Intuitionistic fuzzy ant colony optimization for course sequencing in E-learning

In state-of-art E-learning scenarios, adaptive courses sequencing is important to save the learner loss in hyperspace. Learning material is a unstructured place in hyperspace and adaptive course sequencing light the path to learners for selecting appropriate courses with their knowledge levels. In this work, a method based on adaptive content sequencing using faculty's personal strength is proposed for providing the most suitable content out of the overloaded course material. Further, learning path is optimized using ant colony optimization on Intuitionistic fuzzy data. Results show that objectives of better contents development, coherence among courses, better teaching and learning is achievable by selecting best path in DAG traversal using proposed framework.