences, Cognitive Science, Educational Technology, and Human-Computer Interaction, with awards or award nominations in 3 of these fields.  She serves as Past President of the International Society of the Learning Sciences and Executive committee member of the Artificial Intelligence in Education Society.  She has served as program co-chair for numerous international conferences, workshops, and symposia.  She also serves as Executive Editor of the International Journal of Computer Supported Collaborative Learning and Associate Editor of the IEEE Transactions on Learning Technologies.

Abstract: Computational Discourse Analysis is an active area of Learning Analytics that offers real time insights into social processes that impact learning and performance in online courses. This talk reports on research that leverages this important area of research, specifically in connection with a key discussion property referred to as Transactivity.  In particular, this talk reports on work towards achieving persistent social interaction throughout large online courses in the form of technology-supported team based projects. We propose what we refer to as a deliberation-based team formation procedure to improve the selection and initiation process used to start team-based learning in an effective way.  Results from validation studies demonstrate that project teams formed through this technology-enhanced process are more successful at a collaborative knowledge integration task.  Data from deployment studies in real MOOCs are consistent with these findings and demonstrate the potential of this paradigm for real world impact.  Implications for promising new directions in online group learning will be explored.

Keynote: Continuous Improvement of Educational Technology through Discoveries with Big Data

Dr. John Stamper
Carnegie-Mellon University

Bio: John Stamper is an Assistant Professor at the Human-Computer Interaction Institute at Carnegie Mellon University. He is also the Technical Director of the Pittsburgh Science of Learning Center DataShop. His primary areas of research include Educational Data Mining and Intelligent Tutoring Systems. As Technical Director, John oversees the DataShop, which is the largest open data repository of transactional educational data and set of associated visualization and analysis tools for researchers in the learning sciences. John received his PhD in Information Technology from the University of North Carolina at Charlotte, holds an MBA from the University of Cincinnati, and a BS in Systems Analysis from Miami University.  Prior to returning to academia, John spent over ten years in the software industry including working with several start-ups.

Abstract: Technology advances have made the ability to collect large amounts of data easier than ever before resulting in massive datasets. These massive datasets provide both opportunities and challenges for many fields, and education is no different. Understanding how to deal with extreme amounts of student data is one of the major challenges in educational research today. Although this challenge presents many obstacles, the opportunities to harness big data to make major gains in educational efficiency is also attainable. One area that is especially attractive to the use of big data is adaptive educational systems. Data mining techniques can suggest improvements to the models which drive these systems increasing the overall efficiency of student learning leading to a significant savings in time needed for students to learn.

There will also be some Normal talks:

TEAMMATES: A Flexible Feedback Management System for Education

Dr. Damith C. Rajapakse
National University of Singapore


Abstract: TEAMMATES is an online tool for managing feedback paths in education (e.g. peer feedback among team members). While most Learning Management Systems (LMS) have some support for managing feedback paths, TEMMATES aims to compliment the primary LMS with more flexible and more powerful feedback path management. TEAMMATES can facilitate feedback sessions for instructors and students to submit responses for a set of questions, as done in typical online survey tools. But unlike in typical survey tools, each question in TEAMMATES can have its own feedback path (i.e. who is answering the question about who) and visibility settings (who can see the responses). TEAMMATES is available as a free service from At June 2017, TEAMMATES user community spanned 175,000 users from over 600 universities.

Theatre performances as data: implications for research and teaching

Dr. Miguel Escobar Varela
National University of Singapore

Bio: Miguel Escobar is a theatre scholar, web programmer and translator. His main interests are the digital humanities and Indonesian performance practices. In his research, he applies computational methods to the study of performance and codes interactive platforms to share theatre scholarship online. He is currently Assistant Professor at the National University of Singapore and director of the Contemporary Wayang Archive ( He also coordinates Digital Humanities events in Singapore ( More information is available at

Abstract: This talk gives an overview of projects where theatrical performances were used as sources of data. Video, motion capture and linguistic transcriptions of performances can be used for computational research and also for interactive museum exhibits and other creative projects. Thinking about theatre as data opens up new possibilities for interdisciplinary collaborations but it also creates a challenge for teaching. How to prepare students for this kind of work?

Automated Assessment of Argumentation in Science Classrooms

Dr. Chen Wenli
Nanyang Technological University

Bio: Dr Chen Wenli is an Associate Professor with the Learning Sciences and Technology (LST) Academic Group at the National Institute of Education (NIE), Nanyang Technological University (NTU). Her research interests include computer supported collaborative learning, mobile learning, and learning analytics. She is the editor-in-chief for Journal of Computers in Education, the Associate Editor for Research and Practice in Technology Enhanced Learning, and the Advisor Editor for Asia Pacific Education Review. She has led a number of national scale research projects and published 2 books and more than 70 papers on international peer-reviewed journals. She has won more than 10 Best Paper Awards in international conferences.

She was presented the prestigious “Young Researcher Leader Award” by the Asia-Pacific Society for Computers in Education (APSCE). She received the “NIE Excellence in Teaching Commendation” in 2015 and won the “Nanyang Education Award” from NTU in 2016. She is the Programme Committee Chair for International Conference on Computers in Education (ICCE) 2017, co-Chair for the International Conference of the Learning Sciences (ICLS) 2016, the PC Chair for Global Chinese Conference on Computers in Education (GCCCE) 2014.

Before joining NTU and NIE, she was a journalist with the Xinhua News Agency in China and won several “Best Story” Awards.

Abstract: Collaborative argumentation has been widely recognized as an effective approach for science education as it helps students understand the nature of science, promotes deeper learning of content and enhances knowledge creation. However collaborative argumentation rarely takes place in school because teachers and students lack support in evaluating and reflecting the scientific argumentation. Understanding the significance of both “learning to argue” and “arguing to learn”, this project aims to develop real time automated assessment on both cognitive argumentation and social participation for collaborative argumentation through multi-dimensional learning analytics and visualization. With timely and appropriate assessment and visualization, productively collaborative argumentation processes can be engendered, and the group can be guided to function effectively and efficiently.