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Algorithmic Grading Strategies for Computerized Drawing Assessments

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2018

Conference Session

Assessment & Grading in Mechanics

Tagged Division

Mechanics

Page Count

17

Permanent URL

https://peer.asee.org/27544

Download Count

15

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Paper Authors

biography

Mariana Silva University of Illinois, Urbana-Champaign

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Mariana Silva is an Adjunct Assistant Professor and Curriculum Development Coordinator in the Mechanical Science and Engineering Department at the University of Illinois at Urbana-Champaign. She received her BSME and MSME from the Federal University of Rio de Janeiro, Brazil and earned her Ph.D. in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign in 2009. Besides her teaching activities, Mariana serves as an academic advisor in the Mechanical Science and Engineering department.

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biography

Matthew West University of Illinois, Urbana-Champaign

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Matthew West is an Associate Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. Prior to joining Illinois he was on the faculties of the Department of Aeronautics and Astronautics at Stanford University and the Department of Mathematics at the University of California, Davis. Prof. West holds a Ph.D. in Control and Dynamical Systems from the California Institute of Technology and a B.Sc. in Pure and Applied Mathematics from the University of Western Australia. His research is in the field of scientific computing and numerical analysis, where he works on computational algorithms for simulating complex stochastic systems such as atmospheric aerosols and feedback control. Prof. West is the recipient of the NSF CAREER award and is a University of Illinois Distinguished Teacher-Scholar and College of Engineering Education Innovation Fellow.

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Abstract

Introductory mechanics courses have important learning objectives focusing on students' ability to accurately draw or sketch particular types of diagrams, such as free body diagrams and graphs of shear forces and bending moments in beams. To achieve mastery of these drawing skills it is essential that students have many opportunities to practice and that they receive rapid and accurate feedback on whether they are drawing the correct diagram for a given mechanical problem. With the growing student enrollment in many engineering programs, however, it becomes increasingly difficult to provide prompt and accurate grading using the traditional approach of having students submit hand-drawn diagrams which are graded by a teaching assistant or grader. Instead, an appealing alternative is to use automated computer-based systems for presenting problems to students, in which they draw a diagram or graph on the computer and this can then be immediately graded algorithmically and feedback returned.

The central question addressed by this paper is as follows. When receiving a computer-drawn mechanical diagram or sketch from a student, what algorithmic procedure should be used to grade the submission? We present a general algorithmic framework for grading diagrams that addresses five key functionality requirements: (1) the algorithm should be able to provide students with meaningful feedback about errors in their diagram, (2) the algorithm should be easy to understand for problem authors, and require only data which is readily available to authors, (3) the algorithm should be adaptable to different types of drawings or sketches, (4) the algorithm should be fast to execute, and (5) the algorithm should be robust to unexpected or unusual inputs.

The algorithm we describe satisfies all of these requirements and it was implemented in a computerized drawing system used for a large introductory mechanics course at Midwestern University with approximately 200 students. We present results from three evaluation sources: (1) student interaction data with the system, (2) student affect data reported via survey and anonymous written feedback, and (3) instructor feedback. The results indicate that the system was able to efficiently and robustly grade student diagrams and provide formative feedback, and that students significantly increased the number of practice problems that they solved using this system when compared to the traditional pen-and-paper method previously used.

Silva, M., & West, M. (2017, June), Algorithmic Grading Strategies for Computerized Drawing Assessments Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/27544

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