Decoding Education Policy: Computational Methods for Education Policy Research

Recent developments within Natural Language Processing (NLP) and computational text analysis in conjuncture with the growing availability of digitized open parliamentary data, radically expands the possible scope of research on education policy and political debate.

Utilizing these novel research methods and data sources has the potential to push the limits for education policy research by way of making studies of larger empirical materials, and across longer time dimensions possible. Yet, the full potential of these developments is yet to be realized.

NLP and computational text analysis is rarely implemented in education policy research and the fast-paced methods development in this area is primarily happening within fields such as linguistics and computer science.

Through exploratory workshops we aim to bring together researchers from a broad transdisciplinary network to develop the data, methods and knowledge necessary for NLP analysis within education policy research. The workshops will focus on the case of the marketization of Swedish education.

Four thematic workshops will be implemented, each focusing on an important step in the process of conducting NLP analyses within the education policy field: WS1: Data selection and Filtering, WS2: Data annotation, WS3: Data analysis, WS4: Data visualization.

Workshops 1-3 will be conducted among selected and invited researchers while Workshop 4 will be open to researchers within the fields of Education policy and NLP-research.