Minimising Risk vs Maximising Success: learning analytics intervention strategies

At the other end of the phone is a student. Your learning analytics tool has predicted that they may be at risk of early departure. Under the circumstances they are likely to benefit from a supportive, sympathetic conversation, or perhaps they need a nudge or a jolt to make them realise they need to re-engage.

What approach do you take?

This is, of course, a ludicrous dichotomy, but bear with me.

I read an interesting article about the UK Government’s approach to the Covid-19 pandemic in the Financial Times (sorry, now only available to subscribers). In essence, the piece argues that at every turn the Government’s strategy was, “if this works, everything will be alright”. It was peak optimism, a bit of belief, some plucky bulldog spirit and we’ll pull through. At the time of writing (26th January, 2020) the UK has amongst the highest rates of both infection and deaths per capita of any nation.

I believe that there are many reasons the Government has so far failed catastrophically to manage the coronavirus pandemic, but I find this ‘optimum outcome’ argument quite compelling. The strategy assumes that things won’t get any worse, that the virus won’t mutate, or that perhaps, unlike in the 1918 flu pandemic, the second wave won’t be worse than the first.

The article argues that a second strategy may have been more appropriate and delivered  better outcomes. Instead of hoping that each new strategy would ‘fix’ the pandemic, a different approach would be to assume that things are going to get worse, the virus will mutate and will be spread throughout the country and act accordingly. Instead of assuming that it can be solved, the Government could have taken a strategy of avoiding the worst outcome, taking a minimising harm approach. We may have to live with the disease, but we could live more safely with it. This approach is perhaps more akin to really boring tasks like pre-flight checks, or infrastructure maintenance. It’s dull, but if you assume the consequence of it being wrong is pretty catastrohic, it may be a more useful way to approach a global pandemic.

bungee jumping
It’s bungee jumping, probably an activity that needs to work really hard on minimising risk

What approach do you take?

In summer 2020, my University, NTU, ran a phone campaign to support our students, we used learning analytics data to target those students who appeared to have disengaged from their studies. I’ll write more about this as part of our OfLA Erasmus+ project, and will run through some of the key lessons at this workshop (28th January 2021). We have subsequently extended our activity so that in the first term of 2021, a central team contacted students identified by our learning analytics system as being potentially at risk of early departure.

All our callers received training from a range of professionals in the institution in topics such as how to make a referral to student support services or how to use a coaching approach to support students. We spent a lot of time at the end of each day debriefing the callers and working on ways to make the calls more effective.

I just want to focus on one aspect of feedback from the callers: pitching the initial conversation with the student. We perhaps saw four responses to the calls:

  1. The vast majority of students who answered the phone were grateful for the call. They were interested that someone had called, some students appeared very motivated by the call, but the majority appeared to be more reassured than spurred on.
  2. A smaller number of students were struggling to cope. Our callers encountered students struggling with mental health issues, confidence about coping, isolation or who felt completely lost. Some of the calls were long and led to referrals to professional services. These often left the callers quite drained, but feeling that they had been really helpful to the student spurring to take a first step for themselves.
  3. A number of students chose not to answer the phone. In most cases, a voice mail was left with them, but relatively few called back. In some cases, students appeared to have blocked the number.
  4. The final group expressed degrees of bemusement, denial and mild hostility. No analytics system will be perfect and some students will have been working hard, but not in ways that can be picked up by the system. However, the callers came away feeling that some students just hadn’t understand that their approach to their studies was ‘risky’ and not likely to be successful. In some respects, these were the most difficult calls: the caller could see that there was a potential problem, but couldn’t impress on the student the seriousness of the situation. I suspect this is a problem felt by personal tutors across the globe.

In some respects, I’d want to suggest that the strategy ought to be to optimise success, to challenge every student, to confront them and inspire them to be the best they can be. But I don’t think we can: starting with such an approach would probably alienate more students than it would inspire, and in the case of type 2 students above, potentially catastrophic.

So we adopt a minimising harm starting point.

The caller doesn’t confront; they offer a supportive coaching conversation. As we develop our experience of this process, we hope to develop scripts and approaches so that as the conversation develops the caller can adopt approaches with differing degrees of challenge, but our core focus is safety first. Helping many students a little is, at least for now, a better approach than helping a few students a lot.


Easter Egg (hey you got this far)

One strategy that our callers developed is similar to one adopted by the some of our personal tutors. The callers found that if they explained that the Dashboard had identified that the student had low engagement they were able to usefully dissociate themselves within the conversation. Students were less likely to think that they were being accused of something by the caller and slightly more comfortable at working with the caller (effectively against the Dashboard).



One thought on “Minimising Risk vs Maximising Success: learning analytics intervention strategies

  1. Pingback: Contacting Gen Z Students – part II – Living Learning Analytics Blog

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