An Open Learning Design, Data Analytics and Visualization Framework for E-Learning
Phase 2 (Y3-Y4) of this project aims to facilitate K-12 and MOOC instruction and research by:
(1) Developing open-source analytical and visualization methods for K-12 resource-based e-learning in mathematics.
(2) Articulating design patterns in integrated learning and analytics for computational thinking (CT) in MOOCs and K-12.
Evaluating (1) and (2) on a pilot scale. Analytical and visualization methods are targeted because e-learning in K-12 education is often centered around resources such as item banks and videos, but these platforms lack students’ interaction data in test items, limiting the possibility of further data analytics. CT and mathematics are both important areas in STEM education. CT is a key 21st century competence encompassing knowledge, skills and their application to solve problems. The integrated patterns will provide data-driven feedback to inform e-learning design.
Phase 1 (Y1-Y2) integrated three e-learning technology components for MOOCs:
Learning design tools for course design and integration with specific learning analytics and visualization;
Analytical methods, including learner behavior and predictive analytics
Visualization interfaces to support stakeholder interpretation of learning analytics.
Phase 2 activities will enhance all three components and put them to the test.