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Guide to: Learning Analytics (for staff)

This page provides guidance for staff on the responsible and effective use of learning analytics data to support student learning and teaching enhancement.

Please Note: The guide includes summarised information and therefore, it is essential that you read the related policy and/or regulation in full. To search for specific information, you can use CTRL+F and search for keywords throughout the page.

To be read in conjunction with: Policy on the Use of Data in the Support of Student Learning (Learning Analytics)

Introduction

Learning analytics uses data about students and their activities to understand and improve learning processes and provide better support to students. 

This guide explains how to use learning analytics data appropriately, including when ethical approval is required, how to handle data responsibly, and how to share findings. 

Key Principles of Learning Analytics, as stated in the policy: 

  1. The University, and those individuals or teams carrying out learning analytics, will be transparent to all stakeholders about how data is identified, collected, retained and used from learning and teaching systems. 
  2. Students – individually and/or collectively – should have appropriate access to relevant insights gained from learning analytics. 
  3. The uses of learning analytics should be justified and documented, and should be defined and explained as clearly as possible to stakeholders, including students. 
  4. It is essential that data to be used for learning analytics should be accurate and authentic. Deficiencies or biases in data must be identified and taken into account in the interpretation of data. 
  5. Data should be interpreted in as full a context as possible, with recognition that learning analytics data does not give a complete picture of a student’s learning. 
  6. Learning analytics should be focused on enhancement and should be used to enable as wide a benefit to students as is appropriate. Interventions or actions stemming from learning analytics must be inclusive. 
  7. Learning analytics data or insights from learning analytics will not be used by the University for any purposes beyond those set out in this policy (e.g. for staff performance review). 

Ethical approval

Ethical approval is not required for all learning analytics projects. Whether approval is needed depends on the purpose of the work and how the findings will be used. 

If required, ethical approval must be obtained before the project begins. 

When ethical approval is not required: 

Ethical approval is not required where projects meet all of the following conditions: 

  • Use University of Glasgow data 
  • Are not intended to have externally facing outputs (e.g. conference presentations or publications)  
  • Are carried out within the parameters of the University Learning Analytics Policy  

In addition, ethical approval is not required where projects: 

  • Use secondary data, that is publicly available and already anonymised. 

Examples include:  

  • Monitoring engagement with VLE activities to check if students have participated, as a check on student engagement – for example, a course convener may be trying to establish whether any students need a reminder to participate in a mandatory task, or to find out if students are having problems accessing resources; 
  • A project that uses University of Glasgow data for a routine purpose such as to enhance the quality of learning and teaching provision or to inform curriculum redesign in a School; 
  • An internal investigation that seeks to correlate the extent of VLE engagement with assessment outcomes, the findings of which are used internally to verify the usefulness of resources in helping students demonstrate attainment of intended learning outcomes as a quality assurance indicator. 

When ethical approval is required: 

Ethical approval is required for projects that:  

  • Produce externally shared outputs (e.g. publications, conferences, blogs) 
  • Are defined as research, including Scholarship of Teaching and Learning (SoTL) 
  • Begin as internal projects but later involve external sharing of findings 

Important: In the case that the purpose of a project evolves over time, and the data includes identifiable student information which was not originally collected with consent for this purpose, it cannot be reused without permission. In such circumstances, the University Ethics Committee advises against seeking retrospective permission or ethical approval. 

Examples include:  

  • A study that seeks to correlate the extent of engagement with online study resources with assessment outcomes, the outcomes to be externally disseminated as a journal article; 
  • A SoTL project that uses routinely collected VLE data to seek to correlate the extent of engagement with formative quizzes with assessment outcomes, which is intended for external publication on a blog. 
  • A learning analytics project undertaken for scholarship purposes, where the output will be shared publicly at the University of Glasgow’s annual Learning and Teaching conferred (which is open to external delegates).  
  • If the person carrying out a learning analytics project originally intended to use the findings only internally, to improve learning and teaching practice or inform curriculum design, but subsequently wished to disseminate the findings externally. 

Non-ethical use of data 

If the previously-gathered data involved human participant data which was not anonymised, it would not be acceptable to use it without permission from the participants. In such circumstances, the University Ethics Committee advises against seeking retrospective permission or ethical approval. If needed, advice should be requested from the relevant School/College/Institute Research Ethics Convenor. 

