No abstract

Recent significant advances in automatically predicting user learning styles are described. The system works with new client-based systems that filter Web pages and provide easy, structured, focused, and controlled access to the Internet. A first system called iLessons was embedded within Microsoft Internet Explorer 6 and provided teachers with tools to create lesson Web pages, define zones of the Internet that could be accessed during a lesson, and enforce these settings in a set of computers. A second system enabled students to investigate and collaborate using the Internet. The system filtered Web pages based on the relevance of their contents and assisted students by inferring their learning style (active or reflective) and by recommending pages found by fellow students based on page relevancy, student learning style, and state of mind measured by activity. The system infers learning style in real time by monitoring user activity and recent significant advances in the research are described.

Methods arc described to create more accurate sub sets of user data by introducing dead bands into data clusters. User data is collected and then mined. That produces clusters of data. Dead bands arc then generated to delineate and describe the data in the clusters more accurately. This is accomplished by classifying data inside the newly created dead bands as NOT being in either of two or more clusters. For example, three clusters arc generated f r om two. If the t w o were YES and NO then another set of DON'T KNOW is introduced. The new set improves the precision of choices made using data in the YES and the NO clusters. Dead bands arc introduced by establishing a radius f r om the corners of2-D shapes containing the clusters or by establishing a horizontal or vertical line in parallel with the edges. Each radius or edge encompasses 80% of user data nearest to the corner or edge of the data set. 20% arc outside and excluded f r om their original set. If lines do not overlap, then a dead-band is created to contain user data that is not as confident. That increases the likelihood of accurate decisions being made about the new sets of user data. Case studies are described to demonstrate that.