Q&A: Matthew Mugo Fields on the Current State of Data Analytics in K–12

While many teachers understand the importance of data, integrations can prove burdensome on educators who do not have the background or time to use analytics tools properly. 

According to a 2018 Data Quality Campaign survey, 86 percent of teachers believe data is a crucial tool for an effective educator; however; over half also say they do not have enough time to analyze data, and more than one-third say there is too much data to decipher. 

To learn more about how data is used and where analytics in the classroom is headed, EdTech sat down with Matthew Mugo Fields, executive vice president and general manager of supplemental and intervention solutions for Houghton Mifflin Harcourt. 

MORE FROM EDTECH: Check out how K–12 schools can simplify their data analytics initiatives.

EDTECH: What are some of the most common data collection methods schools use to help their students improve?

FIELDS: There's really a range, and it depends on what schools are looking for. Some schools administer a diagnostic benchmark assessment that students take at the beginning of the school year to give a broader view of their learning profile. 

Others will use formative assessments in the classroom on a daily basis, although I think both should be applied. Students participating in some form of formative assessment connect with the lesson. Being able to have a collection of all of that data gives teachers a view of not just how students are doing in their programs, but also how they are likely to do on the state assessments. 

The combination of long- and short-term assessments calibrate the growth trajectory for students, which is important because now there is, rightly, a real focus on student growth as opposed to just absolute proficiency. 

EDTECH: How can teachers use data collection to help students improve their soft skills, which may not be as easily tracked by data points?

FIELDS: While I don’t think anyone has figured out precisely how to assess for the broadly accepted range of social-emotional learning needs that students have, there is some real work being done there from our vantage point. 

At Houghton Mifflin Harcourt, we have historically focused much more on having curricular content that addresses social-emotional learning needs and blending that with academic content. 

There are folks who are working on behavior management assessments through gamification and rewards. 

Additionally, there are a lot of tools out there that can be implemented at home to serve both parents and students to really emphasize social-emotional learning. Ultimately, data sets used by K–12 schools are not as massive as they need to be to reach really sound scientific conclusions at this point. 

But what is encouraging is that people are endeavoring to figure out how to fix that. The way we view it is that, at the end of the day, teaching and learning should consistently be a functional social-emotional exercise. 

What matters most in learning is the relationship between teacher and student. If we can use technology in really smart, elegant ways to address mundane tasks, teachers can spend their time really building those relationships. 

MORE FROM EDTECH: Read more about how K–12 educators can boost SEL with classroom technology.

EDTECH: Many teachers understand the importance of data, but they are not data scientists. How can schools help support teachers so they feel more comfortable with analytics?

FIELDS: I absolutely agree, data implementation right now can put an unnecessary burden on teachers to try and interpret data when, quite frankly, we as providers should be much more focused on how we serve up actionable insights.

If you think of data analytics as a triangle, you have raw data at the bottom, information in the middle and actionable insights at the very top.

We should have teachers focused on the question, “What does the data mean, and what should I do with it?” These tools should be serving up data-driven findings in a way that helps teachers make classroom improvements that are productive for their students.

This also means making technology solutions easy to use consume. It shouldn't take someone having to read a 50-page manual to understand how to digest analytics. That's not to say that there isn't going to be some training required.

However, if providers and IT leaders can focus on making these services intuitive, there won’t be a need for teachers to stay up late at night, working on Excel spreadsheets because they're getting information from all of these disparate, disconnected discussions about their kids. 

EDTECH: What is on the horizon for data analytics in the next five to 10 years?

FIELDS: I think one big step is developing smart, purposeful ways to deploy artificial intelligence in the service of teachers. When used properly, AI can help teachers accomplish tasks they ordinarily would love to do more often, but just do not have the time for.

One example is observational assessments. By combining AI with speech recognition, teachers can listen to how a student reads and accurately assess that student’s reading ability, which can help educators ascertain what the next step in that student’s learning should be.

AI can also be used in service of the teacher. Certain AI deployments can help them run reports to see how one group is doing relative to another, which can help even the playing field in the classroom, so all students have the same opportunities for success.

This kind of technology will be much more readily available and exciting, quite frankly, because one thing we know is that teachers are often overworked and underpaid. If we can do something about lowering some of that workload on them, it will make teaching a more joyful, rewarding and effective experience.

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