DataShop: An Educational Data Mining Platform for the Learning Science Community @ CREATE Lab, 196 Mercer St, 8F

05/04/2012 2:30 pm – 05/04/2012 4:00 pm In this talk, John Stamper will discuss his vision of creating a true platform for conducting educational data mining and learning analytics research. The talk will focus on his work with DataShop, which is an open data repository and set of associated visualization and analysis tools for educational data. DataShop has data from thousands of students deriving from interactions with on-line course materials and intelligent tutoring systems. The data is fine-grained, with student actions recorded roughly every 20 seconds, and it is longitudinal, spanning semester or yearlong courses. As of April 2012, over 325 datasets are stored including over 70 million student actions which equates to almost 200,000 student hours of data. Most student actions are “coded” meaning they are not only graded as correct or incorrect, but are categorized in terms of the hypothesized competencies or knowledge components needed to perform that action. He will discuss how the repository has been used to make insights on learning and some of the key issues we face in developing an open data repository, including security, privacy, and data diversity. John Stamper is a member of the research faculty 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. 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. John is a Microsoft Certified Systems Engineer (MCSE) and a Microsoft Certified Database Administrator (MCDBA). John was the co-chair of the 2010 KDD Cup Competition, titled “Educational Data Mining Challenge,” which centered on improving assessment of student learning via data mining.