written by: Alan Berg
Universities can improve student retention by delivering the right amount of feedback at the right moment with their online systems. Systems such as Signal (http://www.itap.purdue.edu/learning/tools/signals/) have shown the way. The tools of use are covered by the term Learning Analytics (http://en.wikipedia.org/wiki/Learning_analytics).
Learning Analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Give the student and teacher feedback and advice at the right moment automatically, consistently and with care of detail and then you will have a considerably improved learning opportunity. With the right sized deployment to systems such as Blackboard (https://blackboard.ic.uva.nl/webapps/login/) or Sakai CLE (http://www.communities.uva.nl) you can improve average grades and increase student retention.
Learning Analytics can focus scarce resources on parts of a student study where they are most needed. However, the current tooling is still at an immature level. Issues such as the meaning and standardization of metrics still need to be addressed.
UvA is involved in two pilots following the Learning Analytics path: The first is to build a tool that you can use across platforms, see: Kick off Blog (https://www.surfspace.nl/artikel/702-uva-analytics-kick-off/) and Updates (https://www.surfspace.nl/artikel/789-when-data-has-meaning/). The second is a joint VU & UvA effort (https://www.surfspace.nl/nieuws/467-studenten-van-klankbordgroep-vu-en-uva-staan-positief-tegenover-learning-analytics/).
I am the lucky writer of the pilot code for the first project. The tool runs outside the course management system (Blackboard). However, it still looks like it is part of a Blackboard course. The tool pulls in data from a Google Analytics type tracking system (http://www.google.com/analytics/). It uses a open source equivalent to Google Analytics named PIWIK (http://piwik.org/). Web Analytics tell you a lot about which links are being hit in a website, the type of browser, the number of visits. By enriching the tracking data, and keeping the tool outside the course management system we can later re-use the technology across a wide range of on-line systems. This enlarges the tools usefulness within complex campus wide infrastructures.
The pilot is ongoing and we are learning a lot about our particular methodology. The next stage is to look to deepening the bridgehead with a more aggressive set of follow up projects.
If you are interested in involving yourself further. Please let us know