Contextualizing College Algebra and Calculus with Data Science to Boost Engagement, Understanding, and Performance

About This Grant

This project aims to serve the national interest by implementing and evaluating an innovative instructional model to improve student success and persistence in foundational mathematics courses, which are critical gateways to STEM degree completion. Specifically, this Level 1 Engaged Student Learning project will adopt and adapt the evidence-based hybrid-flipped learning (HFL) approach for teaching College Algebra and Calculus, embedding interdisciplinary data science applications to promote mathematics relevance and data literacy among STEM students. By emphasizing interactive learning, the model promotes deeper student engagement, enhanced conceptual understanding, and improved achievement. By connecting mathematical concepts to meaningful data science applications, the model fosters positive attitudes toward mathematics and supports persistence in STEM pathways. Moreover, by exposing students early to data-driven thinking, the project will attract and prepare a large pool of students for further study and careers in data-intensive fields, responding to the growing national demand for a data-literate workforce. The project seeks to achieve three main goals: enhance student engagement and learning outcomes in College Algebra II and Calculus I courses; inspire early interest and participation in data science education; and build a faculty community dedicated to fostering analytical and problem-solving skills in foundational mathematics. Using Python-based computational notebooks, the project will develop online curricular materials for the HFL model and launch the weekly virtual math applications lab (V-MAL) in College Algebra II and Calculus I courses to support active learning, including collaborative problem-solving and project-based learning of mathematics in data-driven contexts. Thus, helping students develop mastery of math concepts, computational thinking, and analytical and communication skills. To implement and institutionalize this instructional model, the project will engage the introductory mathematics faculty in a suite of professional development activities on adopting the HFL model with a data-centric pedagogy in their teaching. The project will use a mixed-methods research design to study the effectiveness of the instructional model and curriculum in improving student engagement, conceptual understanding, and achievement in introductory mathematics. Thus, providing much-needed evidence to guide the implementation of innovative pedagogies in introductory mathematics education. Evaluation efforts will also examine impacts on students' interest in pursuing data science education. Through targeted dissemination efforts, including conference presentations, blog posts, and journal articles, the project will promote the instructional model and curriculum to encourage adoption by other institutions seeking to make their STEM curricula more data-driven. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.