Automated Pattern Extraction from Mobile Educational Games over Wireless Networks for Advanced Personalized Education

Project Summary
Studying the behavior of students playing the mobile educational games, may open up new possibilities. For example, we may be able to analyze this data for understanding the collective behavior of the students in response to certain lessons and questions with intrinsic educational values. Statistical techniques such as principal component analysis of such data may expose the underlying strengths and weaknesses of the different sub-groups of students. It may also help identify the salient related dimensions of the multi-variate environment that are really controlling the learning process. Such information can be used to customize curriculum, tests, and class-room presentations. This research will develop a system to do these. Specific tasks involve: 1. Support cell-phone-based mobile ad-hoc networks of students.
2. Develop performance data collection modules on-board the cell-phones.
3. Develop policy-driven privacy-preserving distributed data mining (DDM) algorithms for analyzing the distributed data generated by the cell-phone-based educational games using asynchronous algorithms.
 

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