Ko, P., & Mohammadi-Aragh, M. J., & Harris, J. G., & Dyer, J. L., & Sun, Y. (2021, July), Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--38216

\bibitem{asee_peer_38216}
  Ko, P., \& Mohammadi-Aragh, M. J., \& Harris, J. G., \& Dyer, J. L., \& Sun, Y. (2021, July), \emph{Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather}
  Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference.
  10.18260/1-2--38216

Pat Ko, Mahnas Jean Mohammadi-Aragh, Jonathan G. Harris, Jamie Lee Dyer, and Yan Sun.  "Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather".  2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July.  ASEE Conferences, 2021.  https://peer.asee.org/38216 Internet.  14 Dec, 2024

\bibitem{asee_peer_38216}
  Pat Ko, Mahnas Jean Mohammadi-Aragh, Jonathan G. Harris, Jamie Lee Dyer, and Yan Sun.
  "Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather".
  \emph{2021 ASEE Virtual Annual Conference Content Access, Virtual Conference, 2021, July}.
  ASEE Conferences, 2021.
  https://peer.asee.org/38216 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_38216
author = "Pat Ko, Mahnas Jean Mohammadi-Aragh, Jonathan G. Harris, Jamie Lee Dyer, and Yan Sun"
title = "Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather"
booktitle = "2021 ASEE Virtual Annual Conference Content Access"
year = "2021"
month = "July"
address = "Virtual Conference"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/38216}
number = {10.18260/1-2--38216}
}

TY  - CPAPER
AB  - Computational thinking refers to a set of skills necessary to conceptualize data and solve problems with a computer. While rooted in computer science, the skills are general enough to be useful to all professionals in design and problem-solving fields, including engineering. The Next Generation Science Standards (Achieve, 2013) promote developing computational thinking in K-12 students by integrating the skills into the context of other science instruction. However, there are barriers to implementation, including that current K-12 science teachers have not been trained in computational thinking and few tools exist to support teachers who want to integrate computational thinking into classroom learning. 

The NSF-funded 3D Weather Analysis and Visualization (3D Weather) project is creating a contextual framework to support the integration of computational thinking into 7th-12th grade science classes. The 3D Weather project is supporting teachers who are teaching basic meteorology concepts in order to engage their students in the analysis of real weather data visualized in three dimensions. The focus of the project is on providing the professional development that science teachers need in order to write lessons that motivate student learning of and practice with computational thinking. Current 3D Weather professional development includes guidance for computational thinking principles, instruction for downloading and analyzing real weather data, training for visualizing weather data in 3D using existing tools, and support for how to integrate computational thinking into K-12 classrooms. Meteorology was chosen as the context for the computational thinking instruction because it is a science phenomenon that is universally familiar to all: everyone has some experience with weather. The computational thinking skills taught through the 3D Weather project include understanding and manipulating complex three-dimensional data through visualization, and the relationship between input variables and outcomes. 

The inaugural year of the grant was in 2020, but due to COVID-19 restrictions, planned activities were modified and scaled back. Originally scheduled as a one-week online followed by one-week face-to-face training, the project delivered an entirely online pilot version of the two-week professional development in July 2020. Participants included four middle school science teachers. The training consisted of two content modules that covered how weather is affected by 1) temperature and 2) pressure and wind. The professional development included additional modules detailing how to install, navigate, and use Integrated Data Viewer (IDV), a free 3D visualization tool which was designed to be used with geoscience data (Unidata Program Center/University Corporation for Atmospheric Research, 2020). Videos lectures on weather and IDV were made available to the teachers in the form of voice-over PowerPoint slides. In addition, multiple group video conference calls were scheduled with the teachers so that they could ask questions of and provide feedback to the project staff. Survey and interview data were collected from the teachers and will be used to improve future offerings of the training.

In this Work-in-Progress paper, we briefly discuss the motivation for the 3D Weather Analysis and Visualization project, the design of the initial professional development activities, and the top “lessons learned” from our first professional development offering. This paper will be of interest to computing professionals who want to help teachers incorporate computational thinking into K-12 classrooms and to those offering professional development that requires teachers to learn a new computing tool. 

References
Achieve. (2013). Next Generation Science Standards. Achieve, Inc. https://www.nextgenscience.org
Unidata Program Center/University Corporation for Atmospheric Research. (2020). Unidata’s Integrated Data 
   Viewer. https://www.unidata.ucar.edu/software/idv/docs/userguide/index.html


AU  - Pat Ko
AU  - Mahnas Jean Mohammadi-Aragh
AU  - Jonathan G. Harris
AU  - Jamie Lee Dyer
AU  - Yan Sun
CY  - Virtual Conference
DA  - 2021/07/26
PB  - ASEE Conferences
TI  - Work-in-Progress: Incorporating Computational Thinking Instruction into K-12 Using 3D Weather
UR  - https://peer.asee.org/38216
DO  - 10.18260/1-2--38216
ER  -