With support from NSF's Accelerating Discovery program, investigators at Rice University will design and implement the Learning in Education Research Network (LERN) to study and harness the complex factors that propel human learning. The central idea is that carefully designed, large-scale learning studies involving large numbers of students and teachers, coupled with modern data science tools, can provide insight into the factors that underpin student learning. Researchers have long sought to determine the predictors of academic success and persistence since such predictions could enable preemptive interventions for students at risk for failure. However, these studies have usually examined populations of students rather than individuals. The population focus limits prediction to groups of students rather than to individuals. The LERN project intends to take advantage of rapid technological advances to develop individually tailored ways of studying learning, shifting the science of learning away from population-based thinking and towards individual-based thinking. In this way, the project intends to contribute to personalized learning, so individually tailored approaches can be used to address each student's differences in learning and achievement.
The individual-based approach to be studied in LERN is characterized by an emphasis on how individual differences affect learning outcomes, and how these differences interact with learning interventions. The project intends to develop: (1) new data science tools to measure students' individual differences and their interactions with authentic digital learning environments; (2) a large-scale infrastructure to scientifically manipulate students' learning experiences; (3) a suite of targeted experiments and studies that leverage large numbers of students; and (4) a study fusing student individual differences and learning interaction data with socioeconomic and demographic data. LERN will use OpenStax, a digital learning platform, to study multiple factor interactions in learning across large numbers of students in Houston, Texas. The project intends to develop a set of data science tools, including adaptive assessment and digital highlighting and annotation, that will allow examination of individual differences and learning behaviors including locus of control and academic mindset. These efforts are intended to shift the course of educational research from population-based to individual-based, thus setting the stage for personalized learning. These efforts complement the National Science Foundation's focus on Harnessing the Data Revolution by harnessing data science approaches to understand and improve student learning.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
