In recent years, Big Data has been transforming the way educational institutions operate and students learn. Technological advances have made memorization-based educational models irrelevant and meaningless, while the attempts at "innovation" still continue to reinforce behaviors of competition and individualization.
Learning platforms - or Learning Machine Systems - enable educators to create learning experiences aimed to the individual needs, preferences and learning styles, identifying which teaching methods, materials and content are most effective and engaging. In addition of that, the datas generated by those systems helps educators make decisions, adapt learning experiences, improve teaching methods, and measure educational outcomes through advanced analytical tools and algorithms that present patterns, trends, insights, and provide more personalized and up-to-date learning paths, keeping the subjects always relevant to students!

However, dealing with this new dynamic is still a challenge for the vast majority of educational institutions. They store large amounts of data from various sources - such as professional and student information systems, learning management systems, linked platforms and apps - in centralized databases or cloud-based platforms, where they can be accessed, processed and analyzed. This enables, for example, to use predictive analysis to identify students at risk of academic failure and to create intervention strategies to support these students and improve their chances of success.The allocation also optimizes the institution's resources by identifying areas where support or resources are needed more effectively.
Despite all the agility and accessibility that it offers, simplifying administrative processes, leading to better operational efficiency and cost savings, it is crucial to create educational programmes and policies that ensure the proper use of these data. Protecting the sensitive information of educators and students is of most importance. Data privacy laws and regulations that ensure the confidentiality and security of student information need to be included for the quality and integrity of such data.
Another point of attention is the ethical aspect of data use. Personalization of learning collaborates with more plural and diverse environments, but there is still much work to be done on the prejudices generated by algorithms and the potential for discrimination that still permeates the digital world! These issues must be addressed in order to ensure fairer educational practices. Only then will new learning, with the help of big data, be able to achieve its full potential in a revolutionary, responsible and ethical way.