Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 207-211.doi: 10.11896/jsjkx.201000042

• Artificial Intelligence • Previous Articles     Next Articles

Evaluation of Quality of Interaction in Online Learning Based on Representation Learning

WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge   

  1. School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China
  • Received:2020-10-09 Revised:2020-11-04 Online:2021-02-15 Published:2021-02-04
  • About author:WANG Xue-cen,born in 1996,postgra-duate.Her main research interests include smart education and recommendation system.
    ZHANG Yu,born in 1980,Ph.D,lectu-rer,is a member of China Computer Fe-deration.His main research interests include big data in education,social networks,etc.
  • Supported by:
    The National Natural Science Foundation of China(U1811261) and Fundamental Research Funds for the Central Universities(N180716010).

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Abstract: The model of education today has undergone a very significant change,and education is developing in the direction of ubiquity,intelligence and individuation.Online education,represented by MOOCs,is gradually coming into the public field of vision,and the interactivity in online education has become the key to determine the quality of online learning.Some researches show that the interaction in the learning process provides efficient help and effective support for learners,and the feedback of learning process evaluation can effectively improve the interaction effect of learning.Modeling interactions between learners and learning resources is crucial in domains such as e-commerce.Representation learning presents a method to model the sequential interactions between learners and learning resources.Firstly,an interactive network of online learning is established.And then,the learners and learning resources can be embedded into a Euclidean space by using two recurrent neural networks.The evaluation index of the quality of interaction is proposed,which can judge whether the learner's learning effect is up to the expectation.The experiments on real datasets reveal the effectiveness of the proposed method.

Key words: Big data in education, Interactive evaluation, MOOCs, Representation learning

CLC Number: 

  • TP311.13
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