随着互联网技术的迅速发展，网络课程在国内外已经取得了令人骄傲的成果，先后出现了许多优秀的网络课程资源，此外，MOOC作为网络课程的一种新型表现形式，它在教育领域引起了的巨大变革，但是在实际的运行中，网络课程的实施仍面临着很大的问题。资源更新率低，资源管理不当，学习者被动，课程辍学率高，个性化应用服务缺失等问题成为当前绝大数网络课程可持续发展的瓶颈，导致很多课程出现了只有教师的“教”，严重缺少学生的“学”的现象。 本文以数据结构课程为背景，针对上述问题，设计并实现数据结构MOOC系统。首先借鉴MOOC课程的运行模式，分析并设计数据结构MOOC系统运行模式，在学生学习过程和教师教学过程中，教师和学生以及学生和学生之间的互动是系统运行的内在动力，资源是二者互动的基础，因此学习者的学习和资源的构建息息相关。在充分分析了课程运行模式之后，结合传统经典的LTSA框架，对其进行改进，提出数据结构MOOC学习管理框架，主要增加了学习者资源代理元件，学习者成绩代理元件，学习者资源数据库和三级代理模型。设计资源评估模型实现资源代理，设计学习行为评估模型实现学习者成绩代理，设计三级代模式提高资源管理和学习行为评估的时效性和可操作性。最后，在对学生学习效果评估的数据基础上，运用遗传算法设计了智能组卷的组卷策略，并通过实验验证了遗传算法的收敛性和试卷的相关性，达到个性化学习的目的。 总之，数据结构MOOC系统通过对学习管理系统框架的改进，以“学习者”为中心，实现了资源的动态构建，建立了学生学习行为评估模型，设计的智能组卷满足个性化应用的需求，对未来网络课的发展具有重大影响。

With the rapid development of network technology, the online course has made great achievements both at home and abroad, and a number of excellent network course resources have been built. In addition, as a new form of online course, MOOC has caused great changes in the field of education, but in the actual operation, the implementation of the network course is still facing a lot of problems. The problems, the low rate of resource update, improper resource management, passive learners, high course dropout rates, lack of personalized service and so on, have become the bottleneck of sustainable online course development, which leads to the phenomenon that there is only the teacher's "teaching" and a serious lack of students "learning" in a number of courses. Based on the Data Structure Course, this paper designs and implements the Data Structure MOOC system. First, with reference to the operation mode of MOOC course, the learner-centered of Data Structure MOOC system operation mode is designed. In the process of students' learning and teachers' teaching, the interaction between teachers and students is the internal driving force of the system, and the curriculum resources are the basis of the interaction between the two. Therefore, the study of learners and the construction of resources are closely related. After the full analysis of the curriculum mode, according to the traditional LTSA, this paper makes some improvements and puts forward the Data structure MOOC learning management framework. It mainly adds the learner resource proxy component, the learner performance proxy, the learner resource database and the three level agent model. The learner resource proxy component is realized by the design of resource evaluation model. The learner performance proxy is realized by the design of the learning behavior assessment model and the three level agent model can improve the timeliness and operability of resource management and learning behavior evaluation. Finally, based on the assessment of learning effect of students, the intelligent test paper is achieved by making use of the genetic algorithm for the purpose of personalized learning and the convergence of the genetic algorithm and the relevance of the test paper are verified by experiments. In short, through the improvement of the learning management system framework, Data Structure MOOC System can achieve the dynamic construction of resources and establish student evaluation mechanism, and the design of intelligent test paper can meet the needs of personalized applications, which has great influence on the development of the network course in the future.