Multiple Choice Analyzer


How many instructors revise their multiple-choice tests based on their students’ responses? If one wishes to make a better multiple-choice test, a statistical analysis of these responses could be helpful.

We have developed a freely available web-based program to analyze a multiple-choice test, and it requires no programming experience – just your student response data and the answer key in two excel spreadsheets.

The program outputs a pdf file that goes beyond the usual scoring but includes many other metrics, which would help the instructor to revise the test for improving its reliability.

The outputs include difficulty index, discrimination index, Cronbach alpha, item response theory, and distractor analysis. It also outputs if a question should be kept or removed based on four different criteria. The source code is open-access, and we invite others to improve it.

The description is given here at https://www.garrickadenbuie.com/project/mc-test-analysis/

The program can be accessed at https://apps.garrickadenbuie.com/mctestanalysis/

How to make the input excel files is at http://www.eng.usf.edu/~kaw/MCTestAnalysis/MCTestAnalysis_input.pdf

An example of a hypothetical output report is here at https://www.garrickadenbuie.com/project/2017-07-06-mc-test-analysis_files/MCTestAnalysis_Example-Report.pdf

The source code for developers is at https://github.com/gadenbuie/mctestanalysis

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Author: Autar Kaw

Autar Kaw (http://autarkaw.com) is a Professor of Mechanical Engineering at the University of South Florida. He has been at USF since 1987, the same year in which he received his Ph. D. in Engineering Mechanics from Clemson University. He is a recipient of the 2012 U.S. Professor of the Year Award. With major funding from NSF, he is the principal and managing contributor in developing the multiple award-winning online open courseware for an undergraduate course in Numerical Methods. The OpenCourseWare (nm.MathForCollege.com) annually receives 1,000,000+ page views, 1,000,000+ views of the YouTube audiovisual lectures, and 150,000+ page views at the NumericalMethodsGuy blog. His current research interests include engineering education research methods, adaptive learning, open courseware, massive open online courses, flipped classrooms, and learning strategies. He has written four textbooks and 80 refereed technical papers, and his opinion editorials have appeared in the St. Petersburg Times and Tampa Tribune.

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