Learning analytics projects

In response to the increased use of MS Teams for teaching during the pandemic, Information Services reviewed the data available in MS Teams through the lens of learning analytics and data protection.  This project outlined the engagement, activity and well-being data that MS Teams measures, stores and shares with staff and students. It also made some recommendations for privacy and data protection and continued use of MS Teams for teaching.  

More information about this project is available on its project page. 

Learning Analytics on MS Teams project page

This project was a small-scale pilot to explore using OnTask, an open-source learning analytics platform to improve the student experience via timely, personalised and actionable student feedback.  This software uses learning analytics data and instructor-set criteria (if this, then that) to coach students via email.  OnTask can use data from a range of teaching tools and the virtual classroom, and it can use the raw data from these tools or transform the data to create more advanced data sets and workflows. The pilot ran with a fully online MicroMasters course and two campus-based courses with large student enrolments. This project was an important stepping stone in looking at tools to personalise and enhance the distance learning experience and in understanding the effort and expertise needed to manage data-driven learning tools.  

More information about this project is available on its project page.

Learning Analytics for DLAS project page

Adaptive learning platforms aim to use a student’s learning analytics data to adapt the learning journey according to the student’s needs.  Information Services explored several 3rd party adaptive learning platforms for potential use within distance learning programmes. 

The research on analytics in flipped classrooms was primarily focused on the development of methods that allow for understanding the types of strategies and strategy changes learners follow throughout an academic semester, based on the analysis of digital traces recorded by VLEs. These analytics can be used to inform improvement of instructional design and learning experiences. This research was done in collaboration with the University of Sydney, University of South Australia, and University of Belgrade. 

More information about this project can be found in its published paper. 

An analytics-based framework to support teaching and learning in a flipped classroom

To assist European universities to become more mature users and custodians of digital data about their students as they learn online, the SHEILA Project will build a policy development framework that promotes formative assessment and personalized learning, by taking advantage of direct engagement of stakeholders in the development process. It ran from 2016-18. 

More information can be found on the Centre for Digital Education's project pages and on the project's website. 

Supporting Higher Education to Integrate Learning Analytics (SHEILA)

SHEILA Project EU website

From 2015-2017, we ran a 2 year pilot with Civitas Learning using data from our fully online Masters level programmes and courses. The choice of the online Masters programmes as the pilot area was a critical one as it had the advantage of being a readily identifiable and isolatable pilot group that is large enough for the pilot scheme to work within. This was a data rich environment with strong student engagement in the digital learning environments.   

This project allowed us to gain experience of developing learning analytics models, promoted teachers’ and students’ understanding of this area, improved our understanding of where areas of weakness exist in our data collection, and helped to develop a supporting Learning and Teaching Analytics Policy. 

More information can be found on the project webpage.

VLE Student Analytics project page

This project asked: ‘How can University teaching teams develop critical and participatory approaches to educational data analysis?’.  It looked to develop ways of involving students as research partners and active participants in their own data collection and analysis, as well as fostering critical understanding of the use of computational analysis in education.  Its objectives included finding ways to capture and analyse data from a broader range of student learning activity (beyond attendance and the VLE).  It also aimed to find ways to create 'personalities' for the reports by identifying the kind of language students preferred in automatically generated reports and determining an appropriate 'voice' for feedback to each student.   This work was funded by a Principal’s Teaching Award Scheme grant.

You can find more information about the LARC project on the Centre for Research in Digital Education's projects webpage. 

The Learning Analytics Report Card (Centre for Research in Digital Education projects)

The research on video analytics was conducted primarily in collaboration with the University of South Australia, University of New South Wales, University of Sydney, and University of British Columbia. Analytics were developed to study the effects of instructional conditions and experience on adoption of the video annotation software named Online Video Annotations for Learning (OVAL). Analytics were based on the use of digital traces of interaction with OVAL and used in the studies are conducted with students of performing arts and engineering and with faculty members for their academic development. 

More information can be found in this paper related to the project and on OVAL's Github page.

Using Video Annotation Software to Develop Student Self-Regulated Learning

OVAL project GitHub page

Information Services explored some learning analytics options within the Learn and Moodle virtual learning environments, working with a small number of specific courses. Projects and tools included those which allow students to see some of their own data and to help them understand their activity and learning patterns. These projects provided valuable information about student attitudes to data and privacy, which was used to inform several of the other projects listed. 

You can find more information about the VLE Learning Analytics project on the virtual learning environment webpages. 

More information about the VLE Analytics project

The University of Edinburgh is one of the pioneers in the space of massive open online courses. The researchers in Information Services, Centre for Research in Digital Education, School of Informatics, and Institute for Academic Development were actively engaged in analysing digital trace, demographic and success data of the students who were enrolled on several MOOCs. This analysis involved understanding the study patterns, effects of social networks, and other demographic data on the success and experience of MOOC learners. Researchers from the University of Edinburgh collaborated with Technical University of Delft, Massachusetts Institute of Technology, University of Michigan, University of South Australia, University of Texas at Arlington, and University of Memphis. 

Supported by the European Association for Research on Learning and Instruction (EARLI) as a Centre for Innovative Research, the goal of this research was to develop measurements of students’ cognition, metacognition, emotion and motivation during learning in order to support the development of more powerful adaptive educational technologies. This research was done in collaboration with Radboud University Nijmegen, University of Oulu, North Carolina State University, Technische Universität München. 

You can find more information about this project on the Centre for Research in Digital Education's projects webpage. 

Earli Centre for Innovative Research- Multimodal Analytics for Self-Regulated Learning

Related paper: Relevance of learning analytics to measure and support students' learning in adaptive educational technologies (2017)

This project worked on the identification of common problems faced by teachers and students when learning online and aimed to determine the types of learning analytics teachers would find useful to effectively address these problems. The project developed a web-based analytics tool (dashboard) named Loop that supports teachers to more easily interpret learning analytics to help them improve teaching and learning practices. This research was in collaboration with the University of Melbourne, University of South Australia, and Macquarie University. 

More information about this project can be found in the following paper: 

Loop: A learning analytics tool to provide teachers with useful data visualisations

As educators increasingly embrace social technologies to support learning, challenges arise in evaluating the quality and nature of student participation in activities using technology external to the institution's Learning Management System (LMS). This project extended the field of learning analytics  by developing an open-source toolkit for performing sophisticated analysis of learners' engagement in connected learning environments. This project was in collaboration with the University of Sydney, University of Texas at Arlington, University of South Australia, and University of Technology Sydney. 

More information can be found on the project website.

Beyond the LMS website

In 2015, Student Systems planned to: 

• Develop our use of student data to support ways to enhance learning & teaching, the student experience and operational effectiveness

• Focus activity on what could make a difference at School level – provide support, help develop insights and share practice; 

• Focus on the accessibility, visualisation and transparency of data, helping to simplify and manage complexity; 

• Examine the use of dashboards to support these objectives.  

Prototypes were developed in the second half of 2015 using both the BI and Qlikview tools and delivered to a number of forums with senior representatives from Schools and Colleges.  The dashboards received consistent, positive engagement and feedback from the academic community.  

Funding was secured to help move these dashboards from prototype to service for the 2016/17 academic year and the dashboards are now used to provide greater insight to Schools. These dashboards are complementary to the work being done to develop learning analytics for direct, individual student support and better course design.