Bita Akram is an Assistant Professor with the Department of Computer Science at North Carolina State University. Her research lies at the intersection of artificial intelligence and advanced learning technologies with its application on improving access and quality of CS Education. She been actively developing data-driven approaches for assessing students' CS competencies as demonstrated through their interactions with educational programming activities. She has served as the organizer and program committee for venues focused on educational data mining including EDM and CSEDM.
Yang (Arvin) Shi is an Assistant Professor of Computer Science at Utah State University. He has been working towards building data-driven methods for representing program code to enhance the ability of Intelligent Tutoring Systems and benefit student modeling processes for computing education. With a focus on DM/ML approaches applied to CS education, his research interests also include Programming Language Processing, Software Analysis, and Deep Learning. He has served as a program committee (PC) member in conferences across EdTech (EDM, LAK, SIGCSE, ICER) and AI (KDD, AAAI, NeurIPS) disciplines, and co-organized the Educational Data Mining in Computer Science Education (CSEDM) workshop since 2020.
Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh, where he also directs the Personalized Adaptive Web Systems (PAWS) lab. He has been working in the field of adaptive educational systems, user modeling, and intelligent user interfaces for more than 30 years. He published numerous papers and edited several books on adaptive hypermedia and the adaptive Web. He is a founder of CS-SPLICE and has advanced research and infrastructure for CSEDM.
Thomas Price is an Associate Professor of Computer Science at North Carolina State University. His primary research goal is to develop learning environments that automatically support students through AI and data-driven help features. His work has focused on the domain of computing education, where he has developed techniques for automatically generating programming hints and feedback for students in real-time by leveraging student data. He has helped organized a number of efforts at the intersection of AIED, Data Mining and CS Education, including the CS-SPLICE working group on programming snapshot representation and prior CSEDM and CS-SPLICE workshops.
Ken Koedinger is a Professor of Human Computer Interaction and Psychology at Carnegie Mellon University. Dr. Koedinger has an M.S. in Computer Science, a Ph.D. in Cognitive Psychology, and experience teaching in an urban high school. His multidisciplinary background supports his research goals of understanding human learning and creating educational technologies that increase student achievement. His research has contributed new principles and techniques for the design of educational software and has produced basic cognitive science research results on the nature of student thinking and learning. Koedinger directs LearnLab, which started with 10 years of National Science Foundation funding and is now the scientific arm of CMU’s Simon Initiative. 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 co-founder of CarnegieLearning, Inc. that has brought Cognitive Tutor based courses to millions of students since it was formed in 1998, and leads LearnLab, now the scientific arm of CMU’s Simon Initiative. Dr. Koedinger has authored over 250 peer-reviewed publications and has been a project investigator on over 45 grants. In 2017, he received the Hillman Professorship of Computer Science and in 2018, he was recognized as a fellow of Cognitive Science.
Paulo Carvalho is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research explores how AI can revolutionize learning by creating engaging, practice-first environments. He uses data analytics and computational modeling to understand student learning, motivation, and meta-cognition and develop precise models for better learning experiences. He’s currently investigating how generative AI can power these practice-focused approaches, boosting engagement and freeing teachers to provide personalized support.
Shan Zhang is a PhD student in the educational technology program at the University of Florida. Before that, she gained her Ed.M. degree from Harvard University. Her research focuses on multimodal learning analytics, educational data mining, and AI in education and AI education. Shan’s recent work explores integrating AI into K-12 education, applying multimodal learning analytics and natural language processing (NLP) techniques to analyze collaborative learning features and affect in computer science, and math learning environments, and developing learner models.
Andrew (Shiting) Lan is an Assistant Professor in the Manning College of Information and Computer Sciences, University of Massachusetts Amherst. Before that, he was a postdoctoral research associate in the EDGE Lab at the Department of Electrical Engineering, Princeton University, and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering in May 2014 and May 2016, respectively, from the Digital Signal Processing (DSP) group at Rice University. His research focuses on the development of artificial intelligence (AI) and especially natural language processing (NLP) methods to enable scalable and effective personalized learning in education, covering areas such as learner modeling, personalization, content generation, and human-in-the-loop AI.
Juho Leinonen is an Academy Research Fellow at Aalto University. His research focuses on creating better insight into students’ learning with fine-grained learning analytics; using educational technology and artificial intelligence for personalizing course content; and using learnersourcing to create ample learning opportunities for distinct student needs. He has served on the program committee of both computing education focused and educational data mining focused conferences.
Arun Balajiee Lekshmi Narayanan | University of Pittsburgh, USA
Luc Paquette | University of Illinois at Urbana-Champaign, USA
Andrew Petersen | University of Toronto, Canada
Juan Pinto | University of Illinois at Urbana-Champaign, USA
Maria Mercedes T. Rodrigo | Ateneo de Manila University, Philippines
Cliff Shaffer | Virginia Tech, USA
Andy Smith | North Carolina State University, USA
Cansu Tatar | Northern Illinois University, USA
Khushboo Thaker | University of Pittsburgh, USA
Leo Ureel II | Michigan Technological University, USA
Zichao Wang | Adobe, USA
Yingbin Zhang | South China Normal University, China
Zhikai Gao | North Carolina State University, USA
Adam Gaweda | North Carolina State University, USA
Arto Hellas | Aalto University, Finland
Muntasir Hoq | North Carolina State University, USA
Nguyen-Thinh Le | Humboldt-Universität zu Berlin, Germany
Naiming Liu | Rice University, USA
Michael Liut | University of Toronto, Canada
Lauri Malmi | Aalto University, Finland