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Icon for: Hong Qin

HONG QIN

University of Tennessee Chattanooga

REU Site: ICompBio - Engaging Undergraduates in Interdisciplinary Computing f...

NSF Awards: 1852042, 1761839, 1720215, 1453078

2021 (see original presentation & discussion)

Grades 6-8, Grades 9-12, Undergraduate

How should we teach data science? Here, I share my experience of letting students discover stories behind data in an online coding bootcamp for high-school and college students.. There are about ~60 students from more than 10 states participating in the five-day bootcamp, with the youngest student at 13 years old. Students were guided to analyze regional COVID19 data sets with R codes on Google Co-Lab. Students learned to retrieve COVID19 cases, Google mobility data, and weather conditions. Students learned to merge and visualize these heterogeneous data sets, and correlate with local events and policy changes. Based on what they learned from the bootcamp, 28 students submitted written reports and video presentations of their findings. Google Co-Lab is a cloud-based coding platform. R is a popular language in data science. Online coding bootcamp can bring participants from many different geographic regions. The bootcamp material is available at https://github.com/hongqin/R-covid19-bootcamp-2020Dec  

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Discussion from the 2021 STEM For All Video Showcase (13 posts)
  • Icon for: Luiz Oliveira

    Luiz Oliveira

    Researcher
    May 11, 2021 | 08:09 a.m.

    Hello HONG and team! I think what your team did during COVID pandemic was fantastic. I admire how you were able to use the pandemic as an opportunity for students to analyze important data sets and write their own reports. Data analytics in today's world is such an important skill to have, especially given our inevitable reliance on technology and big data. Seeing that you are promoting this to young students is truly amazing!

    Throughout this 5-day boot camp, what would you say were some of the biggest challenges you faced? And how did you overcome them? Thanks, and great job!

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 11, 2021 | 08:42 a.m.

    Hello Luiz, Thanks for your kind words. The most challenging part is how to help the individual students to troubleshoot their individual projects, especially when students tried to use specialized data sets related to their specific projects. We had 40 ~ 70 participants during the 5 days, but only a handful of experienced TAs. I wish we could have more TAs to provide one-on-one tutoring. I am amazed that ~25 students wrote final reports and did video presentations. We offered the coding Bootcamp just before Christmas, and some would-be participants might be preoccupied with holiday events, (which are not advised during the pandemic, but that's the reality). 

  • May 17, 2021 | 09:34 p.m.

    Troubleshooting (especially virtually!) is particularly hard. Sounds like you facilitated some amazing work on highly relevant data. Congrats!

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 11, 2021 | 09:57 a.m.

    Our CoLab based notebooks for the bootcamp are available at https://github.com/hongqin/R-covid19-bootcamp-2...

  • Icon for: NATHAN KIMBALL

    NATHAN KIMBALL

    Facilitator
    Curriculum Developer
    May 12, 2021 | 07:12 a.m.

    Hello Hong, how wonderful you made use of highly relevant data to teach important skills, and with an impressive range of participants as well. I'm wondering about the details of the bootcamp. Did students need to have some programming background?  Also, I'm wondering about the course content, what you actually taught and then how students developed their questions that they investigated for their projects. 

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 12, 2021 | 08:47 a.m.

    Hello Nathan, I used a notebook run on Co-Lab. Students are guided to make changes at specific lines. Many students have no prior experience in R coding. The notebook is at the GitHub repository. 

  • Icon for: Aman Yadav

    Aman Yadav

    Facilitator
    Professor
    May 12, 2021 | 08:34 a.m.

    Great way to teach data science by grounding it in local context around COVID. I was wondering what kinds of prior programming experience did students need to be able to participate in the boot camps? What was the background of teachers who participated? 

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 12, 2021 | 08:49 a.m.

    Hi Aman, many students seem to have no prior R coding. Teachers are mostly science teachers. 

  • Icon for: Susan Warshaw

    Susan Warshaw

    External Evaluator
    May 14, 2021 | 11:27 a.m.

    I am wondering if the program covered design principles for creating effective data visualizations? 

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 14, 2021 | 12:55 p.m.

    No. Our goal was to cultivate students' interest and awareness in data science. 

  • Icon for: David Lockett

    David Lockett

    Facilitator
    Albert Einstein Fellow
    May 14, 2021 | 12:48 p.m.

    Hello Hong. Exciting to learn about this project, and analyzing regional COVID19 data sets with R codes on Google Co-Lab. I'm curious about your research approach, did the specific tools used to visualize these heterogeneous data sets help students better understand R coding? 

  • Icon for: Hong Qin

    Hong Qin

    Lead Presenter
    Associate Professor
    May 14, 2021 | 12:58 p.m.

    Hi David. The bootcamp was meant to raise the awareness and interests of students in data science. We had daily surveys to gauge students' participation and engagement. We also require a final report and video presentation. 

  • Icon for: Lei Liu

    Lei Liu

    Researcher
    May 14, 2021 | 01:23 p.m.

    Thanks for sharing a very interesting and timely project, Hong. I am glad that you included different age groups of students. Did you compare the reasoning difference across age groups? Particularly how different age groups use data differently and how they interpret them differently? I am interested to see if there is any reasoning pattern difference across age groups for emerging topics like COVID that matter to all people's life experiences. 

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