Simple Technology is an Improved Solution for a Post-Pandemic Informative System: A Reference Model

Abstract

COVID-19 pandemic accelerated the digitalization and the implementation of technological tools to distribute knowledge and content to certify the instructional process's steadiness despite the restrictions posed in many nations worldwide. However, multi-models of development and integrations based on multitudes of theoretical and conceptual frameworks made it difficult for deciders during the year - especially in developing countries - to follow a clear path based on their contextual needs. Based on a literature review and historical Data, Learning Ana-lytics research, and its empirical results, this article proposes a data-analytics model for growth. Training/educational technologies help stakeholders use data as intelligence sources to implement technologies that will improve traditional learning procedures without constraining practices. As a result, the paper also suggests, according to the pragmatic results, a 4-year plan applicable in multidi-mensional contexts to enhance the organization's learning capabilities as a whole unit to face future trials. The return on experience in the last year of the pandemic contracts the methodological basis of results. Furthermore, this manuscript's aim is defined by the urgent necessity of post-pandemic solutions to positively safe-guard the future of keeping the wheel of knowledge running for the learners and warrant upcoming transition into using data as a source of developing new learn-ing technologies.