University participation has risen spectacularly. The target of 40% participation should be comfortably met by 2025. The nation has quickly moved from an elite to a mass higher education system. The second equity target has proven more challenging, but progress is being made. The relative proportion of low-SES undergraduate students rose from 16.2% to 17.7% between 2009 and 2014. In the same period, the overall number of undergraduate low-SES students increased by 44%, while other cohorts increased by 30%.

Andrew Harvey, ‘Uncapping of university places achieved what it set out to do‘, June 2016

1

It takes a while to notice something’s wrong. There’s a sound that doesn’t quite belong, although not by much—it’s not like a siren right in your street, or a breaking window. So you catch yourself noticing it, and forget to look up. But five, ten minutes later it’s still there, and look, it’s a helicopter hovering, hanging in the air like a kite. Then it’s looping out in a wide arc and coming back to exactly the same spot. Round and back, round and back, all morning.

Once you’ve seen it, you don’t unhear it. Explanations start unspooling, tumbling over each other, tangling up. It’s hovering over a major intersection, it’s scanning the escarpment where a hiker might have fallen, it’s following a car chase, it’s filming something, it’s hunting for someone. Neighbours come out of their houses and look up. How long have you been hearing that sound? When did it start?

2

In the past few weeks I’ve exchanged thoughts with people about the rise of analytics in higher education, and especially the arrival of personalisation. What separates personal from personalised? This email came:

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Personalisation is the endgame of consumer analytics. It’s the point at which wide surveillance morphs into individual care, without the actual cost of staff. In universities, social data about students layers over all their tracks and patterns as learners, their collisions and intersections, all the half-cooked queries and false starts that no one much intended to share; personalisation lets us zoom in with an unmanned drone to drop off a map to a journey, crafted just for them.

And if we notice they’re drifting from the trail, how could it be a bad thing for us to use our insights to recover them, adjust their progress, set them straight?

It turns out learning analytics is a field where people say “intervention” without unease. We intervene like good people stopping a fight, like bystanders who step in and rescue someone. We come between someone and what fate seems to have stored up for them. It’s a salvationist theology: we know what’s best for others, and we can see when someone’s tilting, and possibly falling right off the wagon.

The problem is that this is exactly the kind of reformism that drives the other kind of intervention, the tough love kind, the governmental kind. We intervene out of faith, prejudice and self-interest. We intervene to help failing students become their better, more successful selves in ways that worked for us. We intervene because we’ve gone on selling the graduate earnings premium like a cheap watch despite all the evidence that the labour market is falling to bits. And we’re hardly disinterested. Our intervening zeal has a grubby side: students can’t be left alone to fail, to make a plan that doesn’t involve us, because their completion has a dollar value, and their success grows our reputation and our market for the future.

So we also intervene because we’re sandbagging our business plan, and our revenue stream. We intervene to ensure that every student who enrols in year X sticks around until year Z, all doing the exact same amount of stuff, at a foreseeable unit cost that enables us to plan. And in service of our interventions, a well scaffolded curriculum works for us like a movie of standard length works for a movie theatre: a business efficiency sold as a unique and transforming experience.

This is why we’re seeing whole divisions appearing whose role is to hover over learning, to track all the things that learners do, to gather data so that our interventions are precisely targeted. This is also why so much effort in the governance of digital learning is focused on getting more students doing more things in the LMS, even though this is one of the least engaging environments for actual learning; it’s why there is increasing policy focus on placing data capture points in curriculum, assessment and feedback; and increasing responsibilities for staff in managing the digital records of student learning.

Behind all this local busywork, there’s a powerful and well-funded research effort that’s being sustained by these changes, and that’s constantly searching for new action. What new data can institutions recruit? What new insights can be drawn out of fresh combinations of things we’ve always known? If for example we can pinpoint the exact moment when attrition risk begins and we can personalise the perfectly automated intervention, can we enrol more and weaker students in better conscience? With sufficient personalisation, and perhaps some upfront investment in digital resources, could more students self-manage their learning, and could someone still be prepared to pay for their experience? What if those students were in large and underserved education markets in developing economies? 

This is the lesson that MOOC pioneers have left behind for us to think about as they pivot into the next phase of their business plan: that analytics, automation and personalisation are the basis of a low-cost and skeleton staff educational experience that can be rolled out anywhere, and that only needs a modest fee-for-access to cover its costs, providing the market reach is wide enough.

But the patterns that analytics can make visible are those that should be starting human conversations, not replacing them. This is why we need to be far less sanguine about twinning analytics with cheap labour—let alone tutor bots—because if this human conversation is going to help students personalise their own learning (and they are surely the right ones to be doing it), it needs staff who are resourced with time, stability, experience and the confidence to hear what students have to say.

3

No two students who quit university do so for the same reason. The decision to leave is part of a complicated story that began long before they arrived, and will go on to deliver future outcomes none of us can see. It involves families, friends, and a muddle of hopes and fears that are political, social and contradictory. This semester I’ve had the privilege of listening to students who left and came back, who are on the verge of leaving, who have changed direction and changed again. The toughest stories to hear are from those who are staying because the risk of leaving seems worse, in this employment market, in this region, at this time, with those family hopes backed up behind them.

Thanks to the data we hold on enrolment, retention and completion, we know these students only as the basis of our claims of policy success. They’re here, they’re meeting all the deadlines and earning grades and moving through the curriculum right on time. Analytics based on tracking failure and discontinuation won’t help them, because their problem isn’t in this terrain at all, but in messier zones of self-doubt, fatigue and anxiety. To understand more about the experience of the student who detaches without leaving, and why this should matter more to us, we need to show up in person, to listen fully, and to let each story stay a whole one.

There are challenges and opportunities facing social and narrative researchers in education: scale, replicability, transferability are all troubled when the focus is on the stories learners tell rather than the observable things they do. But there are explanations that can’t be found by any other means, that can’t be seen by hovering. So let’s have this conversation openly and optimistically, and see what we can add.

#sonar