What kind of digital footprint do students have as a result of their attendance at university?
Swiping a digital identity card to access the school or library, logging into a virtual learning environment, submitting assessments online or using a cloud or network printer to print, charging that same identity card to pay for meals in the canteen or indeed their printing. When using the VLE provided, clicking links, navigation, patterns of use, reading habits and writing habit can all be recorded. Connecting to campus wifi opens up student personal devices to the same potential for data mining – students are leaving behind them both passive and active digital footprints. But are we being honest with our students about the data that is being collected, or indeed that it is being collected at all? Have we explained why this information is being collected and how it will be used and essentially how it will be stored or for how long? Most importantly do the university have a duty of care to ensure students understand the concept of digital footprint so that they can make informed choices about how and when they will participate?
Uses for the data being collected
There are two types of data which can be collected and used with the intent of improving the student experience at a university;
- collecting information about students activities on campus can help manage timetables, staffing, and equipment availability to reduce bottlenecks and improve services.
- Information about reading and writing habits, VLE use and online submissions can be used to better understand teaching and learning, and to personalise or adapt learning to the student’s needs (Siemens, G., 2013).
However, one option I’m hearing spoken about on our course frequently is that of the recommendations algorithm used by commercial sector companies like amazon and Tesco. This algorithm can take information about a student and make recommendations, for instance, students who took your current course also found this course engaging. As has been pointed out in our tweetorial this week, in many cases, this could open up the possibility of options which a student may not have considered otherwise, lead to a path of study and indeed even career choices which may have been missed without such a recommendation algorithm. However, as much as I can see benefits in this and do enjoy the use of this algorithm in my personal life, I can also see the opportunity for misuse by both the information provider and the student themselves.
There has been extensive concern over the black boxing of the algorithms being used, our lack of understanding of how they work, what information is being used and what the intent is. Yeung (2017) talks of reliance on a mechanism of influence in algorithms of this nature called “nudge”. Where essentially the intention is to gently nudge the consumer in the desired direction, think of Tesco and the vouchers they send out fo money off certain items. Nudging customers to come back to the store and use those vouchers and hopefully also spend more money on things they didn’t intend buying until they enter the store. I can see a similar use of the recommendation algorithm in universities. After all, universities are businesses which need the income of students taking courses, therefore encouraging students to buy more courses would be beneficial.
There is another potential for misuse of this algorithm that doesn’t seem to have been addressed in our conversations this week and that is from the student. Depending on the information and recommendations given, could a student chose courses based on the perception that one may be easier than another, from student reviews and feedback, much like the students who currently chose courses based on assessment criteria because they don’t like group work or don’t want to sit an exam? Does this press higher education into a consumer culture against the premise of improving learning or understanding education where students don’t chose studies based on things they want to learn but rather on an easier route to attaining the big bit of paper with the university crest on it?
Siemens, G., 2013. Learning Analytics: The Emergence of a Discipline. The American behavioral scientist, 57(10), pp.1380–1400.
Yeung, K., 2017. “Hypernudge”: Big Data as a mode of regulation by design. Information, Communication and Society, 20(1), pp.118–136.