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An Intelligent Learning Style Prediction on Web Interface Using Temporal Fuzzy Rules

Partheeban N., Dr.Sankar Ram N.

Abstract


E-Learning provides a lot of opportunities for the learners without attending class room sessions. Moreover, it is an essential technology for the successful conduct of distance education programmes in which the learners may be the persons who did not have an opportunity to carry out their education in an educational institute. Moreover, e-learning is not depending on rigid timing and location constraints. It provides multiple educational methods in terms of teaching and learning using multiple content delivery techniques. In the current internet scenario, storage, communication and discussions can be carried out at real time from anywhere in the world at any time. The challenges in e-learning systems can be addressed by using the technological developments which are made in the last few decades including the development of hardware, networking and internet, programming languages, database management systems and cloud computing tools. Moreover, the challenges in the storage and retrieval of e-learning contents include the need for new storage structures and access methods. Learning style prediction is useful for content provision to learners in e-learning environment. In the past, many learning style model have been identified in the literature. However, the grouping of learners needs intelligent analysis in order to form suitable groups. Hence, we propose an intelligent learning style prediction model for identifying the learning behavior of different learners who learn Software Engineering course through e-learning. For this purpose, we propose a new prediction method which is used fuzzy temporal rules as materials and methods which can understand the level of learners and predict their needs for e-learning. The main aim of the proposed system is that it helps to provide different levels of learning materials to the learners on the same subject depending upon their efficiency in learning.


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