LARC learning analytics report 13/03/17

from Dropbox


It seemed only fitting to include my LARC report for the week after discussions around learning analytics. I have annotated it briefly with some notes but it definitely serves the purpose of highlighting the need for context. Without any context it would baffle an outsider, showing a poor level of social interaction, engagement or attendance.

A quick overview is that it was week 9 of a ten week course and two assignments were looming in my mind. I was focused on working on my learning challenge for the first of these and prioritised this along with reading and planning my writing. None of this was captured in the ‘numbers’. It also didn’t compare the week with my fellow classmates but to the course average which meant there was no direct comparison. This might have served to show some of the context as many of the others would be generating similar reports for the same week.

2 Replies to “LARC learning analytics report 13/03/17”

  1. Thanks for sharing your LARC report here, really great to relate this to EDC, and a nice way to tie the two course together in some way.

    The annotations were great – although I do hope you are enjoying the chance to think critically about learning analytics that the LARC offers! Many of the phrases are intentionally provocative!

    I think you’re right to highlight context here. The report appears to be (and is!) entirely unaware of circumstances influencing your ability to generate data in Moodle. Your brief explanation actually reveals so much about your week which the LARC would be unable to pick up on.

    I wonder though, could we build much more sophisticated analytics that *could* measure ‘context’? Would that solve the issues around analytics?

  2. Hi Jeremy, my annotations were an attempt to critically consider the data that was gathered yet not actually used, as well as what was used. So in answer to your question I think context could be measured to some extent without more sophisticated analytics.

    For example, if the report had considered my weekly average with the class weekly average (rather than the displayed course average) then it could have acknowledged ‘something’ going on across the class. So the low numbers would look less stark against the class numbers for each week.

    Going a step further by combining this with the data that it was one week before the end of the course it could have provided supportive feedback such as “Congratulations, you are nearing the end of course, keep up the good work. You may be finding it difficult to stay motivated at this point but don’t forget to login/join conversations for the last week – it may help with your assignments(exams).”

    Essentially, flipping the existing data into supportive encouragement rather than demotivating ….

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