In the fast-moving world of digital transformation, innovation, and technology where we find ourselves in today, data literacy is becoming increasingly important for students to learn early. Big data is set to transform our society and, in many ways, it already has.
Advertisers collect data through social media platforms to target specific demographics. Politicians use data to micro-target specific groups of individuals with perfectly crafted, A/B tested messaging. Brick-and-mortar businesses use data to develop an understanding of the safety of their supply chains and operations in order to keep consumers safe.
Unfortunately, it has been difficult for educational institutions to keep up with the ways that data is transforming our lives. Many schools are slow to incorporate a data literacy curriculum, resulting in students often not encountering data science classes until college, if at all. If today’s students are to be armed with the skills they need to contend with the brave new world of big data, it is important for educators to make collecting and analyzing data fun and accessible.
One of the best ways to teach children about data is through the hard sciences. Chemistry, biology, and physics are all typically included in the curriculum of most schools, and each of these disciplines can incorporate data literacy in specific ways.
Moreover, these subjects are best taught through an interactive, inquiry-based approach that allows students to learn principles by making use of the scientific process. In other words, rather than merely explaining scientific theories in a PowerPoint presentation, educators can bring students into the lab to run experiments, test hypotheses, and learn science for themselves.
Through the scientific process and by running experiments with classmates, students can also learn about data in a way that is much more exciting than simply reading about abstract data science principles in a textbook.
In this way, students can learn about the principles of big data through hands-on, interactive experiences.
Many schools are already taking this approach by allowing students to use data loggers in the classroom. Data loggers are electronic devices that collect and record real-time environmental information. Popular datasets they can log include temperature, humidity, and differential pressure.
Specialized data loggers or sensors can also be used to record velocity, sound, light, pH,and moisture. Data loggers open up a whole new realm of discoveries for students since the information they collect can be observed in real time.
A simple experiment about the boiling point of water takes on new life when students can see the temperature of the water live, perhaps even being displayed on a graph in real time. The ability data loggers give students to actively observe changing environmental conditions live allows them to more easily conceptualize abstract scientific principles. Many teachers also find that students are better able to understand mathematical concepts by seeing a live graph change in real time based on environmental data.
This speaks to the potential of data loggers to enhance students’ higher order thinking and cognition. The use of data loggers in science classrooms can also offer a seamless transition to cross-disciplinary learning. In the process of learning how to collect environmental data, students can naturally move into computer science concepts related to how to store and analyze that data computationally.
Given the increasing importance of computer science skills, the fact that data loggers give students the chance to approach the field in an interactive way that is rooted in real-world observations is a huge benefit. Too often, students are turned off by the abstract nature of data science, but allowing students to analyze and manipulate data about the world around them that the students gathered themselves makes the process far more interesting.
Let’s not forget that learning to use data loggers is valuable in and of itself. In fact, it could help students get prepared for future careers and a college curriculum. That’s because many industries use data loggers extensively to collect and monitor environmental data for quality assurance and public safety reasons.
The healthcare industry uses data loggers to track and record the temperature of storage facilities used to transport sensitive medical products. A timely example includes Covid-19 vaccines.
Covid-19 vaccines are being distributed around the world and must be kept at very low temperatures. Data loggers are used to make sure vaccines do not expire in transit due to excess heat – a process referred to as the cold chain.
The food and beverage industry also uses data loggers extensively to ensure proper temperatures and humidity when transporting food.
As a result, students interested in these fields and many others may get the chance to put their data logging skills into practice in a future job or career.
Many schools are already using data loggers to make many classic experiments more accessible and interactive. Examples include measuring pH, Boyle’s Law, the effects of greenhouse gases, gravity’s influence on falling objects, and how exercise affects heart rate.
Data loggers are significantly faster than analogue data collection tools and techniques. This allows students to finish experiments, collect data, and analyze results more quickly.
Data loggers are more accurate than their analogue counterparts. More accurate readings mean that students can avoid being misled by instrumental error and develop a better picture of the world around them through science.
Data loggers also have significantly more storage capacity than other data collection methods. This is extremely helpful because it means that students can look at the large quantities of data they have collected at a later time. Rather than having to determine the meaning of the data mid-experiment, students can devote their time and attention to large datasets after the experiment is included.
The extra storage capacity of data loggers gives students the opportunity to learn how to detect patterns and signals in large amounts of data, an increasingly important skill in the information age. This offers the perfect segway into data science principles which could include algorithmic scripting, machine learning, or even programming in Python. This crossover into data science gives students the chance to use real-world data in order to learn to think like a computer.
The benefit here is that students will be working with datasets that they themselves collected which pertains to actual events they observed in the world. As a result, they will be more motivated to analyze the data to develop real theories about how the natural world works. Students can even be offered the chance to test those theories or try to disprove them through subsequent experiments and data analysis.
Data loggers offer a terrific opportunity for schools to expose students to science, critical thinking, and data analysis. Many educators have already realized their potential and begun to introduce them into students’ curriculum.
