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The use of Big Data in education (1/2)



Big Data is not only a big trend today: it’s a disruptive innovation that progressively changes every aspect of our daily processes, and perhaps even our society. The education business is one of those where the integration of big data raises the most important number of questions; probably because education has historically been more associated with the notions of subjectivity and independent thinking than other sectors, the use of a massive authoritarian database looks like a threat to free-thinking and learning. MOOCs, papers and courses are written on it, revealing the fears and expectations big data inspire.

cc licensed ( BY NC SA ) flickr photo shared by giulia.forsythe

Big Data, a threatening educative revolution

Big Data uses a series of recent technological innovations to create massive data reserves and new ways to interpret, analyse and apply those using specific algorithms. For a school or university, two different data resources are used: the data within the school, as inside processes or marking algorithms, and the data from outside the school, as the external data bases, the social network contents and the personal data and historic information. Big Data, as it is understood today, is the eco-system formed by these two data resources, enabling the internal research and analytics of the school to integrate and descript the normalised behaviours of students in order to track them.

The use of data in education works in three major operations:

-          Scoring and Grading : a common resource for grades or credits, automatically actualised with every new work by the student,  helping both student and teacher to keep track of their course; such as that used in Colorado public schools.

-          Adaptive learning: everybody knows that each student has their own approach to learning. Use of data enables educational programs and teachers to adapt to each student’s aptitude, based on that student’s point of view and the actual results he achieves.

-          Problem management: authenticity of work, consistency, absences… the control of every aspect of school life becomes easier.


The V's of Big Data in Education

The 3 Big V’s of Big Data are Volume, Velocity and Variety.

-          VARIETY is one the main success factors in the use of big data in education. Instead of offering the same course, work requisites and books to everyone - still true in many cases - ,which very often contain standardised information, it is now possible to provide students with items adapted to his/her own specific ability. VARIETY also refers to the variety of different methods used to track and use a student’s preferences and difficulties: demographic data, localisation, navigation, mail accounts, personal desktop contents...

-          For these two VARIETY assets, VOLUME is the one thing that permits schools to touch the greatest audience, and offer them the most varied course.

-          VELOCITY is the asset that uses stocking and reporting to give reports in movement, sometimes in real time, giving the student what he/she needs - even before the need becomes recognised by the student or the idea of a need appears - offering more precision to answer this need. Real-time auctions are already being developed using this method. This explosion is due to two different technological revolutions: data capacity acceleration and the rapid expansion of the internet and social networks that enable its diffusion.

-          Another V could be VERACITY: the verification of the safety and exactitude of data during the whole process.

-          VARIABILITY: the different processes to use the reclosed data.

-          COMPLEXITY: it is obvious that the better the algorithm used to track and predict students’ behaviors, the more complicated it is.


MOOCs and e-learning platforms already dispose of a very targeted and rich database, enabling them to create very specific courses that are often much more effective than the ones done by competitors. Big Data, first of all, creates a new, simple and effective segmentation of the students. This research is realised real-time by the same systems that provide the courses; it is no longer an estimation but the virtual landscape of the audience. Once a good algorithm has been found and is adapted to a specific audience, course and teaching model, the analysis, tracking and business proposition can be made within a few seconds, and the student is offered personalised results and advice. It can even reveal a potential learning value that a human observer might not have been aware of.

The role of the teacher, therefore, would be to provide the human factor necessary for interpreting and humanising these results, in order to help the student deal with problems and to see those things that weren’t revealed by data information.