The past, present, and future of data in education.
This is Part 2 of our Shaping the Future of Learning series, in which we explore how various innovations and trends have shaped, and will continue to shape, the educational landscape. Part 1 explored the evolution of edtech.
When I was teaching in the late 1990s and early 2000s, teaching was a classroom-focused activity. My ability to teach students to solve quadratic equations or make a coherent argument was based on how I delivered instruction and how well I was able to assess each student’s learning.
The reliance on individual teachers to control the learning experience can be fantastic for some students and pretty horrific for others.
Teachers—we’re only human. Yes, I was dynamic and flexible, empathetic and encouraging. But I also carried implicit biases, a limited attentional capacity at any given time, and strained time and resources while guiding over a hundred students at once.
No matter how hard I tried to evaluate my students—what they were strong in or struggling with, who preferred group or individual work, who responded better to constructive criticism or confidence boosts—there were surely needs that weren’t met. There was only so much observation I could do and data I could track.
We asked so much of teachers before data-driven technology came along to take some important demands off of their plates.
Today, in a post-COVID-19 world, nearly every student in the United States has a device and access to the internet, and each district has access to a plethora of digital content and assessments. What would have taken considerable time and energy for teachers to do manually, software can do in milliseconds. Teachers are then freed up to focus on other aspects of their job that a computer could never do.
All of this digital learning is producing gobs of data: the number of logins, clicks, right and wrong answers, time spent on tasks, and more.
The big question remains: “How does all this data make education better?”
There is incredible potential for learning data to translate to truly transformative instructional support. One example of how modern edtech makes good use of data is MATHia.
The adaptive technology identifies exactly which skills students are struggling with for a given topic. For example, if a student is struggling with volume and surface areas of rectangular prisms, is it because they are making a mistake picking the prism height, computing the area of the base, or calculating the volume? MATHia, based on the data it collects, not only knows which specific step is stumping the student; it also provides targeted instructional support and practice opportunities to build the needed skills.
Imagine how long it would take one teacher to similarly assess and assign individual skill-specific exercises to 30 students in a class!
In addition, teachers who use tools like MATHia can sharpen their instructional efficacy. Our programs give teachers the tell-tale indicators of specific types of student struggle and arm them with the right information to help the most number of students. Think of it like the CliffsNotes version of differentiated instruction.
Our data-powered software guides students down adaptive, personalized learning paths that enable them to get the precise support they need when they need it. At the same time, data also drives continual improvements on the software itself.
In our constant pursuit of improvement, we examine how the data that MATHia collects can help us design more coherent and effective instructional content. It would be impossible to clearly see spots that might trip up students, which we can smooth over, without this data.
This work results in better designed workspaces and activities that leverage the most effective ways to introduce, guide, and support learning. For example, we have made workspaces more efficient by paring back tasks that data showed were extraneous and adding better scaffolding for core instructional objectives that we found students were struggling with.
Because these enhancements are driven by data and analysis, not intuition or tradition, we can feel confident that every update will help students learn even better than before. And we have even more advancements we’re working on to continue to shape the future of learning!
For over 20 years, we have been driven by data to continually improve the efficacy of our math solutions for middle and high school learners, and now we’ve expanded our offerings to include world languages, literacy and ELA, and applied sciences like computer science and physiology. We believe that students are capable of more than they think, and we hold our solutions to that standard as well.
Data and its application to the learning experience is where we at Carnegie Learning can add the most value to education. We help at the teacher and classroom level of learning, as well as at the district and national levels by designing more effective, high-quality programs with the best instructional design.
By applying these data-driven capabilities, teachers become superstars in their classrooms, and students start to see math as approachable, relevant, and something they can excel at. After all, the data clearly tells us—every student can be a “math person.”
Peter is an educator with over 15 years of strategy, education, and product development experience. He led the redesign of a university, built new programs, designed curriculum, and worked within the classroom. At Carnegie Learning, Peter works with an amazing team that focuses on the development of leading edge student- and teacher-focused math products and services.Explore more related to this author