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TechTrends

, Volume 60, Issue 4, pp 381–384 | Cite as

Big Opportunities and Big Concerns of Big Data in Education

  • Yinying WangEmail author
Original Paper

Abstract

Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the concerns over data security, privacy protection, and ethical boundaries of accessing personal digital data are discussed. The article concludes with an invitation to education practitioners, policymakers, and researchers to advance our understanding of big data and better serve students in the digital era.

Keywords

Big data Education Educational policymaking Educational policy implementation Ethics Learning analytics Privacy 

References

  1. Aud, S., Wilkinson-Flicker, S., Kristapovich, P., Rathbun, A., Wang, X., & Zhang, J. (2013). The condition of education 2013 (NCES 2013–037). U.S. Department of Education, National Center for Education Statistics. Washington, DC. Retrieved from http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2013037.
  2. Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: a review and future visions. Journal of Educational Data Mining, 1(1), 3–16.Google Scholar
  3. Bousbia, N., & Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments? In A. Pena-Ayala (Ed.), Educational data mining: Applications and trends (pp. 3–28). Switzerland: Springer.CrossRefGoogle Scholar
  4. Carpenter, J. P., & Krutka, D. G. (2014). How and why educators use Twitter: a survey of the field. Journal of Research on Technology in Education, 46(4), 414–434.CrossRefGoogle Scholar
  5. Charalabidis, Y., Maragoudakis, M., & Loukis, E. (2015). Opinion mining and sentiment analysis in policy formulation initiatives: The EU-community approach. Proceedings of the 7th IFIP 8.5 International Conference, Thessaloniki, Greece, August 30—September 2 (pp. 147–160). Switzerland: Springer International Publishing.Google Scholar
  6. Chung, W., & Zeng, D. (2015). Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security. Journal of the Association for Information Science and Technology. doi:  10.1002/asi.23449.
  7. Cox, D. D., & McLeod, S. (2014). Social media marketing and communications strategies for school superintendents. Journal of Educational Administration, 52(6), 850–868.CrossRefGoogle Scholar
  8. Executive Office of the President of the United States & President’s Council of Advisors on Science and Technology. (2014). Report to the President—Big data and privacy: A technological perspective. Retrieved from https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf.
  9. Hesse, B. W., Moser, R. P., & Riley, W. T. (2015). From big data to knowledge in the social sciences. The Annals of the American Academy of Political and Social Science, 659(1), 16–32.CrossRefGoogle Scholar
  10. IBM. (2013). What is big data? Bringing big data to the enterprise. Retrieved from www.ibm.com.
  11. Johnson, L., R. Smith, H. Willis, A. Levine, and K. Haywood. 2011. The 2011 Horizon Report. Austin, TX: The New Media Consortium. Retrieve from http://net.educause.edu/ir/library/pdf/HR2011.pdf.
  12. Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788–8790.CrossRefGoogle Scholar
  13. Lau, A., & Tsui, E. (2009). Knowledge management perspective on e-learning effectiveness. Knowledge-Based Systems, 22(4), 324–325.CrossRefGoogle Scholar
  14. Looi, C., & Wong, L. (2014). Implementing mobile learning curricula in Singapore schools. A Programme of research from innovation to scaling. Education Technology & Society, 17(2), 72–84.Google Scholar
  15. Macfadyen, L. P., Dawson, S., Pardo, A., & Gašević, D. (2014). Embracing big data in complex educational systems: the learning analytics imperative and the policy challenge. Research & Practice in Assessment, 9(2), 17–28.Google Scholar
  16. Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data: The future of education (ebook only). Houghton Mifflin Harcourt.Google Scholar
  17. McHugh, D. (2015). Traffic prediction and analysis using a big data and visualisation approach. Retrieved from http://leeds.gisruk.org/abstracts/GISRUK2015_submission_20.pdf.
  18. Prpić, J., Taeihagh, A., & Melton, J. (2015). The fundamentals of policy crowdsourcing. Policy and Internet, 7(3), 340–361.CrossRefGoogle Scholar
  19. Reddicka, C. G., Chatfieldb, A. T., & Jaramilloa, P. A. (2015). Public opinion on National Security Agency surveillance programs: a multi-method approach. Government Information Quarterly, 32(2), 129–141.CrossRefGoogle Scholar
  20. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.Google Scholar
  21. Schintler, L. A., & Kulkarni, R. (2014). Big data for policy analysis: the good, the bad, and the ugly. Review of Policy Research, 31(4), 343–348.CrossRefGoogle Scholar
  22. Sindi, H. F. (2005). A machine learning approach for intelligent tutoring systems. WSEAS Transactions on Systems, 4(7), 1053–1057.Google Scholar
  23. Song, Y. (2014). “Bring Your Own Device (BYOD)” for seamless science inquiry in a primary school. Computers & Education, 74, 50–60.CrossRefGoogle Scholar
  24. Topper, A., & Lancaster, S. (2013). Common challenges and experiences of school districts that are implementing one-to-one computing initiatives. Computers in the Schools, 30(4), 346–358.CrossRefGoogle Scholar
  25. Trucano, M. (2014). Big data in education in 2025: A though experiment. The World Bank Blog on ICT Use in Education. Retrieved from http://blogs.worldbank.org/edutech/thought-experiment-big-data.
  26. Wang, Y. (2013). Social media in schools: a treasure trove or hot potato? Journal of Cases in Educational Leadership, 16(1), 83–91.CrossRefGoogle Scholar
  27. Wang, Y. (2016). State education agencies’ use of Twitter: Mission accomplished? Sage Open, 6(1), 1–12.Google Scholar
  28. Wang, Y., & Decker, J. R. (2014a). Examining digital inequities in Ohio’s K-12 virtual schools: implications for educational leaders and policymakers. International Journal of Educational Reform, 23(4), 294–314.Google Scholar
  29. Wang, Y., & Decker, J. R. (2014b). Can virtual schools thrive in the real world? TechTrends, 58(6), 57–62.CrossRefGoogle Scholar
  30. Wang, Y., Sauers, N., & Richardson, J. (2016). A social network approach to examine K-12 educational leaders’ influence on information diffusion on Twitter. Journal of School Leadership, 26(4), 495–522.Google Scholar
  31. Watson, J., Murin, A., Vashaw, L., Gemin, B., Rapp, C. (2012). Keeping pace with K-12 online & blended learning: An annual review of policy and practice. Evergreen Education Group. Retrieved from http://kpk12.com/cms/wp-content/uploads/KeepingPace2012.pdf.
  32. Whitman, W. N. (2015). Trending now: using big data to examine public opinion of space policy. Space Policy, 32, 11–16.CrossRefGoogle Scholar
  33. Wong, L. (2015). Forward: removing seams by linking and blurring. In L. Wong, M. Milrad, & M. Specht (Eds.), Seamless learning in the age of mobile connectivity (pp. v–xv). Singapore: Springer.Google Scholar
  34. Wong, L., Chai, C., Chin, C., Hsieh, Y., & Liu, M. (2012). Towards a seamless language learning framework mediated by the ubiquitous technology. International Journal of Mobile Learning and Organization, 6(2), 156–171.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications & Technology 2016

Authors and Affiliations

  1. 1.Department of Educational Policy StudiesGeorgia State UniversityAtlantaUSA

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