IEDMS BoD elections (2020)

Giora Alexandron

Giora Alexandron is an Assistant Professor in the Department of Science Teaching at Weizmann Institute of Science, Israel. His research centers on AI to assist teachers in K-12 Blended Learning Environments, and on analyzing the behavior of MOOC learners, with specific interest in the relation between assessment design, incentives, and cheating. Giora started his career as an R&D engineer/manager in an AI-for-CAD startup, and after it was acquired, returned to Academia to do a PhD in CS Education. During his postdoc with Dave Pritchard at MIT, working on data mining and MOOCs, he came to know this exciting EDM domain that integrates two of his passions – education and advanced computational approaches.

Since 2015 Giora has served the wonderful EDM community in various ways such as Track Chair and PC member, and he hopes to extend his contribution as a board member. He hopes that, together, we can reach new audiences, and increase our pedagogical impact on education systems around the world.

Tiffany Barnes

Tiffany Barnes is Professor of Computer Science at NC State University. Dr. Tiffany Barnes was founding General Chair for EDM 2008, Program Chair for EDM 2009, and General Chair for EDM 2016, and co-Chair for EDM 2017. Tiffany Barnes has served on executive boards for the International Educational Data Mining Society (2008-present), the AI in Education Society (2016-19), and the ACM Special Interest Group on Computer Science Education (2010-16). She is Chair of IEEE Computer STC Broadening Participation, and founded the Research on Equity and Sustained Participation in Engineering, Computing and Technology (RESPECT) conference in 2015, and was Program Chair for the 2014 Foundations of Digital Games conference.  She has served as Associate Editor for IEEE Transactions on Learning Technologies (2016-present) and the Journal of Educational Data Mining (2008-2010), and guest editor for special issues on AI in Computer Science Education (IJAIED 2017), the Best of RESPECT (Computing in Science and Engineering, 2016, 2020), and Serious Games (IEEE Computer Graphics and Applications 2009). Dr. Barnes is co-director of the STARS Computing Corps, a consortium of colleges and universities that develops leadership and regional partnerships to broaden participation in computing.  Dr. Barnes received the 2009 NSF CAREER award to create the technology for a new generation of data-driven intelligent tutors. Her current projects build data-driven intelligent tutors in STEM and novice programming, mine educational interaction data for actionable insights, research K12 teacher professional development for computer science and computational thinking, and provide research experiences for undergraduates.


Carrie DEMMANS EPP is an Assistant Professor at the University of Alberta and a research affiliate with the UBC Language Sciences Initiative at the University of British Columbia. Carrie joined the University of Alberta, where she teaches courses on human-computer interaction, intelligent user interfaces, natural language processing, and the use of artificial intelligence in educational applications. Before moving to Alberta, Carrie completed a postdoc at the Learning Research and Development Center of the University of Pittsburgh and visiting researcher positions with the Open Learner Models at Birmingham group (UK) and the Graduate School of Language, Communication, and Culture at Kwansei Gakuin University in Japan.

Carrie has been working in the areas of educational data mining, learning analytics, and artificial intelligence in education since she joined the ARIES lab at the University of Saskatchewan as an undergraduate student in 2004. She remained in ARIES to complete her MSc and earned her PhD from the University of Toronto.

Neil Heffernan

Dr. Neil Heffernan is the William Smith Dean’s Professor of Computer Science at Worcester Polytechnic Institute. Before entering academia, Neil taught middle school math and science in the Teach for America program in Baltimore, where he met his wife Cristina. While completing his Ph.D. in Computer Science at Carnegie Mellon University, Neil incorporated his passion for education and focused on educational technologies. In 2003, Neil and Cristina created the first ASSISTments platform, and to this day it remains a free service that is used by 300,000 students across the United States for daily classwork and nightly homework. In October 2016, Dr. Heffernan was asked to present at the White House on the reproducibility crisis in educational research and the need for pre-registration and open data. In December 2016, the Heffernans presented at the White House for a second time on the SRI evaluation that found ASSISTments doubled student learning. He has received national press from U.S. News, Scientific American, The New York Times, The Boston Globe and NPR. Dr. Heffernan has written 60+ papers on learning analytics and over two dozen papers on the results of randomized controlled trials. He was one of the founding members of the EDM society, he has written a slew of EDM papers, and his students have won the “Best Student Paper” award a few times. Dr. Heffernan is a huge fan of open science and he is proud of the fact that over 43 papers have been written by others that use his data set including the Best Paper at EDM 2019 (to be clear, Heffernan was not an author on that paper, he just released the data set).   

Xiangen Hu

Dr. Xiangen Hu is a professor in the Department of Psychology, Department of Electrical and Computer Engineering and Computer Science Department at The University of Memphis (UofM) and senior researcher at the Institute for Intelligent Systems (IIS) at the UofM and is professor and Dean of the School of Psychology 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) Partnership Laboratory at the UofM, and is a 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 has received funding for the above research from the US National Science Foundation (NSF), US Institute of 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.

