Learning analytics work at Manchester Metropolitan University between 2010 and 2012 summarised for a BETT 2013 Seminar

1. Emerging interest in learning analytics
• Why?
– Make better decisions
• Examples
– Choosing a new learning platform
• Statistical analysis of learner demographics, VLE engagement
and exam board outcomes identified important requirements for
our new platform
– Targeting an institutional change programme
• Statistical analysis of NSS & internal survey (10,000+
respondents) identified importance of hygiene factors (course
organisation: timetable, deadlines, etc) for overall satisfaction

2. Learning to interpret learning analytics
• How?
– Joining large datasets on common keys
– Identifying predictive models of ranked factors using
randomForest in R
– Triangulating outcomes with qualitative data
• Institution-wide student survey
• Focus groups
• Student Union ‘shout-outs’ and ‘Student Voice’ reports