IAd-LEARNING: A NEW E-LEARNING PLATFORM
Thursday 15th
from 17:45 pm to 18:30 pm
Room 25
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Technical
Purpose of the talk: Describing the use of Machine Learning and Big Data Techniques to improve the performance of elearning students. Presenting an existing case of an elearning platform (iAdLearn¡ng) and the technology used behind the scenes, to make adaptive/high performance elearning a reality.
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iAdLearning is a new ELearning Platform based on two main principles:
- Adaptive eLearning: iAdLearning analyses how students learn and proposes them learning strategies individually targeted for each platform user. "The platform adapts to the user".
- Nonlinear learning: Users learn by freely navigating through the course contents, according to their interests, while still understanding the original course structure and, if they wish, following the platform recomendations.
iAdLearning is capable of:
- Importing documents, analyzing them and establishing semantic relationships among the different document individual content components creating, as a result, a user navigable graph (knowledge network).
- Allowing free navigation through the knowledge network as well as through the original course structure.
- Discovering user learning patterns and establishing optimal learning paths for each individual platform user based on his/her profile.
- Offering metrics to qualify the quality of the training materials and learning process.
How does iAdLearning Work?
- Document Import:
A set of documents belonging to an eLearning course is imported, analyzed and broken down into semantically relevant fragments called ACEs (Atomic Content Elements). ACEs represent course fragments that can be individually studied and understood. - iAdLearning creates a graph representing the relationships between the different ACEs:
- Structural Relationships: represent connections related to the course structure as initially established by documents authors
- Semantic Relationships: represent connections created due to the similarity of the contents being described by the connected ACEs
