“failure” (CC BY 2.0) by tinou bao When it comes to research I’ve been a bit of failure, especially when measured against some of the more recent strategic and managerial expectations. Where are those quartile 1 journal articles? Isn’t your h-index showing a downward trajectory? The concern generated by these quantitative indicators not only motivated
Last year I started using with Perl to play with analytics around Moodle Book usage. This year, @beerc and I have been starting to play with Jupyter Notebooks and Python to play with analytics for meso-level practitioners (Hannon, 2013). Plotly provides a fairly useful platform for generating graphs of various types and sharing the data.
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
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,
Following on from the last post the following documents how to share the “indicators platform” for analytics via github. It’s largely intended to help @beerc. I doubt there’s nothing (at the moment) that makes this inherently interesting for anyone else. End result The (almost completely useless) end result of this work is this github repository.
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:
This is the third in a series of posts documenting “thinking” and progress around the next step of some bricolage with learning analytics and attempts to make some progress with the Indicators project. The last post in this series revisited some work I did last year. The aim of this post is to consider and
This is the 2nd in 3 posts thinking about learning analytics and how we might engage with it better. The first rambled on about reproducible research and hunted for what we might do. This post is an attempt to reflect on some work I did last year trying to design a collection of analytics to
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
A small group of teacher educators that I work with are starting to explore some research ideas around engagement, initial teacher education, and in particular the questions that arise out of the Quality Indicators for Learning and Teaching (QILT) For that project and others I need to get back into analysing institutional Moodle data. The