IEDMS BoD elections (2022)

Dr. Qiwei Britt He

Dr. Qiwei Britt He is a Senior Research Scientist in Psychometric and Data Science Modelling Center at Educational Testing Service (ETS), managing and conducting research on educational data mining, text mining, sequence mining, process data modeling, psychometric modeling, innovative item type development, and technology-enhanced environment design, in national and international large-scale assessments such as PISA, PIAAC and NAEP as well as in K-12 education assessments and learning projects. She has been leading many research projects funded by NSF, IES, IEA, NCES and OECD in developing methods to analyze complex new data source (e.g., sequential process data and textual data) in educational and psychological assessments. Dr. He currently serves as the principal investigator in an IEA-funded project to investigate students’ computational thinking skills in coding environment with process data and co-leads an IES-funded project in using sequence mining techniques to explore the behavioral patterns of low-skilled adults in digital literacy and numeracy tasks. Dr. He was recently selected into the Educational Evaluation Panel (psychometrics and statistics) for UNESCO Institute for Statistics and was appointed as OECD Thomas J. Alexander Fellow in 2018. Dr. He received a PhD in Psychometrics and Data Science in University of Twente, the Netherlands. Her doctoral dissertation focused on combing text mining techniques and item response theory in psychiatric screening and psychological assessments, which received the Anne Anastasia Distinguished Dissertation Award granted by American Psychological Association Division 5 in Quantitative and Qualitative Research Methods.  

Dr. Collin F. Lynch

Dr. Collin F. Lynch is an Associate Professor of Computer Science at North Carolina State University. His research is focused on the development of robust adaptive educational systems for Ill-Defined domains such as scientific writing, law, and software development.  He also conducts research on social network analysis, graph mining, evolutionary computation, and augmented graph grammars.  His more recent work focuses on adaptive tools for computer science education and the integration of online learning platforms into massive open online courses and blended courses.  He also serves as the Policy Chair for the International Educational Data Mining society.  He has also served as Program Co-Chair for the 2019 International Conference on Educational Data Mining.

Dr Rakesh Agrawal

Rakesh Agrawal is the President and Founder of the Data Insights Laboratories, San Jose, USA. He is a member of the National Academy of Engineering, both USA and India, a Fellow of ACM, and a Fellow of IEEE. He has been both an IBM Fellow and a Microsoft Fellow. He has also been the Rukmini Visiting Chair Professor at the Indian Institute of Science, Bangalore, India, a Visiting Professor at EPFL, Lausanne, Switzerland, and a Visiting Professor and JSPS Fellow at the Kyoto University, Japan. ACM SIGKDD awarded him its inaugural Innovations Award and ACM SIGMOD the Edgar F. Codd Award. He was named to the Scientific American’s First list of top 50 Scientists. Rakesh has been granted 85+ patents and published 200+ papers, including the 1st and 2nd highest cited in databases and data mining. Six of his papers have received “test-of-time” awards.  His papers have been cited 130,000+ times with 35+ of them receiving 500+ citations each and three of them receiving 8000+ citations each (Google Scholar). His h-index is 106. His research formed the nucleus of IBM Intelligent Miner that led the creation of data mining as a new software category. Besides Intelligent Miner, several other commercial products incorporate his work, including IBM DB2, WebSphere, Flowmark and Microsoft Bing.

Recently, Rakesh has been furthering and applying data mining in innovative and novel ways to enhance electronic textbooks and online education. This body of work has already provided technologies for algorithmically identifying deficient sections in a textbook, augmenting textbooks with rich content in multiple formats mined from the Web, and forming study teams with the goal of maximizing overall learning. The results of user studies demonstrating the effectiveness of these technologies have appeared in foremost venues in a series of papers (e.g., WWW2011, CIKM 2011, PKDD 2011, KDD2012, CIKM2013, KDD2014, ICFCA2014, L@S2014, EDM2016, EDM2017). The work for enhancing textbooks was built into the enriched NCERT textbooks bundled with the Govt. of India’s Aakash tablet. Rakesh was elected to the Indian National Academy of Engineering in 2016 for this work.

Specifically related to the Educational Data Mining Society, he is currently a General Chair for the EDM-2023 Conference and a member of the EDM Board. He gave a Keynote at the EDM-2016 Conference. He has published several papers on mining educational data, including in the 2017 and  2016 EDM conferences. He initiated and has sponsored the EDM Test-of-Time Awards since 2017.  They are awarded annually to the most influential paper published at an EDM conference 10 years earlier. He also sponsored this year’s CSEDM data challenge.

Dr Kenneth Koedinger

Ken Koedinger (pact.cs.cmu.edu/koedinger.html) is the Hillman Professor of Computer Science at Carnegie Mellon. He explores how people think and learn by developing and studying technology-enhanced learning. He directs LearnLab (learnlab.org), which provides the world’s largest educational technology data sharing resource (learnlab.org/DataShop) as well as learning analytics sharing (learnsphere.org), and educational and collaboration programs for improving data-driven learning engineering. LearnLab builds on the past success of Cognitive Tutors, an approach to online personalized tutoring that is in use in thousands of schools and has been repeatedly demonstrated to increase student achievement, for example, doubling what algebra students learn in a school year. He was a founder of Carnegie Learning, Inc. (carnegielearning.com) that has brought Cognitive Tutor based courses to millions of students since it was formed in 1998. He recently served as a president of the International Educational Data Mining Society.

