This subtopic covers Evidence-Based Practices from the learning sciences used to develop interventions that demonstrably improve learning outcomes. When the science is unavailable or unclear, learning engineering teams often conduct action research to obtain their own evidence for a design decision.
- Iterative Testing and Data-Driven Refinement: Continuous testing and analysis of learner data ensure that solutions are responsive and optimized. Learning engineering teams use data to make iterative improvements, ensuring interventions are effective.
- Personalization and Adaptivity: Learning sciences research supports the personalization of content based on individual differences in cognition, motivation, and social context. Learning engineering teams use adaptive algorithms to deliver personalized experiences, increasing engagement and efficacy.