Exploring the balance between automation and human intervention in improving final year university student non-completion
2012-05-18T14:59:31Z (GMT) by
This paper examines the research methods used in the 'Pedestal for Progression' project, a project that set out to determine why students at Loughborough University fail to complete their final year. It demonstrates how methods adopted can be used to enhance student experience and improve retention. Initial research with students found that the difference between student experience in initial and final years can be characterised by concern over the independent study required for the dissertation project and associated worries of managing workloads with competing deadlines. Interviews and workshops with students also identified a wider concern about the quality of relationships with technical, administrative and academic staff. In addition, research found that the final year can be flooded by concerns over employability. Fundamental to these issues are student relationships. Using the methods of Service Design and Data Mining the project designed, implemented and assessed a number of initiatives aimed at alleviating these student concerns. Key to the theory of Service Design is the management of points of contact with service providers and vital to Data Mining is the identification of patterns of behaviour that could predict non-completion. Service Design aims to provide customer focused highly desirable services. Whilst, on the other hand, data mining aims to identify signals that determine those at risk of not completing courses. This paper examines the use of Service Design and Data Mining in Higher Education from the results of the project and determined that whilst the methods can be used in a complementary manner, each derives from different paradigms of knowledge.