Do Proctored Exams Matter In Online Classes?

Does the format of assessment (proctored or un-proctored exams) affect test scores in online principles of economics classes? This study uses data from two courses of principles of economics taught by the same instructor to gain some insight into this issue. When final exam scores are regressed against human capital factors, the R-squared statistic is 61.6% for the proctored format exams while it is only 12.2% for the un-proctored format. Three other exams in the class that had the proctored final were also un-proctored and also produced lower R-squared values, averaging 30.5%. These two findings suggest that some cheating may have taken place in the un-proctored exams. Although it appears some cheating took place, the results suggest that cheating did not pay for these students since the proctored exam grades were 4.9 points higher than the un-proctored exam grades although this difference was significantly different at only the 10% level. One possible explanation for this is that there was slightly higher human capital in the class that had the proctored exam although this must have occurred by chance since the students did not know if the exams were going to be proctored in advance so there is no issue of selection bias. A Oaxaca decomposition of this difference in grades was conducted to see how much was due to human capital and how much was due to the differences in the rates of return to human capital. This analysis reveals that 17% of the difference was due to the higher human capital with the remaining 83% due to differences in the returns to human capital. It is possible that the un-proctored exam format does not encourage as much studying as the proctored format reducing both the returns to human capital and the exam scores.

Is the Cheating Risk Always Higher in Online Instruction Compared to Face-to-Face Instruction?

This article analyzes the exposure to cheating risk of online courses relative to face-to-face courses at a single institution. For our sample of 20 online courses we report that the cheating risk is higher than for equivalent face-to-face courses because of reliance on un-proctored multiple choice exams. We conclude that the combination of a proctored final exam, and strategic use cheating deterrents in the administration of un-proctored multiple choice exams, would significantly reduce the cheating risk differential without substantially altering the assessment design of online instruction.

The Relationship of Student Demographics to 10th Grade MCAS Test Anxiety

The intensification of consequential testing situations is associated with an increase in anxiety among American students (Casbarro, 2005). Test anxiety can have negative effects on student test performance (Everson, Millsap, & Rodriguez, 1991). If test anxiety has the potential to decrease students’ test scores, it becomes a factor that can threaten the validity of any inferences drawn between test scores and student progress (Cizek & Burg, 2006). There are several factors that relate closely to test anxiety (Cizek & Burg, 2006). Variables of key influence include gender, socioeconomic status, and teacher-manifested anxiety (Hembree, 1988). Another influence upon test anxiety is students’ participation in academic support programs to prepare them for exit examinations. The purpose of this study was to examine the relationship between 10th grade high school student gender, socioeconomic status, perceived teacher anxiety, and student preparedness with levels of the Massachusetts Comprehensive Assessment System (MCAS) test anxiety. It appears that few studies have examined levels of high school test anxiety in regards to this specific high-stakes MCAS exit exam required for high school graduation. A two-phase sequential mixed-methods research design was used to survey (N=156) 10th grade students represented by a sampling of (n=80) students with low socioeconomic status and (n=76) students with high socioeconomic status regarding their levels of test anxiety in relation to upcoming MCAS testing. A multiple regression analysis was used to measure the relationship between the predictor variables (gender, socioeconomic status, perceived teacher anxiety, and student preparedness) with the criterion variable of student test anxiety using the Test Anxiety Inventory (TAI). Personal interviews with (n=20) volunteer students provided rich explanations of students’ academic self-efficacy, their perceptions of their performance on the upcoming MCAS exam, and their use of strategies to reduce their levels of test anxiety. Personal interviews with (n=12) volunteer school administrators and teachers provided descriptions of their perceptions of how test anxiety affected their students’ performance. A major quantitative finding of this study was that the variables of student socioeconomic status and student ratings of teacher anxiety accounted for the variance in students’ levels of surveyed test anxiety (R2 = .06, p = .033, small to medium effect size). These results indicate that different student populations vary in their readiness skills to successfully participate in consequential testing situations. Consequently, high-test anxious students would require emotional preparation as well as academic preparation when confronting high-stakes testing. The results have the potential to re-shape the format of schools’ MCAS test preparation efforts.

Student Performance in Traditional vs. Online Format: Evidence from an MBA Level Introductory Economics Class

All previous studies comparing online and face-to-face format for instruction of economics compared courses that were either online or face-to-face format and regressed exam scores on selected student characteristics. This approach is subject to the econometric problems of self-selection omitted unobserved variables. Our study uses two methods to deal with these problems. First we eliminate self-selection bias by using students from a course that uses both instruction formats. Second, we use the exam questions as the unit of observation, and eliminate omitted variable bias by using an indicator variable for each student to capture the effect of differences in unobserved student characteristics on learning outcomes. We report the finding that students had a significantly greater chance of answering a question correctly if it came from a chapter covered online.