Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning

Clos, Jeremie; Qu, Rong; Atkin, Jason

Abstract

© 2019, Springer Nature Switzerland AG. Policy making involves an extensive research phase during which existing policies which are similar to the one under development need to be retrieved and analysed. This phase is time-consuming for the following reasons: (i) there is no unified format for policy documents; (ii) there is no unified repository of policies; and (iii) there is no retrieval system designed for querying any repositories which may exist. This creates an information overload problem for policy makers who need to be aware of other policy documents in order to inform their own. The goal of this work is to introduce a novel application area for studying information retrieval models: the information seeking phase of policy design, applied to life-long learning policy-making. In this paper, we address this problem by developing a common representation for policy documents, informed by domain experts, in order to facilitate their indexing and retrieval by users. This position paper highlights the research questions that we aim to answer in our future work and the dataset that we intend to use to do so. Our main contribution is the creation of a unified dataset of policy interventions which can be used for highly specialised information retrieval tasks, and will be released in order to provide the field with the first unified repository of policy interventions in adult education.

Citation

Clos, J., Qu, R., & Atkin, J. (2019). Information Retrieval for Evidence-Based Policy Making Applied to Lifelong Learning. https://doi.org/10.1007/978-3-030-34885-4_41

• Conference Name: International Conference on Innovative Techniques and Applications of Artificial Intelligence (SGAI 2019: Artificial Intelligence XXXVI)
• Start Date: Dec 17, 2019
• End Date: Dec 19, 2019
• Acceptance Date: Sep 2, 2019
• Online Publication Date: Nov 19, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Dec 13, 2019
• Publicly Available Date: Jan 14, 2020
• Publisher: Springer Verlag
• Volume: 11927 LNAI
• Pages: 487-493
• Series Title: Lecture Notes in Computer Science
• Series ISSN: 0302-9743
• ISBN: 9783030348847
• DOI: https://doi.org/10.1007/978-3-030-34885-4_41
• Public URL: https://nottingham-repository.worktribe.com/output/3546261
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-030-34885-4_41