Applications of Big Data in Education
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Applications of Big Data in Education

Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data in Education, Hadoop, Learning Analytics.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1105465

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4815

References:


[1] M. Treder, “The definition of emerging technologies,” 2010. (Online). Available: http://ieet.org/index.php/IEET/more/treder20101206.
[2] T. Olavsrud, “10 ways big data is changing the business of sports,” 2014. (Online). Available: http://www.cio.com/article/2687035/big-data/ 164523-10-Ways-Big-Data-Is-Changing-the-Business-of-Sports.html.
[3] T. Olavsrud, “How big data is helping to save the planet,” 2014. (Online). Available: http://www.cio.com/article/2683133/big-data/howbig- data-is-helping-to-save-the-planet.html.
[4] E. Dumbill, “What is big data? An introduction to the big data landscape,” O’Reilly Radar: Insight, Analysis, and Research about Emerging Technologies, 2012. (Online). Available: http://radar.oreilly.com/2012/01/what-is-big-data.html.
[5] E. Schmidt, “Every 2 days we create as much information as we did up to 2003,” 2010. (Online). Available: http://techcrunch.com/2010/08/04/Schmidt-data/.
[6] J. J. Berman, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information, Waltham, MA: Elsevier, 2013.
[7] K. Laudon and J. Laudon, Management Information System: Managing the Digital Firm, 13th ed. UK: Pearson, 2014.
[8] B. Schmarzo, “Business analytics: moving from descriptive to predictive analytics,” 2014..
[9] NMC, “The horizon report: 2014 higher education edition,” 2014.
[10] D. Conway, “The data science venn diagram,” 2010. (Online). Available: http://drewconway.com/zia/2013/3/26/the-data-science-venndiagram.
[11] B. Poulson, “Techniques and concepts of big data (Online Training Course),” 2014. (Online). Available: http://www.lynda.com/sdk/ Hadoop-tutorials/Techniques-Concepts-Big-Data/158656-2.html.
[12] D. Henschen, “12 Hadoop vendors to watch in 2012,” InformationWeek Connecting the Business Technology Community, 2012. (Online). Available: http://www.informationweek.com/big-data/softwareplatforms/ 12-hadoop-vendors-to-watch-in-2012/d/did/ 1102410?page_number=13.
[13] J. Sheard, “Basics of statistical analysis of interactions data from webbased learning environments,” in Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Eds. Boca Raton, FL: CRC Press, 2011, pp. 27–42.
[14] Y. Rogers, H. Sharp, and J. Preece. Interaction Design: Beyond Human- Computer Interaction, 3rd ed. John Wiley & Sons Ltd, 2011.
[15] K. Card, D. Mackinley, and B. Shneiderman, Readings in Information Visualization: Using Vision to Think. San Francisco: Morgan Kaufmann, 1999.
[16] K. Silius and Kailanto, “Visualizations of user data in a social media enhanced web- based environment in higher education,” Int. J. Emerg. Technol. Learn., vol. 8, no. 2, pp. 13–19, 2013.
[17] W. Hamalainen and M. Vinni, “Classifiers for educational data mining,” in Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Eds. Boca Raton, FL: CRC Press, 2011.
[18] P. D. Antonenko, S. Toy, and D. Niederhauser, “Using cluster analysis for data mining in educational technology research,” Educ. Technology Research and Develop, vol 60, pp. 383-398, 2012.
[19] R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” in Proceedings of the 1993 ACM SIGMOD Conference, 1993.
[20] A. Picciano, “The evolution of big data and learning analytics in american higher education,” J. Asynchronous Learn. Networks, vol. 16, no. 3, pp. 9–20, 2012.
[21] IBM, “Analytics for achievement: Understand success and boost performance in primary and secondary education,” Somers, NY, 2014.
[22] D. Niemi and E. Gitin, “Using big data to predict student dropouts: Technology affordances for research,” in Proceedings from the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in Digital Age, 2012.
[23] P. Willging and S. Johnson, “Factors that influence students’ decision to dropout of online courses,” J. Asynchronous Learn. Networks, vol. 13, no. 3, pp. 115–127, 2009.
[24] J. Kay, I. Koprinska, and K. Yacef, “Educational data mining to support group work in software development projects,” in Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Eds. Boca Raton, FL: CRC Press, 2011, pp. 173 – 185.
[25] D. Godoy and A. Amandi, “Link recommendation in e-learning systems based on content-based student profiles,” in Handbook of educational data mining, C. Romero, S. Ventura, M. Pechenizkiy, and R. Baker, Eds. Boca Raton, FL: CRC Press, 2011, pp. 273 – 286.
[26] NMC, “The horizon report: 2011 edition,” 2011.
[27] T. Peckham and G. McCalla, “Mining student behavior patterns in reading comprehension tasks,” in Proceedings of the 5th International Conference on Educational Data Mining, 2012.
[28] M. Korolov, “10 big myths about big data. Network World,” 2014. (Online). Available: http://www.networkworld.com/article/2173703/ software/10-big-myths-about-big-data.html.
[29] V. Protonotarios, G. Stoitsis, K. Kastrantas, and S. Sanchez-Alonso, “Using multilingual analytics to explore the usage of a learning portal in developing countries,” J. Asynchronous Learn. Networks, vol. 17, no. 2, pp. 101–117, 2013.
[30] K. Xiangsheng, “Big data x-learning resources integration and processing in cloud environment,” Int. J. Emerg. Technol. Learn., vol. 9, no. 5, pp. 22–26, 2014.
[31] J. Yoo and M.-H. Cho, “Mining concept maps to understand university students’ learning,” in Proceedings of the 5th International Conference on Educational Data Mining, 2012.