Learning Analytics

Learning Analytics is 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.

We use data about students and their activities to help understand and improve educational processes, and provide better support to learners. It should be for the benefit of students, whether assisting them individually or using aggregated and anonymised data to help other students or to improve the educational experience more generally.

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Learning Analytics and the Student Experience Plan

Learning Analytics is placed within Success Objective 5: Retention and Achievement
SO5.4
Scope and invest in a Learning Analytics tool to provide effective visual dashboards based on data that demonstrably impacts on learner engagement/success (e.g. VLE usage, library usage, attendance monitoring).This will be used by Personal Coaches to support students either at risk of withdrawing through low levels of engagement, or who can be encouraged to succeed at a higher level. This will also assist in meeting our duties under the Strategic Equality Plan.

The case for Learning Analytics

The issues of retention and attainment have always been complex problems, and difficult to solve with a single approach. However, advances in technology, and specifically educational technology are opening doors that allow us to look more closely at these issues. Learning analytics can contribute to addressing the issues of student retention and student attainment.

The new General Data Protection Regulation (GDPR) adds bite to the current data protections laws and tasks the Information Commissioners Office (ICO) with ruling on how we use Learning Analytics

We have developed the following:

  • a Learning Analytics code of practice
  • a USW Learning Analytics policy
  • Changes our T&C and consents at enrolment and progression.

Current Interpretation of this from JISC is we:

  • Don’t have to ask for consent for the use of non-sensitive data for analytics, this can be considered as legitimate or public interest
  • Consent is sort for use of all sensitive data
  • The institution needs to ask for consent to take interventions with students on the basis of the analytics

Follow this link for more information from the ICO.

The JISC Architecture

We are exploring three core products:

  • JISC’s ‘Study Goal’ is a student app that borrows ideas from fitness apps, allowing students to see their learning activity, set targets, record their own activity, and share this as a social network
  • Tribal’s ‘Student Insight’ seeks to provide a predictive analytic service
  • Data Explorer is the first we are rolling out to staff.

Data Explorer provides a series of dashboard that visualises data such as VLE activity and grades. It is hoped that further data such as attendance and library usage will be made available as we move forward. It will be piloted from September as part of the Personal Academic Coaching project.
It’s function is to explore live and historic data from the VLE and Quercus and pull this into a series of dashboards and visualisations. These can help inform the conversations PAC’s have during coaching sessions.

We are planning training to run alongside the Introduction to Personal Academic Coaching training sessions.
View more information about dates/locations on our Help and Support pages.