Data Literacy in Freshman Integrated Science Part 1

Data Literacy in Freshman Integrated Science Part 1

This year, we’re taking a very intentional approach to working with data in our Freshman integrated science course, lovingly titled Chemical and Physical Systems and even more lovingly referred to as Science 9. We tweak this course every year as we vertically align our curriculum with the Middle School and develop departmental goals for data and computational thinking. This hasn’t been an easy road as we wrestle with the time it takes to provide meaningful learning experiences and the necessity to cover a broad range of science content. Our current iteration consists of a two pronged approach to introducing students to basic data literacy: ubiquitous use of Excel, and activities in vPython tied to our Science concepts. Ultimately, by the end of the year I would like to have a data literacy proficiency scale (https://www.marzanoresearch.com/resources/tips/ps_tips_archive) that we can use in future years to properly assess students as they become fledgling scientists and data scientists.

 

First, while it isn’t new or flashy (dare I say sexy?), Excel is still a standard for organizing and processing data. We plan to use Excel in every lab that involves any type of data collection this year. There are so many basic skills that Excel can help to develop. To name a few: understanding data types, organizing information, automating calculations, visualizing information, graphical analysis, and trend analysis. We want to break them of the thought that Science is simply textbooks and theory. Real Science involves math (an absolute shock to some 9th graders) and sometimes messy numbers we have to make sense of.

 

In a recent activity, each lab group had an Atwood Apparatus that allowed them to calculate acceleration of moving weights based on gravity. We asked the question, “What is the relationship between force, mass, and acceleration?” Students used photogates and data collectors to measure the velocity of moving weights in a variety of combinations, organized their data in Excel, then used the Excel graphing tools to create a velocity vs. time graph. They could easily calculate the acceleration from here, and see the connection between a greater net force, and an increase in those acceleration values.

 

We can already see expected challenges popping up in our labs so far. Students have a difficult time communicating what data means and often get lost or confused about what certain numbers mean or even what units are attached to them. Communicating what data trends they see will be a focus moving forward. This will be something to mull over as we think about standards to assess.

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