Computational organization of didactic contents for personalized virtual learning environments

• Introduces multilevel structure of contents.
• A multilevel structure of concepts allow an automatized and personalized content presentation of contents.
• Introduces proximal learning patterns for personalization.
• Employ of artificial intelligence in computational organization of didactic contents.
• Artificial neural network on student's classification in proximal learning patterns.

This paper presents an organization model for personalized didactic contents used in individual study environments. For many students the availability of contents in a general form might not be effective. A multilevel structure of concepts is proposed to provide different presentation combinations of the same content. Our work shows that it is possible to personalize the didactic content in order to encourage students, by using proximal learning patterns. These patterns are obtained from the analysis of the actions of students with positive results in the individual content organization. The system uses artificial intelligence techniques to reactively organize and personalize content. Personalization is made possible by means of an artificial neural network that classifies the student's profile and assigns it a proximal learning pattern. Expert rules are used to mediate and adjust the contents reactively. Experimental results indicate that the approach is efficient and provides the student a better use of the content with adaptive and reactive personalized presentation.