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Development of a Cognitive Tutor for Learning Truss Analysis

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Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

Teaching Statics

Tagged Division

Mechanics

Page Count

14

Page Numbers

24.413.1 - 24.413.14

Permanent URL

https://peer.asee.org/20304

Download Count

35

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Paper Authors

biography

Paul S. Steif Carnegie Mellon University

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Paul S. Steif is a Professor of Mechanical Engineering at Carnegie Mellon University. He received a Sc.B. in engineering from Brown University (1979) and M.S. (1980) and Ph.D. (1982) degrees from Harvard University in applied mechanics. He has been active as a teacher and researcher in the field of engineering education and mechanics. His research has focused on student learning of mechanics concepts and developing new course materials and classroom approaches. Drawing upon methods of cognitive and learning sciences, he has led the development and psychometric validation of the Statics Concept Inventory – a test of statics conceptual knowledge. He is the co-author of Open Learning Initiative (OLI) Engineering Statics, and he is the author of a new textbook Mechanics of Materials, published by Pearson.

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Levent Burak Kara Carnegie Mellon University

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Luoting Fu

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Abstract

Development of a cognitive tutor for learning truss analysis  Statics poses many conceptual challenges to students and offers exposure to realisticsystems and to a style of analysis important throughout engineering: subsystem isolation.The ability to solve problems is a principal goal of statics; yet students traditionallyreceive the least contact with instructors as they practice solving problems. With thecomputer, one may be able to construct environments that offer instruction and feedbackto 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 abilityof the computer to ascertain, judge, and give feedback on what students have done. Onthe one hand, one could give students a blank piece of paper (or a computer tablet) andask them to conduct their analysis; but it would be quite challenging to interpret whatstudents are drawing and respond to it. On the other hand, we could ask students a seriesof multiple-choice questions, with carefully chosen answers; while the responses areinterpretable, they give no indication if students could independently solve a full problemon 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 ripefor effective computer-assisted problem solving: students can have reasonably broadlatitude to solve truss problems, while the computer can track students’ work in detail andprovide 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 ofskills or knowledge components needed to solve trusses, as well as the common incorrectactions typical of novice learners. We show how the errors typically committed bystudents 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.  

Steif, P. S., & Kara, L. B., & Fu, L. (2014, June), Development of a Cognitive Tutor for Learning Truss Analysis Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/20304

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