Building the K-12 teacher data scientist
In the coming months, a deluge of technologies and products will flood the educational space. These new technologies and products will seek to address the aftermath of the coronavirus pandemic from a learning loss perspective. The ed-tech space will be a major space to watch as companies position themselves, their technologies, and products for user adaptation.
At my core, I am a teacher; however, I am also Data Scientist. I can clearly document my journey towards Data Science which slowly began more than five years ago with my entrance into the ed-tech space but truly crystallized over the past year and a half.
I was the ambassador evangelizing technology products to teachers, warming them up to tech, and brand loyalty. I found myself in the middle of what I would describe as the ed-tech civil wars between two giants winning the space for market share in one of the largest education markets in the country.
Working in the consulting/implementation client-facing space in the trenches allowed me to stay on the ground, influence, and teach, but also had the opportunity to hear about metrics, KPIs, and business intelligence. I was also curious about the black screens I saw as I strolled through open office spaces with things I did not understand. It was code, in various languages.
Exactly one year and a half ago I was in my zone, I was going into schools a few times a week working with teachers. Then it all stopped. I had the clear goal of becoming a data scientist, and two months prior I began structured coursework for my learning through Lambda School. With schools closed, work priorities shifted towards more backhouse ‘research’ opportunities about remote learning and how Covid19 would transform the K-12 educational space.
The educational space we will return to when students are fully back in schools must address the learning disruption, measuring both losses and gains. New products will emerge taking into consideration what we have yet to learn and even about how we learn. Data, cleaned, analyzed and information extracted will drive the development process of these products that should be student and teacher-friendly.
I am specifically excited about the role that data science will play in the K-12 educational space. How will machine learning and AI tools will help teachers find solutions to close learning gaps? How can teachers utilize technologies like virtual teaching assistants and chatbots to individualize student learning? Recommendation systems, like the ones we see while shopping on Amazon product or watching a movie on Netflix, is another example of how we can use data science in K-12 environments.
Instead of movies, academic content based on mastery of learning standards will drive the recommendation engines continuously moving students along, increasing cognitive demands but also remediating when necessary. Natural Language Processing will also be a part of the data science toolkit in the K-12 space.
The increasing prevalence of devices, hybrid environments(remote/blended), instructional text-based communications such as chats between teachers and students, are forms of unstructured data that can be analyzed and provide valuable learning insights.
Providing schools, principals, and teachers with academic intel for appropriate student interventions based on a robust internal data collection system are some of the possibilities of what we can do. Data science in the K-12 arena allows us opportunities to use educational data in timely ways providing earlier academic interventions.
Respectfully taking into consideration the technological gains teachers have made over the past year we can begin to reshape narratives around student academic data while utilizing old, new, and emerging metrics, both quantitative and qualitative in non-punitive ways that allow educators how to make better decisions.
Whether gaps in literacy or math are widened when socioeconomic metrics are factored in, these applications and technology products must be holistically inclusive. Their development and engineering teams should have an awareness of their own biases and mitigate these factors with inclusive team members. Why not teachers?
While teachers relish the day when they can get back to their classrooms, the deluge of coming technologies and products which are needed must be seamlessly and purposefully integrated to address not only the challenges schools will face but also respect the technological gains teachers have made.
