[{"id":126,"date":"2020-07-28T13:40:52","date_gmt":"2020-07-28T17:40:52","guid":{"rendered":"http:\/\/edspace.american.edu\/amytrietiak\/?p=126"},"modified":"2020-08-12T10:22:18","modified_gmt":"2020-08-12T14:22:18","slug":"big-data-learning-analytics-and-the-role-of-assessment-in-online-learning","status":"publish","type":"post","link":"https:\/\/edspace.american.edu\/amytrietiak\/2020\/07\/28\/big-data-learning-analytics-and-the-role-of-assessment-in-online-learning\/","title":{"rendered":"Big Data, Learning Analytics and the Role of Assessment in Online Learning"},"content":{"rendered":"

Higher education is facing a number of challenges in the twenty-first century. Some of the top challenges include declining student enrollments, financial difficulties, and decreased state funding, to name a few (Wiley Education Services, n.d.). In a 2016 survey by the Babson Survey Research Group, two-thirds of academic leaders polled said that online education is integral to their strategic plan.<\/p>\n

As a result, online enrollment trends are on the rise. A report from the Education Department’s Integrated Postsecondary Education Data System shows that the number of students taking at least one online class at either a public or private nonprofit institution increased from 30 percent in 2017 (Ginder, Kelly-Reid, & Mann, 2018) to 31.6 percent in 2018 (US Department of Education, 2019). The number of students enrolled exclusively online increased from 30.4 percent in 2017 (Ginder, Kelly-Reid, & Mann, 2018) to 32.5 percent in 2018 (US Department of Education, 2019). While the rate of growth seems to\u00a0be decelerating when compared to previous years, online education continues to fuel\u00a0the\u00a0growth in overall enrollment in higher education\u00a0(Lederman, 2019).<\/p>\n

Despite the popularity of online learning for students, perceptions of quality still remain for academic leaders. In a 2015 survey, 29 percent of chief academic officers polled believed\u00a0online classes are\u00a0not as valuable as face to face classes (Allen, Seaman, Poulin, & Straut, 2016). The larger body of research, however, shows no significant difference in student learning outcomes when comparing the two modalities (Cavanaugh & Jacquemin, 2015; Kemp & Grieve, 2014). In fact, some studies show that online students may even outperform their face to face learner counterparts (Means, Toyoma, Murphy, Bakia, Jones, 2009). Regardless of the medium, quality course design is imperative\u00a0to maximize student learning (Muller, Gradel, Deane, Forte, McCabe, Pickett, Piorkowski, Scalzo, & Sullivan, 2019). Furthermore, assessment of student learning is a critical competency of online educators (Martin, Kiran, Kumar, & Ritzhaupt, 2019).<\/p>\n

In this literature review, I plan to explore innovations in the assessment of online learning, particularly with the use of big data and learning analytics. In an era of heightened accountability and decreasing student enrollments, Dringus (2012) argues that higher education is especially interested in big data and learning analytics as it relates to student learning online.<\/p>\n

As a full-time faculty member in a fully online master’s degree program, I have seen first-hand how engaging and rewarding online teaching and learning can be. I am disheartened by the negative perceptions of online learning despite the body of research to support that learning outcomes are similar between the modes of delivery. With the proliferation of technology and web tools available to support online learning efforts, I am interested in the concepts of big data and learning analytics (LA). More specifically, I am interested in how these concepts can be leveraged to assess student learning in the online classroom.<\/p>\n

Big Data in Business<\/h2>\n

Living in the digital age, technology is pervasive. People today have GPS devices on their smartphones, watches that can track and monitor their heart rates, and televisions that can predict viewing habits. A digital footprint is created every time you log on to or engage in\u00a0a transaction on a digital device\u00a0(Marr, 2017). This digital footprint is translated into a data set that can be sorted, quantified, and analyzed\u00a0in order\u00a0to understand the user’s behavior (Yupangco, 2017).<\/p>\n

For example, in my preliminary search for information on big data, I explored some mainstream articles to better understand how the business sector\u00a0might be using big data analytics to drive decision making. While reading an article\u00a0on\u00a0Entrepreneur.com<\/a>,\u00a0I took a screenshot to capture the image, as well as the\u00a0pop-up, that emerged on my screen:<\/p>\n

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(Balkhi, 2019)<\/p><\/div>\n

The timing of the pop-up message shown above was impeccable. Not only does this image showcase a practical example of\u00a0how Amazon uses data to upsell customers based on the purchasing history of previous buyers, but the pop-up at the bottom of the screen also reads:<\/p>\n

Entrepreneur Media, Inc. values your privacy. In order to understand how people use our site generally, and to create more valuable experiences for you, we may collect data about your use of this site (both directly and through our partners). By continuing to use this site, you are agreeing to the use of that data. For more information on our data policies, please visit our Privacy Policy (Entrepreneur, n.d.).<\/p><\/blockquote>\n<\/div>\n<\/div>\n

