There exist numerous resources online to gain the desired level of knowledge on any topic. However, this complicates the process of selecting the most appropriate resources. Every learner differs in terms of their learning speed, proficiency, and preferred mode of learning. This paper develops an adaptive learning management system to tackle this challenge. It creates a customized course for every student based on their level of knowledge, preferred mode of learning and continuously updates the course based on their learning speed. The material is filtered from a knowledge base that is dynamically updated using web scraping and ranked using feedback from students on the relevance and quality of each material. The model is tested in two phases: the content generation algorithm and the learnability of the system as a whole. The evaluation is done both quantitatively and qualitatively and validated with statistical analysis. Real-time testing of the system shows state-of-the-art performance.

在线上存在大量资源，以获得有关任何主题的所需知识水平。但是，这使选择最合适的资源的过程变得复杂。每个学习者的学习速度，熟练程度和偏好的学习方式都各不相同。本文开发了一种适应性学习管理系统来应对这一挑战。它会根据每个学生的知识水平，偏好的学习方式为他们创建定制的课程，并根据他们的学习速度不断更新课程。资料是从知识库中过滤出来的，该知识库使用网络抓取功能进行动态更新，并使用学生对每种资料的相关性和质量的反馈进行排名。该模型分两个阶段进行测试：内容生成算法和整个系统的可学习性。评估在数量和质量上进行，并通过统计分析进行验证。系统的实时测试显示了最先进的性能。