Possible uses of academic analytics

The following is a slightly edited copy of a message I’ve just sent off to the Learning Analytics GoogleGroup set up by George Siemens. I’m into reuse. Essentially it tries to highlight a small subset of the uses of learning analytics that I see as most interesting.

Some colleagues and I have been taking some baby steps in this area. In terms of trying to understand where we might end up doing we’ve started talking about three aspects. All very tentative, but can highlight a small subset of potential uses.

I have perhaps used a broad definition for analytics.

1. What?

This is the visualisation of what has happened in learning.

Lots of work to be done here in terms of finding out what patterns to look for and how to represent them.

For example, we took factors identified by Fresen (2007) as promoting quality as a guide to look for particular patterns.

Use #1 – Analytics can be used to test theories/propositions around learning and teaching. Perhaps by supplementing existing methods.

Fresen, J. (2007). A taxonomy of factors to promote quality web-supported learning. International Journal on E-Learning, 6(3), 351-362.

2. Why?

Once we’ve seen certain patterns, the obvious question is why did that pattern arise?

e.g. A colleague found a pattern where, on average, the older a distance education student was, the more they used the LMS.

The obvious question is why? Various theories are possible, but which apply? The 2nd person to comment on the above post is a psychology research masters student who is just completing some research attempting to identify an answer to they why question.

Use #2 – Analytics can be used to identify areas for future research.

3. How?

How can you harness analytics to improve learning and teaching.

Rowan made the point about using analytics to encourage changes in policy. I’ve seen this happen. Some early analysis at one institution showed that very few course websites had a discussion forum and even fewer used it effectively. Policy changed.

Use #3 – Analytics can inform policy change.

As we work/worked in areas supporting university academics in their teaching we were most interested in this question. In particular, we were interested in how analytics could be used to improve academics teaching.

Use #4 – Analytics can be used to encourage academics to reflect on their teaching.

e.g. Another colleague used analytics to reflect on how his conceptions of teaching matched what analytics showed about what happened within his courses.

Use #5 – Analytics can be used to encourage students to think differently about their learning.

Presenting students with different visualisations of what they are doing (or not doing) around learning can also encourage them to change practice.

I’ve heard reports that use of the SNAPP tool has achieved this. I’ve heard similar reports about the
use of the Progress Bar block for Moodle.

It’s possible to see a common trend in the last few uses. To some extent analytics is being used to improve “distributed cognition” in terms of putting some more smarts into the environment, which in turn becomes more likely to be seen and acted upon by policy makers, students or teachers.

However, what these people do in response to the improved knowledge they have, is still fairly open. I have a particular interest in how to encourage and enable these folk to use their improved knowledge in useful and interesting ways.

Which in turns will generate changed behaviour and hopefully changed use of the system. This takes us back to the what question at the start.

Use #6: Analytics can be an important component to on-going learning about what works and what doesn’t in learning and teaching.

3 thoughts on “Possible uses of academic analytics

  1. Pingback: The growth potential and ethics of academic analytics. « Col's Weblog

  2. Not sure if it’s in the same ballpark, but I’d be interested to see if learning analytics would cover tracking AIs. If not, it could certainly be used to support tracking AIs to give an automated indication as to whether the student has reached a sufficient level of understanding to continue to the next topic.

    1. G’day Tony,

      AI seems to be the almost automatic assumption of many folk involved with analytics. I have to say that I’m not convinced, but not entirely skeptical.

      The work that I may do, would almost certainly be without AIs. Aimed more at providing scaffolding for human beings to make judgements. In some cases, this might eventually be aided by AIs.

      One of the reasons, is that I’m not sure most academics can specific what is a sufficient level of understanding in any meaningful way. Given the diversity of people/learners, I’m also not sure that there is an objective way to do this.

      Basically, I think AIs or similar “intelligent” technologies (e.g. the semantic web) are very limited in what they can do and the folk pushing them often over state what can be achieved in real situations (i.e. outside the “lab”).

      However, I’m not certain enough to rule it out completely.


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