The Official Blog of Forecast5 Analytics

Jason Schoenleber
Jason Schoenleber

Data Visualization, Student-Performance, Student Achievement, College and Career Readiness, ESSA

Data Refining: How to Organize Student Performance Data to Guide Curriculum Decisions

Posted on Feb 6, 2018 9:00:00 AM

In the first few months of every new year, I notice a lot of articles with tips and tricks about how to organize belongings. I’m sure spring-cleaning advice is just around the corner. They’re popular topics because when our “stuff” is organized, we know where to find what we’re looking for. It’s a nice feeling.

The articles got me thinking about conversations I have had with school district administrators lately. As we talk about ways to analyze student readiness, the problem is often not that they don’t have enough data about their students. It’s quite the opposite. Most school districts produce and store treasure troves of information about students that’s housed in multiple data systems and managed by a variety of departments. One manager told me his district has 600 measurements available for every student.

Lots of data is good, but unless you know what’s valuable you can’t do anything with it. How do we help school districts “declutter” their data so they can make better, more informed decisions? I suggest we adopt a new approach – data refining – to empower administrators to more easily understand what’s hiding in all those student indicators.

Beyond ESSA:  How to Drive Strategic Decisions by Tapping Required Student Data Measurements

Data refining leads to better curriculum decisions

The goal of organizing isn’t about cleaning. It’s about getting to a state of permanent orderliness that makes everyday life simpler so there’s more time to do what you really want.

The same is true for data refining. There’s a bit of work up front to figure out what data you need to access, analyze and use to make decisions that improve student performance. But, once the model is in place, the process of using data to make informed decisions is so much easier.

For example, a world languages department is considering a new online interactive program that enables students to practice their speaking skills. To get ideal pricing, it’s in the best interest of the school district to sign a five-year contract. The department head needs to understand how many seats to purchase for each level of its Spanish, French, Chinese and Italian courses.

The typical solution is to look at enrollment figures for the past few years as the guide. This approach is missing the opportunity to use all the data available to the department head to make a better-informed decision. But, the complexity of uncovering that data can be a roadblock because the required time and effort is just too great.

What if the school district could run an analysis that considers other factors that influence interest in specific languages and enrollment figures for higher-level courses? A quick refinement of what data sets are considered might reveal growing demand for world languages in the middle schools, identification of the growth of a sub-group of students as identified by demographic info that show an interest in studying a specific language, and a decline in interest in an advanced world studies class.

Student Performance Data Holds the Answers to Closing Achievement Gaps

By combining all these student indicators, the district might discover a trend indicating anticipated interest in the Chinese program that extends through senior year, thus requiring more seats than could not have been predicted from only looking at past enrollment numbers. At the same time, the analysis shows a slight drop-off in the number of students likely to take the Advanced Placement Spanish class when they are seniors.

Armed with this info, the school district and department head are able to more accurately predict what curriculum support is needed for each language program, purchase the right amount of seats, and save money on the overall cost of the online interactive program.

Organizing doesn’t mean deleting data

The beauty of data refining is it doesn’t require school districts to sift through or discard data. Rather, it’s about knowing where to find and combine subsets of student indicators to uncover trends and better understand what’s really happening in the district.

For administrators, Forecast5's 5Lab™ can be their “personal organizer.”  5Lab is an analytics platform that pulls data in the right format as needed from disparate student information systems into one sandbox. There’s no need to go through complicated steps to extract data from several sources and populate complex spreadsheets. 5Lab can pull data as needed so analytics can be run on a regular or as needed basis.

Data refining is about keeping all the data resources a school district maintains. The goal is to understanding how to find and use the right data at the right time to guide curriculum decisions and ultimately improve student performance.  Spend time consuming data, rather than formatting and cleansing data.

 Improve student performance with 5Lab


Jason Schoenleber is a Product Manager with Forecast5 Analytics in Naperville.  Prior to working at Forecast5, Jason worked for 3 years as a Solutions Consultant for Enfos Inc., advising corporate remediation directors on managing their financial debt obligation data through a SaaS platform and template modeling.  He received his Bachelors in Marketing from the University of South Dakota, and is currently pursuing his Masters in Business Administration from Northwestern University’s Kellogg School of Management.

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