News Story
OU Analyse has been highlighted in the inaugural issue of Academic Quality Standards
Nancy Pontika, Wednesday 31 Jan 2024KMi’s OU Analyse, also known as the Early Alert Indicators Dashboard (EAID), is widely used at the OU to enhance student retention and has been highlighted as part of the inaugural issue of Academic Quality & Standards.
In this issue, Janet Hunter, a senior lecturer at the OU, described how she utilised the EAID to pinpoint students showing lower engagement with the module. At the outset of the module, an email was dispatched to Associate Lecturers (ALs), reminding them of their EAID access and urging its utilisation to gauge student engagement. ALs could then direct their attention to individual students, referring to those who exhibited no response to Student Support Teams.
This proactive strategy yielded higher completion and pass rates in the 2021J term compared to other Level 1 modules in the School. ALs acknowledged the effectiveness of the nudges, motivating them to check on students with whom they might not have regular interactions. The positive outcomes prompted a higher engagement with the EAID across departments, involving AL ‘super-users’ to identify at-risk students and notify ALs. Hunter suggests using the filtering capabilities of the EAID to concentrate on specific student groups for each module.
The success story of OU Analyse (EAID) represents a significant step forward in enhancing student success and academic achievement at the Open University.
Miriam Fernandez, Professor of Responsible Artificial Intelligence, and part of the team behind the research and development of the EAID, said: “It is fantastic to see how colleagues around the university are using the EAID to support our students and help them towards success. We are very grateful for the continuous feedback and support from colleagues around the OU and want to encourage everyone to use it!”
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The OU Analyse project is piloting new machine learning based methods for early identification of students who are at risk of failing.
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