g:i ag:i aImproving Educational Technology through Discoveries in Big Data - AIM Analyticsg:i a - g:i a


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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.

John StamperJohn 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.  


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