Development of a cognitive tutor for learning truss analysis Statics poses many conceptual challenges to students and offers exposure to realistic systems and to a style of analysis important throughout engineering: subsystem isolation.The ability to solve problems is a principal goal of statics; yet students traditionally receive the least contact with instructors as they practice solving problems. With the computer, one may be able to construct environments that offer instruction and feedback to students while learning, without the presence of an instructor. But there is a trade off:between how much freedom students have to create solutions to problems and the ability of the computer to ascertain, judge, and give feedback on what students have done. On the one hand, one could give students a blank piece of paper (or a computer tablet) and ask them to conduct their analysis; but it would be quite challenging to interpret what students are drawing and respond to it. On the other hand, we could ask students a series of multiple-choice questions, with carefully chosen answers; while the responses are interpretable, they give no indication if students could independently solve a full problem on their own. We refer to this as the latitude-interpretation trade-off.The analysis of trusses via method of joints and method of sections is a topic that is ripe for effective computer-assisted problem solving: students can have reasonably broad latitude to solve truss problems, while the computer can track students’ work in detail and provide feedback. Truss analysis presents this opportunity because the forms of solutionsare well structured and the common range of student errors can be identified.In this paper, we describe the development of a cognitive truss tutor. The tutor is deemeda cognitive tutor in the sense that there is an underlying cognitive model for the set of skills or knowledge components needed to solve trusses, as well as the common incorrect actions typical of novice learners. We show how the errors typically committed by students in solving truss problems are also allowed by the tutor. We explain how thetutor’s design imposes modest constraints on user actions relative to fully free paper-and-pencil solving, but still enable full interpretation of student work. Students have used thetutor in place of written homework in regular statics courses, and data on this usage hasbeen collected. Results from initial analysis suggest that students commit fewer errors asthey use the tutor.
Paul S. Steif, Luoting Fu, Levent Burak Kara. (2014). Development of a Cognitive Tutor for Learning Truss Analysis. American Society for Engineering Education Annual Conference. June 15-18, 2014. Indianapolis, Indiana.