基于课程知识图谱的个性化资源推荐系统的应用研究 毕业论文定稿+初稿+任务书+开题报告+答辩PPT+答辩稿+运行环境+知识图谱源文件+论文检测查重报告+项目源码及数据库文件
基于课程知识图谱的个性化资源推荐系统的应用研究
摘要：近年来，随着教育信息化、个性化教育及K12等新理念的涌现，传统教育方式正逐步向信息化、智能化方向转型。这一转变极大地激发了学生对不受时空限制的学习方式的浓厚兴趣。然而，当前市场上的推荐系统在为学生提供学习资源时，往往未能精准匹配学生的个性化需求，导致推荐效果不佳。同时，这种信息数字化的新型学习方式在带来便捷的同时，也引发了信息冗杂、形式繁杂等问题，使得系统检索变得愈发困难。
为解决上述问题，本研究致力于开发一个基于课程知识图谱的个性化资源推荐系统。该系统旨在适应不同学生的多样化学习需求，通过构建精细化的知识结构，为学习者提供更加精准、个性化的学习资源推荐。本研究采用MySQL作为后台数据库，利用HTML技术实现系统前端页面的设计，同时选用Python作为后台开发语言，结合Django框架，完成整个系统的界面显示和数据交互功能。
基于课程知识图谱的个性化资源推荐系统，主要针对互联网上学习资源过多，导致学生难以快速定位所需知识点的问题。系统主要包含以下四个核心功能模块：用户模块、用户行为采集模块、个性化推荐模块以及后台管理模块。用户模块负责用户信息的注册、登录及管理；用户行为采集模块通过收集学生的学习行为、成绩、兴趣等多维度数据，构建学习者画像；个性化推荐模块则基于课程知识图谱和学习者画像，运用先进的推荐算法，为学生提供个性化的学习资源推荐；后台管理模块则用于系统管理员对系统资源、用户信息及推荐策略等进行管理和维护。
通过本系统的开发与应用，我们期望能够帮助学生快速找到适合自己的学习资源，提高学习效率，同时促进个性化学习的实现，培养学生的自主学习能力、批判性思维和创新能力。此外，该系统还可为教师提供个性化的教学辅助工具，帮助他们更好地了解学生的学习情况，从而优化教学方法和策略，进一步提升教育质量。
关键词：知识图谱；个性化课程资源；推荐系统；Django；MySQL
Applied research of personalized resource recommendation system based on course knowledge graph
Abstract：In recent years, with the emergence of new concepts such as education informatization, personalized education and K12, the traditional education methods are gradually transforming to the direction of informatization and intelligence. This shift has greatly stimulated students' strong interest in learning methods that are not limited by time and space. However, the current recommendation system in the market often fails to accurately match the personalized needs of students when providing learning resources for students, resulting in poor recommendation effect. At the same time, this new learning method of information digitization not only brings convenience, but also causes problems such as miscellaneous information and complicated forms, which makes the system retrieval more difficult.
To address the above problems, this study aims to develop a personalized resource recommendation system based on the course knowledge graph. The system aims to adapt to the diverse learning needs of different students, and to provide learners with more accurate and personalized learning resource recommendation by constructing a refined knowledge structure. In this study, MySQL is used as the background database, HTML technology is used to realize the design of the front-end page of the system, and Python is selected as the background development language, combined with the Django framework, to complete the interface display and data interaction function of the whole system.
The personalized resource recommendation system based on the course knowledge map mainly aims at the excessive learning resources on the Internet, which makes it difficult for students to quickly locate the required knowledge points. The system mainly includes the following four core function modules: user module, user behavior acquisition module, personalized recommendation module and background management module. The user module is responsible for the registration, login and management of user information; the user behavior collection module collects students 'learning behavior, achievement, interest and other multidimensional data; the personalized recommendation module is based on course knowledge map and learners' portrait and uses advanced recommendation algorithm to provide personalized learning resource recommendation; the background management module is used for the system administrator to manage and maintain the system resources, user information and recommendation strategies.
Through the development and application of this system, we expect to help students quickly find suitable learning resources, improve learning efficiency, promote the realization of personalized learning, and cultivate students' independent learning ability, critical thinking and innovation ability. In addition, the system can also provide teachers with personalized teaching AIDS to help them to better understand the students' learning situation, so as to optimize the teaching methods and strategies, and further improve the quality of education.
Key words: knowledge graph; personalized course resources; recommendation system; Django; MySQL
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