Articles

Student Guide to Learning Analytics

TLDRoffon

The "digital footprints" left when you use university systems combined with grades and past academic history provide additional information to you along with university staff to support your learning.

Introduction

This guide sits alongside the University of Greenwich's Learning Analytics Policy and our Statement of Principles for Learning Analytics.  A key principle is to be completely transparent about all aspects of our use of Learning Analytics. We want you to understand exactly what data is being collected, how it is being processed and what we will be doing with the information. As our use of Learning Analytics develops this guide will be updated.

Learning Analytics are the "digital footprints" left when you use Moodle and other university systems, which combined with data such as grades and past academic history provide additional information to you, your lecturers, tutors and support staff to support your learning.

As a student at the University of Greenwich through registration along with your engagement with your studies we are able to collect the data required, and currently/already use it to review aspects of our modules and manage our use of resources more efficiently. However using this data for Learning Analytics providing you, your lecturers, tutors and support staff with additional information to support your learning and ensure you reach your full academic potential is new.

The resulting picture from using the data in this way can give you a better idea of how your learning is progressing, help us to understand how we can best support you to meet your goals and achieve your full potential while at the University of Greenwich.

A future use for Learning Analytics is to provide predictive indicators for achievement by comparing a learner's patterns of activity and achievement with those of previous groups of students. While we will not use predictive indicators in the first year of implementation it is our aim to introduce these.  We expect these indicators to help us identify more easily those who may need additional support with their academic studies, which will enable us to contact them to see how we can help. Further information on the purpose of learning analytics is available.

We will be transparent about the data collection, sharing, consent and responsibilities, but learning analytics metrics will not be used for assessment purposes and will not contribute to coursework grades.

In collaboration with Jisc, the UK's expert body for digital technology and digital resources in higher education, the university analyses a large amount of data about teaching and learning events through a Learning Analytics processor. Using a model tailored to our university systems, a measure of engagement for each individual will be generated and compared with class or group averages. Results that differ from the average can be identified allowing interventions put in place for individuals.

Learning Analytics can help you when you start university to give you an accurate perception of how your learning is progressing. This can be valuable information throughout your studies.

Presenting Your Information to You

The Learning Analytics Student App, Study Goal, developed by Jisc, is available from the APP or equivalent Android Store. You will also have a link from our own University of Greenwich student app. Study Goal shows you information on your learning activities. A score is shown for 'engagement', calculated from details of your digital footprint and your engagement with an activity.

Your 'attainment' i.e. your marks and grades are also displayed in the app, though these are provisional, therefore may change until confirmed by a Board of Examiners.

If you consent, emails or text messages may also be sent to you automatically suggesting additional support or resources that could help your studies. Messages may also be sent to congratulate you on good performance or improvement.

Learning Analytics also helps you by giving your personal tutor richer information on your progress, which they may use to discuss your progress in regular meetings, or contact you to check you feel you are on track, or to arrange a meeting to review your progress.

Staff Access

Tutors have access to a tool called Data Explorer containing data about your engagement and attainment which they can use to provide a focus for conversations between the two of you.

Tutors or members of support staff may also receive automated alerts about students whose patterns of study suggest they may need additional tailored support. Along with other information these alerts help tutors decide if they should contact students. It is important to note that the risk scores are only predictive indicators: the model will always have a degree of inaccuracy, therefore cannot indicate with certainty what grade a student will achieve. But it helps us to prioritise those students who are most likely to require additional help. The types of help that will be available will be formalised more fully as we roll out our Learning Analytics systems, and are likely to include, among other things, recommendations of additional class resources, referral to specific study skills programmes, and general advice about managing workloads. We will share more with you as the package of interventions is developed.

We are using the following data for Learning Analytics to support your learning.

Data Security

All data are used in compliance with the Data Protection legislation and in accordance with the University's Data Protection Policy along with Student Privacy Notice :

  • Background information: your name, identifiers used by the [University / College], date of birth, ethnicity, gender, declared disabilities, contact details, entry qualifications, whether your parents were in higher education, your socio-economic background, whether you are an overseas student, contact details, and a link to the photo we hold of you. Two of these data categories are sensitive or special data, Ethnicity and Disability.  We are including these within our models with the aim of improving our monitoring of equality of opportunity and to improve the accuracy of the models. They will be used for Statistical Purposes only.
  • Details about your course, the modules you are taking, and your tutors.
  • Details of your assessments, marks and grades obtained.
  • Details of your activity in Moodle, Panopto and any other Virtual Learning Tool(s) you may use: logins, resources viewed, assessments submitted and graded, and session timeouts.
  • Details about your attendance.
  • Details about your library usage.
  • Responses to surveys.

We are currently developing a mechanism to allow you to access all of your personal data that is in our Learning Analytics processor in a meaningful, accessible format. If any of the personal data is inaccurate, you will have the right to correct that.

Your personal information is safe and secure and subject to strict security procedures in compliance with the General Data Protection Regulation 2018.

Only those members of staff who work in collaboration with the data processors to check the accuracy and adequacy of the modelling and those members of staff who have a professional requirement to support you are permitted to view the analytics about you individually.

Your personal tutor will be able to view data about your engagement, attainment and going forward, any predictions made.

Your survey responses will not be visible in either of the Learning Analytics visualisation tools (Study Goal and Data Explorer), but will be used to improve the predictive indicator of outcomes and to improve our courses.

If you have any concerns about the university using your data for the purposes of Learning Analytics, or wish to gain access to your data, please discuss this with your personal tutor.

Your data is also combined with other students' data to help us improve our courses and support all students better overall. You will not be individually identifiable as fields such as your student ID are encrypted before it is sent to a central learning records warehouse managed by the university's contracted agents (currently Jisc and Civitas), and hosted on servers in which are both physically secure, and located in the European Economic Area where Data Protection and Privacy legislation is fully in place.

These contracted agents are not allowed to share your data with any third party unless a contract is in place between the university and the third party which protects your data. Neither the university nor the contracted agent will sell your data to any third party.

Legal Bases Used for Including Data in Learning Analytics

Universities are designated as 'public authorities' for the purposes of the General Data Protection Regulations,  Data Protection Act 2018 (the bill extends the definition to all bodies subject to the Freedom of Information Act).

Guidance on the GDPR from the Information Commissioner Office indicates that the public task basis is likely to apply to much of the processing done by Universities although some processing may use the legal bases of legitimate interest or consent. For example, a University might rely on public task for processing personal data for teaching and research purposes; but a mixture of legitimate interests and consent for alumni relations and fundraising purposes.

The University has decided to use 'public task' as the basis for processing Learning Analytics data, as Learning Analytics relates to the University's core learning and teaching functions and how we support student retention, progression and attainment.