Using pedagogical and psychological insights to train analysts using confidential data

Green, Elizabeth; Ritchie, Felix

Abstract

With researchers increasingly gaining access to confidentiality data through restricted environments, interest has grown in the training of those researchers to protect confidentiality and to use the secure facility effectively. Researcher training, where it exists, often tends to focus on the ‘chalk-and-talk’ approach or its digital equivalent, the aim is to ensure that the researchers are informed of their legal obligations and so take responsibility for their actions. Although popular, there are multiple problems with this approach. First, it is of limited pedagogical effectiveness. Second, it assumes that information delivery is the purpose of the training. Third, it does not take account of attendees’ attitudes when attending the course. Fourth, it creates an ‘us and them’ barrier between trainers and trainees. An alternative approach to training researchers has been in place in the UK since 2017. It uses good pedagogical practice to increase the effectiveness of training. It uses psychological models of behaviour and attitudes to engage attendees and shape future behaviours. The aim of the course is to build a shared sense of community and trust, rather than information delivery, in line with good data governance practice. This paper describes the experience of designing and running the course. Multiple organisations and trainers were involved in design and delivery, improving feedback but creating its own problems in terms of trainers’ different preferences. Overall, the approach has been highly successful, and has become the model for other organisations. However, the model does place higher demands on the trainer than the traditional model. We also briefly touch on how the move to online teaching in the pandemic has learned from the face-to-face experience.

Citation

Green, E., & Ritchie, F. (2023). Using pedagogical and psychological insights to train analysts using confidential data. Journal of Privacy and Confidentiality, 13(2), https://doi.org/10.29012/jpc.842

• Journal Article Type: Article
• Acceptance Date: Jun 30, 2023
• Online Publication Date: Nov 30, 2023
• Publication Date: Dec 6, 2023
• Deposit Date: Aug 2, 2023
• Publicly Available Date: Dec 12, 2023
• Journal: Journal of Privacy and Confidentiality
• Electronic ISSN: 2575-8527
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 2
• DOI: https://doi.org/10.29012/jpc.842
• Public URL: https://uwe-repository.worktribe.com/output/10997761