A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials

It has been recognized that in order to drive Intelligent Tutoring Systems (ITSs) into mainstream use by the teaching community, it is essential to support teachers through the entire ITS process: Design, Development, Deployment, Reflection and Adaptation. Although research has been done on supporting teachers through design to deployment of ITSs, there is surprisingly little discussion about support for teachers' Reflection - the ability to draw conclusions from ITS usage, and Adaptation - adapting the content to better meet the needs of students. We describe our work on developing analysis tools and methodologies that support reflection and adaptation by teachers. The work was done in the context of helping teachers understand student's behavior in Adaptive Tutorials by post-analysis of the system's data-logs. We used a hybrid solution - part of the data-mining effort is teacher driven and part is automated. We tested our approach by comparing the results of expert analysis of two Adaptive Tutorials with and without an automated Refinement Suggestion Tool, and found it to be a useful teacher's aid. By using this tool, teachers act as 'action researchers', confirming or disproving their hypotheses about the best way to use ITS technology.