An Explanatory Machine Learning Framework for Teacher Effectiveness in STEM Education
Effective Years: 2024-2026
This project aims to serve the national interest by developing explanatory machine learning methods for the study of teaching effectiveness in STEM education. There is consistent evidence that teachers vary widely in their effectiveness but conventional analytic methods have largely failed to explain why and under what contexts teaching and teachers vary. Explanatory machine learning methods hold significant potential in developing fundamental knowledge and theories of equitable and effective STEM teaching because they can track complex features, processes and patterns inherent in and implied by theories of teaching in ways where conventional methods fall short. In this project, we examine the extent to which we can leverage machine learning methods to identify and explain profiles, pathways and practices (e.g., who teachers are, what teachers know, what teachers believe, perceive and experience, what teachers do) that produce student learning and how these profiles and practices vary across STEM education contexts. The outcomes of this project have the potential to accelerate research on the theory and practice of effective teaching, teacher preparation, teacher development and student learning. This is a three-year BCSER: Individual Investigator Development project in STEM Education Research within Research on STEM Learning and Learning Environments.
The fields of STEM education and teacher development have made substantial progress in developing sophisticated theories of teaching and learning and instruments and measures that support and operationalize research on those theories (e.g., teacher knowledge, culturally responsive teacher self-efficacy, classroom observations). Recent literature has, however, noted that there is a mismatch between the complexity found in our theories of effective teachers and teaching and the prevailing methods we use to analyze those theories. For example, theories suggest that teaching is a highly interactive, adaptive, nonlinear and context-dependent practice; yet the field has almost exclusively drawn on simple linear regression models that cannot readily detect and analyze these complex patterns. There is a growing recognition of the need to craft, develop and grow methodologies specific to the purposes of STEM teaching and learning research. This project aims to fill this gap by developing and adapting explanatory machine learning methods (e.g., neural networks) to analyze studies of teaching effectiveness and examining the extent to which these methods can predict, explain and contextualize effective teaching in ways that outperform conventional methods. The results have the potential to broadly build capacity and impact the field by identifying complex features and profiles of effective teaching within and across contexts and developing scalable machine learning methods that are broadly applicable to STEM education studies. This project is supported through a partnership with the Bill & Melinda Gates Foundation, Schmidt Futures, and the Walton Family Foundation. This project is also supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research in the core areas of STEM learning and learning environments, broadening participation in STEM fields, 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.