Applying design science to develop a systems framework for securing digital learning: Insights from resource-constrained educational settings

Krishna Teja Perannagari & Bnrajan Mythiri

International Journal of Technology Management and Sustainable Development2026https://doi.org/10.1386/tmsd_00117_1article
AJG 1ABDC C
Weight
0.50

What the paper says

To support literacy development and personalized learning, digital devices have become integral to modern education. Despite their educational benefits, digital environments also carry the risk of exposing users to harmful content. To address this issue, the study applies design science and constructive research methodology to develop a systems framework. The research is carried out in phases, involving stakeholder interviews, a review of content monitoring practices and the creation of an artefact-based solution informed by experts in curriculum design, educational technology, cybersecurity and policy development. The framework centres on the digital citizenship curriculum and is supported by enablers like allow listed device, policy directives, digital public infrastructure and monitoring toolkit, which collectively ensure end-to-end security. To ensure rigour and relevance, the framework is evaluated against established information systems models and structured using the human–technical–environmental systems framework.

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https://doi.org/https://doi.org/10.1386/tmsd_00117_1

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@article{krishna2026,
  title        = {{Applying design science to develop a systems framework for securing digital learning: Insights from resource-constrained educational settings}},
  author       = {Krishna Teja Perannagari & Bnrajan Mythiri},
  journal      = {International Journal of Technology Management and Sustainable Development},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1386/tmsd_00117_1},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.