Developing a Computational Curriculum Framework for Archival Education

Facilitators: Jenny Bunn (UCL/UK), Mark Hedges (KCL/UK) and Richard Marciano (UMD/US)

This half-day curriculum development workshop acted as a forum for sharing emerging practice and developing curricula for what has recently been labelled Computational Archival Science.

The understanding that the discipline and practice of archival science requires reinvention in the light of digital technologies is not new, and there have been numerous initiatives and developments to address this. In the UK context, we have seen the launch of new Master’s level programs in digital curation and digital asset management, and technical traineeships organised by The National Archives. In cultural heritage institutions, the concept of ‘collections as data’ has emerged, where computational methods and tools are leveraged to work with archival (and other) collections. More broadly, the challenges for practitioners and researchers who work with archival collections, and the enhanced possibilities for scholarship through the application of computational methods and tools to the archival problem space, as well as, more fundamentally, through the integration of ‘computational thinking’ with ‘archival thinking’, have led to the identification of Computational Archival Science as a new, transdisciplinary field of study, concerned with the application of computational methods and resources to large-scale records/archives processing, analysis, storage, and access, with aim of improving efficiency, productivity and precision, in support of recordkeeping, appraisal, arrangement and description, preservation and access decisions, and engaging and undertaking research with archival material (e.g. Marciano et al., 2018).

The aim of this workshop was to bring together people either involved in or with an interest in these developments, in order to reflect on what has been achieved so far, to share emerging practice, and to consider how the work of developing curricula (whether undergraduate, postgraduate and CPD) centered on computational treatments of archival collections might best be taken forward. In particular, the workshop aims to identify some of the building blocks for curricula designed to prepare the next generation of archivists (and information professionals more broadly) to meet the evolving needs of working with digital collections.

Marciano, R., et al. (2018). Archival records and training in the Age of Big Data. In J. Percell , L. C. Sarin , P. T. Jaeger , J. C. Bertot (Eds.), Re-Envisioning the MLS: Perspectives on the Future of Library and Information Science Education (Advances in Librarianship, Volume 44B, pp.179-199). Emerald Publishing Limited.