Data-based EMIS and learning analytics
- 1. UNESCO EDUCATION SECTOR International Conference on Artificial Intelligence and Education Data-based EMIS and education data analytics Beijing People’s Republic of China 16 – 18 May 2019 Transforming current EMIS from a school- based aggregated administrative data management system an integrated and dynamic learning management systems that can effectively support real-time decision making in every aspect of education sector management From Into EMIS: Education Management Information System
- 2. UNESCO EDUCATION SECTOR International Conference on Artificial Intelligence and Education Data-based EMIS and education data analytics Beijing People’s Republic of China 16 – 18 May 2019 From IntoCurrent EMIS AI-powered LMS Performance Curricula Financing Professional development Learning resources International assessment (PISA, TIMSS) • Learning Analytics • Data dashboard • Predictive decision-making algorithms ü Formulating responsive policies and plans ü Monitoring and evaluating education outcomes (metrics on quality and relevance education) ü Real-time decision making - Organised group of information and documentation service - Simple metrics: enrolment, attendance, and grade completion - Information on access and participation in education Needs: data are complete, Reliable, regularly collected and can be aggregated and system Integration
- 3. UNESCO EDUCATION SECTOR 3 Gap in educational data and weak Data Quality Gaps in educational data coverage with respect to SDG 4 indicators SDG4, “Ensure inclusive and equitable quality education and promote lifelong opportunities for all” Source: UNESCO Institute for Statistics (2018). Concept Note:The Investment Case for SDG 4 Data
- 4. UNESCO EDUCATION SECTOR 4 Gap in educational data and weak Data Quality Gathering data in areas with poor internet connection – solution function both offline and online Data Accuracy – validation mechanism implemented How ProFuturo addresses Data Challenges Poor infrastructure Data from several sources and in different formats Best practice Decision-making based on data Enhancing capacity on identifying of relevant indicators, data reporting (Dashboard), use of insights/indicators (Advanced Analytics) and developing apps (Teacher Assistant) Data integration and consistency Fixing data redundancies Data-driven organisation
- 5. UNESCO EDUCATION SECTOR 5 Learning analytics and AI to enhance teaching and learning AI tutoring system to personalise education Advanced Data analytics techniques (visual analytics, Dimension reduction- Principal component analysis (PCA), Chi square..) Teacher assistant dual teacher mode (teacher-virtual assistant) AI assessment tools Computational Thinking as a key competency for learners AI- human interaction AI-powered Professional development Main real applications Challenges – Topics to address Standards AI Ethics, Privacy and transparency Open source Data Hub
- 6. UNESCO EDUCATION SECTOR 6 Thank you Paula Valverde Head of Product and Innovation. Telefonica @paula_valver