• Baepler, P., J. Walker, and M. Driessen, 2014: It’s not about seat time: Blending, flipping, and efficiency in active learning classrooms. Comput. Educ., 78, 227236, https://doi.org/10.1016/j.compedu.2014.06.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bezanson, J., A. Edelman, S. Karpinski, and V. B. Shah, 2017: Julia: A fresh approach to numerical computing. SIAM Rev., 59, 6598, https://doi.org/10.1137/141000671.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowen, E. G., 1950: The formation of rain by coalescence. Aust. J. Sci. Res., 3A, 193, https://doi.org/10.1071/PH500193.

  • Czaplicki, E., and S. Chong, 2013: Asynchronous functional reactive programming for GUIs. Proc. 34th ACM SIGPLAN Conf. on Programming Language Design and Implementation, New York, NY, ACM, 411422.

    • Search Google Scholar
    • Export Citation
  • Davenport, C. E., 2019: Using worked examples to improve student understanding of atmospheric dynamics. Bull. Amer. Meteor. Soc., 100, 16531664, https://doi.org/10.1175/BAMS-D-18-0226.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deslauriers, L., L. S. McCarty, K. Miller, K. Callaghan, and G. Kestin, 2019: Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proc. Natl. Acad. Sci. USA, 116, 19 25119 257, https://doi.org/10.1073/pnas.1821936116.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eberlein, T., J. Kampmeier, V. Minderhout, R. S. Moog, T. Platt, P. Varma-Nelson, and H. B. White, 2008: Pedagogies of engagement in science. Biochem. Mol. Biol. Educ., 36, 262273, https://doi.org/10.1002/bmb.20204.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elliott, C., and P. Hudak, 1997: Functional reactive animation. Proc. Second ACM SIGPLAN Int. Conf. on Functional Programming, Amsterdam, Netherlands, ACM, 263273, https://doi.org/10.1145/258948.258973

    • Search Google Scholar
    • Export Citation
  • Fairbrother, J., C. Nemeth, M. Rischard, J. Brea, and T. Pinder, 2018: Gaussian Processes.jl: A nonparametric bayes package for the Julia language. arXiv, https://arxiv.org/abs/1812.09064.

  • Freeman, S., S. L. Eddy, M. McDonough, M. K. Smith, N. Okoroafor, H. Jordt, and M. P. Wenderoth, 2014: Active learning increases student performance in science, engineering, and mathematics. Proc. Natl. Acad. Sci. USA, 111, 84108415, https://doi.org/10.1073/pnas.1319030111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haak, D. C., J. HilleRisLambers, E. Pitre, and S. Freeman 2011: Increased structure and active learning reduce the achievement gap in introductory biology. Science, 332, 12131216, https://doi.org/10.1126/science.1204820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hallar, A. G., I. B. McCubbin, and J. M. Wright, 2011: CHANGE: A place-based curriculum for understanding climate change at Storm Peak Laboratory, Colorado. Bull. Amer. Meteor. Soc., 92, 909918, https://doi.org/10.1175/2011BAMS3026.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harsh, J. A., M. Campillo, C. Murray, C. Myers, J. Nguyen, and A. V. Maltese, 2019: “Seeing” data like an expert: An eye-tracking study using graphical data representations. CBE Life Sci. Educ., 18, ar32, https://doi.org/10.1187/cbe.18-06-0102.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hinds, W. C., 1999: Aerosol Technology: Properties, Behavior, and Measurement of Airborne Particles. 2nd ed. John Wiley and Sons, 483 pp.

    • Search Google Scholar
    • Export Citation
  • Hudson, J. G., and S. S. Yum, 2001: Maritime–continental drizzle contrasts in small cumuli. J. Atmos. Sci., 58, 915926, https://doi.org/10.1175/1520-0469(2001)058<0915:MCDCIS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, D. C., and Coauthors, 2018: GiovineItalia/Gadfly.jl: V0.7.0. Zenodo, https://doi.org/10.5281/zenodo.1284282.

  • Kosslyn, S. M., 1989: Understanding charts and graphs. Appl. Cognit. Psychol., 3, 185225, https://doi.org/10.1002/acp.2350030302.

  • Kruger, J., and D. Dunning, 1999: Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Pers. Soc. Psychol., 77, 11211134, https://doi.org/10.1037/0022-3514.77.6.1121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malmaud, J., and L. White, 2018: TensorFlow.jl: An idiomatic Julia front end for TensorFlow. J. Open Source Software, 3, 1002, https://doi.org/10.21105/joss.01002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mogensen, P. K., and A. N. Riseth, 2018: Optim: A mathematical optimization package for Julia. J. Open Source Software, 3, 615, https://doi.org/10.21105/joss.00615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perkel, J. M., 2018: Why Jupyter is data scientists’ computational notebook of choice. Nature, 563, 145146, https://doi.org/10.1038/d41586-018-07196-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petters, M. D., 2018: A language to simplify computation of differential mobility analyzer response functions. Aerosol Sci. Technol., 52, 14371451, https://doi.org/10.1080/02786826.2018.1530724.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rackauckas, C., and Q. Nie, 2017: DifferentialEquations.jl—A performant and feature-rich ecosystem for solving differential equations in Julia. J. Open Res. Software, 5, 15, https://doi.org/10.5334/jors.151.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raymond, T., 2007: Non traditional courses for applying STEM knowledge. ASEE Conf., Honolulu, HI, American Society of Engineering Education, AC 2007-438.

    • Search Google Scholar
    • Export Citation
  • Rogers, R. R., and M. K. Yau, 1989: A Short Course in Cloud Physics. 3rd ed. Butterworth-Heineman, 290 pp.

