PDA 676: Behavioural “Nudges” in Learning Technology


My nod of respect to Josh Bersin: my Behavioural Nudges in Education Maturity Model

For the course on Digital Literacy, we had to do another literature review paper on a topic of our choice. Lately I’ve been intrigued by the idea of Nudge Theory popularised by Drs. Richard Thaler and Cass Sunstein. Essentially the idea of a “Nudge” is to introduce ways to incentivise people to make a choice more beneficial to him or her. These can take many forms, which I won’t summarise here. My favourite example is a school cafeteria which provides precut fruit and places it at eye- or reach-level, whilst sugary dessert is placed in a harder-to-reach spot. The fruit is easy to obtain and has the benefit of already being cut up– easy to eat! The sugary dessert is still available but you have to work just that tiny bit harder to get it.

I looked at what behavioural nudges are available in learning technologies, and what type of behaviour they might be trying to encourage in students. I then categorised the examples into the proposed Maturity Model above. I found that there isn’t much available, and that most research in this area is coming out of the United States. Furthermore that most institutions of higher learning struggle to even implement the basics with their Learning Management Systems (LMS) (Level 1). There are some very interesting programmes out there, though, getting into using more data, combining rudimentary predictive analytics with personal coaching to help university students successfully graduate (Level 3).

I didn’t find any case studies at the highest level of maturity, true predictive nudging. Nudging strategies at this level, were they to exist, would acknowledge that student success is not solely reliant on academic achievement. Furthermore, the data to flag correlations lives in other systems outside the LMS and beyond algorithms based on historically successful students. I propose universities look at the student as a whole person, factors about how the university is organised, and better data about why students drop out as potential additions to a predictive analytics algorithm and nudging model.

Read my final paper here.