The project is exploring the use of concepts from cognitive load theory to develop educational materials and teaching strategies that focus on providing problems appropriate to a student's prior knowledge so that the material is more likely stored in long-term memory for use in future situations. Results show that an overloaded working memory leads to students who can solve the problems they are working on, but who do not store the material in long-term memory for use later. Part of this overload comes from the material itself; the other part comes from instruction designs that lead to increased mental effort and no schema acquisition and construction. To provide a proof-of-concept for this approach, materials are being developed for a course in electric circuit analysis. The goal of this instructional change is to improve a student's ability to solve complex, ill-defined problems. This program is developing three different set of materials: a rating scale for electric circuit problems using a community of experts, a set of completion problems which guide students in forming appropriate schema for long term memory storage, and a set of advanced problems which measure a student's ability to apply course material in novel situations. Evaluation efforts, conducted with the help of an outside evaluator, are measuring changes in students' ability to solve both standard and novel problems by scoring student work with standard rubrics. Project materials and results are being disseminated through presentation and publication in the engineering education outlets and by posting material on the instructor's website and on special interest websites (Connections at Rice University and Problem-Based Learning Clearinghouse at University of Delaware). The broader impacts include the dissemination of the material and the potential for adapting this approach to other engineering and science course.
