This project aims to serve the national interest by conducting basic research about the neurobiology of learning in undergraduate life sciences education. This Level 1 STEM learning and learning environments project aims to broaden understanding of who is affected by modeling-based instruction in undergraduate life sciences, and how they are affected. Modeling is vital to STEM professions and national standards call for modeling instruction at all levels of STEM education. Despite behavioral work indicating that modeling-based instruction enhances STEM learning, there is a critical gap in understanding what mechanisms make it effective and which students benefit most from this instructional approach. The proposed project draws on the body of knowledge about STEM expertise from cognitive science. Specifically, as disciplinary experts interact with or create models of biological systems, they continuously engage in error checking to evaluate a model?s credibility and simultaneously inhibit information that is irrelevant to the task. Specific regions of the experts' brains become activated as they evaluate models. These regions include the lateral prefrontal and anterior cingulate regions, which are linked to error detection and inhibition. These observations suggest that error checking and inhibition are integral brain functions for development of STEM expertise. However, it is not known whether undergraduate STEM students, who are not yet disciplinary experts, use similar processes and associated neural systems during modeling-based instruction. It is also not yet known whether error checking and inhibition influence students? long-term retention of disciplinary and modeling knowledge. A deeper understanding about how modeling-based instruction benefits students and for whom it is most effective may be used to develop more effective STEM instruction to meet the needs of all students.
The goals of this project are to (a) describe the nature and extent to which undergraduate life sciences students engage in error detection and inhibition during model evaluation, and (b) determine how these processes are associated with long-term knowledge retention. Interviews with students will enable characterization of the types and extent of error detection. Brain scans using fMRI will determine the functional neural responses. Comparative interviews and neurological scans after one year of instruction will determine how variation in ability to detect errors predicts long-term retention of conceptual and model-based knowledge. Results from this research will be disseminated at research conferences and through peer-reviewed literature. The research will inform development of a modeling-based instruction case study on learning for undergraduate life science students. The proposed project is a joint effort between the University of Nebraska-Lincoln and Michigan State University and is supported by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.
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.