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As technology permeates the lives of people around the world, there is a tremendous amount of data to be harvested (Watson, Wilson, Drew, & Thompson, 2017). In the corporate sector, this\u00a0data is used by large companies to improve business practices\u00a0and, ultimately, to bolster the bottom line.<\/p>\n

In a\u00a02017 article on Harvard Business Review online, the author shares\u00a0more\u00a0specific examples of how fortune 1000 businesses are using big data. As seen in the graphic below, the top three areas where\u00a0businesses are realizing\u00a0the most value from collecting and analyzing data are in\u00a0decreasing expenses, fostering innovation, and launching new products or services (Bean, 2017).<\/p>\n

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(Bean, 2017)<\/p><\/div>\n

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These concepts are further explained in a blog by the College of William and Mary’s Raymond A. Mason School of Business\u00a0(n.d.). Here, the author\u00a0shares some more practical examples of how large companies use big data to improve business practices. For instance, UPS could save $50 million a year by mapping routes that are just one mile shorter for each of their 55,000 routes traveled per day. Banks can track and monitor customer purchasing behavior in order to better detect fraudulent activity, and entertainment providers, like Netflix, can make predictions about which movies or shows a user\u00a0might enjoy based on their current viewing preferences.<\/p>\n

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From improving business practices to enhancing the customer experience, big data has many applications for companies around the world. With this kind of success, it should come as no surprise that institutions of higher education are following suit (Watson, Wilson, Drew, & Thompson, 2017). Given the challenges that universities face with declining overall enrollments and financial difficulties (Wiley Education Services, n.d.), more and more universities are paying attention to big data and learning analytics. Learning analytics holds promise to help institutions find ways to increase student retention and improve student outcomes for a brighter future (Dietz-Uhler & Hurn, 2013).<\/p>\n

Big Data and Learning Analytics in Education<\/h1>\n
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The idea is simple yet potentially transformative: analytics provides a new model for college and university leaders to improve teaching, learning, organizational efficiency, and decision making and, as a consequence, serve as a foundation for systemic change (Long & Siemens, 2011)<\/p>\n<\/blockquote>\n

Bart Collins, Clinical Professor at Purdue University, (n.d.) describes some of the more specific benefits that can be realized for institutions of higher education. At the student level, access to analytics helps provide more information about academic performance. Using the analogy of a Fitbit or Apple watch, Collins explains how data can allow the user to notice patterns in behavior and make changes based on the feedback, and “compare progress towards learning goals” (Collins, n.d. para. 3). At the instructor level, educators can monitor student behaviors to better understand how course materials\u00a0are being utilized. Furthermore, instructors may even be able to understand how utilization of course materials relates to learning outcomes (as measured by grades). When patterns emerge, instructors can make changes to the course structure or materials in order to best meet student needs. Lastly, analytics can provide administrators with information about program performance and enrollments. For example, learning analytics may be able to provide answers to questions like, “which courses are students finding the most engaging?” or “are there student characteristics or engagement patterns that are associated with program retention?” (Collins, n.d., para. 8).<\/p>\n

The table that follows depicts some broader examples of how learning analytics are\u00a0being operationalized in higher education:<\/p>\n

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(Dietz-Uhler & Hurn, 2013, p.19)<\/div>\n
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Applications in Online Learning<\/b><\/h3>\n

The evolution of online learning coupled with the rise of big data has provided new opportunities for applications in online learning (Watson, Wilson, Drew, & Thompson, 2017). Just as users leave a digital footprint behind in the digital transactions they make every day (Marr, 2017), students enrolled in online learning leave a footprint in the online learning environment. In the realm of online learning, big data can be defined as “the data that is created by learners while they are taking an eLearning course or training module” (Pappas, 2014, para. 2). For example, when a student engages with a learning module in an online course, elements of the interaction are tracked and recorded (Dringus, 2012). This might include contributions or replies in a discussion forum, test scores, or media views. The parameters surrounding what information is recorded and where it is housed are established by the learning environment (or web tool utilized), such as the learning management system, social media platform, or other educational technology tools (Pappas, 2014). This information can be collected over time for a large number of students, and data mining algorithms can detect patterns to inform teaching and learning models (Bienkowski, Feng, & Means, 2012).<\/p>\n

The ability to make discoveries from aggregated data is the hallmark of educational data mining, or EDM (Avella, Kebritchi, Nunn, Kanai, 2016). “EDM develops methods and applies techniques from statistics, machine learning, and data mining to analyze data collected during teaching and learning” (Bienkowski, Feng, & Means, 2012, p. 9).<\/p>\n<\/div>\n<\/div>\n<\/div>\n