My notes from this meeting, comments, corrections and thoughts welcomed.
10:25 – 10:35 Update on Jisc’s learning analytics programme Paul Bailey, Jisc
10:35 – 11:00 Update on data transfer to the learning records warehouse, Uniform Data Definition and Data Processing Agreement Rob Wyn Jones, Jisc
11:00 – 11:15 Tea / coffee
11:15 – 12:00 Learning analytics: implementing an institution wide strategy Kevin Mayles, Open University
12:00 – 12:30 Student Success Plan (Intervention Management Tool) and Student App
Michael Webb, Jisc
12:30 – 13:30 Lunch and product demos from Blackboard, Civitas Learning, Hobsons, Tribal and Solutionpath
13:30 – 14:15 Vendor panel – with representatives from leading learning analytics vendors Richard Burrows, Blackboard
Deepak Colluru, Civitas Learning
Jamie O’Connell, Hobsons
Adam Cooper, Tribal
Richard Gascoigne, Solutionpath
Lindsay Pineda, Unicon
Chair: Niall Sclater, Jisc
14:15 – 15:00 Interventions Raj Chande, Behavioural Insights
14:45 – 15:15 Intervention strategies at your institution – discussion
15:15 – 15:30 Tea / coffee
15:30 – 15:55 Report from Jisc Learning Analytics Pilot Dale Davies, The City of Liverpool College
10:25 – 10:35 Update on Jisc’s learning analytics programme Paul Bailey, Jisc
85+ interested, 35 active, Discovery – agreed 28, completed 20, reported 12, Pre-implimentation 18, Implimentation 7,
New short readiness Questionnaire
Working on new Checklist for Initial Implimentation. This will become a webpage on the blog for anyone to work through.
New activities:
- Library Analytics Labs (May-Oct 16)
- College Analytics Labs (Sept 16)
Data Explorer: To allow institutions to explore their data. Target: Learning Technologists, Data Analysts. Partner: Touchbox.
10:35 – 11:00 Update on data transfer to the learning records warehouse, Uniform Data Definition and Data Processing Agreement Rob Wyn Jones, Jisc
- Predictive model requires 12-36 months
11:15 – 12:00 Learning analytics: implementing an institution wide strategy Kevin Mayles, Open University
Using historical data on current students to make perdications.
30 variables identified associated with predication:
- Demographic
- Student – module
- Student – previous study / motivation
- Qualification / mode of study
- Student in previous OU study
Formative and summative data used.
Predictions generated weekly and each tutor will get acess to their tutees data.
This system has to be set up per module so is not ready to roll out to all of the modules in the OU.
Students want the university to notice them when they are struggling and not have to tell the university they are struggling.
OU found that Socio-constructive Learning design increased retention but decreased student satisfaction.
My thoughts: [Do students perceive hard unpleasant work as better education? – too nice / fun =? not educational worthy?]
12:00 – 12:30 Student Success Plan (Intervention Management Tool) and Student App Michael Webb, Jisc
13:30 – 14:15 Vendor panel – with representatives from leading learning analytics vendors Richard Burrows, Blackboard; Deepak Colluru, Civitas Learning; Jamie O’Connell, Hobsons; Adam Cooper, Tribal; Richard Gascoigne, Solutionpath; Lindsay Pineda, Unicon; Chair: Niall Sclater, Jisc
14:15 – 15:15 Interventions Raj Chande, Behavioural Insights
Behavioural insights:
E- Easy
A- Attractive
S- Social
T- Timely
Letter – directing people to webpage which has a form on it, somewhere => Letter directing people directly to the form they’re required to complete.
Some great examples how little changes to how you present the information can have a huge effect on outcomes.
Experiment! try a change and monitor effect.
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