In the virtual world, our activity is recorded in various databases, which are leveraged by multiple companies and industries to offer more personalized services. This information is analyzed to identify behavioral or consumption patterns, which then guide decision-making regarding processes or changes that should be implemented within the organization for continuous improvement. In this context, the analysis of what is known as Big Data is becoming increasingly common. In short, Big Data refers to a vast amount of information generated in real time from various online interactions—such as those on social media—which can be collected and analyzed for future use.
In the field of e-learning, the analysis of Big Data to optimize learning experiences is known as Learning Analytics. This approach has driven the implementation of technologies such as xAPI (Experience API), a software designed to collect data from any online course, which can be integrated into LMS (Learning Management Systems). Its use enables the evaluation of key parameters such as student performance, individual preferences, and difficulties in specific areas of learning. This information can then be used by e-learning specialists to gain a broader understanding of how users assimilate knowledge and, based on this analysis, adapt teaching techniques and methods according to user interactions with the courses, ultimately offering a more personalized learning experience.
Practically speaking, the importance of Big Data application in e-learning lies in its ability to transform information into actions that address various shortcomings in course design or implementation. In this sense, learning analytics enhances teaching processes in three key ways:
- Course Improvement through Prediction: By analyzing user interactions with courses, it is possible to predict future behaviors and subsequently modify course implementation to make the learning process more effective.
- Resource Selection: Learning analytics also facilitates the selection of the most appropriate resources to enhance individual learning, as it assesses which tools or types of assessments are best suited for different courses.
- Personalized Feedback: This type of analysis allows for specific feedback to be provided and guides users toward activities that help them address their particular areas of improvement.
As we have seen, learning analytics not only provides a clear description of a user’s performance on a lesson and identifies patterns in their course interaction, but also enables predictions based on statistical models, allowing the implementation of personalized improvement plans. In short, the potential of Big Data analysis in e-learning lies in its ability to process large volumes of information to quickly understand patterns that reveal how users learn and interact with online courses.
