Theory of Change: a new approach to data by Schools Library Services in the UK

THEBRIDGE, Stella and HARRIS, Gillian (2019) Theory of Change: a new approach to data by Schools Library Services in the UK. Paper presented at: IFLA WLIC 2019 - Athens, Greece - Libraries: dialogue for change in Session 190 - School Libraries.

Bookmark or cite this item: https://library.ifla.org/id/eprint/2547
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Language: English (Original)
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Abstract

Theory of Change: a new approach to data by Schools Library Services in the UK

For a long time, Schools Library Service (SLS) Managers in the UK have wanted a means to collect and analyse meaningful data from the schools they serve, in order both to measure the impact of SLSs in the enhancement of teaching and learning in primary and secondary schools and to demonstrate the value for money their services provide. A sub-group of SLS managers within the professional body ASCEL (Association of Senior Children’s and Education Librarians), worked with Sharon Markless from Kings College London, a respected academic in this field, to develop a Theory of Change framework fit for use across all SLSs, many of whom operate with very different subscription models. A national approach enables SLSs to not only gauge their own value and effectiveness, but also share data and develop their services. It also benefits schools by drawing on their data to show the value of the school library and its services to its stakeholders. The attraction of Theory of Change has been in the ability to create a robust framework allowing for the build-up of relevant data which can be applied to benefits and outcomes across the UK regionally and nationally, creating from existing collection methods a wealth of data in a much shorter space of time than can be achieved by traditional methods of surveying schools. This is because existing data collection is coupled with replicable group evaluation activities that can yield results more effectively. Surveys are rarely answered by an adequate sample, are very time-consuming for analysts and rely on absolute clarity of understanding both of question setters and respondents to achieve meaningful results. The framework looks at all the opportunities to collect segments of data and then merge them to create an overall picture. This paper details the background to SLSs and why this data collection is needed, the process of defining the framework and the benefits many will now be able to reap from adopting the framework both to collect data and to contribute to wide-reaching evaluation outcomes.

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