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Title: Advances in Graduate Training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM)
Innovations by engineers and physical scientists working at the frontiers of microbiome engineering and discovery requires in-depth understanding of microbiome systems with parallel skills in bioinformatics and biostatistics. Despite the importance of integrating bioinformatics and biology into graduate student training in fields outside traditional biological sciences, academic institutions remain challenged with including these disciplines across departmental boundaries. Furthermore, it is critical for students in engineering, bioinformatics, and biostatistics to understand fundamentals behind the biological systems they model, and for biology students to gain competencies in applying bioinformatics and biostatistics to biological questions. To address these needs, we developed the Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM) graduate training partnership between Duke University and North Carolina Agricultural and Technical State University, which was funded by the National Science Foundation Research Traineeship (NRT) program. IBIEM’s goals include training interdisciplinary groups of students to: (a) transform conceptualization and develop skills for application of quantitative biology in microbiome areas; (b) perform cutting edge research requiring interdisciplinary team skills; and to (c) communicate their research across disciplinary barriers and to diverse audiences. The pedagogical framework adapted to foster trainee engagement is learner-centered teaching which emphasizes the importance of selfdirected learning with parallel ongoing assessment to optimize student outcomes. Since IBIEM trainees’ goals as well as entry-level knowledge and skills across disciplines varied greatly, program implementation was found to be challenging and required rigorous evaluation and refinements for effective training across disciplines and skill levels. A comprehensive program evaluation over five years found that the strongest learning and skills outcomes were linked to several “best practices”. Early provision of depth in fundamentals in R programming and reproducible research was found to be critical to “jump start” students without programming backgrounds. Addition of an overview of microbiome experimental design and analysis added important context as to how and where in the research process informatics fits into design progression and was highly motivating to students. Course modality was found to impact trainee outcomes with in-person classes that included hands-on practice and feedback showing greater improvements in training outcomes over hybrid, flipped and virtual course modalities. Furthermore, introduction of low, medium, and high level “challenges” along with in-person tutoring was found to be impactful in building a common foundation to span expertise levels and for engaging students across entry and advanced levels. Training impacts peaked during year four with cumulative implementation of revised strategies. Innovative training revisions and inclusion of critical elements was strongly linked to program satisfaction and ratings of advances in technical, professional and career skills as well as post-training carry over into trainees’ own research and leadership in their labs and careers. Furthermore, this training collaboration and partnership provided the foundation and training model for the newly funded NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) for work in the critical area of engineering the microbiome in built environments. more »« less
Abstract The next generations of science, technology, engineering, and math (STEM) workers are being trained in college and university classrooms by a workforce of instructors who learn pedagogical practice largely on the job. While inclusive instructional practices and their impacts are increasingly well-studied, this training is difficult to instill within the professional development that most STEM professors receive before teaching their students. The Science Teaching Experience Program for Upcoming PhDs (STEP-UP) at the University of Washington was built to prepare future professors for inclusive excellence by guiding them through the literature in education research and providing them a space to practice active and inclusive teaching techniques. This study of STEP-UP uses a design-based approach to understand graduate trainee and undergraduate perceptions of the most salient aspects and outcomes of the program. Our study found that trainees used opportunities to practice inclusive teaching methods with a cohort of their peers, and crucially that these methods were evident in trainee-taught courses through multiple lines of evidence. STEP-UP-trained instructors used inclusive teaching strategies that helped students to feel socioemotionally supported. This study offers a model program that fosters inclusion and equity in undergraduate STEM classrooms through improving teaching professional development for graduate students.
Chapman, MA
(, 2021 American Society for Engineering Education Virtual Annual Conference)
null
(Ed.)
The publication of the first human genome in 2001 transformed biomedical research. Since then, an explosion of new sequencing technologies has required engineers and computer scientists to invent computational methods to analyze and interpret the ever-growing data. Now, large-scale biological data encompasses many types of ‘omics’ datasets, including genomes, transcriptomes, proteomes and metabolomes, and each of these new datasets has created a new set of analytical challenges. To meet this need, the field of bioinformatics has expanded significantly, but there is still a large need for engineers and scientists to work in this inherently interdisciplinary field. Properly trained bioinformaticians have expertise in computer science/engineering and understand the biological and medical context underlying their work. Therefore, the development of robust bioinformatics training programs is critical to educate the next generation of bioinformaticians. Although undergraduate degree programs in bioinformatics exist, providing students with hands-on bioinformatics skills through immersive research experiences is necessary to prepare students for graduate work. Thus, this work describes a recently funded NSF – International Research Experience for Students (IRES) site: US-Sweden Clinical Bioinformatics Research Training Program targeted at training students from diverse educational backgrounds to prepare them for authentic bioinformatics research experiences. Given the inherent interdisciplinary nature of bioinformatics, it is extremely difficult to design a training program that prepares students from different backgrounds (computer science, bioengineering, computational biology, biology) to be successful in a bioinformatics research group. Therefore, this ‘Work-in-Progress’ describes the pre-departure training program developed for this IRES site and the initial lessons learned.
