June 24, 2017
June 24, 2017
June 28, 2018
This paper describes the development and first offering of a new graduate course entitled "Fundamentals of Predictive Plant Phenomics," which is part of a recently awarded National Science Foundation Graduate Research Traineeship (NRT) award to Iowa State University. The focus of this particular NRT award is to train engineering, plant science, and data science graduate students in the area of predictive plant phenomics (P3), with the goal to develop researchers who can design and construct crops with desired traits to meet the needs of a growing population and that can thrive in a changing environment. To meet this goal, the P3 NRT program will train next generation crop scientists to have broad technical skillsets as well as strong "soft skills" in communication and collaboration. A companion paper (Dickerson et al., 2017) provides an overview of the P3 NRT program, whereas this paper focuses on a new course developed as part of the P3 NRT.
One of the challenges associated with providing the students in the P3 NRT program with the needed multidisciplinary skills to thrive is to ensure that all students have a common knowledge base in engineering, plant sciences, and data sciences, no matter their background. The goal is to get all students communicating in the same language. The course "Fundamentals of Predictive Plant Phenomics" was developed to meet this challenge. The course planning took nearly one year and incorporated input from faculty with various disciplinary backgrounds. The actual course is coordinated by an engineering faculty member and taught through a series of guest lecturers covering various plant science, data science, and engineering topics over a 15-week period. In addition to the three 50-minute lectures per week, a 3-hour laboratory each week provides an experiential learning opportunity where students can apply the knowledge they learn in the lectures. The first offering of this course occurred in fall 2016, with 16 enrolled students, 7 from engineering disciplines, and 9 from plant and data science programs. Lessons learned from the first offering of this course are summarized in this paper. The course is providing the needed background so students can develop a successful research topic in the area of predictive plant phenomics and communicate with others in this broad multidisciplinary field. Because the course is a leveling or survey of three disciplines, and each student has a good background in at least one of the three, it has been challenging to keep all students interested and engaged for all lectures (but not labs). To address this challenge, expanding the application of Inquiry-Based Learning approaches during the lecture period in future years is proposed.
Heindel, T. J., & Lawrence-Dill, C. J., & Dickerson, J. A., & Schnable, P. S. (2017, June), An Interdisciplinary Graduate Course for Engineers, Plant Scientists, and Data Scientists in the Area of Predictive Plant Phenomics Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/27577
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