Capturing schools' digital capacity: Psychometric analyses of the SELFIE self-reflection tool

Abstract

Results from self-reflection tools for schools' digital capacity can lead to evidence-based decisions within the school community and/or the development of an action plan for a better integration of digital technologies. Thus, it is important that the information derived from self-reflection tools is complete, accurate, and relevant. However, usually self-reflection tools do not show evidence of the quality of the information provided. In this paper, we focus on SELFIE, a new, comprehensive, and customisable self-reflection tool for schools' digital capacity, and we analyse the quality of the information that it provides. In particular, we look at discrimination and difficulty item parameters (using item response theory), we analyse the reliability (using Cronbach's alpha and Omega) and the construct validity (using confirmatory factor analysis) of its core items. We find support for the tool quality and conclude that schools using SELFIE are provided with accurate information on their digital capacity. Additionally, we discuss ideas for further improving the tool and future research work. The innovative design of the SELFIE tool and the psychometric analyses of its core items are a novelty in the field of schools' digital capacity and can provide insights for the development of self-reflection tools for school communities.

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