The education industry is gradually improving with the rapid development of information technology. The learners use networks and computers to alter the traditional instructional framework based on educational information technology and achieve personalized learning. This teaching method emphasizes each learner’s identity and autonomy. However, due to the huge number of learning resources available on the Internet, students lack relevant courses, clear learning tasks, and the connection between various knowledge points, resulting in an unsatisfactory effect on the learning process. Knowledge maps for different learner types are created using historical learners’ conceptual knowledge and the segmentation and correlation technique of big data knowledge maps. Using a big data method in this process will automatically generate a set of weak conceptual learning pathways. For this problem, in the era of big data, people put forward the concept of knowledge map and used the algorithm based on the big data knowledge map to study the personalized learning path for college French. The content, structure, and relationship of college French knowledge points can be accurately expressed using this method, which is preferred by college administrators and teachers. This paper aims to investigate the personalized learning path for college French using a big data knowledge map, starting with the characteristics of a college French field of study. This study provides technical support in the establishment of a big data knowledge map based on a learning path recommendation framework. So, after the performance of several commonly used learning path recommendation algorithms, three French students have been selected at random for learning path planning. The results show that personalized learning path planning can be realized based on a knowledge map pre-repair relationship and objective attributes. In the analysis, not only the proposed technique is compared with the conventional optimization approach, but also a comparison study on the benefits of several learning effect prediction models is also performed. The results of this study suggest that this algorithm has a high learning efficiency and that the effective implementation of recommendations produced using our proposed strategy has a significant advantage.

中文翻译：

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基于大数据知识图谱的高校法语学习个性化学习路径

随着信息技术的快速发展，教育行业也在逐步完善。学习者利用网络和计算机改变传统的以教育信息技术为基础的教学框架，实现个性化学习。这种教学方法强调每个学习者的身份和自主性。然而，由于网络上的学习资源数量庞大，学生缺乏相关的课程、明确的学习任务以及各个知识点之间的联系，导致学习过程效果不尽如人意。利用历史学习者的概念知识和大数据知识图谱的分割和关联技术创建不同学习者类型的知识图谱。在这个过程中使用大数据方法会自动生成一组弱概念学习路径。针对这一问题，在大数据时代，人们提出了知识图谱的概念，并利用基于大数据知识图谱的算法研究大学法语个性化学习路径。利用这种方法可以准确表达大学法语知识点的内容、结构和关系，深受高校管理人员和教师的青睐。本文旨在从某大学法语专业的特点入手，利用大数据知识图谱探索大学法语个性化学习路径。本研究为建立基于学习路径推荐框架的大数据知识图谱提供了技术支持。所以，在对几种常用的学习路径推荐算法进行性能测试后，随机选取了三名法国学生进行学习路径规划。结果表明，基于知识图谱预修复关系和目标属性可以实现个性化学习路径规划。在分析中，不仅将所提出的技术与传统的优化方法进行了比较，而且还对几种学习效果预测模型的好处进行了比较研究。这项研究的结果表明，该算法具有很高的学习效率，并且使用我们提出的策略产生的建议的有效实施具有显着优势。随机选择了三名法国学生进行学习路径规划。结果表明，基于知识图谱预修复关系和目标属性可以实现个性化学习路径规划。在分析中，不仅将所提出的技术与传统的优化方法进行了比较，而且还对几种学习效果预测模型的好处进行了比较研究。这项研究的结果表明，该算法具有很高的学习效率，并且使用我们提出的策略产生的建议的有效实施具有显着优势。随机选择了三名法国学生进行学习路径规划。结果表明，基于知识图谱预修复关系和目标属性可以实现个性化学习路径规划。在分析中，不仅将所提出的技术与传统的优化方法进行了比较，而且还对几种学习效果预测模型的好处进行了比较研究。这项研究的结果表明，该算法具有很高的学习效率，并且使用我们提出的策略产生的建议的有效实施具有显着优势。不仅将所提出的技术与传统的优化方法进行了比较，而且还对几种学习效果预测模型的好处进行了比较研究。这项研究的结果表明，该算法具有很高的学习效率，并且使用我们提出的策略产生的建议的有效实施具有显着优势。不仅将所提出的技术与传统的优化方法进行了比较，而且还对几种学习效果预测模型的好处进行了比较研究。这项研究的结果表明，该算法具有很高的学习效率，并且使用我们提出的策略产生的建议的有效实施具有显着优势。