Dr Tanja Käser

Tanja Käser is an assistant professor at the School of Computer and Communication Sciences and head of the Machine Learning for Education laboratory in EPFL, Switzerland. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning. Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich, Switzerland.  Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University, USA. Tanja Käser received her PhD Tanja Käser from the Computer Science Department of ETH Zurich. In her dissertation, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award 2015. Tanja is serving as a Program Chair for the EDM 2023 Conference in Bangalore, India.

Dr. Mykola Pechenizkiy

Mykola Pechenizkiy is full professor and chair of Data Mining at the Department of Mathematics and Computer Science, TU Eindhoven, the Netherlands. His main expertise and research interests are in predictive analytics on data evolving over time. He leads Trustworthy AI interdisciplinary research studying foundations of robustness, safety, trust, reliability, scalability, fairness, interpretability and explainability of AI and their applications in industry, healthcare and education. He works in close collaboration with industry on developing novel techniques for informed, accountable and transparent predictive and prescriptive analytics. He serves on the program committees of the leading data mining, machine learning and AI conferences. He served two terms as President of IEDMS and is currently part of its Board of Directors.

Dr. Andrew Olney

Andrew M. Olney previously served as Editor for the Journal of Educational Data Mining and continues to serve as Associate Editor, a position he also holds with IJAIED. He co-chaired and served as the
local organizer for EDM2013 and has held various EDM organizing committee roles for the past decade. Andrew is presently Professor in both the Institute for Intelligent Systems and Department of Psychology at the University of Memphis. His primary research interests are in natural language interfaces with a focus on educational applications.

Dr. Philip I. Pavlik Jr.

Phil Pavlik is an Associate Professor in the Institute for Intelligent Systems and Psychology Departments at the University of Memphis (UofM). He is the Cognitive Psychology Graduate Director in Psychology and also directs the Optimal Learning Lab (OLL), which focuses on the application of adaptive learning methods using educational data mining, learning science, and cognitive modeling. Dr. Pavlik completed his dissertation research with John Anderson in Carnegie Mellon University’s (CMU) Psychology Department and postdoc with Ken Koedinger in Carnegie Mellon’s Human-Computer Interaction Institute. His recent research centers around the MoFaCTS (Mobile Fact and Concept Training System) project, which the OLL uses for experimental and adaptive learning experiments, and LKT (Logistic Knowledge Tracing), a framework for adaptive learner modeling. Dr. Pavlik has published more than 20 posters, short papers, full papers, workshops, and tutorials over the last 15 years at the society’s conference and journal since its inception. He was a proceedings author for the 3rd conference in Pittsburgh and a conference chair for the 6th conference in Memphis.

Dr Sharon Hsiao

Dr. Sharon Hsiao is Assistant Professor & David Packard endowed junior fellow in the Department of Computer Science & Engineering at Santa Clara University. Prior to SCU, Dr. Hsiao was an assistant professor at Arizona State University and Columbia University. Dr. Hsiao’s research focuses on educational technologies, computational modeling, visual analytics, and adaptive technology for personalized learning. Her research methodology involves AI and HCI approaches, applied data science and machine learning techniques in researching effective technology to help people learn. Dr. Hsiao served as Program Chair (EDM’2021), senior program committee (since 2016), JEDM track chair, publicity chair of EDM conference. She has also co-organized the annual workshop of Educational Data Mining in Computer Science Education (CSEDM) since 2018. She is currently co-editing a special issue of JEDM on CSEDM to harness and showcase the results from the past workshops.

Dr. Nigel Bosch
Nigel Bosch is an Assistant Professor in the School of Information Sciences and the Department of Educational Psychology at the University of Illinois Urbana–Champaign (UIUC). Previously, he completed a PhD in computer science at the University of Notre Dame, and was a postdoctoral researcher for two years at the National Center for Supercomputing Applications. His work primarily concerns machine learning, algorithmic fairness, and human-centered computing, particularly in educational contexts. He is especially curious about how students’ backgrounds and identities relate to learning outcomes in computer-based learning environments and to their experiences in those environments. He has published over 50 papers in journals and conference proceedings, including 6 papers that have won best paper or best student paper awards (including at EDM 2015 and 2017). Nigel is also Program Co-chair for the 2022 edition of the EDM conference. In the EDM world, he is most excited about possibilities for improving the accessibility and impact of EDM papers by transforming papers to accessible, searchable web-based representations (and making this process as straightforward as possible for authors).

Dr. Olga C Santos

Olga C. Santos (Eng., Ph.D awarded by IEEE) is Associate Professor and leads the PhyUM research line at the Artificial Intelligence Department at UNED, the Spanish Open University. She also coordinates the Master on Research on Artificial Intelligence. Her current research interests focus on applying data mining techniques to support personalized affective psychomotor learning. In 2014 she received the Young Researcher Award of the IEEE Technical Committee on Learning Technology. She has participated in 16 international projects (UE, National), published over 150 papers (Google Scholar: h=30; #cites=3300) and co-chaired several workshop series (TUMAS-A, RecSysTEL/EdRecSys, PALE, RSyL) and conferences (EDM, AIED, UMAP, EC-TEL). In particular, she organized as Conference Chair the 8th edition of the Educational Data Mining conference (EDM 2015) that was held in Madrid (Spain). She was also Workshops and Tutorial Chair in EDM 2014, Sponsor Chair at EDM 2020 and JEDM Track Chair at EDM 2021. In addition, she is a member of the Editorial Board of JEDM. She is also involved in other related communities such as AIED and UMAP.