ABSTRACT

Today we are in an era where drastic advancements in networking and information technology are in action. The learning process has also taken these advancements, as a result of which e-learning came to the scene. Personalization in e-learning will improve the performance of the system. Recent researches are concentrating on providing adaptability to the learning management systems, depending upon the varying user needs and contexts. Adaptability can be provided at different levels .Providing an adaptive learning path according to the context of the learners’ is an important issue. An optimal adaptive learning path will help the learners in reducing the cognitive overload and disorientation, and thereby improving the efficiency of the Learning Management System (LMS). Ant Colony Optimization (ACO) is a widely accepted technique since it provides an adaptive learning path to the learners. Meta-heuristic which is used in intelligent tutoring systems provides the learning path in an adaptive way. The most interesting feature of ACO is its adaptation and robustness in an environment where the learning materials and learners are changing frequently. In this paper we can have a look through the existing ACO approaches towards providing an adaptive learning path and an introduction towards an enhanced attribute ant for making the e-learning system more adaptive.