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Big Data can kill unmotivated students

Big Data can kill unmotivated students
Big Data can kill unmotivated students

Big Data and personalized learning. The storage of massive data can allow teachers to identify patterns in the way of learning of each class or each student and that they can adapt the contents and the methodology to each case. It is what is known as Adaptive Learning. Big Data can kill unmotivated students.

What is the big data?

Big Data is a term that we have heard continuously for some time and that is presented as the panacea in the management of practically any field. In education it could be, although there is still a long way to go.

The generic definition of Big Data could be the massive storage of data of a certain activity and its study in search of patterns that can predict future behaviors.

Big Data deals with the massive storage of data of a certain activity and its study in search of patterns that can help predict future behaviors

How can Big Data help education?

Big Data can have a huge impact on education, the key is to be able to determine which variables of all that technology can offer, are necessary to analyze to improve the aspects that we want to improve.

The process begins when the data is collected, then it must be categorized and studied to establish behavioral parameters that help us predict what may happen in the future in order to anticipate.

An example: we know from the previous study of data that if a student scores between a 4 and a 6 in his first three Chemistry exams there is a high probability that he will fail the fourth, this pattern that has been observed in the past, will help to reinforce the preparation of this group of students for the next exam.

Advantages of Big Data for Publishers

Through the analysis of the use of its content: activities, books, resources … publishers will be able to know what content has been better or worse understood by the times they have gone through a topic or how long it has taken to solve the exercises correctly.

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Or for example, if there is a correlation in the topics, when many students have had to return to topic 5 while studying 11 … these are just examples of what Big data can do for publishers, with the ultimate goal of making content more efficient for schools.

Big Data in school education and also at home

Once again, with examples is how we can best illustrate the scope of what mass data collection is capable of giving:

How long did it take a student to complete an exercise? What is the mean number of errors in the class before answering a problem correctly? What were the problems of a Physics student before changing schools? Do the same students who fare worse in Physics have more trouble doing math?

The limit is in people and not in machines, so it is essential to prioritize the analysis of what we want to solve first and most importantly define the appropriate variables to achieve it.

The limit of what could be analyzed is in people and not in machines, so it is essential to prioritize the analysis of what we want to solve first and most importantly, define the appropriate variables to achieve it

A teacher, for example, can see if a student takes a very short time to answer questions on a test and that he does it in the wrong way. If this pattern is repeated, it could be deduced that the student could have a problem of hyperactivity, since he rushes in excess and with an adverse result.

At this point, Big Data can recognize individual patterns and become a very useful tool for an Adaptive Learning model

Adaptive learning

Adaptive Learning starts from a very clear premise, not all students learn in the same way. The same content can be difficult, easy, boring or interesting depending on the student.

Until today, the trend was to treat all students in a very similar way, sharing the same content. But thanks to technology and data collection, it is now possible to determine the characteristics of each student and move towards a more personalized education with the aim of preventing a student from feeling ahead or behind the class and thus avoiding that Enter demotivation.

Not all students in a class learn in the same way, adapting as much as possible to each case can make no student feel unmotivated due to the lack of challenges or lost in the subject and therefore unmotivated

Technology, Big Data and the correct analysis of professionals are the tools that will allow to define these patterns in learning.