We're in the final days of this open course. It would be helpful for planning future courses on learning analytics if you could provide reflections on the course design, your learning takeaways, or general reflections. What worked? What did you learn? what didn't work? what frustrated you? how have your views on learning analytics changed as the course progressed?
George Siemens
Discussions started by George Siemens
Yesterday, during Linda's excellent presentation (recording is here: http://www.learninganalytics.net/?page_id=71 ), I was struck by how closely aligned learning analytics are with state and national level metrics and standards. I find this to be a bit disconcerting. Analytics, from my perspective, should primarily offer new insight into learning and options for learning interventions, rather than as a measuring tool for state, provincial, or national standards. Standards are a target for the purpose of comparison - mainly for administrators and politicians. Does anyone share my concerns? Or what am I missing?
Despite drowning in data, most organizations do not take a strategic view on how to use that data to improve learning and knowledge exchange.
Consider a quick sampling of the data a typical university collects:
1. On enrolment: previous schools attended, where the learner lives, emergency contact info, health info, entrance exam scores, etc.
2. Once enrolled - courses registered, attendance, grades, use of library services, organizations the learner is involved with, if they use the university "meal card" - dietary habits, books purchased from the book store, and so on
3. In courses: attendance, grades, assignments, perhaps clickers, if online: which articles the learner has read, how much time in the LMS, which students she has interacted with, etc.
The value of analytics lies in traversing data silos (but that raises ethical issues). Most universities have a dept focused on institutional stats...but that generally has a marketing focus. What has to happen before organizations begin to consider using student-generated data for improving learning? Learning design? Or, in corporate settings, how employees help and support each other?
Consider a quick sampling of the data a typical university collects:
1. On enrolment: previous schools attended, where the learner lives, emergency contact info, health info, entrance exam scores, etc.
2. Once enrolled - courses registered, attendance, grades, use of library services, organizations the learner is involved with, if they use the university "meal card" - dietary habits, books purchased from the book store, and so on
3. In courses: attendance, grades, assignments, perhaps clickers, if online: which articles the learner has read, how much time in the LMS, which students she has interacted with, etc.
The value of analytics lies in traversing data silos (but that raises ethical issues). Most universities have a dept focused on institutional stats...but that generally has a marketing focus. What has to happen before organizations begin to consider using student-generated data for improving learning? Learning design? Or, in corporate settings, how employees help and support each other?
Quick question: are you expecting to attend the LAK11 conference? https://tekri.athabascau.ca/analytics/
If you are, any suggestions on informal chats/get togethers/etc?
If you are, any suggestions on informal chats/get togethers/etc?
Visualization is an effective way of distilling messages down to a few key patterns and then use those patterns to compare other similar messages. Consider this wordle of state of the union addresses: http://image.guardian.co.uk/sys-files/Guardian/documents/2011/01/26/State_of_the_union_2011.pdf
Wordles are somewhat useless - they provide little emphasis on the tone of an information source - i.e. "economy" could be used to mean positive job growth, not negative. A wordle doesn't provide much help in this regard. You have to know a bit of the context in order for wordles to be useful. Some of the tools within IBM's manyeyes are more helpful as they track word patterns, use, and connectedness.
Wordles are somewhat useless - they provide little emphasis on the tone of an information source - i.e. "economy" could be used to mean positive job growth, not negative. A wordle doesn't provide much help in this regard. You have to know a bit of the context in order for wordles to be useful. Some of the tools within IBM's manyeyes are more helpful as they track word patterns, use, and connectedness.