========================================================== Call for Participation Workshop on PERSONALIZING EDUCATION WITH MACHINE LEARNING ==========================================================
To be held in conjunction with Neural Information Processing Systems (NIPS 2012) December 8, 2012 Lake Tahoe, Nevada, USA http://www.cs.colorado.edu/~mozer/Admin/PersonalizingEducationNIPS2012/ Overview ======== The field of education has the potential to be transformed by the internet and intelligent computer systems. Evidence for the first stage of this transformation is abundant, from the Stanford online AI and Machine Learning courses to web sites such as Kahn Academy that offer on line lessons and drills. However, the delivery of instruction via web-connected devices is merely a precondition for what may become an even more fundamental transformation: the personalization of education. In traditional classroom settings, teachers must divide their attention and time among many students and hence have limited ability to observe and customize instruction to individuals. Even in one-on-one tutoring sessions, teachers rely on intuition and experience to choose the material and stye of instruction that they believe would provide the greatest benefit given the student's current state of understanding. In order both to assist human teachers in traditional classroom environments and to improve automated tutoring systems to match the capabilities of expert human tutors, one would like to develop formal approaches that can: * exploit subtle aspects of a student's behavior---such as facial expressions, fixation sequences, response latencies, and errors---to make inferences about the student's latent state of knowledge and understanding; * leverage the latent state to design teaching policies and methodologies that will optimize the student's knowledge acquisition, retention, and understanding; and * personalize instruction by providing material and interaction suited to the capabilities and motivational state of the student. Machine learning provides a rich set of tools, extending classical psychometric approaches, for data-driven latent state inference, policy optimization, and personalization. Years ago, it would have been difficult to obtain enough data for a machine learning approach. However, online interactions with students have become commonplace, and these interactions yield a wealth of data. The data to be mined go beyond what is typed: Cameras and microphones are ubiquitous on portable devices, allowing for the exploitation of subtle video and audio cues. Because web-based instruction offers data from a potentially vast collection of diverse learners, the population of learners should serve useful in drawing inferences about individual learners. Mining the vast datasets on teaching and learning that emerge over the coming years may both yield important insights into effective teaching strategies and also deliver practical tools to assist both human and automated teachers. The goal of this workshop is to bring together researchers in machine learning, data mining, and computational statistics with researchers in education, psychometrics, intelligent tutoring systems, and designers of web-based instructional software. Although a relatively young journal and conference on educational data mining has been established (educationaldatamining.org), the field hasn't had as much contact with machine learning theoreticians as one would like. Potential Participants ====================== If you would like to present at the workshop, please send an extended abstract (1 paragraph - 2 pages) by September 16, 2012 to personalizingeducation2...@gmail.com. Include a preference for manner of presentation (10 minute report, 20 minute talk, poster). Acceptance decisions will be announced by October 1, 2012. We hope to draw participants from diverse academic backgrounds, including: * machine learning theoreticians interested in formal approaches to teaching from a computational perspective * AI researchers interested in computer vision and EEG to recover information about an individual's affective and mental state * established researchers in intelligent tutoring systems * psychologists studying practical aspects of human learning and memory * developers of web sites that collect large volumes of student data * distinguished educators, including educators who can discuss the current state of the classroom and educators in the vanguard of the e-ducation revolution Organizers ========== Mike Mozer Institute for Cognitive Science University of Colorado Javier Movellan Institute for Neural Computation UC San Diego Robert Lindsey Department of Computer Science University of Colorado Jacob Whitehill Department of Computer Science and Engineering UC San Diego Deadline Summary ================ September 16: Abstract submission October 1: Participant notification December 8: Workshop
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