The Effect of Information Technology on Economic Education

Abstract

The author evaluated the effect on student performance of using a new information technology (IT) enhancement that permits students to participate in the recording of lectures that can be downloaded later from the Internet. The author compared two sections of the same Intermediate Microeconomics class and observed the sample students to be representative; the empirical model accounted for any differences in student characteristics between the comparison and test groups. Model results show that students exposed to the IT enhancement performed about 2 percentage points better on their final exam than did the comparison students; however, the difference was not statistically different from zero. The author concluded that the use of IT appears to not have any substantive influence on student performance.

Keywords:

• attendance
• economic education
• information technology
• Internet
• student performance

JEL codes:

• A2
• A22

Scott J. Savage is an associate professor at the University of Colorado, Boulder (e-mail: [email protected]). He thanks Nicholas Flores, Peter Kennedy, Mark-Andreas Muendler, Mark Siegler, Donald Waldman, and the seminar participants of the 82nd Western Economic Association meetings. The University of Colorado's Economics Club provided financial support for the information technology used in the experiments and Andy Bartlett, Dale Hinkle, and Donna Phillips provided technical assistance. Blake Redabaugh provided excellent assistance with student records.

Notes

*Significant at the .05 Type I error level.

^†Significant at the .1 Type I error level.

*Significant at the .05 Type I error level.

**Significant at the .01 Type I error level.

^†Significant at the .1 Type I error level.

*Significant at the .05 Type I error level.

**Significant at the .01 Type I error level.

1. Agarwal and Day (1998), Brown and Leidholm (2002), and Sosin et al. (2004) are exceptions and are reviewed in the next section. In a related literature, Chan, Miller, and Tcha (2005) and Flores and Savage (2007) examined how IT enhancements in administration and in the classroom, respectively, affect economics students’ satisfaction.

2. A tablet is a notebook with a touch screen operated by a digital pen. The instructor faces the classroom while talking and writing, and he or she projects the presentation onto a screen with an overhead projection unit. The lecture presentation is simultaneously recorded with a wireless microphone and Web camera.

3. Tegrity Instructor Software allows instructors to deliver effective multimedia instruction by teaching naturally and annotating PowerPoint, video, screen recording, or simply writing and drawing on empty pages. See http://www.tegrity.com/instructor_software.php.

4. Students register for classes through the CUConnect online portal. They indicate their preferences and the system allocates a section on a first come first served basis. Wait-list requests are prioritized by economics major senior, economics major junior, and everyone else. Students may be admitted directly with overriding permission from the instructor. There were no overriding admissions in the sections used in these experiments, and no student transferred between the comparison and test sections before or during the semester.

5. The comparison and test sections had the same final exams.

6. The estimated coefficients on SENIOR and JUNIOR measure differences in attendance rates between seniors and sophomores (the omitted category) and juniors and sophomores, respectively.

7. Ideally, students should have the prerequisites to take ECON 3070. However, the admission system does not prevent students without the prerequisites from enrolling. Typically, these students simultaneously enroll in the prerequisites and ECON 3070.

8. By evaluating the effect of random assignment on student performance only, this definition of T provides the cleanest evaluation of the effect of the IT enhancement. Given the description of the mobile multimedia podium, this is the policy-relevant evaluation because students cannot be given access to recorded lectures over the Internet without some exposure to the tablet computer and peripheral technologies.

9. The Breusch-Pagan test, χ²(1) = 0.48, p > |χ²| = .49, indicates that the errors in the general model of performance, model (I), do not have different variances between the comparisons and test groups. Similar statistics, not reported, also indicate that this form of heteroskedasticity is not present in specifications (II) through (IV).

10. Figure 1 plots the residuals from the general model for all n = 80 sample students and shows there is no single observation that is far removed from the rest of the observations. Estimates of the regression parameters do not appear to be influenced by outliers.

11. One reason study time has relatively little explanatory power is that some students are smart and do not need to study much, whereas other students are not so smart and need to study a lot. This means it is possible for two students with the same GPA to choose different quantities of study time, but, because of differing innate ability, they score exactly the same final grade. Time spent studying was also be proxied by JOB_HOURS (i.e., 0 = worked zero job hours per week, 1 = < five, 2 = 5–15, 3 = 15–25, 4 = 25–35, and 5 = > 35); the more hours worked at a job, the less time spent studying. The results of a regression using JOB_HOURS as the proxy for time spent studying (instead of STUDY_TIME), not reported here, were similar to those reported for model (II) in Table 5.

12. Even if it was more precisely estimated, the economic significance of the effect of the IT enhancement was relatively small. Equation (3) indicates that ΔA_i =λ/μ = 2.235/0.320 = 6.98. This indicates that the effect of the IT enhancement is equivalent to missing about 1 in 13 classes.

13. No other demographics were available for the Summer 2005 class to make additional comparisons.