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Jonathan D. W. Kahl

The Atmospheric Science program at the University of Wisconsin—Milwaukee regularly offers the general education course Survey of Meteorology, serving over 400 students each year. This article describes a systematic inquiry into the teaching and learning goals of the course and the adequacy of current methods used to assess student performance. Following a survey of the six faculty members with teaching responsibilities for the course, common student learning goals of meteorological content and the application of meteorological concepts to observations were identified. Student surveys, designed to assess both the extent to which these learning goals were being met as well as the depth of learning, were administered to 241 students during the 2005–06 academic year. Results indicate that 80% of students surveyed met the content learning goal, while the application learning goal was met by only 66% of students. A deeper level of application learning, involving pattern recognition and the separation of concepts into component parts, was achieved by only 45% of the students. A comparison of student survey results with course grade distributions indicates that current grading practices are adequate for assessing the content learning goal but are inadequate for assessing the application learning goal.

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Melanie M. Colavito, Sarah F. Trainor, Nathan P. Kettle, and Alison York

time to facilitate the development and application of actionable science is needed ( McNie 2013 ; Meadow et al. 2015 ; Wall et al. 2017b ). Table 1. Key modes of science production in boundary organizations as used in this paper. This paper uses case study research to assess the Alaska Fire Science Consortium (AFSC)—a boundary organization focused on fire science and management in Alaska—and to develop a deeper understanding of how boundary organizations and knowledge coproduction work in

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Peter J. Walton, Morgan B. Yarker, Michel D. S. Mesquita, and Friederike E. L. Otto

; however, having enough bandwidth to be able to download can be problematic to some participants. This course structure though does reflect more closely Kolb’s (1984) model of experiential learning, where it is suggested that participants will form deeper understanding concepts (in this case climate modeling) through actively participating in an exercise. The final point to be taken from comparing the two courses is the issue of certification. As well as wanting to develop their own knowledge

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David R. Perkins IV, Jennifer Vanos, Christopher Fuhrmann, Michael Allen, David Knight, Cameron C. Lee, Angela Lees, Andrew Leung, Rebekah Lucas, Hamed Mehdipoor, Sheila Tavares Nascimento, Scott Sheridan, and Jeremy Spencer

learning, a natural research inquiry surfaced: how do we enhance the teaching and learning of biometeorology in higher education to both promote the discipline and elicit deeper learning into common meteorological, climatological, and biological concepts? As a result of this inquiry, the second international workshop held by the ISB’s SNP group sought to tackle this very issue. Transforming core tenants of biometeorology and education into collaborative initiatives on an international scale was a key

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Lisa M. PytlikZillig, Qi Hu, Kenneth G. Hubbard, Gary D. Lynne, and Roger H. Bruning

explanations are essential for enhancing their ownership of the new knowledge they are developing. In addition, explanation also can facilitate the learning of new skills, deeper learning, and better integration of new knowledge with prior knowledge ( Ainsworth and Loizou 2003 ; Chi et al. 1989 , 1994 ; Renkl 2002 ; Roscoe and Chi 2008 ; Roy and Chi 2005 ). Meanwhile, peer discussion can enhance many important outcomes, including understanding, critical thinking, and construction of complex knowledge

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A. Hicks and B. M. Notaroš

they are capable of transferring learning from one dataset to another and are not limited to specific parameters inherent either to the dataset (e.g., resolution, color, or size) or capturing method (e.g., hardware imperfections reflected in data). Therefore, a classifier properly trained with a CNN can be utilized by a variety of image-capturing in situ devices. Research into deep learning has extended their ability to process complex data without major changes to the algorithm. Finally, CNNs are

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

(e.g., Gagne et al. 2017 ; Campos et al. 2019 ). It is now clear (see references cited as examples in the body of this essay) that AI approaches, including recent advances in ML technology, such as Transfer Learning and Long and Short Term Memory Networks (LSTMs; Hochreiter and Schmidhuber 1997 ), Deep and Extreme Learning ( Schmidhuber 2015 ; Goodfellow et al. 2019 ), and Computer Vision, have the potential to meet increasing requirements for and by nowcast and forecast products, including

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Thomas P. Leahy, Francesc Pons Llopis, Matthew D. Palmer, and Niall H. Robinson

-moving vessels. Therefore, if there are some misclassified T-7 (Sippican) probes as Deep Blue (Sippican) or vice versa, the final outcome on fall-rate corrections will be minimal ( Kizu et al. 2011 ). 5. Conclusions and discussion This study has demonstrated that applying machine learning to the classification of XBT probe types allows for an improvement in the accuracy over the current state-of-the-art method ( Palmer et al. 2018 ). This approach also has the advantage that subjective a priori expertise is

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Tao Zhang, Wuyin Lin, Yanluan Lin, Minghua Zhang, Haiyang Yu, Kathy Cao, and Wei Xue

parameterization schemes, such as convection and cloud schemes. Meanwhile, new discoveries from the machine learning framework can be further verified through physics-based interpretation, having the potential to lead to a deeper understanding of the genesis mechanism of TCs. We plan to extend such applications in separate works. Acknowledgments This work is supported by the CMDV Project to Brookhaven National Laboratory under Contract DE-SC0012704 and Brookhaven National Laboratory’s Laboratory Directed

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S. L. Sellars

Initiative; associate director, Center for Machine Learning and Intelligent Systems, UCI. WORKSHOP HIGHLIGHTS. A noticeable theme throughout the workshop was that technological advances in hardware and software have allowed data-driven approaches to emerge as powerful tools that can be used in the era of big data and “deep analysis.” In addition, many of these technologies allow for massive data transfers, storage, and analysis approaches—necessary features to process enormous and often complex datasets

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