Inferring learning style from the way students interact with a computer user interface and the WWW

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.