This dissertation presents a scalable web framework leaning system, Web-based Learning System (WebLS), addressing the distance learning scenario. Since the speed popularity of the Internet infrastructure and World Wide Web Services that have become the most commonly used information platform and an important medium for education; and expand to the Web-based e-Learning model. The Web-based e-Learning is not subject to the boundary of time or space that has greatly enhanced the effectiveness of online distance learning. The WebLS aims at bringing together the most promising web technologies and standards, in order to attain a scalability and highly availability online learning environment. Moreover, the scalable web framework includes a SCORM based learning management system (named LMS), a server cluster infrastructure, a learning content management service, an information and content repository (named LMS database), and an agent system supporting the innovative solutions taken to implement scalability, availability, portability, reusability, and standardization. The WebLS can store and provide Web access portal to learning contents from teachers, voluntaries, and institutions that lack resources or expertise to offer curriculums over the Internet. So, in the first we design and implement the web-based learning managemnt sytem, Learning Management System (LMS), which conform the e-Learning standard, SCORM 1.2 specification, that established by ADL, and satisfy the requirements of the basic functionality at online web-based learning. Besides, in point of the research topic of learning behavior analysis, we propose a study result for extracting better learning path, Experience Matrix System with Time Fragment Extraction (EMST), which can analyse the learner¡¦s study behavior in Web-based learing environment. Then the information is used to explore, analyse students¡¦ learning path in order to find out the suitable learning path for the more learners. As masses of learners concurrently enter the learning system, the system is often unable to serve such a massive workload, particularly during peak periods of learning activity. We use the server-cluster architecture as a way to create scalable and highly available solutions. However, hosting a variety of learning contents from different owners on such a distributed server system faces new design and management problems and requires new solutions. This dissertation describes the research work we are pursuing for constructing a system to address the challenges faced by hosting learning content on a server farm environment.