Data-Driven Kids

Evolutionarily-Relevant Ways To Improve Teaching Abstract Skills

Data projects from third grade students.

My third graders have an obsession with data. As teachers, we often talk about how our teaching is data-driven. However, my kids themselves are truly data-driven. Our latest math unit focused on collecting, sorting, representing, and drawing conclusions from both categorical and numerical data. When I would tell the kids that it was time to begin math for the day, they would cheer. This took me by surprise because when I first opened the curriculum guide, my initial reaction was, “my kids are going to hate this!” I thought the concepts were complicated and very making graphs was procedural and potentially dull. I falsely believed it was going to be overly challenging for my kids and they would not be engaged.

In reality, my students were hooked from day one! To introduce the unit, the curriculum had them each dictate their favorite foods and we collected their answers as a set of data. Groups of students then got a copy of the data set and had to sort the foods into categories and draw conclusions about preferences of their classmates. They did this for over an hour, sorting and resorting, it was by far the longest math block we had ever had!

Over the course of the unit they created their own surveys, collected data from their peers in multiple classes, used rulers to collect numerical data, and analyzed and constructed bar graphs, pictographs, and line plots. Even in their free time, they were making their own line plots on scraps of paper and discussing questions they wanted to have answered.

My question is this: do I have a group of kids this year that are destined to become future data scientists or is there some evolved mechanism supporting engagement in this mathematics unit?

Primary and Secondary Knowledge

I’ve written before about the difference between primary and secondary knowledge as it relates to what kids learn through play. Coined by David Geary (1995), primary knowledge refers to skills and information that was important for the survival/reproduction of our ancestors over deep evolutionary history. Human cognition was thus likely shaped by natural selection to readily and easily acquire these skills through organic experiences (e.g., how a baby learns to speak and the ability to understanding emotions in others). Contrasted with primary knowledge is secondary knowledge. Secondary knowledge refers to information and skills that are more culturally-based and came along much, much later in evolutionary history. Due to the novel nature of secondary skills, the acquisition of these skills is more complicated and does require explicit instruction and thus schooling (e.g., phonics instruction or learning to read).

It is evolutionarily unnatural for kids to be forced to learn exclusively secondary skills in schools. Engagement in learning and enjoyment of school often suffer as a result (see Gruskin & Geher, 2018). However, by keeping children’s evolved mechanisms for learning primary skills in mind, teachers can create lessons that better meet student needs while still addressing critical secondary skills.

So how does this relate to my students’ obsession with data? Collecting and analyzing data by comparing, adding, and subtracting large numbers are all secondary skills. Had I taught these concepts in isolation through teacher-driven lessons, guided practice on the board, workbook pages, and meaningless data, my students would likely have shown little to no interest. However, by simply changing abstract numbers into “class data,” students were driven by the motivation to learn about their peers. As they polled their classmates, they were learning about information that had the power to impact their social relationships. They were learning about preferences of others, comparing ingroups and outgroups based on interests, etc. These social skills are all considered primary knowledge and are thereby highly motivating for students. The use of secondary skills (the math content) was not what was driving student interest in data. Rather, the math content was simply tools that students needed to access the primary information, and were thus deemed necessary.

Real-World Connections

There is a second reason, based on evolutionary-theory, that I believe this math unit was successful. The short background is that I am currently working on some research projects in conjunction with Glenn Geher’s Evolutionary Psych. Lab at SUNY New Paltz. I have shared this fact with my third graders and explicitly shown them how the work that they are doing is actually similar to the work that the college students do (e.g. collecting and analyzing data sets). While the level of the work is very different, the underlying ideas are the same.

Under the conditions in which children evolved to learn, all learning was connected to the day-to-day life of adults in nomadic tribes. The were no schools. Rather, ancestral learning occurred when groups of children first observed adults engaged in daily tasks and then mimicked this adult work through mixed-age play (see Gray, 2013). Now however, children don’t often see the work that adults do. What’s more, kids don’t see how the work that they do in school actually relates to the real-life skills that adults use. Although many adults read, write, and do math at work, kids don’t often see this as they are in school during this time. By schooling children the way that we do, we are actually sending the message (albeit unintentionally) that much of what they are doing is work for children and won’t matter when they grow up.

When my students saw older college students using the same type of skills that they use, it gave meaning and context to the work we were doing in our classroom. My students felt like the work was important. Rather than feeling like they were wasting time on some abstract skill, they were excited to be doing the same work that college students do.

Takeaways

In summary, by taking advantage of children’s intrinsic drive to learn primary skills as well as adding meaningful context, I was able to achieve more success in teaching complex secondary skills. My third graders willingly and fully engaged in abstract math concepts due to the personal connections that were involved. Their behaviors were better than other parts of the day and their end-of-unit assessment results were high.

There are some key takeaways that teachers should consider:

• Make learning relevant to students’ lives and interests. This connection will help link complex secondary skills with more innate primary skills.

• Make the purpose for learning explicit. How will the skills that kids are working on in school prepare them for life outside of school? How do adults use these same skills?

• Connect learning to “experts”. Not every teacher is connected to a research lab, but every teacher does have friends or family engaged in different careers. By sharing those connections, kids can see examples of how their learning relates to adult experiences.

By giving students an education that connects their school experiences with their evolved learning mechanisms and the real world, they are more likely to grow into lifelong learners who willingly engage with complex skills. These traits will serve them well with whatever path they decide to follow in life. But who knows, maybe my class will grow up to be the next generation of data scientists!

References:

Geary, D. C. (1995). Reflections of evolution and culture in children’s cognition. Implications for mathematical development and instruction. American Psychologist, 50, 24 –37.

Gray, P. (2013). Free to learn. New York, NY: Basic Books.

Gruskin, K., & Geher, G. (2018). The Evolved Classroom: Using Evolutionary Theory to Inform Elementary Pedagogy. Evolutionary Behavioral Sciences, 12, 1-13.