ULUL-ILM: The design of web-based adaptive educational hypermedia system based on learning style

This paper explains about the architecture of ULUL-ILM : a web-based Adaptive Educational Hypermedia System (AEHS) that focuses on student's learning styles. It enables to recognize the student's learning style automatically in real time by means of Multi Layer Feed-Forward Neural network (MLFF). The MLFF is embedded to the system because of its ability to generalize and learn from specific examples, ability to be quickly updated with extra parameters, and speed in execution, making it suitable for real time applications. The system then enables to present and recommends a variety of learning contents adaptively towards each of the student's learning style identified in the student model through the adaptation model. The system then analyzes the learning content on each of the learning material, and then comes up with the generated teaching strategies by means of the teaching strategy generator and fragment sorting. The result of that analysis is called domain model. The adaptation model enables the system to adaptively presents the content, based on the student's learning style by combining the fragment sorting and adaptive annotation technique. The course player in ULUL-ILM enables the system to adaptively presents the content with various teaching strategies towards each of student's learning style. The purpose of ULUL-ILM is to provide the AEHS that can recognize student' learning style automatically in real-time and then presents the learning content adaptively based on their learning style. This paper is intended to elaborate the architecture of ULUL-ILM along with its user, domain and adaptive modeling technique used.