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Reducing Difficulty Variance in Randomized Assessments

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Technical Session 6: Modulus Topics Part 2

Tagged Division

Computers in Education

Page Count

13

DOI

10.18260/1-2--33228

Permanent URL

https://peer.asee.org/33228

Download Count

223

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Paper Authors

biography

Paras Sud University of Illinois, Urbana-Champaign

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Paras Sud led this work as his thesis project for his B.S. in Computer Science from the University of Illinois at Urbana-Champaign. He's currently working in industry.

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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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Craig Zilles University of Illinois, Urbana-Champaign

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Craig Zilles is an Associate Professor in the Computer Science department at the University of Illinois at Urbana-Champaign. His research focuses on computer science education and computer architecture. His research has been recognized by two best paper awards from ASPLOS (2010 and 2013) and by selection for inclusion in the IEEE Micro Top Picks from the 2007 Computer Architecture Conferences. He received the IEEE Education Society's Mac Van Valkenburg Early Career Teaching Award (2010), campus-wide Excellence in Undergraduate Teaching (2018) and Illinois Student Senate Teaching Excellence (2013) awards, the NSF CAREER award, and the Univerisity of Illinois College of Engineering's Rose Award and Everitt Award for Teaching Excellence. He also developed the first algorithm that allowed rendering arbitrary three-dimensional polygonal shapes for haptic interfaces (force-feedback human-computer interfaces). He holds 6 patents.

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Abstract

When exams are run asynchronously (i.e., students take it at different times), a student can potentially gain an advantage by receiving information about the exam from someone who took it earlier. Generating random exams from pools of problems mitigates this potential advantage, but has the potential to introduce unfairness if the problems in a given pool are not identical difficulty. In this paper, we present an algorithm that takes a collection of problem pools and historical data on student performance on these problems and produces exams with reduced variance of difficulty (w.r.t. naive random selection) while maintaining sufficient variation between exams to ensure security. Specifically, for a synthetic example exam, we can roughly halve the standard deviation of generated assessment difficulty levels with negligible effects on cheating cost functions (e.g., entropy).

Sud, P., & West, M., & Zilles, C. (2019, June), Reducing Difficulty Variance in Randomized Assessments Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33228

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