Universidad de Vigo (SPAIN)
About this paper:
Appears in: INTED2016 Proceedings
Publication year: 2016
Pages: 2108-2116
ISBN: 978-84-608-5617-7
ISSN: 2340-1079
doi: 10.21125/inted.2016.1443
Conference name: 10th International Technology, Education and Development Conference
Dates: 7-9 March, 2016
Location: Valencia, Spain
If we can get the right information flowing through the minds of students, perhaps we can improve their success. We can potentially help transform the classroom from the 19th century to the 21st. The byproducts of all this data are the new insights that can drive decisions making in new ways.

The possibilities are numerous and succinctly expressed in the following statement: “… in order to extract more value from data, you have to increase its surface area”.

Data's thickness and the difficulty in bending it into shape has long been an impediment for organizations, requiring a class of people who have the knowledge and skill to bring data together from different sources, combine them, analyze them, and find patterns previously hidden.

There are a number of pressing issues for education, including how to:
(1) increase educator effectiveness;
(2) harness insights from learning experiences;
(3) deliver education for all that is also tailored to individual learners needs;
(4) equip students with relevant skills for their future careers

By that, along with interaction, cooperation and collaboration exists another important issue related to adaptability. In an educational system it is primordial to predict and adapt to new situations and subjects, according to the learner cognitive capacities.

Unfortunately, learning and collaborative tools in general are not always designed to be effective as the Learning Objects that normally are designed to be more comprehensive and universal. Furthermore, the educational material should be suitable for the abilities and skills of any user and must be provided multiple approaches – also the Learning Structures must have a careful approach to usability and "real time" adaptive capacities to reach the best results.

Big Data is a term recently acquired to define a subset of procedimental analysis. However Big Data has become more of a priority in scientific, industrial and public sectors than the education sector.

Often we forget that the real advantage in analyzing data is: speed, "don't get lost in the data - be focus", be critical, take notion of institutional capacity, think in a long term scale.

This paper makes four critical contributions:
1 – Shows a different perspective how we can see and must analyze the data produced in the educational environment;
2 - Shows how Big Data can be used from the beginning of school times to improve drastically the performance of learning, increasing educator effectiveness;
3 - Shows what are the possibilities and the limits of Big Data in improving education;

The methodology used will be based in a three years field research were we compare some cognitive factors in a multidisciplinary environment. This environment is completely controlled and all the tests are conducted according to the cognitive factor studied. This research will be cyclical and will provide us with data that permits to analyze all the aspects of the learning environment by “intelligent agents”.

More recent conclusions of the obtained data – during this three years - permit us already affirm that we achieved 75% of the four main issues referred in the 5th paragraph. We believe that increasing the dataset will allow us to reach the fourth objective of the 5th paragraph, thus becoming it a cross-structure to the entire educational environment.
Education, Interaction, Cooperation, Adaptability, Educational system, Intelligent agents, Learning objects, Learning structures, Usability, Learning