Looking for the Little Things in Big Data

by Guest Author

This post was guest authored by David S. Noffs M.P.H., Ed.D. 

Since 2001 Dr. David S. Noffs has worked at Columbia College Chicago as an adjunct faculty member in the Interactive Arts and Media department teaching sound design, web design and programming. In addition, he has worked at Columbia’s Center for Innovation in Teaching Excellence as an instructional technologist and designer since 2005. In 2015, David joined Northwestern University as a faculty developer in the School of Professional Studies teaching Information Design and Strategy.

The importance of learning analytics

I recently attended an online webinar on Canvas Analytics conducted by Jackie Wickham here at the School of Professional Studies. It occurred to me just how little most online instructors I have encountered in my work as an instructional technologist actually make use of the wealth of information that lies in the recesses of their own courses. I wondered why learning analytics, a field that has grown rapidly over the past five years, has often been overlooked, or seen as unimportant, or even frowned upon by many instructors. Are they afraid of what it will tell them? Does it sound too much like Big Brother? Or is it that instructors simply do not know what learning analytics is, where to find it, or what to do with it? Learning analytics has been defined as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs” (Siemens & Gašević, 2012).

While online teaching is certainly not new, many teachers are new to online teaching, which is different to traditional face-to-face teaching. Many new online teachers I work with as a faculty trainer do not fully understand what all the fuss is about when it comes to learning analytics, data analysis, predictive analytics, and even the popular big data concept. The terms are often misused and confusing, and new online instructors will tell you they are already pre-occupied with discussion boards, assignments, and making sure their students are online and engaged. The irony is that learning analytics can actually help them do all of that and illuminate their own understanding of the human beings inhabiting their virtual classroom.

Teachers in traditional classrooms have long sought an insight into the minds of the faces that stare back at them in the lecture hall. For online instructors, learners are revealed through their habits, writings, reactions, and social behaviors, all documented, graphed, and packaged neatly thanks to modern technology, clever algorithms, and creative software. But the enthusiastic vision of learning analytics advocates and promise of transforming education has been dampened by resistance, skepticism, and debate over the use of data to do more than enhance the teacher/learner relationship.

Advances in learning analytics tools

When I first began putting my own classes online in 2005, the software at the time lacked much of the sophistication of today’s analytics tools. User data inside a learning management system (LMS) both at the classroom and enterprise level was the prized possession of a few skilled data analysts and programmers. While there was plenty of data being kept and used by the software and machines, it was all but hidden from teachers, and user or student logs were often inaccessible. However, over the past ten years, LMS’s like Canvas, Moodle, D2L and Blackboard, have upped their games considerably.

In Canvas, for example, online instructors can easily access analytics from their course homepage. This includes course statistics, active discussions, assignment reports, active students, and student access logs. In addition, there are course analytics that can be sorted by assignments, discussions, students, participation, and grades. You can find information on students and generate “Interactions Reports”. This includes the student’s last interaction, current score, final score, and ungraded assignments (Wickham, 2016).

One exciting new analytics tool I have been trying out here at Northwestern School of Professional Studies is called Nebula. Nebula is not a Canvas product, but rather the product of a team of professors and programmers here at Northwestern. Developed by Seyed Iravani and Jackie Ng, Nebula presents discussion boards as a graphic of connected nodes (Morris, 2016). According to Professor Iravani, at Northwestern Engineering, after he and Ng were disappointed with low discussion participation rates in a course they were running, they “… wanted to discover how to encourage (their) students to participate more.” So they teamed up with Bill Parod and Jacob Collins in Northwestern IT and developed Nebula. The result is a tool that provides a graphic representation of real human interaction, levels of activity and, logically, student interest…something this instructor finds enormously useful and important when striving for a more learner-centered online course environment.

Keeping learners at the center

At the center of learner-centered education is learner-generated data. It would seem a foregone conclusion that online instructors would agree a learner-centered environment should be driven by the learners and their own connections. However, learning analytics as a field has found itself cast into a debate about the purpose and direction that the field should take. On the one hand, advocates George Siemens and Phil Long (2011) see learning analytics as eventually moving beyond the LMS and leading to what they call an “Intelligent Curriculum.” They view it as disrupting the traditional view of courses and state that, “Analytics in education must be transformative, altering existing teaching, learning, and assessment processes, academic work, and administration”. On the other hand, educators like Melanie Booth (2012) caution that, “…even though learning analytics offers powerful tools and practices to improve the work of learning and assessment, well-considered principles and propositions for learning assessment should inform its careful adoption and use. Otherwise, learning analytics risks becoming a reductionist approach for measuring a bunch of ‘stuff’ that ultimately doesn’t matter. In my world, learning matters.”

Despite the divergent views, there can be no doubt that learning analytics is a byproduct of the information age and online education that has enabled us to view the education process and learning from a new perspective; one that educators have been slow to embrace for whatever reasons.

I recently attended a sales pitch session from one online vendor trying to sell a new data analytics tool that would plug in to an LMS. I was left with a slight chill when the sales team talked about quantifying the content of an online discussion forum to predict which students would succeed and which would not in a course. Can such powerful algorithms turned on the teacher/student relationship actually remove the humanity of our courses? It seems there are some deep questions we may have to face as technology challenges our perceptions of the education process. But one thing is certain, whether developing an outcomes based training program or designing a 21st century Intelligent Curriculum, online educators have a lot to learn from learning analytics.



Booth, Melanie. (2012). Learning Analytics: The New Black. Educause Review,. Retrieved from http://er.educause.edu/articles/2012/7/learning-analytics-the-new-black

Long, Phillip D., & Siemens, George. (2011). Penetrating the Fog: Analytics in Learning and Education. Educause Review,. Retrieved from http://er.educause.edu/articles/2011/9/penetrating-the-fog-analytics-in-learning-and-education

Morris, A. (2015). New App Shows Online Discussions as Interactive Graphs. Retrieved from http://www.mccormick.northwestern.edu/news/articles/2015/11/new-app-shows-online-discussions-as-interactive-graphs.html

Siemens, G., Gašević, D. (2012). Special Issue on Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 1-163.

Wickham, J. (2016). Canvas Analytics Quick Start Guide. Retrieved from http://dl.sps.northwestern.edu/learning-tech/canvas-guides/

3 responses to “Looking for the Little Things in Big Data

  1. I am really curious what Intelligent Curriculum is and how that might work! Sounds really interesting, and may alter the way a learner specializes their area of expertise. I agree with your thought on predictive technology on student outcomes from discussion posts. That seems scary and I wonder how my contributions would fair in a program like that. There is also something to be said for letting knowledge grow organically if learners know there are certain criteria that make their algorithms more attractive, I believe this will hamper the discovery spirit that I’ve found most enjoyable as a learner myself making posts and reading others.

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