Data wranglers: human interpreters to help close the feedback loop

Abstract

Closing the feedback loop to improve learning is at the heart of good learning analytics practice. However, the quantity of data, and the range of different data sources, can make it difficult to take systematic action on that data. Previous work in the literature has emphasised the need for and value of human meaning-making in the process of interpretation of data to transform it in to actionable intelligence. This paper describes a programme of human Data Wranglers deployed at the Open University, UK, charged with making sense of a range of data sources related to learning, analysing that data in the light of their understanding of practice in individual faculties/departments, and producing reports that summarise the key points and make actionable recommendations. The evaluation of and experience in this programme of work strongly supports the value of human meaning-makers in the learning analytics process, and suggests that barriers to organisational change in this area can be mitigated by embedding learning analytics work within strategic contexts, and working at an appropriate level and granularity of analysis.