SPOTLIGHT: Data Driven Student Success
A new article summarizes work being conducted to use Learning Theory and Analytics to Improve Undergraduate STEM Education (NSF DRL 1420491).
malt•lab research focuses on three inter-related questions pertaining to motivational and metacognitive processes as they influence cognition and learning:
- What are the cognitive and metacognitive processes that lead to learning in technology-enhanced learning environments?
- How do students’ motivations (e.g., achievement goals and self-efficacy) influence their metacognitive and cognitive processes?
- Can we design tools and instructional practices that improve motivation, scaffold metacognition, and lead to robust learning?
We employ a combination of quantitative, qualitative, and data mining approaches to conduct our research. Here’s one example of the work we’re doing to improve STEM Learning at UNLV.
Please explore this site and get to know the people in the lab and the work we do. If you have questions about the work, are interested in joining the lab, or pursuing a collaboration, please contact us.