Personalizing Mathematics to Maximize Relevance and Skill for Tomorrow's STEM Workforce

This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase student motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM) by developing and testing an intervention system to engage students in meaningful STEM problem posing and solving and by providing an on-line tool to assist teachers in guiding the process.

This is a collaborative project involving three different institutions to use personalization strategies to strengthen students' algebraic interest and skills in STEM pathways. The project will build an environment to support algebra students' learning key content in the context of their STEM career interests by posing and solving story problems. The problems posed are based on algebra content, related to STEM career paths, and embedded in a technological interface. The innovation, of using personalized problem-posing tasks to entice students to recognize, formulate, and solve problems, framed by how mathematics is used in STEM careers, is a compelling strategy to engage students and promote STEM learning.

The project will examine an instructional intervention for high school algebra that connects course content to the STEM careers to increase students' interest in those fields. In addition, within the career pathways construct, the study will investigate the effect of personalized problem posing as well as problem solving on students' algebra skill development. Specifically, the project will examine how different scaffolding approaches to STEM career problem-posing (e.g., expert video vs. examples) affect the problems posed by students and the interest and knowledge that they gain. The findings of the study have the potential will build evidence for both personalization and student generation of problem-solving tasks on improving student learning in algebra and interest in STEM fields. The study seeks to confirm a taxonomy of three features necessary for successful personalized learning: content should be personalized so that it aligns to student interests, problems must be sufficiently granular to more precisely align to student interests, and students must perceive ownership in the process.

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.