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.
Highlights
A system was first described in this journal that assessed student learning style and activity from the way that they interacted with their User Interface [1]
Newer systems attempted to meet this need, such as iLessons, a Web browser-based system embedded within Microsoft Internet Explorer [5] that was created during the research
Feedback from teachers using iLessons suggested that while allowing and denying specific areas of the Internet was an effective way of controlling the misuse of the Internet during a lesson and of focusing students’, they were not able to use the Internet to carry out their own research
Summary
A system was first described in this journal that assessed student learning style and activity from the way that they interacted with their User Interface [1]. Results were used to recommend suitable pages found by other students with a similar learning style. Web pages that could predict some student learning styles based on their behaviour while browsing the Internet. That work in [1] and the new work described in this paper were tested with groups of University students who had different learning styles while they performed various vi engineering research tasks on the WWW and an agent monitored their activity. The volunteers completed an Index of Learning Styles (ILS) Questionnaire that defined. On their learning styles and the questionnaire results were compared with those predicted by the automatic models for each student
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