Week 7: Open Learning Analytics

Date: March 5 - March 11

Over the past six weeks, we've looked at the trends that drive learning analytics, cases and examples, concerns around privacy, and tools and techniques practitioners use. Given how quickly analytics are developing in education, this course is at best an introduction to the topic. This week we'll turn our attention to a practical vision held by SoLAR members for open analytics. The concept paper is linked in the readings below. In few areas of life would we allow significant decisions to be made on our behalf without knowing the criteria driving those decisions. Can you imagine visiting a doctors office and being given medication without any explanation of why? or what it's supposed to do? Unfortunately, much of what happens online in recommendations and personalization happens behind a curtain: we don't know how or why we are receiving the content or the recommendations that we encounter. This is a concern, particularly evident in learning. Algorithms should be transparent so learners know how decisions are being made on their behalf. Institutions should be able to adjust weighting schemes to reflect their unique cultural context.


The Filter Bubble: http://www.ted.com/talks/eli_pariser_beware_online_filter_bubbles.html

Open Learning Analytics concept paper: http://solaresearch.org/OpenLearningAnalytics.pdf

Slideshare presentation on open analytics: http://www.slideshare.net/gsiemens/iadis-shanghai

Openness: Why Learners should know about and influence how decisions are made about their learning:

Live Sessions

A discussion with Simon Buckingham Shum on open learning analytics will be held Tues, Mar 6 at 1 pm Mountain time (http://www.timeanddate.com/worldclock/fixedtime.html?iso=20120221T13&p1=80 to different timezones). The session will be held here in Blackboard Collaborate: https://sas.elluminate.com/m.jnlp?sid=2008104&password=M.13942243B0B58B854EE5D590716D09