Dream or Nightmare? The Ethics of Learning Analytics

Just because it [academic data] is accessible doesn’t make it ethical (Boyd & Crawford, 2012).

What are the implications of higher education institutions collecting student data and compiling a multitude of reports based upon students’ online clicks, page views, time logged on, and electronic notes? Do educators have a responsibility to tell students what they are doing? These are interesting questions that education institutions should be wrestling with in this era of Big Data.

I’ve written several posts recently about learning analytics, emphasizing the need for meaningful analysis, student-centered reporting, and transparency. However, I admit to having reservations about analytics and its role in education. Not only is there potential for abuse and manipulation of data, but I am concerned about privacy and student rights. Surprisingly, there has been little written about the moral implications and the potential nightmares that analytics could create. Simon Buckingham Shum a leading scholar in learning analytics research, led a talk Learning Analytics: Dream or Nightmare? for EDUCUASE’s online spring focus session, (webinar recording available here). Shum discusses Big Data in education focusing on analytics in K-12 and higher education settings. Shums’ talk promotes deep thought as he highlights  the positive aspects of academic analysis but also its dark side. In this post I highlight some of the concerns put forth by Shum, the ethical considerations we as educators should be concerned about, and the questions we should be asking.

Simon Buckingham Shum, EDUCAUSE Webinar, 2012

Questions to Ask
If we look at where the idea of using Big Data to improve productivity and growth came from, we need look no further than to the business sector. Businesses thrive on analyzing customer data, sales, market performance and inventory logistics. Can we apply the same principles to education?  In his talk, Shum asks a rhetorical question about analytics using the slide at the right to emphasize his point. BusinessAnayltics.edu or LearningAnalytics.com? Can we treat academic data the same way as business’ treat data?  IBM thinks so. I attended a webinar several months ago where IBM was sharing a case study about a college where its analytics platforms were implemented.  I was uneasy throughout the  webinar, I heard over again and again words such as  strategy, performance, achievement, strategic planning. Rarely did I hear the words, student, learning or development. I am not suggesting that IBM is incapable of providing valuable expertise, I am only using this sliver of insight I gained through a brief webinar to highlight the bigger issue, which is the need to ask questions that include, ‘how  should we approach educational data?’, ‘who should have access?’ and ‘how does academic data differ from other types of data?’.

What do students think?
Speaking of breadth, do students know the depth and breadth of data that is collected about them within the academic platform (LMS such as Moodle or Blackboard) they use consistently? And if they do, what are their responses? Some students will not care, yet others may be vehemently opposed. However, when students are involved in the discussion of how to use the data, and are part of the conversation, which Shum suggests, the concern of privacy and ethics becomes clearer. Transparency is essential. Grand Rapids Community College blog features an excellent article, Obligation of knowing: Ethics of Data Collection and Analytics, which suggests using transparency to create trust. Letting students know how data will be used, and how they will benefit is a good place to start.

Simon Shum, Webinar

Simon Shum closes his webinar with two slides, the first with an image of a man holding a magnifying glass, asking ‘who gets to hold magnifying glass’, implying that educators should be considering not only who should be analyzing and viewing student data but why. The final slide, an image of a student holding a mirror, suggests that analytics should be used as a mirror for learners to become more reflective, and less dependent. Yet it is up to the institution to determine how data will be used which will determine the result— either a nightmare scenario where analytics breed resentment and myopia, or a dream scenario. In the dream scenario, analytics can create a generation of tools that support and develop learners, where students become self-directed, responsive and armed with the skills needed for the 21st century.


Photo Credit: Personal Data, by Charlie Collis (highwaycharilie), Flickr

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