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I love learning analytics – why you should too

Learning analyticsI was talking with a client this afternoon and I mentioned learning analytics. Oh, he said, I love learning analytics. Yeah, I smiled, me too.

 

I can’t imagine having that conversation with many people. So, in this post, I am going to share my enthusiasm on some key points, with some hints on where to start.

 

Learning analytics is starting to gain traction across all areas of education. It is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. 1

 

So why are learning analytics so important?

 

Learning analytics isn’t just an educational trend. Recently there has been a rise of business articles about big data. ‘You can’t manage what you don’t measure,’ is a common phrase in business. When making business decisions, you seek out and access data to assist in your decision making process. Big data allows managers to measure and know more about their businesses, and directly translate that knowledge into improved decision making and performance. 2

 

So why would our learning environments be any different?

 

Learning analytics has different meanings for different contexts. For a practitioner it might be about gaining an understanding of what your learners are doing and how you can do things better. For an organisation, it might be about measuring ROI and ensuring the allocation of resources to the right areas and capturing learners before they become disengaged.

 

The problem is, there is so much data (and if you use Moodle, upcoming changes mean that there will be more data available 3). So it is important to have strategies to help you manage.

 

Mark Drechsler (NetSpot) has an intuitive way of considering analytics. He suggests you consider:

  • Target: There is so much data that is available, it is important to be able to focus on what you need.
  • Consumer: Different groups will need different data (and use that data in different ways)
  • Scope: Data can come from a number of locations, from inside your LMS or other systems (like Google analytics)
  • Automation: Data and analysis can be automatic or manual.

 

For me, the first question I ask when talking about analytics is, what is the problem you are trying to solve (or what do you want to achieve).

 

Using the considerations above, this leads to a set of questions to ask:

  • What data are you targeting? Is it the student, subject level, faculty or organisational level? Identify the data that will best align the outcomes you are looking to achieve? For example, if you are looking to reduce student disengagement (and increase retention), develop a profile of a student who is at risk and target the data that can assist in the identification. This might be log-in rates or participation in events.
  • Who is going to use the data? Is it for the students, teachers/lecturers or the organisation? For example, if you are looking to increase student retention, will the information be provided to the student or the teacher/lecturer?
  • Where is the data coming from? Is it just your LMS or are you combining this with data from other sources.  For example, will your LMS provide the information or will another system.
  • What type of task is the data gathering/analysis? Is it an automatic task or manual? For example, do you have a system that will automatically gather and interpret the data or will individuals have to do it?

 

This is just a set of starting questions that I have started to use when talking with clients about learning analytics, to assist in focusing the discussion around.

 

If you’d like to chat about learning analytics and how to use them to achieve your outcomes, send us an email at hello@klevar.com

 

If this little bit of sharing the love has prompted you to want to investigate more, here are some links you might want to explore:

 

If you get a chance to hear Mark Drechsler (from Flinders Uni) present on the subject, its really worth it –

 

Frances Kneebone’s MoodleMoot (2013) presentation on using Google analytics, go to: http://youtu.be/Ro7rR3c4n_U

 

A really great blog about the importance of narrative when exploring data: http://blogs.hbr.org/cs/2013/05/the_value_of_big_data_isnt_the.html

 

Educause summary of readings: http://www.educause.edu/library/learning-analytics

 

Horizon report: http://net.educause.edu/ir/library/pdf/HR2013.pdf

 

Journal of Educational Technology and Society (July, 2012): http://www.ifets.info/journals/15_3/ets_15_3.pdf

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1 http://en.wikipedia.org/wiki/Learning_analytics

2 McAfee, A and Brynjolfsson, E 2013, “Big Data: The Management Revolution”, Harvard Business Review, October
http://hbr.org/2012/10/big-data-the-management-revolution/ar/1

3 cf: http://www.slideshare.net/moodler/2013-mootau13 and http://docs.moodle.org/dev/Logging_2

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