How to apply for ethical approval 

  • Submit an application to the relevant College Ethics Committee before starting the project 
  • Seek advice from the Committee if unsure whether approval is needed 
  • Seek additional guidance where the project involves data relating to protected characteristics 

Data in a Learning Analytics Project

Types of data in Learning Analytics 

The Learning Analytics Policy lists commonly used data types, though this is not exhaustive. Data will evolve as systems and practices develop. 

Generally, data falls into two categories:  

Student engagement data 

  • General access data (e.g. Moodle logs) 
  • Enrolment data (e.g. MyCampus)  

Student performance data 

  • Asynchronous discussion participation (e.g. in Moodle or Microsoft Teams) 
  • Synchronous online participation (e.g. in Zoom or Microsoft Teams) 
  • Course completion data (e.g. Moodle course completion option) 
  • Feedback to tutor from students (structured and/or unstructured) 
  • Assessment results/completion (e.g. from Moodle or MyCampus) 

 Interpretation of data in Learning Analytics 

Learning analytics data represents only a partial view of student learning and must be interpreted carefully. 

Principle 5 of the Learning Analytics Policy highlights that data should always be considered in context, recognising that it does not provide a complete picture of a student’s experience or learning. 

Staff should: 

  • Treat individual datasets as snapshots rather than definitive evidence 
  • Use multiple data sources where possible (triangulation) 
  • Avoid assumptions about engagement or learning based on limited indicators 

Examples of cautious interpretation:  

  • Higher levels of visible interaction (e.g. discussion forum activity) are often assumed to indicate more active or effective learning. However, this reflects a particular view of learning and may overlook independent or less visible forms of engagement that are not captured in interaction data. 
  • Data showing that a student has clicked on or accessed resources does not mean they have meaningfully engaged with them, or that learning has not taken place elsewhere (e.g. offline). These types of “activity data” are only partial indicators of learning and can have limitations, including inconsistent predictive value, potential bias, and variation across disciplines. 

GDPR compliance

Before starting a learning analytics project: 

  • If you intend to undertake a Learning Analytics project involving personal data, a DPIA (Data Protection Impact Assessment) must be completed. Undertaking a DPIA provides an opportunity to draw out the intended processing activities and purposes, which in turn helps in identifying the applicable legal basis, any potential security or other risks, and ensures the processing is proportionate to the project’s aims and that it begins with appropriate safeguards in place. 

Data Retention 

The retention of data depends on:  

  • whether data is identifiable, anonymised or pseudonymised 
  • the legal basis for processing 

Please see the Data Protection & Freedom of Information Office on data retention for research data and record retention schedules. 

Sharing findings

With students:  

Sharing findings with students is optional, but staff are encouraged to consider: 

  • whether sharing insights would support learning 
  • how meaningful or robust the findings are 
  • the potential benefit to the student experience 

Good practice includes: 

  • being transparent about what data is collected and how it is used 
  • recognising students as stakeholders in their data 
  • involving students, where appropriate, in discussions about data use 

Within the University: 

Sharing findings internally requires careful consideration, especially where data relates to identifiable students. 

Before sharing, consider: 

  • Is the data aggregated or individual-level? 
  • Has it been anonymised? 
  • Could individuals still be identified (e.g. small cohorts)? 
  • Would students reasonably expect this data to be shared? 
  • Do you have permission to share it? 

Privacy and ethical considerations 

Learning analytics operates in a context where: 

  • data collection can feel intrusive if not handled transparently 
  • there may be power imbalances between institutions and students 
  • third-party platforms (e.g. external learning tools) introduce additional data risks 

Staff should be aware of: 

  • who has access to student data 
  • how data is stored and processed 
  • the implications of using external systems 

Any concerns should be raised with the Data Protection and Freedom of Information Office.

Follow-up support

Depending on your query, the following may be of use:  

More information on Learning Analytics: 

  • Jisc have assembled a range of helpful resources and guidance on learning analytics in UK higher education. Jisc also promote their own learning analytics system (Predictor), to which the University of Glasgow is not subscribed. 

FAQs

Can I get ethical approval to use data that has already been gathered for a different purpose / secondary data? 

Ethical approval is not required for the use of secondary data, that is existing data collected by others, if the data is in the public domain and has been anonymised by the time it became secondary data. 

What is the difference between anonymous and pseudonymous data? 

Please see the guidance on anonymous and pseudonymous data from the Data Protection & Freedom of Information Office.