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PAPER SUBMISSION

To submit a paper fill in this template and send it to educonf@cemapre.iseg.utl.pt


with the word “submission” in the subject of the email.
The deadline for the submissions is the 15th of October

1.

Title of Paper

Effects of Internet Usage on Academic Performance in a University Setting: Evidence from Portugal










2.

Author's Position

Ryan Turner




Author’s Institutional Affiliation (include city/country)

Carnegie Mellon University – Pittsburgh PA; Institúto Superior Técnico – Lisbon




Author's email address

rjturner@cmu.edu










3.

Second Author’s Name (if any)







Second Author's Position







Second Author’s Institutional Affiliation (include city/country)







Second Author’s email address













4.

Additional Author(s)’ Name(s) in order of authorship (if any)






Additional Author(s)’ Position(s) in order of authorship







Additional Author(s)’ Institutional Affiliation (include city/country)







Additional Author(s)’ email(s) in order of authorship













5.

Presenter (Presenting Author)

Ryan Turner










6.

Three (3) Keyword Descriptors




a.

MSC

62P25, 91G70, 97B40

b.

JEL

C33, I23, O33










7.

THE ABSTRACT




a.

Introduction, Background, and Objectives

The Internet can be beneficial to students but can also be a disruptive distraction. Many schools are anxious to deploy Information and Communications Technologies (ICTs) such as Wifi networks, but the literature remains undecided as to whether, and in what contexts, Wifi is academically “productive”. In the context of education, we believe that the Internet is both an indispensable resource, and a significant source of distraction. We seek to understand the net effect of Wifi Internet on student performance in the context of higher education.


b.

Theoretical or Conceptual Framework (if applicable)

A variety of literature intersect to cover educational technology policy and Internet usage in the context of education, and we must take care in our analysis to differentiate broadband from narrowband, fixed from Wifi, Wifi from mobile, and so forth. Our results run contrary to previous results for the same country but in secondary education, raising further questions about how different implementation strategies and deployment contexts can yield different outcomes.


c.

Research Methods, Samples or Data Sources

We construct a panel of student semester grades and Wifi usage using two data sets from the Engineering School at the University of Porto (FEUP) in Porto, Portugal. The former data set provides cumulative grade points earned and number of courses completed each semester between Fall 1999 and Spring 2010 (22 semesters), as well as basic student information (admissions scores) for roughly 21,000 students. The latter provides Wifi usage as hours online and megabytes transferred, per student per semester between Fall 2006 and Spring 2009 (6 semesters).
We regress academic performance on Wifi usage, employing first-differences to control for time-constant unobserved effects, and we also control for several student-specific and semester-specific factors. In particular, we use Application Grade (student's test scores used to apply to FEUP) to control for prior student performance, and as proxy for socioeconomic controls, which are unavailable. To increase the robustness of our results (due to a reduced number of observations in some regressions), we run regressions with and without the main control variables to see if the two populations show substantial differences (they do not).
Supposing that productive Wifi usage outweighs unproductive usage, we hypothesize that Wifi usage is positively related to academic performance.


d.

Method of Analysis

For each of several models, we perform eight first-difference OLS regressions of performance (grades and number of courses) on Wifi usage (hours and megabytes), and with usage separated by day and night. We interact curricular year with Wifi usage to control for differences in the effect of Wifi for each curricular year; for instance, we expect graduating master's students to be more productive than first-year undergraduates in the way they use Wifi. We believe that student Wifi productivity is commensurate with maturity. Finally, we control for differences among majors by interacting major dummies with Wifi usage.


e.

Findings

We find a consistent, positive relationship between Internet usage and student performance, which is robust for several models and measures. Models controlling for usage specific to major and curricular year, and for separate day and night usage, reveal that certain categories of students tend to use Wifi more productively; in particular, Civil Engineering students, daytime users, and students closer to graduation (master's students). Other categories are less productive, such as Mechanical Engineering students and nighttime users. We also find an unequal relationship between the effect for megabytes and time, suggesting that the application in use is important in explaining the “productivity” of the connection.


f.

Conclusions, Scholarly or Scientific Significance, and Implications

We conclude that Wifi usage has a demonstrably positive relationship with academic performance. This is significant from a policy standpoint because it validates the widespread investment that has been made in ICT and Wifi networks in schools. We recognize, however, the need for additional research on exactly why Wifi has a positive effect on performance, and therefore how this positive effect can be reproduced in other contexts. At present we provide only a correlation, though we are actively pursuing an instrumental variable that may allow us to assert causality. Nevertheless, the robustness of our results, across several models, is noteworthy.










8.

References

Angrist, J., & Lavy, V. (2002). New Evidence on Classroom Computers and Pupil Learning. The Economic Journal, 112(482), 735–765.
Arabasz, P., & Pirani, J. (2002). Wireless networking in higher education (Vol. 2; Tech. Rep.). Boulder, CO: EDUCAUSE Center for Applied Research.
Belo, R., Ferreira, P., & Telang, R. (2011, April). The Effects of Broadband in Schools: Evidence from Portugal. Carnegie Mellon University.
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Kubey, R. W., Lavin, M. J., & Barrows, J. R. (2001). Internet Use and Collegiate Academic Performance Decrements: Early findings. Journal of Communication, 51(2), 366–382.

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Odaci, H. (2011, August). Academic self-efficacy and academic procrastination as predictors of problematic internet use in university students. Computers & Education, 57(1), 1109–1113. doi: 10.1016/j.compedu.2011.01.005
Tindle, E. (2002). Pathological Internet Use (PIU) in University Students: A New Addiction. In The 6th pacific rim first year in higher education conference proceedings. Christchurch, New Zealand: Queensland University of Technology.
Underwood, J. (2009, November). The impact of digital technology. Becta. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/3059815
Underwood, J., Ault, A., Banyard, P., Bird, K., Dillon, G., Hayes, M., ... Twining, P. (2005, June). The impact of broadband in schools. Becta ICT Research. Retrieved from http://dera.ioe.ac.uk/1616/
Unleashing the Potential of Educational Technology. (2011, September). Washington, D.C.: Executive Office of the President, Council of Economic Advisors.


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