An intelligent system to support planning interactive learning segments in online education

Abstract Today many e-learning platforms use intelligent services for the purpose of creating a personalized learning environment. This learning environment must be adapted for the personalized student-learning style. The aim of this chapter is to propose, develop, and explain implementation of a personalized intelligent system. This system will suggest student additional learning resources. Those resources will support the students’ immersive learning process which will lead toward better outcomes of learning activities. Online e-learning systems must implement methods and evaluation techniques for the successful of different teaching paths. This is not an easy task since there are many technical challenges still uninvestigated. Every e-learning system must have different interactive learning segments. Those segments can be in the form of text, video, image, quizzes, etc. Segments have been defined as learning objects and they are entities in e-learning system courses. Data analytic techniques have emerged as the most appropriate method for the automatic detection of student-learning models. In this chapter we will use those techniques for analyzing learning paths. Paths can be composed from four different learning objects. Learning objects will be implemented in Moodle environment. Generalized sequence patterns will be mapped, and the activity module observer will be used to track students learning behavior. Results of the tracking will be used to develop intelligent systems for the planning of interactive learning segments.