Increasingly, K-12 school systems are facing tough accountability standards set both by federal statutes such as “No Child Left Behind†and by state-mandated standardized testing programs. These standards hold K-12 administrators to an extremely high level of accountability — student performance is viewed as a direct reflection of the school, rather than of the student, as is typically true in higher education.
Despite the mounting pressure, technology to assist K-12 schools in complying with these standards is just beginning to evolve. Even the most advanced school districts have just begun to extend the data incorporated in their Student Information Systems (SISes) beyond classroom assignments, attendance records, grades and bus routes to include standardized and state test results and local assessments. Gathering the student data into one central location enables the school districts to analyze the data and develop individualized student learning plans. This approach, however, is the exception rather than the rule.
To improve the quality of education and compete for federal and state funding, school systems must be able to capitalize on the value of their vast amount of student data. Gathering and managing the student data within a warehouse or data mart is a critical step. Equally important is the next step — analysis — which finds the patterns and relationships among the data points and turns it into usable, actionable information. A category of analytical software that is already familiar to the business world but has just recently been introduced in academia is predictive analytics.
Know What’s Next, Now
Broadly, predictive analytics is a class of software — which includes data mining, text mining, Web analytics and similar analytic technologies — that connects data to effective action by drawing reliable conclusions about current conditions and future events. Organizations across a broad range of industries — from banking and insurance to pharmaceuticals and retail sales — are discovering the benefits of leveraging business knowledge by applying sophisticated analytic techniques to their enterprise data. This data analysis provides them with insights into what their customers’ needs are today and what their needs will be tomorrow
Divining Information From Data
The Lafourche Parish (La.) School Board, which oversees 30 schools and more than 15,000 K-12 students, wants to ensure that their students receive a quality education, and that it maximizes its limited budget dollars to achieve that goal. The Board wants to measure progress by improving test scores, and then use those test results to enhance learning for students in their region by analyzing test scores of fourth and eighth grade students. In addition, it wants to deliver the information that results from this test score analysis to school administrators and teachers, who can put the information to good use by developing strategies and making decisions that will improve the quality of education for Lafourche Parish students. The state’s “Reaching for Results†reform initiative includes a testing program to improve educational curricula and to minimize “social promotion,†the practice of passing students to the next grade even if they do not have the skills needed to succeed.
Using predictive analytics, the Lafourche Parish School Board compares test scores of individual students against others in the same school and then in the entire school system. The school board also analyzes the scores against demographic and other available data and then shares the reports with educators so that lesson plans can be tailored to address specific areas of need.
The Lansing (Mich.) School District has 40 schools and more than 17,500 students under its jurisdiction. Tracking students’ scholastic performance from kindergarten through high school is an important component to ensuring academic success. Most Michigan school districts have used a state-designed tool that, unfortunately, was not adequately equipped to integrate and analyze the vast amount of information that comprised each student’s file, including grades and scores from a battery of local and national exams.
Lansing Schools are now using statistical analysis software to quickly integrate and analyze data. The schools’ database team delivers reports that help school administrators evaluate a student’s successes and challenges, and moreover, understand which school programs are proving beneficial.
The lesson learned is that K-12 school can better serve their students by understanding the needs of the individual student – much in the same ways businesses need to understand their customer’s needs. There is a huge amount of student data with which to work — most of it is an untapped gold mine. By using predictive analytics, school systems are better able to report and comply with state and federal standards; and, most importantly, meet the educational needs of their students.
Email: Ken Stehlik-Barry
