London, United Kingdom
University College of Estate Management (UCEM) has been developing a strategic and data-driven approach to improve student success and retention. As part of this work, the institution has adopted predictive analytics for its Blackboard Open LMS environment. The data gathered through analytics has helped prove to UCEM their strategy to achieve student success has in fact been working quite successfully.
UCEM is a leading provider of supported online education for the Built Environment, with over 95 years’ experience of providing high quality learning opportunities. At any one time, it has over 3,500 students from more than 100 countries benefiting from its qualifications taught by tutors with extensive industry experience.
Since 2015, UCEM has been developing an institutional strategy, called “No Student Left Behind,” to support student retention and success. This approach is aimed at optimizing all aspects of the teaching and learning process in order to ensure that students have the best opportunities for succeeding while studying at the institution.
“UCEM is passionate about delivering an excellent student experience, and central to this is enabling students to reach their potential and succeed in their studies,” says Peter Stone, UCEM’s Learning Technology Innovation Manager. The approach is reinforced through regular communications within the institution, including the sharing of team success stories.
In order to identify students with potential risk of failing or dropping out and intervene when necessary, the institution has decided to develop a learning analytics strategy and has adopted X-Ray Learning Analytics, Blackboard’s predictive analytics, for its Blackboard Open LMS environment.
“Learning analytics provides an easy-to-use and visually engaging way to identify students potentially in need of further support, and the ways in which we use that information is guided by No Student Left Behind” says Gethin Edwards, Retention & Data Officer at UCEM. Learning analytics also provides more detailed information about student interactions, which is helping to enhance module design and delivery.
Enhancing the Learning Process
UCEM is still at an early stage in their learning analytics journey. X-Ray Learning Analytics was implemented on their Virtual Learning Environment (VLE) in September 2016, and has over 30 reports available for UCEM that provide a wealth of information.
“The new wave of data is exciting, but it is also a challenge to identify where to start and what to focus on,” says Stone. No Student Left Behind has helped direct UCEM’s focus, with the learning analytics reports helping them identify how students engage with the VLE, with each other and with the learning resources.
The institution has already benefited from having access to the learning analytics data. For Edwards, the most significant example is the way in which it has informed the decision to amend UCEM’s semester structure. In the institution’s current semester structure, students can study up to three modules concurrently, and learning analytics
provided evidence of the extent to which students divert their attention between modules driven by assessment deadlines. These paths in their activity conflict with the weekly learning activities designed into each module. Hence, after analyzing student’s data, UCEM is looking to implement a semester structure in which students study one module at a time, sequentially, in a semester, allowing them to fully focus their attention and efforts, in a bid to further support student retention and success.
Large Enrollment Modules
UCEM modules range in size from 5 to nearly 800 students. The large number of students in some of the modules can be a challenge in online learning. Stone explains that X-Ray Learning Analytics has validated UCEM’s current approach, whereby they place students into groups of approximately 45 students supported by a tutor.
Blackboard has developed a case study on UCEMi that showed there were no meaningful differences in student behavior in large enrollment classes as compared to smaller ones. The case study provides a detailed look about the way learning analytics data has been used by Blackboard data centers to demonstrate that large-enrollment modules do not affect student performance at UCEM.
It doesn’t mean that module size doesn’t matter entirely, but rather that UCEM’s program virtually eliminated the negative effects that might be expected from large enrollment modules. “The evidence from the learning analytics has helped us feel confident in our approach,” says Edwards.
Close Relationship with Blackboard’s Development Team
Early adoption has been crucial to UCEM’s strategy. As one of the first institutions to license X-Ray Learning Analytics, the university college became an active part of the product development process.
One of the major developments on the reports resulting from this close-working relationship is the option to filter reports by tutor group, which facilitates the information management process, providing them with an additional layer of data to drill into.
“We feel the feedback we have provided has enabled Blackboard to enhance their product, and this has also been beneficial to us. Being able to access the data using a tutor group filter is a great example of a way in which this close working has supported us,” Stone says.
By working closely with technology vendors in the early stages of product development, UCEM has had the opportunity to actively inform design decisions based on their own experience.
Now that UCEM has access to reports by tutor group, they’re planning to embed the use of learning analytics at the tutor group level. “This will involve in-depth discussions with our academic team, including module leaders and tutors” says Edwards. UCEM will be using the learning analytics reports to monitor the way in which different groups engage with the VLE and learning tools, and also how the students engage with each other. They are particularly interested in exploring the extent to which student profile affects student engagement, retention and success.
UCEM’s team is also interested in developing a student dashboard. “We’re still exploring the data ourselves, but, at some point in our journey, we would like to explore the development of a student dashboard,” says Edwards.
“We’re very much looking forward to continuing the journey. At an early stage, we’ve made very good progress, but we still have a lot more to explore and learn from the reports, which makes it very exciting.” he concludes.
Would you like to learn more about predictive learning analytics?
Peter Stone, UCEM’s Learning Technology Innovation Manager.
Gethin Edwards, Retention & Data Officer at UCEM.
AFP Niklas Hallen
BLACKBOARD. (2017). Using learning analytics to understand student success in large enrollment courses [PDF]