Dutta, Prashanta; Seo, Soobin; Probst, Tahira; Hewa, Joseph M
(, ASEE)
While the demand for interdisciplinary knowledge is undeniable, there are formidable challenges when offering graduate education to Engineering students. To address that, we designed an educational research project that delves into the effectiveness of an interdisciplinary National Science Foundation (NSF) Research Trainee (NRT) program for engineering students studying robotics and autonomous systems. This newly funded NRT program aims to train next-generation scientists and engineers with professional skills through interdisciplinary courses such as leadership, business, and psychology in addition to cutting-edge technical knowledge in the field. We are using retrospective surveys and content analysis to identify student experience with interdisciplinary training and education programs. Both quantitative and qualitative analysis evidenced an increased level of confidence in soft skills such as interdisciplinary understanding, communication, and collaboration skills throughout participating in the interdisciplinary NRT program.
Patil, P; Schulte, C; Rebol, M; Sikka, N; Hood, C; Amdur, R
(, Telemedicine and e-Health)
Background: Medical procedure training often requires constant feedback and different educational interventions. Analyzing gestures within the context of medical procedure training helps trainees better understand critical maneuvers that ensure the successful completion of a procedure. Most gesture feedback involves an instructor suggesting an alteration of the trainee's form or position. This type of feedback is often difficult to convey within telehealth procedure education. For example, remote training of medical procedures is difficult for trainees when they do not have the same type of in‐person interaction with the instructor. These challenges exist in various scenarios such as online physical exam education for medical students or medical procedure training for rural/disaster/wilderness scenarios. Since few tools exist to overcome this challenge, we developed a software program that uses data processing and OpenPose to quantify gestures to help remote trainees learn new procedural skills. Methods: Novice healthcare providers were recorded during an ultrasound‐guided central venous catheterization (US‐CVC) training session. Each trainee was paired with one physician instructor, who modeled and helped assist with completing the procedure. For this feasibility study, various gestures throughout the training were analyzed using video data to identify which gestures might be especially useful for completing the procedure. With our software, a single frame capturing the precise moment at which each of the individuals physically placed the central line needle into the mannequin was then processed. Keypoint data from both arms were further processed to identify critical angles for the insertion of the syringe. Both, left and right, arm angles of the trainees were then compared to the instructor's respective angles to assess whether trainees were mirroring the instructor's gestures, indicating successful procedure completion. Results: 7 trainees and 6 instructors were analyzed from the cohort consisting of 10 trainees and 10 instructors. A total of 13 frames were processed by the OpenPose algorithm and a total of 325 keypoints (25 keypoints per individual) were collected. The instructor's left arm angle was positioned at 163.1 degrees (SD = 9.7), while holding the ultrasound probe and their right arm angle was 109.9 degrees, while holding the syringe. The mean of the trainee's left arm angles was 160.9 degrees (SD = 12.7) and the mean of the trainee's right arm was 102.1 degrees (SD = 18.4). For the left arm, the mean difference between trainee and teacher was 2.24 ± 20.31 degrees, (95% CI ‐16.54 to 21.03 degrees), p = .78. For the right arm, mean difference was 7.84 ± 14.88 (95% CI ‐5.92 to 21.60), p = .21. Since each trainee was matched to a particular teacher as that trainee's gold standard, we used 2‐tailed paired t‐tests to examine differences between trainee and teacher angles for each arm. In this pilot data, the trainees' arm angles did not differ significantly from their teachers' angles. Discussion: This study's results suggest that trainees had similar arm angles to the instructor. The significance of these findings suggests that there is a way to quantitatively measure if a trainee successfully completes a procedure through a video. Assessing whether trainees effectively perform the procedure is challenging, especially from a 2‐D video. Yet, some of these limitations may be overcome with quantitative gestural analysis. Remote medical procedure training stands to benefit from this form of feedback as it is often difficult to convey to trainees how to alter their position over video conferencing alone. Instructors can suggest a change in the trainee's gestures with real‐time data, allowing the trainee to adjust and successfully complete the procedure. Our findings illuminate the utility of quantitative gesture analysis to overcome the challenges of communicating qualitative gestures and help trainees learn new procedures and maneuvers through telehealth‐related video platforms.
