3 Reasons to Get Students' Hands Dirty with Data More Often • Talk Tech With Me

It seems like lately, topics about data, big data, and data privacy have been at the forefront of many conversations. This makes a lot of sense considering that just about everything is collecting some sort of information on us these days- where we go, our habits, what we like, and what we buy.

For Christmas this past year, my brother got a Google Home. Now my three-year-old nephew can start playing his favorite song or turn the hallway light blue without lifting a finger and simply starting his request with “ok, Google…”. This is the Internet of Things which can be really cool, but it also has other implications for life as we know it. For the purpose of this post, I’m focusing on what this means for skills our students need to be prepared for the next steps after twelfth grade. If you’re interested in topics related to data such as privacy and how much information is too much to give away, I recommend checking out the Note To Self’s Privacy Paradox. It’s a five-part podcast series dedicated to taking back your digital identity.

Now, regardless of how you feel about all this data that’s being collected, the fact of the matter is that students will be working with this data one day, regardless of their field and career choices. Below are three big reasons that I think it’s important to give kids more opportunities to work with and make sense of data.

1- Data Analyzation is a Crucial Skill For Jobs of the Future

Aside from just my own observations mentioned above, a number of articles have been written and studies have been conducted about the skills people will need to do jobs in the future. One that I have referenced often when talking to teachers is the Future Work Skills of 2020 study (conducted in 2011). The infographic below summarizes the studies findings.

Another popular study, that was actually used by ISTE for the Student Standards refresh last year, is the Future of California’s Workforce study that “analyzes key drivers that will reshape the STEM
disciplines and landscape of work in California through 2032.” The year 2032 sounds pretty far off, doesn’t it? When in reality, students born last year will only be 16 years old in 2032! Many of us, including myself, will still be responsible for educating the kiddos who need to be prepared for the next steps after high school in 2032. This study reflects similar findings to the first study I mentioned. Take a look at this nice little Future Framing document that ISTE created to summarize this study. I use it when I’m working with teachers to help them understand what the future jobs they’re preparing students for may look like. Here are a few takeaways from this second study in regards to data.

Each of the four future job descriptions on page 1 requires heavy data analyzation skills. The jobs range from fields in health care to law enforcement to the study of the mind and the study of matter.
2 of the 8 Drivers and Implications of change identified on pages 2 and 3 are:
- Big Data- basically, everything is collecting data on us, so every field will begin to use this data to inform their business decisions
- Computational Thinking- The Internet of things (everyday items being connected to the internet and collecting data on its usage) will require everyday people, not just programmers, to have a basic understanding of computational thinking and digital data.

2- It’s in the ISTE Student Standards

The ISTE Standards for Students reflect these studies of the future workforce. The ISTE Standards have been developed based on global research and feedback from students through policy makers and everyone in between from all over the world. They identify important skills students need to compete for next steps and jobs with their peers on a global scale. This is so important because students aren’t just competing for jobs and internships with the district next door or even just others in their state or country, but they are competing globally in our global economy. Standard 5, Computational Thinker, requires:

Students formulate problem definitions suited for technology-assisted methods such as data analysis, abstract models and algorithmic thinking in exploring and finding solutions.
Students collect data or identify relevant data sets, use digital tools to analyze them, and represent data in various ways to facilitate problem-solving and decision-making.

3- Google Sheets & Forms Make it Easy!

Google Sheets & Forms makes it incredibly easy to have students start analyzing and interpreting data. With Google Forms, the teacher can collect the entire class’ individual results into one spreadsheet. Then the teacher can share the spreadsheet with students to make their own copy and analyze the results of the entire class. Students could also set up their own Google Form to collect survey responses and then analyze the results in the spreadsheet.

You can also use Sheets without a Google Form. Think of any lesson that you would normally be collecting or looking at a data set. Plug it into Google Sheets and then insert a chart to view the data visually, or use a pivot table to start understanding the data better… and heck, create a chart out of your pivot table! If this is sounding like a foreign language to you, that’s all the more reason to start doing these types of activities with students. In their future (not yours), they need to feel comfortable with data and spreadsheets as we saw in the future job skills studies above.

The image below is an example of a motion lab that a 6th grade science teacher does with her students. She usually has them chart out their results on pen and paper and then hand-draw their chart. The teacher came to me to see how she may be able to incorporate technology to make this activity stick with students a little better. I suggested she have the students create a table in Google Sheets, insert their answers there and then insert a chart to visualize the results. It was an easy way to introduce her students to spreadsheets, data, charts, and computational thinking (what happens to the chart if we change the results?).