Key Information Retrieval System for MOOC Videos

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.