Technologies for Data Collection, Processing and Communication in Education in Emergencies
Mapping Practices and Opportunities in the MENA Region and Globally
The effective integration of technology in the Education in Emergencies (EiE) sector has great potential to improve the availability, quality and use of data for stakeholders globally, but the sector faces unique challenges in introducing these new technologies. Particularly, EiE interventions serve populations burdened by poverty, displacement, and violence and, therefore, have unique data privacy and protection protocol requirements. How, then, can technology best serve the needs of EiE practitioners? And what specific principles should practitioners follow when adopting a new technology? The study team answers these questions via an analysis of interviews with 35 EiE, education, and technology professionals, classifying data technologies currently in use among the three main phases of the data life cycle: data collection, processing, and communication. Based on the results of these interviews and review of the literature, the study team proposes eight guiding principles for practitioners as it relates to technology use across the data life cycle. Two of these principles should act as a framework for all decisions: (1) Do no harm, and (2) Follow General Data Protection Regulation (GDPR) and similar standards. Three of these principles help practitioners to identify when a new technology is appropriate: (3) New technology does not mean better technology, (4) Coordinate among stakeholders, adapting technologies using a systems-thinking approach and (5) Develop innovations in collaboration with local organizations and end-users. The final three principles help practitioners in deploying the technology successfully: (6) Introduce new technology through the lens of social and behavior change, (7) View technology as a long-term investment, and (8) Nurture a culture of data feedback loops and data-driven decision making.
The study team also shares recommended practices related to each phase of the data life cycle. For data collection, the study team recommends highlighting duty of care, the need to deploy multiple modes of data collection, the importance of design, and the need to tailor technological solutions to the culture in which it is being deployed. For data processing, the study team recommends investing in strengthening analytical support, building capacity for staff to better leverage data, building expertise in multiple platforms, automating data validation processes, and developing processes to share analyses. Finally, for data communication, the study team recommends identifying data champions within the organization, creating a broad culture of data sharing within the organization, providing data in a timely fashion, following data visualization best practices, simplifying reporting, ensuring system maintenance, and using row-level security to limit access to data.
Aligned with these principles and best practices, the greatest opportunities for technology in EiE that can further strengthen existing technological systems and equip decision makers with key information on learners, teachers, schools, and systems are identified. These opportunities include expanding use of existing platforms to collect data, empowering beneficiaries, field staff, schools, and teachers with data through feedback loops, and exploring new promising technologies, particularly data collection through learning management systems, automation of data cleaning and analysis, sector-appropriate machine learning techniques, and chat-bots and enhanced information access.