The next frontier for big data is education.

As a matter of fact, profound technological and methodological changes are already sweeping across the education sector.

Student-level data collection is still a rare occurrence in the US public schools. Colleges and universities, on the other hand, do not think twice about tracking and analyzing unique data patterns in a practice referred to as “learning analytics.” The recorded, logged, and reviewed information is used to predict academic performance and drop-out rates of college students.

Big Data for Better Decisions

Although school districts nationwide have gathered standardized test information for many years, only federal workers and school principals have access to it. Few teachers have helpful data they can understand and use for their decisions in classrooms. Not in Atlanta public schools though. The city’s schools help teachers with their day-to-day decision making by providing them with well-curated collections of student-level data. The experiment is bold and complicated in equal measure.

Before the introduction of big data analytics, information was used punitively. After cross-referencing information stored in disparate systems, teachers and administrators were reprimanded for their shortcomings. Many were afraid to lose their classroom autonomy; some even resorted to cheating.

Atlanta public schools experiment is an audacious attempt to give data analytics a better reputation.

Instead of turning educators into statisticians, the Atlanta district provides them with easily digestible data. Now, teachers have dashboards with color-coded charts and other handy visualizations. It has become extremely easy for them to zero in on students’ performance. The categorization of learners’ academic achievement by subjects, domains, and levels allows teachers to make better decisions. The educators structure their pacing and lesson plans based on hard data not on guesswork.

Over the next few months, more than 3, 000 teachers of the Atlanta district will have access to data analytics. Thus, they will be able to serve each learner based on their individual needs, which is a ground-breaking achievement.

The Atlanta school district is not a pioneer in educational data tracking. Universities have been monitoring their students for quite some time. “We want to understand the story behind that data,” says Bart Rienties who heads the data collection program at the Open University. By analyzing even mundane series of events such as trips to the university library, it is possible to provide learners with individualized instructional support.

Four Applications of Data Analysis in Education

There are several ways in which big data can benefit the provision of educational services:

  1. Improvement of Student Results

Data analytics can be leveraged to improve academic achievement levels among students. Currently, the student performance is measured along a single dimension – answers to tests and exams. It doesn’t have to be so anymore. Each student leaves a unique trail of data, which can be captured, logged, and analyzed. The use of big data can help educators better understand individual behaviors of learners such as sources they use for preparation, time they take to answer a question, etc. These and similar data points can be processed almost instantaneously, thereby proving each learner with unique feedback. The analysis of data trails can also be used for detecting students who have used help with writing papers, thereby eradicating contract cheating.

  1. Customization of Study Programs

Another way to use big data in educational settings is to create individualized study programs for each learner. It doesn’t matter how many students attend a school – each one can have their unique program. This approach becomes feasible if a school offers a mix of online and offline learning. The online classes can be attended at a student’s own pace. The combination of the two approaches allows learners to have customized lessons online and offline guidance.

  1. Reduction of Dropout Rates

The introduction of data analytics into schools and colleges can drastically reduce dropout rates, which are currently embarrassingly high. By using predictive analytics, educational institutions across the country can gain valuable insights into future outcomes of their students. Furthermore, it is possible to run a scenario analysis on a certain course before it is introduced into a school curriculum. Thus, the need for costly trial-and-error can be minimized. By monitoring graduates’ performance on the job market, educators can help college hopefuls choose suitable courses.

  1. Targeted International Recruiting

Predictive analytics can help educational institutions in understanding more variables in the application process. The gained insights will let colleges and universities to adjust their recruitment approaches and better allocate their marketing funds. The wealth of data can also be utilized by students willing to make better application choices.

There is no denying that big data can revolutionize the educational industry in the US. Smarter students will have a beneficial influence on organizations and society. Therefore, it is a high time to embrace educational data analytics and usher in a brighter future.