Probably the highest profile technology amongst senior managers and leaders at this time is the use of analytics to support teaching, learning and assessment.
Using the data that institutions gather on a regular basis for the purposes of analysis, looking for patterns is one that has gained traction over the last few years. There are also others who wonder if this analysis of data and patterns is useful and allowing us to make informed decisions about learners.
Jisc have released a new report: Learning Analytics in Higher Education: A review of UK and international practice (PDF). Drawing on eleven case studies, they examine why institutions are deploying learning analytics, and what the benefits are for learners. They also discuss the main data sources being drawn upon by institutions and the technical architecture required.
The emphasis of the report is on investigating the evidence for learning analytics: what impact it’s having, and to what extent the algorithms can actually predict academic success.
I have always seen analytics as a tool to support and enhance existing decision making and support, that was already in place. The analytics reinforcing an existing view, or bringing to light patterns that were previously hidden.
Analytics in my opinion doesn’t replace good teaching decisions, support and intervention strategies, it helps inform them, so that we can ensure all learners receive the support and advice they need. Which is why I am also pleased to see in the report, that they also look at how institutions are carrying out interventions to attempt to retain students at risk, and provide better support for all students as they progress through their studies.
The interventions arising from analytics are probably the most important aspect of analytics, otherwise why bother?
The main report summarises the case studies. The full individual case studies are:
- Traffic Lights and Interventions: Signals at Purdue University
- Analysing use of the VLE at the University of Maryland, Baltimore County
- Identifying at-risk students at New York Institute of Technology
- Fine-grained analysis of student data at California State University
- Transferring predictive models to other institutions from Marist College
- Enhancing retention at Edith Cowan University
- Early alert at the University of New England
- Developing an ‘analytics mind-set’ at the Open University
- Predictive analytics at Nottingham Trent University
- Analysing social networks at the University of Wollongong
- Personalised pathway planning at Open Universities Australia