Introduction

Intended Learner

This learning module is intended for high school students who are interested in expanding their understanding of how information plays a critical role in communicating science concepts, how data analysis methods allow for increased confidence in conclusions, and how effective communication for a public audience can utilize a combination of images, text, and interactive media.

Desired Educational Outcomes

It is important to know where you are planning to go so here are a few targeted educational outcomes we will be attempting to address during this program. These standards are simply statements about what a student is expected to do and are listed among the ‘computational thinking competencies’ described by ISTE.

Computational standards from ISTE:

  • (1b) Learn to recognize where and how computation can be used to enrich data or content to solve discipline-specific problems and be able to connect these opportunities to foundational CT practices and CS concepts.
  • (2e) Communicate with students, parents and leaders about the impacts of computing in our world and across diverse roles and professional life, and why these skills are essential for all students.
  • (3a) Model and learn with students how to formulate computational solutions to problems and how to give and receive actionable feedback.
  • (4a) Design CT activities where data can be obtained, analyzed and represented to support problem-solving and learning in other content areas.
  • (5c) Use a variety of instructional approaches to help students frame problems in ways that can be represented as computational steps or algorithms to be performed by a computer.

Functional Educational Outcomes

Be able to:

  • improve your ability to communicate scientific information
  • import and analyze large datasets
  • use principles of design in your visualizations
  • implement analysis and plotting in R and RStudio
  • write with R Markdown and combine text and graphics
  • integrate data analysis and visualization into your research project workflow

About the Structure

The Computational Thinking portion of this program is divided into four seminars and two discussions about current topics. The seminars are split into a ‘thinking part’ and a ‘doing part’. You might think of it as abstract plus concrete or ‘minds on’ and ‘hands on’.

Computational Tools

For this portion of the program, we will use the programming language R and the most common integrated development environment called RStudio. Specifically, you will most likely use RStudio Cloud which is hosted online, but you also may use RStudio Desktop if you choose to install that software.

Topics

  • Seminar I: Design for Scientific Communication & Intro to R
  • Seminar II: Data Visualization and Plot Examples
  • Seminar III: Applied Data Visualization: Agriculture Bioengineering
  • Seminar IV: Introduction to Quantitative Analysis: T-test and Linear Regression
  • Current Topics I: The R Graph Gallery
  • Current Topics II: Technical Reports with R Markdown