EduTECH AU 2015, Day 1, Higher Ed Leaders, Panel Discussion “Leveraging data for strategic advantage” #edutechau

A most distinguished panel today. It can be hard to capture panel discussions so I will do what I can to get the pertinent points down. However, the fact that we are having this panel gives you some indication of the importance of this issue. Getting to know your data will make it easier for you to work out what to do in the future.

University of Wollongong (UoW) have set up a University-wide approach to Learning Analytics, with 30 courses in an early adopter program, scaling up over the next two years. Give things that they have learned.

  1. You need to have a very clear strategic approach for learning analytics. Learning analytics are built into key strategies. This ties in the key governing bodies and gives you the resources.
  2. Learning analytics need to be tied into IT and data management strategies – separating infrastructure and academics won’t work.
  3. The only driver for UoW is the academic driver, not data and not technology. All decisions are academic. “what is the value that this adds to maximums student learning, provide personalised learning and early identification of students at risk?”
  4. Governance is essential. UoW have a two-tier structure, a strategic group and an ethical use of data group. Both essential but separate.
  5. With data, and learning analytics, comes a responsibility for action. Actions by whom and, then, what action? What are the roles of the student, staff and support services? Once you have seen a problem that requires intervention, you are obliged to act.

I totally agree with this. I have had similar arguments on the important nature of 5.

The next speaker is from University of Melbourne (UoM), who wanted to discuss a high-level conceptual model. At the top of the model is the term ‘success’, a term that is not really understood or widely used, at national or local level. He introduced the term of ‘education analytics’ where we look at the overall identity of the student and interactions with the institution. We’re not having great conversations with students through written surveys so analytics can provide this information (a controversial approach). UoM want a new way, a decent way, to understand the student, rather than taking a simplistic approach. I think he mentioned intersectionality but not in a way that I really understood it.

Most of what determines student success in Australia isn’t academic, it’s personal, and we have to understand that. We also can’t depend on governments to move this, it will have to come out of the universities.

The next speaker is from University of Sydney, who had four points he wanted to make.

He started by talking about the potential of data. Data is there but it’s time to leverage it. Why are institutions not adopting LA as fast as they could? We understand the important of data-backed decision making.

Working with LA requires a very broad slice across the University – IT, BI, Academics, all could own it and they all want to control it. We want to collaborate so we need clear guidance and clear governance. Need to identify who is doing what without letting any one area steal it.

Over the last years, we have forgotten about the proximity of data. It’s all around us but many people think it’s not accessible. How do we get our hands on all of this data to make information-backed decisions in the right timeframe? This proximity applies to students as well, they should be able to see what’s going on as a day-by-day activity.

The final panellist is from Curtin University. Analytics have to be embedded into daily life and available with little effort if they’re going to be effective. At Curtin, analytics have a role in all places in the Uni, library, learning, life-long learning, you name it. Data has to be unified and available on demand. What do users want?

Curtin focused on creating demand – can they now meet that demand with training and staffing, to move to the next phase of attraction?

Need to be in a position of assisting everyone. This is a new world so have to be ready to help people quite a lot in the earlier stages. Is Higher Ed ready for the type of change that Amazon caused in the book market? Higher Ed can still have a role as validator of education but we have to learn to work with new approaches before our old market is torn out form underneath us.

We need to disentangle what the learner does from what the machine does.

That finished off the initial panel statements and then the chair moved to ask questions to the panel. I’ll try and summarise that.

One question was about the issue of security and privacy of student information. Can we take data that we used to help a student to complete their studies and then use that to try and recruit a new student, even anonymised? UoW mentioned that having a separate data ethics group for exactly this reason. UoW started this with a student survey, one question of which is “do you feel like this is Big Brother”. Fortunately, most felt that it wasn’t but they wanted to know what was going to happen with the data and the underlying driver had to be to help them to succeed.

Issuing a clear policy and embracing transparency is crucial here.

UoM made the point that much work is not built on a strong theoretical basis and a great deal of it is measuring what we already think we care about. There is a lot of value in clearly identifying what works and what doesn’t.

That’s about it for this session. Again, so much to think about.



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