6. Effective learning analytics challenge
» Rationale
› Organisations wanted help to get started and have access to
standard tools and technologies to monitor and intervene
» Priorities identified
› Code of Practice on legal and ethical issues
› Develop basic learning analytics service with app for students
› Provide a network to share knowledge and experience
» Timescale
› 2015-16—test and develop the tools and metrics
› 2016-17—transition to service (freemium)
› Sep 2017—launch, measure impact: retention and achievement
7. What do we mean by learning analytics
»The application of big data techniques such as machine
learning and data mining to help learners and
institutions meet their goals
»For our project
› Improve retention (current project)
› Improve attainment (current project)
› Improve employability (future project)
› Personalised learning (future project)
8. Toolkit and community
»Blog: http://analytics.jiscinvolve.org
»Reports
› Code of practice for learning analytics
› The current state of play in UK higher and further
education
› Learning analytics in higher education: a review of UK and
international practice
»Mailing list: analytics@jiscmail.ac.uk
»Network meetings
9. Learning analytics architecture
»Cloud based
»Modular approach
»Various best of breed suppliers
»Organisations select options that suit them best
»All data remains securely accessible to owner only
»Scalable and expandable
10.
11. Current engagement
»Expressions of interest: 85
»Engaged in activity: 35
»Discovery to Sep 2016:
› Agreed: 28
› Completed: 18
› Reported: 17
»Learning analytics pre-implementation: 12
»Learning analytics implementation: 7
12. Future engagement
»From Sep 2016:
› “Readiness toolkit” with a diagnostic set of questions and
support materials leading to implementation
› Start-up guidelines to get ready for learning analytics
implementation
»Further details will be announced via the email list:
analytics@jiscmail.ac.uk
17. What is involved in learning analytics
»SoLAR (Society for Learning Analytics Research)
› “learning analytics is the measurement, collection,
analysis and reporting of data about learners and their
contexts, for purposes of understanding and optimising
learning and the environments in which it occurs”
18. What data are involved
»Learner details
»Records of achievement
»Learning activities—attendances
»Course and module information
»Teaching staff details
»Historical data
»Other learner context information—use of resources
»Mappings and cross referencing between all above
19. What important considerations are involved
»Should students be asked for consent to use their data?
»Can students access all the data held about them?
»Should interventions always be human-mediated?
»What sources/good indicators of student engagement
or activity exist?
»What problems might occur capturing learning data?
»Who leads the implementation of learning analytics in
your organisation?
»What is the strategic driver—quality, finance, learner
satisfaction?
20. Who is involved in learning analytics
»Learners
»Teaching staff
»Teaching management
»Organisation management
»Systems users
»Technical and data management staff
»Analysts
»Legal staff
»Others?
22. Readiness assessment prototype
»Assessment areas:
› Culture and vision
› Strategy and investment
› Structure and governance
› Technology and data
› Skills
»First step in identifying requirements and
recommending actions
23.
24.
25.
26. Have a go
»Divide into groups of organisations at a similar stage of
learning analytics implementation
»In your group, encourage a volunteer with connected
device to facilitate completion of form at:
› http://bit.ly/la-readiness
»Together agree completion of responses and discuss
appropriateness of this method of initial assessment
and the choice of questions
»Prepare a few of the most pertinent points raised to
report back to larger group
27.
28. Questions and comments
»Jisc platform of interest?
»Would you like to use the service?
»Who will coordinate at your organisation?
»What are your next steps?
»How can Jisc help further?
»Your questions…
29. jisc.ac.uk
Except where otherwise noted, this work
is licensed under CC-BY-NC-ND
Information at analytics.jiscinvolve.org
Paul Bailey paul.bailey@jisc.ac.uk
George Munroe george.munroe@jisc.ac.uk
Announcements on analytics@jiscmail.ac.uk
Contacts
Editor's Notes
Please edit this slide to represent the session you are running at ConnectMore
What do we mean by learning analytics. The service we are developing will collect data and undertake statistical analysis of historical and current data derived from the learning process to create models that allow for predictions that can be used to improve learning outcomes.
Models are developed by “mining” large amounts of data to find hidden patterns that correlate to specific outcomes
E.g. Mine VLE event data to find usage patterns that correlate to course grades
The service will provide predictive models initially for retention (identify students at risk of failing) and attainment (identifying students at risk of not achieving a specified level of attainment).
In the future we will look to offer predictive models to support employability and personal/adaptive learning.
The project consists of the learning analytics architecture (next slide), a toolkit and community.
These consist of a blog with reports and information to assist institutions with readiness to implement learning analytics and technical implementation of the Jisc service.
There are three reports all linked from the blog a Code of Practice for Learning Analytics, A report from 18 months ago that reviewed current state of learning analytics in the UK and a more recent report on the evidence base for the effectiveness of learning analytics with 12 international case studies.
If you want to be involved and keep informed about the development of the service then join the analytics jiscmail list
We also hold quarterly network meetings which are promoted via the blog and jiscmail list
Overview of learning analytics architecture.
Red items are components that will include the tools in the project (Tribal student insight, Unicon/Apereo LAP and Student Success Plan, Student App) but also alternative third party or institutional tools.
We have ~400 people on the Jiscmail list and a pipeline of interested institution's (50+ HE, 20+FE). We are actively engaging with 35 institutions, 28 in discovery institutional readiness and 12 in beta implementations.
From Sept 16 we’ll be introducing a new institutional readiness process to help institutions get ready for implementing learning analytics. This will consist of an overview workshop to introduce the service and an diagnostic assessment tool, institutions will complete the assessment tool and then undertake appropriate actions to address recommendations.
For institutions who are ready to start implementation there will be set of guidelines to get set-up with data collection and visualisations, ready to implement a predictive analytics solution and the student app.
Details will be announced via the jiscmail list – so join it to participate.
