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Learning path combination recommendation based on the learning networks

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Abstract

Discovering useful hidden learning behavior pattern from learning data for online learning platform is valuable in education technology. Studies on learning path recommendation to recommend an appropriate resource for different users are particularly important for the development of advanced online education. However, it may suffer from low recommendation quality for beginners or learner with low participation. In order to improve the recommendation quality, a learning path combination recommendation method based on the learning network (LPCRLN) is proposed. In LPCRLN, it introduces complex network technology. Based on the characteristics of courses and learners, the course network and learner network, respectively, are constructed, and then learners are divided into three types. Finally, the recommendation is made in different scenarios according to the learner’s learning records. In this study, a series of experiments have been carried out. By comparisons, experimental results indicate that our proposed method is able to make sound recommendations on appropriate courses for different types of learners with significant improvement in terms of accuracy and efficiency.

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Acknowledgments

This work is supported by the National Social Sciences Foundation Project (Grant Nos. 17BTQ069, 18BGL101) and the Zhejiang Natural Science Foundation Project (Grant No. LY19F020007).

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Correspondence to Hong Liu.

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Liu, H., Li, X. Learning path combination recommendation based on the learning networks. Soft Comput 24, 4427–4439 (2020). https://doi.org/10.1007/s00500-019-04205-x

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Keywords

  • Learning path combination recommendation
  • Learning network
  • MOOC
  • Scenarios