Andrew Lan

Andrew Lan is an assistant professor in the College of Information and Computer Sciences, University of Massachusetts Amherst (UMass). Prior to joining UMass, he was a postdoctoral research associate at Princeton University after receiving his M.S. and Ph.D. degrees from the Digital Signal Processing group at Rice University. He was also affiliated with OpenStax, a nonprofit organization that provides a series of open-source textbooks used by over 4 million students worldwide and 60% of US Colleges and Universities, where he contributed to the development of artificial intelligence (AI) algorithms behind the OpenStax Tutor personalized learning platform. 

His research focuses on the development of human-in-the-loop machine learning (ML) methods to enable scalable, effective, and fail-safe personalized learning in education by collecting and analyzing massive and multi-modal learner and content data. This massive and multi-modal learner and content data can be collected in both traditional classrooms and online learning platforms, e.g., during massive open online courses (MOOCs). His vision is to build an AI system that enables a world in which every learner has access to high quality, affordable, and personalized learning. He is active in both the EDM and AI/ML communities and has co-organized a series of workshops on machine learning for education; see for details.

Noboru Matsuda

Dr. Noboru Matsuda is Associate Professor of Computer Science at North Carolina State University.  Noboru’s research interests are rooted in the innovation of cutting-edge AI technologies to enhance learning as well as to advance theories of how people learn. He is particularly interested in artificial intelligence technologies for students to learn, teachers to teach, and researchers to understand how people learn and fail to learn. Noboru’s scholarly expertise thus spans computer science, cognitive science, learning science, and education. Noboru is widely known by his research on SimStudent ( where the research team develops an artificial intelligence that learns cognitive skills from examples and through guided problem solving, and studies its application (e.g., intelligent authoring, learning by teaching). In recent years, he has launched a research project on pragmatic learning engineering where the research team studies artificial intelligence technologies that facilitate authoring of adaptive online courseware (e.g., question generation, skill mining, and contents validation). Noboru loves programming and swimming when he is not dreaming of the future of education.

Zachary Pardos

Dr. Pardos, an Associate Professor at UC Berkeley in the Graduate School of Education, studies adaptive learning and AI. His research focuses on knowledge representation and recommender systems approaches to using behavioral and semantic data to map out paths to cognitive and career achievement in K-16. He earned his PhD in Computer Science at Worcester Polytechnic Institute with a dissertation on computational models of cognitive mastery. After completing his PhD in 2012, he spent one year as a Postdoctoral Associate at the Massachusetts Institute of Technology applying adaptive learning paradigms to massive open online courses. At UC Berkeley, he directs the Computational Approaches to Human Learning research lab, teaches in the Graduate School of Education and the Division of Computing, Data Science, and Society, and is an affiliated faculty in Cognitive Science. He is a long time contributor to the field, with 27 papers published in EDM, two receiving student best paper awards (one as a student, the other as advisor), and was program co-chair in 2014. Additionally, Pardos conducts work and contributes academic service in adjacent communities, serving on the 2020 program committees for ACM’s RecSys, CHI, and AAAI conferences and organizing field growing activities in the social sciences supported by the American Educational Research Association. 

Anna Rafferty

Anna Rafferty is an associate professor of computer science at Carleton College. She earned her doctorate in computer science from the University of California, Berkeley, and her work blends computational cognitive science, computer science, and education. In her research, she’s particularly interested in closing the loop between assessing student learning and taking actions to address misunderstandings or help students learn more efficiently, and recently, she has begun investigating questions surrounding equitable impact across student groups of algorithms that adaptively personalize. Her work has been published in a variety of venues, including JEDM, Cognitive Science, and the Journal of Educational Psychology, and has been funded by the NSF. Dr. Rafferty served as one of two Program Chairs for EDM 2020 and is part of the AAAI Undergraduate Consortium organizing committee, building on her passion for undergraduate education and increasing access to research for a diverse range of students.

Cristóbal Romero

Cristóbal Romero is Full Professor at the University of Córdoba in Spain and member of the KDIS (Knowledge Discovery and Intelligent Systems) research group.  His main research interests are the application of data mining/learning analytics and artificial intelligence techniques to educational data/environment/domain. He has published more than 150 papers in books, journals and conferences, 50 of which have been published in Thomson-Reuters Impact Factor (IF) journals and some of them are important EDM (Educational Data Mining) surveys/reviews. He was also the co-editor of several special issues and two books regarding EDM specific topics. He was a founding officer of the international EDM society and he has served in the program committee of a great number of international conferences about education, personalization, data mining, AI and he has organized as Program Chair and Conference Chair several editions of EDM conference. Currently, he is associate editor of the IEEE Transaction on Learning Technologies journal.

John Stamper

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 has been heavily involved in the Educational Data Mining community and has a wealth of knowledge around IEDMS. He served as conference chair, program chair, poster chair, sponsorship chair, and local chair of EDM conferences. Prior to returning to academia, John spent over ten years in the software industry including working with several start-ups.