Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (10): 79-85.doi: 10.11896/jsjkx.240400087

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

Key Information Retrieval System for MOOC Videos

ZHAO Bocheng1, BAO Lantian1, YANG Zhesen2, CAO Xuan1, MIAO Qiguang1,3,4   

  1. 1 School of Computer Science and Technology,Xidian University, Xi'an 710071,China
    2 Beijing Aerospace Automatic Control Institute,Beijing 100854,China
    3 Xi'an Key Laboratory of Big Data and Intelligent Vision,Xi'an 710071,China
    4 Key Laboratory of Collaborative Intelligence Systems,Ministry of Education,Xidian University,Xi'an 710071,China
  • Received:2024-04-15 Revised:2024-07-03 Online:2024-10-15 Published:2024-10-11
  • About author:ZHAO Bocheng,born in 1989,Ph.D,associate professor,is a member of CCF(No.J0076M).His main research intere-sts include interactive learning,deep reinforcement learning,multi-objective game confrontation,etc.
    MIAO Qiguang,born in 1972,professor,doctoral supervisor,is a councillor of CCF(No.09025D).His main research interests include computer vision,smart education and multi-modal large language models.
  • Supported by:
    National Natural Science Foundations of China(62272364),National Science and Technology Major Project(2022ZD0117103),Key Research and Development Program of Shaanxi Province(2024GH-ZDXM-47) and Teaching Reform Project of Shaanxi Higher Continuing Education(21XJZ004).

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Abstract: Thanks to the rapid advancement of Internet technology,online education platforms,particularly massive open online courses(MOOCs),have increasingly captured public attention.MOOCs represent a revolutionary educational approach,effectively eliminating the geographical boundaries inherent in traditional education models and fostering the worldwide dissemination of elite educational resources.These courses empower learners to cherry-pick courses based on their unique interests,create flexible study schedules,monitor their progress,and revisit materials as needed.Despite their versatility,current MOOC platforms still struggle to pinpoint precise knowledge nuggets within lecture videos.This often leads learners to constantly scrub through the video timeline,searching for relevant segments,thereby disrupting the learning continuum.In view of this situation,we introduce a MOOC video knowledge extraction algorithm,leveraging a multi-level binary matching attention mechanism model.This algorithmic framework integrates subtitle text recognition and generation,subtitle segment extraction,a knowledge point extraction model,and a retrieval module.Experimental results show that,compared with the current knowledge point extraction model,the method of this system has achieved the optimal performance on some key indicators on multiple datasets such as Inspec,NUS,Krapivin,SemEval,KP20k,which fully proves the potential and value of this system in practical applications.

Key words: Online education, Massive open online courses, Video retrieval, Key phrase generation, Knowledge location

CLC Number: 

  • TP18
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