基于在线教育数据挖掘的个性化学习策略研究

Research on Personalized Learning Strategies Based on Online Education Data Mining

近年来,随着移动互联网的快速发展,各种新型的在线学习平台开始涌现,越来越多的人参与其中。在线平台中课程丰富,种类繁多,如何选择课程进行高效学习学习仍然是开放性的问题。通过对在线教育数据进行分析挖掘,构建用户个性化学习策略的模型。首先通过情感分析模型对课程评分进行打分,形成用户的喜好矩阵,然后利用推荐算法,给新用户推荐个性化的课程。实践结果表明,该方法能在某种程度上帮助用户进行个性化高效学习。

With the rapid development of mobile Internet, various new online learning platforms have emerged, and more and more people are participating. There are a wide variety of courses on the online platform, but how to choose courses for efficient learning is still an open question. This paper builds a model of user personalized learning strategies by analyzing and mining online education data. First, the course scores are scored through the sentiment analysis model to form the user’s preference matrix, and then the recommendation algorithm is used to recommend personalized courses to new users. The practical results show that this method can help users to learn personalized and efficient to some extent.