A framework for infrastructuring sustainable innovations in education

ABSTRACT

Learning scientists have historically been interested in understanding how learning happens and in creating innovations to facilitate learning in real-world situations. Recently, the field has recognized that advancing standalone innovations is not enough to address systemic problems in education; instead, the focus must be broadened to sustain these innovations. Drawing on an interdisciplinary body of literature on infrastructure, this paper presents a framework—the IMPROV framework—that offers theoretical, methodological, and practical tools for infrastructuring innovations in the learning sciences. Infrastructuring can be defined as the ongoing process of creating functional infrastructures for activities in a particular context. I describe six inter-connected areas of infrastructuring to highlight ways that learning designers and researchers could surface existing infrastructures, facilitate coherence-making negotiation activities, engage practitioners in collaborative design, and continuously evaluate infrastructuring activities. It is hoped that such advances will increase the relevance and sustainability of innovations in education and provide infrastructure to further improve them.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ To cover publications across multiple disciplines, literature searches using search terms such as “infrastructure*” were conducted in multiple databases including Scopus, Web of Science, and Google Scholar. An AI-based literature search tool named Elicit (which searches across the Semantic Scholar corpus) was also used to iteratively build a list of references.

² According to Merriam-Webster dictionary, second (verb) in this context means “to release (someone, such as a military officer) from a regularly assigned position for temporary duty with another unit or organization.”

Additional information

Funding

The material is based upon work partially supported by the National Science Foundation under Awards 1657009 and 2101413.