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Mobile Networks and Applications

, Volume 24, Issue 1, pp 34–46 | Cite as

MOOCRC: A Highly Accurate Resource Recommendation Model for Use in MOOC Environments

  • Hao Zhang
  • Tao Huang
  • Zhihan LvEmail author
  • Sanya Liu
  • Heng Yang
Article

Abstract

With the rapid development of MOOC platforms, the online learning resources are increasing. Because learners differ in terms of cognitive ability and knowledge structure, they cannot rapidly identify learning resources in which they are interested. Traditional collaborative filtering recommendation technologies perform poorly given sparse data and cold starts. Furthermore, the redundant recommended content and the high-dimensional and nonlinear data on online learning users cannot be effectively handled, leading to inefficient resource recommendations. To enhance learner efficiency and enthusiasm, this paper presents a highly accurate resource recommendation model (MOOCRC) based on deep belief networks (DBNs) in MOOC environments. This method deeply mines learner features and course content attribute features and incorporates learner behavior features to build user-course feature vectors as inputs to the deep model. Learner ratings of courses are processed as supervised labels with supervised learning. The MOOCRC model is trained by unsupervised pretraining and supervised feedback fine tuning; moreover, the model is obtained by adjusting the model parameters repeatedly. To verify the effectiveness of the MOOCRC, an experimental analysis is conducted using learner elective data obtained from the starC MOOC platform of Central China Normal University. Real course enrollment data are used to verify the classification accuracy of the MOOCRC. The experimental results show that the MOOCRC has greater recommendation accuracy and converges more quickly than traditional recommendation methods.

Keywords

MOOCs Recommender systems Collaborative filtering Deep Belief Networks Classification predictions 

Notes

Acknowledgments

We are grateful to the volunteers for capturing the data. This research is supported by the National Key Research and Development Program of China (no. 2017YFB1401300, 2017YFB1401304), the National Natural Science Foundation of China (no. 61702211), and the Self-Determined Research Funds of CCNU from the Colleges’ Basic Research (nos. CCNU17QN0004 and CCNU17GF0002).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Hao Zhang
    • 1
  • Tao Huang
    • 1
  • Zhihan Lv
    • 2
    Email author
  • Sanya Liu
    • 1
  • Heng Yang
    • 1
  1. 1.National Engineering Research Center for E-LearningCentral China Normal University (CCNU)WuhanChina
  2. 2.School of Data Science and Software EngineeringQingdao UniversityQingdaoChina

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