The Incorporation of Big Data in Mathematical Training for the Fourth Industrial Revolution

Introduction

The construction of massive data and the dizzying evolution in information and communication technologies invite us to be constantly updated to face change and meet the demands that new generations, globalization and living conditions demand.

According to Mayer and Cukier (2013), a new influenza virus was discovered in 2009. The new strain, which combined elements of the viruses that cause avian and swine flu, was called H1N1 and spread rapidly. In a matter of weeks, public health agencies around the world feared a terrible pandemic would occur. Some commentators warned of an outbreak similar in scale to the 1918 Spanish flu, which affected half a billion people and caused tens of millions of deaths. In addition, no vaccine against the new virus was available. The only hope public health authorities had was to slow down its spread. Now, to do so, they first needed to know where it had already manifested.

In the United States, the Centers for Disease Control and Prevention (CDC) asked doctors to alert them to new cases of the flu. Still, the outlook for the pandemic that emerged was always a week or two late. There were people who could feel sick for days before going to the doctor. Transmitting the information to the central organizations took time, and the CDC only tabulated the figures once a week. With a disease spreading faster and faster, a two-week lag is an eternity. This delay completely obfuscated the public health agencies at the most crucial moments.

A few weeks before the H1N1 virus made headlines, engineers at the internet giant Google happened to publish a remarkable article in the scientific journal Nature. This piece caused a sensation among health officials and computer scientists, but otherwise went largely unnoticed. The authors explained in it how Google could “predict” the spread of winter flu in the United States, not just nationwide, but even by specific regions, and even by states. The company did this by studying what people were looking for on the internet. Since Google receives more than three billion queries daily and archives them all, it had tons of data to work with.

Google took the fifty million most common search terms used by Americans and compared that list with CDC data on the spread of seasonal flu between 2003 and 2008. The intention was to identify those affected by the influenza virus by through what they were looking for on the internet. Others had already tried doing this with internet search terms, but no one had as much data, processing power, and statistical expertise as Google.

Although the Google staff assumed that searches could focus on obtaining information about the flu - by typing in phrases like “remedies for cough and fever” - that was not the point: since they were not sure, they designed a system that did not mattered. All this system did was look for correlations between the frequency of certain information searches and the spread of the flu over time and space. They processed a staggering four hundred and fifty million different mathematical models to test the search terms, comparing their predictions with the flu cases recorded by the CDC in 2007 and 2008. Therefore, they hit a goldmine: Their software found a combination of forty-five search terms that, when used together in a mathematical model, showed a strong correlation between their prediction and the official figures for the disease throughout the country. Like the CDC, they could tell where the flu had spread, but, unlike the CDC, they could do so in near real time, not a week or two later.

So in 2009, when the H1N1 crisis broke out, Google's system proved to be a more useful and timely indicator than government statistics, with their natural reporting lag. In addition, public health officials got an invaluable information tool that is based on big data, “big data”: the ability of society to take advantage of information in new ways, to obtain useful insights or valuable goods and services significant.

Derived from the above, in this chapter it is proposed to include Big Data in mathematical education and training as well as in the educational sphere in general, since it is of the utmost importance to link these sectors to be able to develop different competencies demanded by the society of the knowledge in conjunction with the fourth industrial revolution.

In this way, the objective of the chapter is to determine the importance of the incorporation of big data in mathematical training according to the context of the fourth industrial revolution through a documentary research that will be of interest to teachers who are dedicated to mathematics training of students in this area of knowledge.