Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 67-78.doi: 10.11896/jsjkx.240500002

• Technology and Application of Intelligent Education • Previous Articles     Next Articles

Learning Pattern Recognition and Performance Prediction Method Based on Learners'Behavior Evolution

HUANG Chunli1, LIU Guimei1, JIANG Wenjun1, LI Kenli1, ZHANG Ji2, TAK-SHING Peter Yum3   

  1. 1 College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China
    2 University of Southern Queensland,Queensland 4350,Australia
    3 Department of Information Engineering,The Chinese University of Hong Kong,Hong Kong 999077,China
  • Received:2024-05-06 Revised:2024-07-16 Online:2024-10-15 Published:2024-10-11
  • About author:HUANG Chunli,born in 1994,Ph.D candidate,is a member of CCF(No.U0106G).Her main research interests include smart education,behavior analysis and learning optimization.
    JIANG Wenjun,born in 1982,Ph.D,professor,Ph.D supervisor,is a senior member of CCF(No.34099S).Her main research interests include smart education,learning optimization,social network analysis and recommendation system.
  • Supported by:
    National Natural Science Foundation of China(62172149).

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Abstract: Online learning provides learners with open and flexible learning opportunities,but suffers low learning engagement and unsatisfactory academic performance.Existing works on academic performance prediction mainly study how behaviors will impact performance from a static perspective,and neglect learners' behavior evolution over time and lack a deep understanding of learning patterns and learners' motivations,which are the key factors in learning performance.Therefore,a method of perfor-mance prediction based on learners' learning pattern and motivation is proposed to model the effects of learners' patterns and motivations on their performances.First,we quantify learning efficiency based on learners' efforts and gains and construct the dynamic evolution sequence of learning efficiency with time.Then,we cluster learners' behavior and identify four typical learning patterns combined with the actual learning scenarios.Based on this,learning pattern recognition and motivation prediction mode-ling are designed.The final performance prediction model is constructed by combining them with the bi-directional long-and short-term memory networks.Furthermore,we conduct a detailed and in-depth data analysis on each type of learning patterŃs efforts and gains in eight online courses.Comparative experiments show that the proposed model performs better on several metrics,with improvements ranging from 6.9% to 29.2%.Our work will help online learners,teachers,and platforms accurately understand learners' learning states and improve online learning performance.

Key words: Online learning, Behavior evolution, Learning pattern recognition, Learning motivation prediction, Learning perfor-mance prediction

CLC Number: 

  • TP391
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