  • Rothenberg, D., and C. Wang, 2016: Metamodeling of droplet activation for global climate models. J. Atmos. Sci., 73, 12551272, https://doi.org/10.1175/JAS-D-15-0223.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shah, P., and J. Hoeffner, 2002: Review of graph comprehension research: Implications for instruction. Educ. Psychol. Rev., 14, 4769, https://doi.org/10.1023/A:1013180410169.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snyder, J. J., J. D. Sloane, R. D. P. Dunk, and J. R. Wiles, 2016: Peer-led team learning helps minority students succeed. PLOS Biol., 14, e1002398, https://doi.org/10.1371/journal.pbio.1002398.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soltis, R., N. Verlinden, N. Kruger, A. Carroll, and T. Trumbo, 2015: Process-oriented guided inquiry learning strategy enhances students’ higher level thinking skills in a pharmaceutical sciences course. Amer. J. Pharm. Educ., 79, 11, https://doi.org/10.5688/ajpe79111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spencer, J. N., 1999: New directions in teaching chemistry: A philosophical and pedagogical basis. J. Chem. Educ., 76, 566, https://doi.org/10.1021/ed076p566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stark, P. B., and R. Freishtat, 2014: An evaluation of course evaluations. ScienceOpen, https://doi.org/10.14293/S2199-1006.1.SOR-EDU.AOFRQA.v1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steeneveld, G.-J., and J. Vilà-Guerau de Arellano, 2019: Teaching atmospheric modeling at the graduate level: 15 years of using mesoscale models as educational tools in an active learning environment. Bull. Amer. Meteor. Soc., 100, 21572174, https://doi.org/10.1175/BAMS-D-17-0166.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanamachi, R. L., D. T. Dawson, and L. C. Parker, 2020: Students of Purdue Observing Tornadic Thunderstorms for Research (SPOTTR): A severe storms field work course at Purdue University. Bull. Amer. Meteor. Soc., 101, E847E868, https://doi.org/10.1175/BAMS-D-19-0025.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tufte, E. R., 2001: Visual Display of Quantitative Information. 2nd ed. Graphics Press, 197 pp.

  • Twomey, S., 1959: The nuclei of natural cloud formation part II: The supersaturation in natural clouds and the variation of cloud droplet concentration. Geofis. Pura Appl., 43, 243249, https://doi.org/10.1007/BF01993560.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Violin, C. R., and B. M. Forster, 2018: An introductory module and experiments to improve the graphing skills of non-science majors. J. Microbiol. Bio. Educ., 20, 20.3.59, https://doi.org/10.1128/jmbe.v20i3.1863.

    • Search Google Scholar
    • Export Citation
  • Walker, J. D., S. H. Cotner, P. M. Baepler, and M. D. Decker, 2008: A delicate balance: Integrating active learning into a large lecture course. CBE Life Sci. Educ., 7, 361367, https://doi.org/10.1187/cbe.08-02-0004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wickham, H., 2016: Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, 266 pp.

  • Wilkinson, L., 1999: The Grammar of Graphics. 2nd ed. Springer, 408 pp.

  • Wilkinson, M. D., and Coauthors, 2016: The FAIR guiding principles for scientific data management and stewardship. Sci. Data, 3, 160018, https://doi.org/10.1038/sdata.2016.18.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Interactive Worksheets for Teaching Atmospheric Aerosols and Cloud Physics

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  • 1 Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, North Carolina
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Abstract

Student-centered active learning pedagogies improve learning outcomes and increase the engagement of underrepresented groups. Implementing such pedagogies requires interactive tools for students to manipulate inputs and use the outputs to construct knowledge. This work introduces interactive worksheets for teaching about atmospheric aerosol and cloud physics and describes the toolchain to create and deliver the content. The material is appropriate for upper-level undergraduate and graduate instruction with pedagogy based on process-oriented guided inquiry learning. Students playfully interact with physical relationships and atmospheric models. Two examples are the interaction with an aerosol–cloud parcel model for simulating the early stage of cloud formation and the interaction with the Bowen model for simulating the formation of rain by coalescence. Photos, text, figures, and software associated with the project are free to be shared and free to be adapted. In addition to focusing on discipline-based learning objectives, the worksheets emphasize interacting with real-world data and practicing graph comprehension. Hosting the content in the cloud ensures reliable and scalable delivery to any device with a browser and Internet access. The worksheets are designed to be used in a student-centered active learning classroom but can also be used in an online setting.

Supplemental material: https://doi.org/10.1175/BAMS-D-20-0072.2

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Markus Petters, mdpetter@ncsu.edu

Abstract

Student-centered active learning pedagogies improve learning outcomes and increase the engagement of underrepresented groups. Implementing such pedagogies requires interactive tools for students to manipulate inputs and use the outputs to construct knowledge. This work introduces interactive worksheets for teaching about atmospheric aerosol and cloud physics and describes the toolchain to create and deliver the content. The material is appropriate for upper-level undergraduate and graduate instruction with pedagogy based on process-oriented guided inquiry learning. Students playfully interact with physical relationships and atmospheric models. Two examples are the interaction with an aerosol–cloud parcel model for simulating the early stage of cloud formation and the interaction with the Bowen model for simulating the formation of rain by coalescence. Photos, text, figures, and software associated with the project are free to be shared and free to be adapted. In addition to focusing on discipline-based learning objectives, the worksheets emphasize interacting with real-world data and practicing graph comprehension. Hosting the content in the cloud ensures reliable and scalable delivery to any device with a browser and Internet access. The worksheets are designed to be used in a student-centered active learning classroom but can also be used in an online setting.

Supplemental material: https://doi.org/10.1175/BAMS-D-20-0072.2

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Markus Petters, mdpetter@ncsu.edu

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