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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