Abstract
With the development of Internet technologies and the increasing demand for knowledge, increasingly more people choose online learning platforms as a way to acquire knowledge. However, the rapid growth in the types and number of courses makes it difficult for people to make choices, which leads to a series of problems, such as unsystematic learning processes and a low learning efficiency. Based on the current course situation of MOOC (massive open online courses) platforms, this paper proposes a new automated construction method for course knowledge graphs. A course knowledge graph is constructed by annotating the pre-knowledge of each course and calculating the similarity between courses, and it is displayed using the Neo4j graph database platform. After completion of the course knowledge graph, the knowledge graph of the courses is used to study learning path recommendation algorithms, including rule-based and machine learning based algorithms, and to perform a comparative analysis using the higher education formation program of a university.
Supported by the National Natural Science Foundation of China (No. 61977003).
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Chen, H., Yin, C., Fan, X., Qiao, L., Rong, W., Zhang, X. (2021). Learning Path Recommendation for MOOC Platforms Based on a Knowledge Graph. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management . KSEM 2021. Lecture Notes in Computer Science(), vol 12816. Springer, Cham. https://doi.org/10.1007/978-3-030-82147-0_49
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DOI: https://doi.org/10.1007/978-3-030-82147-0_49
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