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Despite calls over the past twenty years to develop graduate STEM education models that prepare students for the new post-graduate workforce, few innovations in graduate STEM education have been disseminated. Given the diversity of graduate candidates’ prior skills, preparation, and individual career aspirations, modernizing STEM graduate programs is needed to support and empower student success in graduate programs and beyond. In this session, participants will learn about new research into the development and implementation of a personalized learning model (PLM) for graduate STEM education employed in a chemical engineering department at an R1 university. Our PLM integrates student-centered approaches in coursework, research, and professional development in multiple programmatic components. The key components of our PLM include guided student creation of independent development plans (IDPs), modularization of graduate courses and professional development streams, scaffolding curricular instruction to prioritize independence and mastery, using IDPs for directed research and career discussions and assessment of student performance and learning, and evaluation of the program from current students, alumni, faculty, and industry partners. Our comprehensive PLM plan is designed to maximize impact through personalized learning touchpoints throughout all aspects of graduate training. This presentation will focus on one element of our PLM, the modularization of the chemical engineering core graduate courses. To ensure the learning in core graduate courses reflects the diverse needs of chemical engineers, we generated a body of knowledge (BOK) for graduate chemical engineering in collaboration with our technical advisory board (TAB), which included chemical engineers and people that work with chemical engineers from industry, national labs, academia, and entrepreneurial representatives. We started by collecting the learning objectives (LO’s) from all core chemical engineering courses: Thermodynamics, Kinetics and Reactor Design, Transport Phenomena, Mathematical Methods, and Issues in Research and Teaching. The LO’s were refined by alignment with course assignments and activities and re-written using the most accurate Bloom’s Taxonomy verbs in collaboration with an instructional designer. We utilized GroupWisdomTM for group concept mapping of the new LO’s and provided an opportunity for the TAB to add new LO’s, identified by the individuals in the TAB to be critical for success in each member’s occupation. LO’s for the chemical engineering core courses were sorted on the level of knowledge (undergraduate, graduate, and specialized) and rated for importance by the TAB. Using GroupWisdom’sTM analytic tool for creating a similarity matrix for sorting the level of LO’s, multidimensional scaling, and hierarchical cluster analysis, all core course LO’s have been grouped into 6 clusters. These clusters, along with the current course list and the LO importance ratings, helped us visualize converting chemical engineering core courses into 1-credit hour modules. This restructured curriculum offers opportunities for ensuring that students have learned the requisite prior knowledge by review of essential undergraduate principles, streamlining essential graduate-level material, and supporting self-directed learning through the selection of specialized modules that align with students’ research and career goals. This proposed approach shifts core graduate chemical engineering education to an asset-based system, addressing knowledge gaps and ensuring rigorous, tailored learning experiences.
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A New Personalized Learning Approach Towards Graduate STEM Education: A Pilot in Chemical Engineering
Despite calls over the past two decades to develop and deploy graduate STEM education models that prepare students for a variety of careers outside of academia, few innovations have emerged to meet students at their current skill and preparation levels when entering their graduate studies while also considering students’ individual desired career paths. The U.S.’s current approach to graduate STEM education does not emphasize preparing students with the professional skills and experience outside the lab. Further, students from differing socioeconomic and underserved backgrounds are often not adequately supported. Through a National Science Foundation Innovations in Graduate Education (IGE) award, the University of Pittsburgh Swanson School of Engineering is creating and validating a personalized learning model (PLM) for graduate education within the Department of Chemical and Petroleum Engineering. The goal of this model is to transform and modernize graduate STEM education through a personalized, inclusive, and student-centered approach, which will, in turn, advance existing knowledge on the relationship between personalized learning and student outcomes. The principles of personalized learning guide the PLM. The PLM is comprised of five components. The first three components provide an intentional approach to learning: Instructional Goals developed for each student based on a learner profile and individual development plans (IDP), a purposeful Task Environment that breaks the traditional three-credit coursework into modules and co-curricular professional development streams, and a resolute approach to Scaffolding Instruction that leads to mastery in the student’s area of focus. The last two components provide feedback and reflection: Assessment of Performance Learning quantifies students’ progress, and Reflection and Evaluation, where improvement opportunities help the student to develop further. Incorporating personalization at every touchpoint of a graduate student’s academic journey creates an authentic, customized, student-centered approach to graduate education. This paper describes in detail the model and the literature behind its development, along with assessments used to guide students.
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- Award ID(s):
- 2325599
- PAR ID:
- 10639490
- Publisher / Repository:
- ASEE Conferences
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18260/1-2--46464
