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Active STANDARD GRANT National Science Foundation (US)

A Personalized Learning Model for STEM Graduate Education

$5M USD

Funder National Science Foundation (US)
Recipient Organization University of Pittsburgh
Country United States
Start Date Oct 01, 2023
End Date Sep 30, 2026
Duration 1,095 days
Number of Grantees 3
Roles Principal Investigator; Co-Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2325599
Grant Description

Most U.S. graduate engineering programs traditionally follow a “one-size-fits-all” approach that focuses narrowly on research skills, is slow to incorporate industry trends, and defaults to training students for careers as university professors. Further, students are often assumed to start at the same knowledge level, thereby disregarding differences not only in academic preparation, but in students’ background, including socioeconomic, sociocultural, prior work experience, and professional development.

Not accounting for these factors negatively affects inclusivity. This National Science Foundation Innovations of Graduate Education (IGE) award to the University of Pittsburgh will (1) break the traditional one-size-fits-all approach to graduate STEM education by creating and validating an approach focused on personalized learning, and (2) generate the knowledge required to broadly deploy this innovation within and outside the University of Pittsburgh.

The approach will be piloted in Chemical Engineering, where the impact on both students and faculty will be rigorously assessed. The personalized approach directly addresses issues of inclusivity by removing barriers to participation by underrepresented minorities. The innovation of this project lies in the integration of its components into a department-wide model that can be translated to any STEM field, directly addressing a well-known challenge in the STEM community: diffusion of educational innovations.

That is, it does not rely on the personalization of a single course or a particular focus on professional development. This project will advance scientific knowledge on the connection between personalized learning and student outcomes.

This project will create and validate a personalized learning model (PLM) for graduate STEM education. It is guided by an overarching theory of change to quantify the impact on both students and faculty. Key components of the model include (1) establishing Instructional Goals for each student through learner profiles, strength finders, and individual development plans, (2) defining the Task Environment through one-credit, modular classes that provide flexibility and content customization, along with a set of co-curricular activities organized around industry, academia, and entrepreneurship, and (3) Scaffolding the Instruction to provide pedagogy that leads to independence and mastery in the students’ area of focus.

Strategy (4), Assessment of Performance and Learning, enables tracking of the students’ progression towards their instructional goals, followed by guided (5) Reflection and Evaluation. Qualitative and quantitative methods, both formative and summative, will be employed, guided by internal experts in evaluation and assessment, by an Education Innovation Advisory board, and by an external evaluator.

The first goal of the innovation is to create and deploy the PLM, and the second goal is to provide the knowledge base to deploy this innovation beyond chemical engineering to other STEM fields within and beyond the University of Pittsburgh. Roger’s Diffusion of Innovation will be used to create and implement “how to” workshops to disseminate findings within Pitt and beyond.

Thus, a significant broader impact is the development of a new approach to graduate training that, once proven impactful, can be disseminated nationally across multiple STEM disciplines.

The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.

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.

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University of Pittsburgh

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