InterJournal Complex Systems, 1018
Status: Accepted
Manuscript Number: [1018]
Submission Date: 2004
Dynamics of Innate Spatial-Temporal Learning Process: Data Driven Education Results Identify Universal Barriers to Learning
Author(s): Wenjie Hu ,Mark Bodner ,Edward G. Jones ,Matthew R. Peterson

Subject(s): CX.41



Spatial-temporal (ST) reasoning - thinking in patterns - data from computerized STAR (ST Animation Reasoning) video games designed to dynamically enhance students learning mathematical concepts ( are gathered over the internet. ST reasoning has been shown to be innate to the structured columnar cortex and to be highly trainable. The data containing fundamental ST information (learning curves) are analyzed and then sent back to teachers so that students can be trained efficiently in an interactive manner. Here we report the use of data mining techniques to examine the dynamics of the learning process - Data Driven Education (DDE). The learning curves for each STAR game, played a number of times on several days, are grouped into different categories according to contours, identifying the different phases of learning. We present our first DDE results from > 2,200 2nd graders on one STAR game showing plateaus in the learning curves. These plateaus are then identified with universal sharp barriers to learning related to specifics in the game design common to many computer games. Simple changes in the design of this game will be tested to see if these sharp barriers to rapid learning are removed. Further DDE studies will not only provide fundamental information on how learning occurs, but may also form the basis for a revolution in education.

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