Hauser, Adam J.; Verma, Jahnvi; Holley, Karri A.; Bandi, Thejesh N.
(, Proceedings of the 55th Annual Precise Time and Time Interval Systems and Applications Meeting)
While over one-third of the U.S. economy and much of our national security infrastructure directly depends on precision timing, there has been to date no educational workforce development program in the US dedicated to training young talent in the timekeeping technologies that underpin our society. The Alabama Collaborative for Contemporary Education in Precision Timing (ACCEPT) Program is a new, 5-year National Research Traineeship program funded by the National Science Foundation, designed to train the next generation of graduate (MS and PhD) degree holders in a field of critical important to our nation. ACCEPT will provide a comprehensive training and educational opportunity for trainees from physics, mathematics, and engineering. Trainees will combine coursework across these three departments with professional development in critical areas identified by precision timing experts (teamwork, leadership, ethics, communication), and put their training into practice via research experiences with ACCEPT partners, student-led initiatives, and networking at conferences and workshops. In this paper, we present the current objectives, vision, and methodology of our new program, initial steps toward building a comprehensive training facility, and initial research and demonstration projects.
Kelly, Glenda T, Granek, Joshua, Gunsch, Claudia K, Graves, Joseph L, and Singleton, David. Advances in Graduate Training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM). Retrieved from https://par.nsf.gov/biblio/10530592.
Kelly, Glenda T, Granek, Joshua, Gunsch, Claudia K, Graves, Joseph L, & Singleton, David. Advances in Graduate Training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM). Retrieved from https://par.nsf.gov/biblio/10530592.
Kelly, Glenda T, Granek, Joshua, Gunsch, Claudia K, Graves, Joseph L, and Singleton, David.
"Advances in Graduate Training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM)". Country unknown/Code not available: ASEE. https://par.nsf.gov/biblio/10530592.
@article{osti_10530592,
place = {Country unknown/Code not available},
title = {Advances in Graduate Training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM)},
url = {https://par.nsf.gov/biblio/10530592},
abstractNote = {Innovations by engineers and physical scientists working at the frontiers of microbiome engineering and discovery requires in-depth understanding of microbiome systems with parallel skills in bioinformatics and biostatistics. Despite the importance of integrating bioinformatics and biology into graduate student training in fields outside traditional biological sciences, academic institutions remain challenged with including these disciplines across departmental boundaries. Furthermore, it is critical for students in engineering, bioinformatics, and biostatistics to understand fundamentals behind the biological systems they model, and for biology students to gain competencies in applying bioinformatics and biostatistics to biological questions. To address these needs, we developed the Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM) graduate training partnership between Duke University and North Carolina Agricultural and Technical State University, which was funded by the National Science Foundation Research Traineeship (NRT) program. IBIEM’s goals include training interdisciplinary groups of students to: (a) transform conceptualization and develop skills for application of quantitative biology in microbiome areas; (b) perform cutting edge research requiring interdisciplinary team skills; and to (c) communicate their research across disciplinary barriers and to diverse audiences. The pedagogical framework adapted to foster trainee engagement is learner-centered teaching which emphasizes the importance of selfdirected learning with parallel ongoing assessment to optimize student outcomes. Since IBIEM trainees’ goals as well as entry-level knowledge and skills across disciplines varied greatly, program implementation was found to be challenging and required rigorous evaluation and refinements for effective training across disciplines and skill levels. A comprehensive program evaluation over five years found that the strongest learning and skills outcomes were linked to several “best practices”. Early provision of depth in fundamentals in R programming and reproducible research was found to be critical to “jump start” students without programming backgrounds. Addition of an overview of microbiome experimental design and analysis added important context as to how and where in the research process informatics fits into design progression and was highly motivating to students. Course modality was found to impact trainee outcomes with in-person classes that included hands-on practice and feedback showing greater improvements in training outcomes over hybrid, flipped and virtual course modalities. Furthermore, introduction of low, medium, and high level “challenges” along with in-person tutoring was found to be impactful in building a common foundation to span expertise levels and for engaging students across entry and advanced levels. Training impacts peaked during year four with cumulative implementation of revised strategies. Innovative training revisions and inclusion of critical elements was strongly linked to program satisfaction and ratings of advances in technical, professional and career skills as well as post-training carry over into trainees’ own research and leadership in their labs and careers. Furthermore, this training collaboration and partnership provided the foundation and training model for the newly funded NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) for work in the critical area of engineering the microbiome in built environments.},
journal = {},
publisher = {ASEE},
author = {Kelly, Glenda T and Granek, Joshua and Gunsch, Claudia K and Graves, Joseph L and Singleton, David},
}
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