Learning Analytics Project (LeAP)
Providing insights to maximise student potential at City
What is learning analytics?
Learning analytics is “the application of analytic techniques to analyse educational data, including data about learner and teacher activities, to identify patterns of behaviour and provide (frequent) actionable information to improve learning and learning-related activities.” (Van Harmelen, M. & Workman, D. (2012). CETIS Analytics Series: Analytics for Learning and Teaching).
Learning analytics uses specialised software that pulls together existing student learning data from multiple university sources into one system. By having student learning data in one system, staff can obtain a clearer picture of student engagement with educational activities across modules and programmes without having to look at multiple systems.
Staff may use learning analytics data for two main purposes:
- To identify students who are not engaging with their studies and provide those students with appropriate, tailored, and timely support to help them succeed. The support offered, also known as ‘interventions’, can be done on an individual or group basis. These interventions can ensure that students receive appropriate learning support. For more on interventions, see Frequently Asked Questions.
- To understand and improve teaching and learning, enriching students’ educational experiences. For example, virtual learning environment (VLE) data can show how often students access different resources and resource types, influencing module learning design. Staff may also choose to combine different learning analytics data types – such as VLE, attendance, and assessment data – for further learning insights, discovering, for example, that students’ consistent use of a certain resource correlates with higher final grades. These insights may be shared with incoming students to encourage engagement.
How does City use learning analytics?
City’s Learning Analytics Project (LeAP)
City is currently piloting and embedding its learning analytics service and system through the Learning Analytics Project (LeAP), project-managed and worked on by members of the Learning Enhancement and Development (LEaD) team. LeAP’s key focus is on how learning analytics and student interventions can be used to understand and support student engagement with their studies, which in turn can lead to better retention and progression of students.
From autumn 2020, City has piloted Jisc’s Data Explorer (DataX) learning analytics system, a learning data hub with a dashboard interface that displays and interprets students’ dynamic (i.e. behavioural) data. Using a traffic-light system – also known as RAG (Red, Amber, Green) indicators – it indicates individual student, module and course engagement patterns. From September 2022, three data sources will be used by the learning analytics system to determine student engagement. These are:
- VLE (i.e. Moodle) data
- Student record system (i.e. SITS) assessment data
- Attendance (i.e. SEAtS/CountMeIn) data.
LeAP has a number of connections to and interdependencies with other City work. LEaD, IT and Student & Academic Services work closely together to ensure that requirements and objectives across all areas are taken into consideration.
Phase 1 (2016-2020)
From 2016 – 2020, the team delivered LeAP Phase 1, which aimed to understand how learning analytics could benefit City. Key stakeholders included LEaD, IT, MAfS (Modernising Administration for Students) & Schools. This project involved taking part in the co-design of Jisc’s DataX system. The team also ran consultations with staff and students on the use of learning analytics, trialled the DataX system and produced a list of recommendations for moving forward.
Phase 2 (2020-2023)
The current phase of this project is LeAP Phase 2. This will be a 3-year project from 2020-23. This project aims to:
- Pilot and embed a core learning analytics service City-wide with support and training provision, focusing on student engagement.
- Implement Jisc’s DataX learning analytics system and provide access to data for all relevant City staff
- Create a code of practice and a policy (or amendments to existing policies) to support learning analytics.
- Make recommendations and improvements to the service, based on investigations into interventions, benefits and data sources.
- Support staff in the use of a learning analytics system to design and implement appropriate interventions to support student learning.
- Explore the further potential of learning analytics (e.g. in learning design) through research and evaluation.