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Best Practices for Using Algorithmic Calculated Questions via a Course Learning Management System

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Tips of the Trade: Best Practices, Expanded Advice, and Strategies for Implementable Course Improvement

Tagged Division

New Engineering Educators

Tagged Topic

Diversity

Page Count

22

DOI

10.18260/p.26377

Permanent URL

https://peer.asee.org/26377

Download Count

1809

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Paper Authors

biography

Gillian M. Nicholls Southeast Missouri State University

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Dr. Gillian M. Nicholls is an Assistant Professor of Quantitative Methods at Southeast Missouri State University. Her research interests are in applying statistical analysis and optimization to supply chain management, transportation management, and engineering education. She holds the B.S. in Industrial Engineering (Lehigh University), Masters in Business Administration (Penn State University), M.S. in Industrial Engineering (University of Pittsburgh.), and Ph.D. in Industrial Engineering (University of Pittsburgh). Prior to entering academia, Dr. Nicholls was a practicing industrial engineer in the freight transportation industry. Address: Donald L. Harrison College of Business, Southeast Missouri State University, One University Plaza – MS 5815, Cape Girardeau, MO 63701; telephone (+1) 573.651.2016; fax: (+1) 573.651.2992; e-mail: gnicholls@semo.edu.

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William J. Schell IV P.E. Montana State University Orcid 16x16 orcid.org/0000-0001-8626-1671

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Dr. William J. Schell holds a Ph.D. in Industrial and Systems Engineering – Engineering Management from the University of Alabama in Huntsville and M.S. and B.S. degrees in Industrial and Management Engineering from Montana State University (MSU). He is an Assistant Professor in Industrial and Management Systems Engineering at MSU with research interests in engineering education and the role of leadership and culture in process improvement and serves as an Associate Editor for both the Engineering Management Journal and Quality Approaches in Higher Education. Prior to his academic career, he spent 14 years in industry where he held leadership positions focused on process improvement and organizational development.

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Neal Lewis University of Bridgeport

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Neal Lewis received his Ph.D. in engineering management in 2004 and B.S. in chemical engineering in 1974 from the University of Missouri – Rolla (now the Missouri University of Science and Technology), and his MBA in 2000 from the University of New Haven. He is an associate professor in the School of Engineering at the University of Bridgeport. He has over 25 years of industrial experience, having worked at Procter & Gamble and Bayer. Prior to UB, he has taught at UMR, UNH, and Marshall University. Neal is a member of ASEE ,ASEM, and IIE.

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Abstract

Textbook publishers have created online algorithmic problem banks for books that have high enrollment. These banks allow an instructor to assign problems from the book with students submitting their answers via an online interface. The algorithmic problems ensure that each student gets a different combination of problem parameter values. Problems are graded automatically with partial credit and immediate feedback available. Instructors benefit by not needing to grade the problems. Students benefit by potentially having multiple attempts to solve each problem with feedback in between attempts. However, these online resources are only available for large enrollment courses where it is financially feasible for publishers to create them, and there is normally an extra cost to the students for access.

For instructors teaching courses without such publisher resources or for those wanting additional assignments outside the publisher systems, many commonly used Learning Management Systems (LMS) have similar functionality. A number of the LMS packages in current use such as Blackboard™, Moodle™, Brightspace™, and Canvas™ have the capability for creating calculated quiz questions with algorithmic features. This allows an instructor to design an online question with a range of values for one or more parameters in a problem such that each attempt will have a different correct answer.

This paper presents best practices for designing and using algorithmic calculated questions for quizzes and/or homework. The paper discusses ways to build the questions in advance, test possible answer combinations, and design likely wrong answers for partial credit and feedback. The pros and cons of using these calculated questions are reviewed. Examples and actual experiences from using the questions demonstrate that this is a beneficial way for instructors to enrich the learning experience while streamlining grading. This is an efficient way for a new engineering educator to gradually build a set of automated problems that can be modified to create new problems with minimal additional effort.

Nicholls, G. M., & Schell, W. J., & Lewis, N. (2016, June), Best Practices for Using Algorithmic Calculated Questions via a Course Learning Management System Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26377

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