Computer Science ›› 2022, Vol. 49 ›› Issue (4): 269-281.doi: 10.11896/jsjkx.210500125

• Artificial Intelligence • Previous Articles     Next Articles

Personalized Learning Task Assignment Based on Bipartite Graph

TAN Zhen-qiong1, JIANG Wen-Jun1, YUM Yen-na-cherry2, ZHANG Ji3, YUM Peter-tak-shing4, LI Xiao-hong1   

  1. 1 School of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;
    2 Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong 810014, China;
    3 Zhejiang Lab, Hangzhou 310012, China;
    4 Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China
  • Received:2021-05-18 Revised:2021-12-09 Published:2022-04-01
  • About author:TAN Zhen-qiong,born in 1997,postgraduate,is a student member of China Computer Federation.Her main research interests include data mining,task allocation,intelligent education and learning optimization.JIANG Wen-jun,born in 1982,Ph.D,professor,is a senior member of China Computer Federation.Her main research interests include social network analysis,user behavior analysis and opinion mining,intelligent education and learning optimization.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(62172149,61632009),Open Project of Zhejiang Lab(2019KE0AB02) and Natural Science Foundation of Hunan Province of China(2021JJ30137).

Abstract: “Learning” is a complex event.Individual's learning effect is affected by many factors.Moreover, different individuals have different learning habits.Therefore, it is challenging for students to plan their learning schedule reasonably according to their own characteristics.Although some general theoretical strategies for task management have been proposed, the differences among individuals are usually neglected.Furthermore, existing research cannot provide a calculation method to form a specific task mana-gement schedule.To this end, this paper tries to explore students'learning characteristics by deeply studying the relation between learning efficiency and time factor through data analysis.Based on this, it quantifies personalized learning efficiency.Furthermore, it exploits the bipartite graph method to construct the learning task assignment scenario, and designs adaptive utility function according to different learning goals.Then, a dynamic allocation algorithm TLTA based on transfer learning is proposed to formulate a reasonable schedule for students.Finally, a large number of experiments are carried out on real learning datasets, and the results validate the effectiveness and applicability of the proposed work.

Key words: Bipartite graph, Learning effect, Task allocation, Time factor, Transfer learning

CLC Number: 

  • TP391
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