The next step is to automate the process of evaluating students problem solving strategies. Artificial neural networks may be quite useful for this purpose. The maps produced by Analysis require teacher interpretation, are not quantitative and don t provide real time feedback to students. The neural networks, on the other hand, employ pattern recognition software programmed to detect the subtle patterns that translate to success, distinguishing among strategies in a quantifiable way. We re trying to quantify the problem solving process in a way that is simple and informative enough that teachers will want to use it, but also research oriented enough that teachers interested in pursuing advanced degrees will be able to explore the subject further, Stevens says.
Efforts are also focused on teaching instructors how to program their own content.
We have K 12 teachers who are now writing grants, giving talks and returning to school for advanced degrees in this material, Stevens reports. In addition, approximately 80 high school and middle school teachers have attended three day UCLA workshops, where they learn how to integrate the programs into the classroom setting. We re looking at what students need to know prior to using the program, what types of follow ups are desirable and what supplementary materials are needed if students are to record their observations and results, says Stevens, who is actively pursuing such questions with researchers in UCLA s Graduate School of Education and Information Studies. Over the 12,000 times people have run the immunology problems, essentially no two students have followed exactly the same sequence of tests, he says. The learning of complex skills is very individualistic, and yet we continue to stand up and lecture to large groups of students as if they were all the same.