We are excited to announce that LabXchange's Data Science–Driven Science Education (DSDSE) project has recently debuted a new type of learning resource, the spreadsheet simulation, to further support the building of critical, hands-on skills in data science. Designed for those with no prior experience using spreadsheets, Osmosis Data Analysis and Interpretation teaches the essentials of using spreadsheet tools for scientific data analysis and interpretation. In this interactive simulation, learners will engage with data to calculate percent mass changes, create bar graphs and box plots, and interpret variability.

Spreadsheet Simulation: Osmosis Data Analysis and Interpretation

As shown below, this learning experience guides users through the process of analyzing datasets in a spreadsheet environment. Learners learn about data types, how to write formulas, and how to create and edit data visualizations, all with built-in hints and feedback available if they get stuck.

Learners are given example data to analyze. The goal is for them to gain the skills to analyze their own data—either from their classroom or from a virtual experimental protocol simulation such as Osmosis and Medicine: Why Is Saline Used for Intravenous (IV) Fluids?, which is part of LabXchange's Core Concepts in Biology (AP Biology) cluster. These transferrable skills give students a feeling of ownership over their data without needing prior experience with writing code. The techniques they learn will be relevant to popular, real-world spreadsheet platforms such as Microsoft Excel and Google Sheets.

LabXchange is best known for experimental protocol simulations, wherein learners perform an experiment in a virtual lab environment; spreadsheet simulations are designed to accompany those protocols and close the loop between experimentation and data analysis.

Learning Objectives

By engaging with this resource, students will learn how to:

1. Calculate percent change in mass using a spreadsheet.
2. Create and analyze data visualizations using a spreadsheet.
3. Explain the importance of multiple trials in experimentation to mitigate the effect of error.
4. Compare/contrast the outcomes of reporting mean vs. reporting median.

This learning resource is part of the Data Visualization pathway, which covers the fundamentals of communicating and interpreting data through graphs and charts.

About the Data Science–Driven Science Education Project

With generous support from the U.S. Department of Defense (DoD) STEM, the DSDSE project was launched in 2023 as an ambitious initiative aimed at educating the next generation of learners on the the incredible real-world importance of data science and data literacy.

The project will provide sustainable, long-term data science resources for high school students and educators that will augment national digital literacy by integrating data science with existing high school STEM curricula and building educator capacity to confidently lead students in data science explorations.

Learn more in the DSDSE project announcement and visit the DSDSE project page to stay up to date on what's coming next.