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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:69913", author = "Faisal Kalota", title = "Applications of Big Data in Education", 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.", volume = "9", number = "5", pages = "1607-6", }
