Online proctoring discount: The role of measured stressors

Abstract

The authors of this article examine the effect of online proctoring on exam scores with samples of proctored and unproctored students in an upper-level economics class. They document a proctoring discount (i.e., the tendency for lower scores on proctored exams versus on unproctored exams) and a relationship with students’ perceptions measured by trust and a belief in a perfect online proctoring system. The authors suggest that the elimination of the proctoring discount is a worthwhile goal of the educational community as online learning and assessment activities have become increasingly popular since the COVID-19 pandemic.

Keywords:

• Academic integrity
• online assessment
• online proctoring

JEL CODES:

• A20, A22, I23

Disclosure statement

The authors have no financial interest or benefit from the direct application of this research. They also received no financial support for the creation of this research.

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data are not available.

Notes

1 Yates and Beaudrie (2009) obtain findings for final grades in online math courses with proctored and unproctored exams consistent with Beck (2014), i.e., no performance difference associated with proctoring.

2 Alessio et al. (2018) focus on the time-spent-on-exam differentials for proctored and unproctored students, finding results consistent with Harmon and Lambrinos (2008).

3 All data collected in support of this study was approved by the university’s IRB, and the authors charged with handling the raw data attended the requisite mandatory training.

4 Students recognized by the university as needing assistance due to a disability were dropped from our sample as a suitable accommodation is often extended testing time or the use of technology or human-based reading assistance.

5 We note that our article’s results use only the exam scores and do not include any bonus points for survey participation or practice exams.

6 Table 1 reports 139 observations, and table 2 reports 194 observations. This difference in the number of observations arises because 1) not all student information is reported in the university administrative records, and 2) each student appears twice as two exams are used as separate observations in the regression analyses.

7 A correlation matrix is available upon request; it offers no evidence of significant multicollinearity among adopted variables.

8 A detailed summary of all questions used in this research and their mean values is available upon request.

9 Trust*ProctorPerfect is kept in the analysis for the specification of eq. (3) and eq. (4) in the spirit of controlling for a potential interaction.

10 The total effects are, in turn, −34.309 + (7.967*2.42) = −15.029, and −36.402 + (8.143*2.42) = −16.696. The 2.42 is equal to the average of ProctorPerfect when Proctor = 1.

11 Our sample did not show different results by PriorGPA groupings of students vis-à-vis the proctoring discount, nor did a Proctor and PriorGPA interaction term prove significant. Further, the university does not require a standardized nationally normed test score for admission, and few students had one in the official university records.