This paper stems from the need to identify the sustainability bottlenecks in schools' digital transformation. We developed the conceptual model of the smart, digitally enhanced learning ecosystem to map transformation processes. We posit that the notion of sustainability is central to conceptualize learning ecosystems' smartness. The paper presents the mapping results of Georgian public schools' data using the interviews from 62 schoolteachers, ICT managers, and school principles. The qualitative content analysis revealed that even the schools with comparative digital maturity level could not be considered as smart learning ecosystems that are transforming sustainably. The findings call for the design of technology integration in the school as a dynamic transformation that balances two sustainability intentions-to stabilize the current learning ecosystem with its present needs, while not compromising its pursuit to test out possible future states and development towards them. We suggest schools build on the inclusion of different stakeholders in digital transformation; nourishing their resilience to ruptured situations; widening the development, testing, and uptake of digitally enhanced learning activities; weaving internal networks for sharing new practices; conducting outreach to change the socio-technical landscape; and developing feedback loops from learning, data, and information flows to manage the changes.
This paper discusses the factors that determine school’s digital learning ecosystem smartness. A dataset was collected from 52 schools in Ghana, Georgia and Estonia. Qualitative school observations and interviews were transformed to the quantitative categories and compound variables using the grid-based approach. We found three distinctive digital learning ecosystem types that described some possible developmental stages in the ecosystem. Discriminant analysis revealed two functions. Most dominant compound variables in the first function were the top-down external provision of digital resources and ICT incentives. The second function characterizes with bottom-up proactiveness of the schools. Path modelling between the compound variables revealed the growing complexity in connectivity among the mediating, transformative and flow components, that determines the smartness of learning ecosystem. Such interconnected components form specific fitness niches which have been co-created in organizations through collective effort, making school ecosystem responsive to the socio-technical regime and externally provided opportunities in the countries.
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