How Big Data Is Shaping the Future of Education

At the close of the 20th century, education in a college in Spain, the U.S., or Chile was very similar to what could be found in a classroom at the University of Bologna in Italy in 1088 (note: it is considered the oldest university in the world). Granted, the subjects had changed and the classical trivium and quadrivium had given way to a proliferation of topics in the sciences and the arts, as well as specialization in university degrees. But the format of the teacher, the blackboard, and the desks was not far removed from a medieval classroom. Digital transformation and big data applied to education were still to come.

In this article you will find:

Nanotexture infographic

Gradually, however, a revolution was being introduced in terms of devices or hardware: first projectors, then computers, then tablets, and digital whiteboards.

Then came the boom in online education or e-learning, with channels on YouTube and other platforms.

Today we are in a third phase: the era of big data and artificial intelligence applied to education.

All of this is coming together in a new generation of study spaces: smart classrooms. The growing importance of this approach has crystallized into a new term that encompasses all these technological advances: edtech.

For this article, we will focus on the application of big data, which is one of the most recent innovations.

How is big data applied to education?  

In educational environments, the application of big data consists of the creation of protocols for collecting student data, creating databases, and subsequent analysis.

Data collection is known as Educational Data Mining and is a discipline that began to be talked about at the turn of the century, with the first international conference held in 2008.

Student data includes their academic results, but also the time they spend using various tools, the speed at which they solve problems, the sources they use, or the questions they find most difficult to answer. It is even possible to measure the degree of student boredom.   

This type of tool will allow teachers to quickly detect students who have (or will have) problems and provide them with personalized attention.

Of course, for the use of educational big data to be efficient, it is first necessary to advance in digitization and the use of devices and software, which is still a pending subject in many academic centers. To summarize, there are two main aspects of the academic application of big data:

Educational big data approaches

Purdue University

  1. At the level of schools, institutions, and public bodies. It allows obtaining a snapshot of parameters such as school dropout or the anticipation of possible behavioral problems. These databases can also include the socio-economic background of students as a risk factor. An example of this is the Course Signals program that Purdue University pioneered in the USA in 2007. According to the university, its software has enabled it to increase student retention by up to 21%.
  2. At the teaching and course level. This application of big data revolves around the day-to-day work of teachers, whether in face-to-face or online education. So to speak, it is an application in the field that allows educational decisions to be modulated almost in real-time.

Some benefits (present and future)

Big data in education is still in its early stages. For now, its greatest impact is being felt in online learning. For example, knowing which parts of the program spark the most interest or what is the optimal duration for each lesson is just a click away.

However, it promises to profoundly transform the relationship between students and teachers. And, hopefully, make life easier for the latter. 

The main benefits of big data applied to education are:

  1. Reduction of the dropout rate by detecting risk factors.
  2. Personalization of curricula and learning rhythms.
  3. Reduction of the teaching workload
  4. Optimization of performance evaluation
  5. Real-time student monitoring
  6. Development of career plans based on strengths and weaknesses.

Examples of big data software applied to education

Big data software for education

There are already numerous analytical software tools that allow leveraging the huge amount of data generated in the educational context. In addition to solving general issues such as those we have already addressed, they are applied in specific areas of learning.

  1. The Sparx Maths tool is a software that combines big data and machine learning to adaptively analyze the answers to more than 32 000 mathematics questions. Depending on the student’s path, the optimal questions are posed to fill the gaps detected. Knowre is another tool with similar applications in the field of mathematics.
  2. BrightBytes is a big data tool for primary and secondary education that combines masses of anonymized data with specific student data. The platform covers everything from basics such as attendance to issues such as social and emotional learning.
  3. Tableau takes a visual approach to represent the data, so teachers can see at a glance which students are falling behind, individual performance, and benchmarking against other schools and colleges in the region or country. 
  4. Another tool based on massive data analysis is Blackboard. This system is based on gamification, i.e., the use of techniques and resources from games and video games to motivate students. It is a Learning Management Software platform (LMS or learning management software). This software makes it possible to anticipate student problems and improve communication with teachers.

In a society where continuous training and learning are already a reality, big data is destined to optimize people’s careers throughout their lives. Theoretically, someone could submit all their educational data to an analysis to detect gaps or find areas of academic development of interest.

Education is just one of the many fields where substantial changes are taking place thanks to the application of big data.

If you want to know more examples and applications of this technology, you can take a look at the article we have dedicated to it here.

Sources: Carnegie Mellon University, Upicus, Career Foundry, Wiley Interdisciplinary Reviews,

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