Understanding systems conditions for sustainable uptake of learning analytics

My current institution is – like most other universities – attempting to make some use of learning analytics. The following uses a model of system conditions for sustainable uptake of learning analytics from Colvin et al (2016) to think about how/if those attempts might be enhanced. This is done by summarising the model; explaining how

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Adding a custom library and a semi-real application – Indicators notebook(s)

So the indicators notebooks/platform is on github. The one and only bit of analysis is almost completely useless and still requires a fair bit of set up code. The aims in this post are Add in a custom library for connecting to the data source. Add an indicator/notebook that does something kind of useful. Hopefully,

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Jupyter notebook indicators platform: Baby step #1 – Finding an ORM

The last post documented early explorations of Jupyter notebooks ending with a simple query of a Moodle database. This post takes the first baby steps toward some sort of indicators platform using Jupyter notebooks, Python and github. The focus here is to find some form of ORM or other form of database independent layer. Problem:

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Thinking about (more) reproducible research and learning analytics

There are moves afoot to revisit some of the earlier research arising from Indicators project and hopefully move beyond. To do that well we need to give some thought to updating the methods we use to analyse, share and report what we’re doing. In particular, because we’re now a cross-institutional project. The following captures some

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Dashboards suck: learning analytics' broken metaphor

I started playing around with what became learning analytics in 2007 or so. Since then every/any time “learning analytics” is mentioned in a university there’s almost an automatic mention of dashboards. So much so I was lead to tweet. @s_palm Well everyone knows that “real” LA requires a dashboard — Don Quixote Jones (@djplaner) June

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