Examining the Effect of Proctoring on Online Test Scores

Helaine Mary Alessio, Nancy J. Malay, Karsten Maurer, A. John Bailer, Beth Rubin


Online education continues to grow, bringing opportunities and challenges for students and instructors. One challenge is the perception that academic integrity associated with online tests is compromised due to undetected cheating that yields artificially higher grades. To address these concerns, proctoring software has been developed to address and prevent academic dishonesty. The purpose of this study was to compare online test results from proctored versus unproctored online tests. Test performance of 147 students enrolled in multiple sections of an online course were compared using linear mixed effects models with nearly half the students having no proctoring and the remainder required to use online proctoring software. Students scored, on average, 17 points lower [95% CI: 14, 20] and used significantly less time in online tests that used proctoring software versus unproctored tests. Significant grade disparity and different time usage occurred on different exams, both across and within sections of the same course where some students used test proctoring software and others did not. Implications and suggestions for incorporating strategic interventions to address integrity, addressing disparate test scores, and validating student knowledge in online classes are discussed.


online education, academic integrity, online test, grades

Full Text:



Allen, I.E., and Seaman, J. (2015). Grade level: Tracking online education in the United States. Babson Survey Research Group and Quahog Research Group, LLC. Last access on February 3, 2016: http://www.onlinelearningsurvey.com

Beck, V. (2014). Testing a model to predict online cheating: Much ado about nothing. Active Learning in Higher Education, 15(1), 65-75.

Berkey, D., and Halfond, J. (2015). Cheating, student authentication and proctoring in online programs. New England Journal of Higher Education, July 20. Last access on February 3, 2016: http://www.nebhe.org/thejournal/cheating-student-authentication-and-proctoring-in-online-programs.

Corrigan-Gibbs, H., Gupta, N., Northcutt, C., Cutrell, E., and Thies, W. (2015). Deterring cheating in online environments. ACM Transactions on Computer-Human Interaction, 22(6), Article 28. Last access on February 3, 2016: DOI: http://dx.doi.org/10.1145/2810239.

Etter, S., Cramer, J.J., and Finn, S. (2007). Origins of academic dishonesty: Ethical orientations and personality factors associated with attitudes about cheating with information technology. Journal of Research on Technology in Education, 39(2), 133-155.

Grijalva, T.C., Nowell, C., and Kerkvliet, J. (2006). Academic honesty and online courses. College Student Journal, 27(3), 180-185.

Harbin, J. L., and Humphrey, P. (2013). Online cheating: The case of the emperor's clothing, elephant in the room, and the 800 lb. gorilla.

Journal of Academic and Business Ethics, 7, 1-6.

Jones, I.S., Blankenship, D., and Hollier, G. (2013). Am I cheating? An analysis of online students’ perceptions of their behaviors and attitudes. Psychology Research, 3(5), 261-269.

King, C.G., Guyette, R. W., & Piotrowski, C. (2009). Online exams and cheating: An empirical analysis of business students’ views. The Journal of Educators Online, 6, 1, 1-11.

Ladyshewsky, R.K. (2015). Post-graduate student performance in ‘supervised in-class’ versus ‘unsupervised online’ multiple choice tests: implications for cheating and test security. Assessment and Evaluation in Higher Education, 40(7), 883-897. DOI: 10.1080/02602938.2014.956683.

Montgomery, D. (2013). Experiments with Random Factors. In Design and Analysis of Experiments (8th ed.). New York: Wiley.

Moten Jr., J., Fitterer, A., Brazier, E., Leonard, J., and Brown, A. (2013). Examining online college cyber cheating methods and prevention methods. The Electronic Journal of eLearning, 11, (2), 139-146.

Newton, D. (2015). Cheating in online classes is now big business. The Atlantic, (4). Last access on February 3, 2016: http://www.theatlantic.com/education/archive/2015/11/cheating-through-online-courses/413770.

Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D., and R Core Team (2015). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1. http://CRAN.R-project.org/package=nlme.

Raines, D.A., Ricci, P., Brown, S.L., Eggenberger, T., Hindle, T., and Schiff, M. (2011). Cheating in online courses: The student definition. The Journal of Effective Teaching, 11(1), 80-89.

SAS Institute Inc. 2012. Base SAS® 9.4 Cary, NC: SAS Institute Inc.

Stuber-McEwen, D., Wisely, P., and Hoggatt, S. (2009). Point, click, and cheat: Frequency and type of academic dishonesty in the virtual classroom. Online Journal of Distance Learning Administration, 12(2). Last access on February 3, 2016: http://www.westga.edu/~distance/ojdla/fall123/stuber123.html.

Verbeke, G. and Molenbergh, G. (1997). Linear Mixed Models in Practice: A SAS-Oriented Approach. Springer New York.

Watson, G., and Sottile, J. (2010). Cheating in the digital age: Do students cheat more in online courses? Online Journal of Distance Learning Administration, 13(1) Last access on February 3, 2016: http://www.westga.edu/~distance/ojdla/spring131/watson131.html

WCET (2009). Best Practice Strategies to Promote Academic Integrity in Online Education by WCET, UT TeleCampus, and Instructional Technology Council. Last access on February 3, 2016: http://wcet.wiche.edu/sites/default/files/docs/resources/Best-Practices-Promote-Academic-Integrity-2009.pdf.

Wickham,H, and Francois, R. (2015). dplyr: A Grammar of Data Manipulation. R package version 0.4.3. http://CRAN.R-project.org/package=dplyr.

Wickham, H. (2009) ggplot2: Elegant graphics for data analysis. Springer New York.

Yates, R.W., and Beaudrie, B. (2009). The impact of online assessment on grades in community college distance education mathematics courses. American Journal of Distance Education, 23, 62—70.

DOI: http://dx.doi.org/10.24059/olj.v21i1.885

Copyright (c) 2017 Online Learning Journal