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Smart analytics in education

Jay Liebowitz, Orkand Endowed Chair in Management and Technology, University of Maryland University College

Big data is a “big” deal and analytics are needed in the education market to help ensure student, faculty, and institutional success. The future looks bright for those institutions willing to take a few steps detailed below. But first, let’s validate the analytical approach in education through a few examples.

Imagine improving your high school graduation rate from 25 percent in 2005 to 80 percent in 2012. Through predictive analytics, a school system in Tennessee used big data analytics to identify “at risk” students during their early schooling and beyond. They found children who were most at risk of dropping out of school (even back in elementary and middle schools), and then provided the right networking and support services intervention to help those students succeed. And it worked, as evidenced by the dramatic increase in graduation rate. That’s the power of big data analytics in education [Lamont, 2013]!

Another example is the EDUCAUSE Learning Initiative. It found that developing and employing learning analytics can help envision and build a new model for improving teaching and learning [Dias and Fowler, 2012]. As a case in point, Yakima Valley Community College applied data analysis to increase student success [Dulin et al., 2012]. By focusing on course pass rates and sequence completion in english and math, more students succeeded in future courses and smoother course enrollment patterns emerged [Dulin et al., 2012].

According to Darrell West (2012) at the Brookings Institution in Washington, DC, the potential for improved research, evaluation, and accountability through data mining, data analytics, and web dashboards in education is great. Schools, however, must understand the value of a data-driven approach to education [West, 2012].

This strategy is also clearly stated in the U.S. Department of Education’s report on enhancing teaching and learning through educational data mining and learning analytics [U.S. Dept. of Education, 2012].

Donald Norris, President of Strategic Initiatives Inc., and Linda Baer, interim VP for Academic and Student Affairs at Minnesota State University, Mankato, further echo these points in their EDUCAUSE report on “Building Organizational Capacity for Analytics” (Norris and Baer, 2013). They define “learning analytics” at the course and department level, and “academic analytics” at the institutional, regional, and national levels. The work of George Siemens, professor at Athabasca University, on analytics in higher education further supports the importance of applying analytics in the big data educational environment.

Even though there is a great need and potential for analytics in education, the space is making slow progress. In the McKinsey Global Institute analysis across 20 sectors, in every category except talent, education is least prepared for ease of data capture, has the least capacity for IT intensity, least reflects the data-driven mind-set, and is the least likely to have overall data availability [Norris and Baer, 2013].

So what needs to be done and where should we start?

  1. We must instill an “Analytics IQ Culture” within the education sector. As Norris and Baer (2013) point out, the culture and behaviors (data-driven mind-set) of institutions must change to optimize student success.
  2. We must gather the resources (both in talent, financial, and moral commitment) to use analytics to tackle strategic issues that are at the heart of the institution. These include both student and faculty related issues. At the University of Maryland University College, a new Center for Innovation in Learning has been created to address new ways to improve learning through big data analytics in higher education.
  3. We must continue to address outcome measures, versus simply system and output metrics [Liebowitz, 2013].

References

  • Dias, V. and S. Fowler (2012), “Leadership and Learning Analytics Brief,” EDUCAUSE Learning Initiative, www.educause.edu/eli, November.
  • Dulin, W., S. Delquadri, and N. Melander (2012), “Yakima Valley Community College: Using Near-Real-Time Data to Increase Student Success,” in Game Changers: Education and Information Technologies (D. Oblinger, ed.), EDUCAUSE.
  • Lamont, J. (2013), “In the Realm of Big Data”, KMWorld, www.kmworld.com, April.
  • Liebowitz, J. (ed)(2013), Big Data and Business Analytics, Taylor & Francis.
  • Norris, D. and L. Baer (2013), “Building Organizational Capacity for Analytics”, EDUCAUSE, February.
  • U.S. Department of Education (2012), “Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics,” Office of Educational Technology, Washington, D.C.
  • West, D. (2012), “Big Data for Education: Data Mining, Data Analytics, and Web Dashboards,” Brookings Institution, Washington, D.C., September.

 

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