Imagine a class of 20 students. Some perform better, others worse. Some students prefer natural sciences, others humanities. Some have better discipline, others struggle with it. Now multiply this by 11 grades and try to understand which subjects each student needs help with and who can be given more challenging assignments.

Academic success is a combination of multiple factors. You'll need to consider the interconnection and significance of each factor to develop a program for a student. Then repeat the process 19 more times.

Now imagine you have data about what and how it affects student outcomes.  More precisely — what influenced results in the past. This is exactly where Big Data comes to the rescue.

What is Big Data and how it works in education

Big Data refers to massive data arrays. From these, it's possible to extract many useful but not immediately obvious patterns and trends. 
In the context of education, several types of big data are distinguished:

• personal student data: age, gender;
• data about student results: attendance, grades, homework completion.
• learning data: which e-textbooks are used, online courses, page views, test completion;
• predictions of student participation in various school activities.

Thanks to its ability to generalize and identify patterns, Big Data technology helps make the learning process more productive. 

Benefits of using Big Data in education

Using Big Data in education provides numerous advantages, including the possibility of personalized learning, which allows adapting educational programs to individual needs of each student, as well as improving monitoring of academic performance and attendance, helping to identify and solve problems in time. Additionally, big data analysis contributes to resource optimization and workload distribution among educators, increases the efficiency of educational processes by identifying the most successful teaching methods, and automates routine administrative tasks, allowing teachers and administrators to focus on more important aspects of the educational process.

Finding an individual approach

Data analysis allows educators to better understand each student's needs and develop personalized learning programs.

For example, American educational startup AltSchool launched a Big Data-based project to improve academic performance of students under 12 years old. They record everything happening in the classroom from different angles. Facial expressions, speech, vocabulary, children's gestures, etc. — all this allows teachers to understand the classroom atmosphere. Although the idea of installing cameras in classrooms and analyzing children's behavior sparks debate among parents, the startup sees this as the future of adaptive and flexible education.

Improving grades

Big Data helps identify problems at early stages and take preventive measures. For example, if data shows that a student is starting to fall behind in certain subjects, an educator can intervene in time and provide necessary support.

Platforms like Canvas and Google Classroom offer teachers  analytics to track student progress, analyze their engagement, and identify areas where additional support is needed. This allows teachers to implement evidence-based learning and intervention strategies tailored to individual student needs.

Helping educators

Data analysis allows educators and administrative staff to better plan their work and allocate resources. This helps focus on key tasks and improve the educational process.

Imagine a situation — data analysis shows that an English teacher is overloaded because they have too many students in classes and many administrative tasks. The learning management system collects and analyzes information about the number of students in each class, teaching loads, and teachers' administrative tasks.

Based on this data, the school administration decides to redistribute students by creating additional classes and hires an assistant to help with administrative work. This allows the teacher to focus on teaching and improve education quality, while also reducing their workload and stress.

The role of AI and educational technologies in using Big Data

AI facilitates big data analysis — it automates processing and identifies patterns. This approach already has successful examples.

Adaptive learning programs

Systems like DreamBox and Knewton use AI to analyze data about student progress and adapt learning materials in real time.

DreamBox is an adaptive educational platform for teaching mathematics. It uses AI to analyze data about student progress and adapts learning materials in real time. As a result, students receive personalized lessons. The platform provides teachers with detailed reports and analytics to track performance and adjust the learning process.

Knewton is an adaptive learning system that uses AI to create personalized educational programs. The platform analyzes data about each student — knowledge, skills, and behavior, to suggest learning materials and assignments that match their level.

Data analysis platforms

BrightBytes and Tableau help schools with tools to visualize and analyze information.

BrightBytes collects data from various sources and presents it in a convenient format for analysis. Using BrightBytes, educators and administrators notice trends, identify problem areas, and adjust the curriculum.

Tableau is one of the industry standards for data visualization. The program is used in various industries, including education. In schools, Tableau is used to create visual charts and diagrams based on records of academic performance, attendance, test results, and other data. Tableau allows easy analysis and interpretation of massive amounts of information.

Multitask platforms for educational institution management

There are also more comprehensive solutions for educational institutions. For example, Mojo helps schools automate more than 30 key processes, including learning process planning, schedule management, and electronic grade book completion. Teachers can create learning courses, assign them to students, and conduct criteria-based assessment. Mojo also provides lesson substitution management, report keeping, and independent diagnostics.

The system creates a digital student profile, tracking their achievements, competition participation, points, and ratings. It also supports behavior monitoring, medical support, and attendance tracking to ensure student safety. Mojo assists in organizing school admissions, library management, and conducting surveys.

School administration can use tools for organizing admissions and informing participants in the educational process. Individual system settings and reference management are available for each school. Overall, Mojo increases educator efficiency and improves student performance through data and analytics usage.

How to get started with big data in your school

• Start small. Begin with analyzing basic data such as attendance and grades, and gradually expand the range of analyzed information.
• If you want everything at once, use comprehensive data solutions such as Tableau, Power BI, or Qlik Sense
• Keep learning. Continuously learn new methods and technologies for data analysis to stay current with the latest trends.