计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (10): 17-32.doi: 10.11896/jsjkx.240400088

• 智能教育技术及应用 • 上一篇    下一篇

基于深度学习的个性化学习资源推荐综述

周洋涛, 褚华, 朱非非, 李祥铭, 韩子涵, 张帅   

  1. 西安电子科技大学计算机科学与技术学院 西安 710071
  • 收稿日期:2024-04-15 修回日期:2024-07-08 出版日期:2024-10-15 发布日期:2024-10-11
  • 通讯作者: 褚华(hchu@mail.xidian.edu.cn)
  • 作者简介:(zhou_yt@stu.xidian.edu.cn)
  • 基金资助:
    西安电子科技大学教育教学改革重点项目(A2304);中央高校基本科研业务费项目(ZYTS24092,QTZX24072,QTZX24085)

Survey on Deep Learning-based Personalized Learning Resource Recommendation

ZHOU Yangtao, CHU Hua, ZHU Feifei, LI Xiangming, HAN Zihan, ZHANG Shuai   

  1. School of Computer and Technology,Xidian University,Xi'an 710071,China
  • Received:2024-04-15 Revised:2024-07-08 Online:2024-10-15 Published:2024-10-11
  • About author:ZHOU Yangtao,born in 1998,postgra-duate,is a member of CCF(No.J4627G).His main research interests include recommendation system,educational big data and knowledge graph.
    CHU Hua,born in 1977,Ph.D,associate professor,is a member of CCF(No.K8691M).Her main research interests include recommendation system,educational big data,object-oriented programming and intelligent software engineering.
  • Supported by:
    Key Project of Education Teaching Reform of Xidian University(A2304) and Fundamental Research Funds for the Central Universities(ZYTS24092,QTZX24072,QTZX24085).

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摘要： 随着信息技术与教育教学的深度融合,新型在线教育作为智慧教育的核心组成部分,为学习者提供了便捷的在线学习平台与丰富的学习资源。然而,在线教育模式的蓬勃发展也催生了“知识过载”与“知识迷航”等显著问题,极大地限制了学习者的学习增益与效率。近年来,学习资源推荐作为一种实现信息过滤与处理的关键技术手段,旨在分析学习者的历史学习行为,捕获其中蕴含的学习需求,最终实现千人千面的学习资源推荐服务。精准的个性化学习资源推荐能够有效解决在线教育场景中“知识过载”与“知识迷航”难题,实现个性化在线教育,已成为各大在线学习平台中不可或缺的核心功能之一。同时,随着深度学习技术的不断发展,基于深度学习的个性化学习资源推荐已成为计算机与教育交叉领域的研究焦点。因此,以“如何实现个性化学习资源推荐”和“如何实现对推荐结果的评估”两个问题为导向,对现有的研究工作进行了多维度、多层次、系统性的总结分析。首先,从场景特性、推荐目标、深度学习技术、边信息集成方式以及推荐模式5个维度对学习资源的个性化推荐过程进行分类与总结,以解答“如何实现个性化学习资源推荐”的问题;其次,从数据集、评估指标、以及实验方式3个方面对推荐结果的评估过程进行归纳与比较,并提供所有开源数据集的统一下载链接,以解答“如何实现对推荐结果的评估”的问题;最后,从对当前学习资源推荐方法自身局限性的攻克以及对外部新兴技术的利用与融合两个方面探讨了学习资源推荐未来的研究趋势。

关键词: 智慧教育, 学习资源推荐, 个性化, 深度学习, 知识图谱

Abstract: With the deep integration of information technology and education,novel online education,as a pivotal component of smart education,provides learners with convenient online e-learning platforms and rich learning resources.However,the rapid development of online education modes has also led to significant challenges such as “knowledge overload” and “knowledge dis-orientation”,which severely limits learners' educational gains and efficiency.In recent years,learning resource recommendation,as a key technology for information filtering,aims to analyze learners' historical behaviors,capture their underlying learning needs,and ultimately achieve personalized learning resource recommendation services.Accurate personalized learning resource recommendations can effectively address the challenges of “knowledge overload” and “knowledge disorientation” in online education,making it an indispensable core function in major online e-learning platforms.In addition,with the continuous advancement of deep learning technologies,research on deep learning-based personalized learning resource recommendation has become a focal area of interdisciplinary study in computer science and education.Therefore,this paper systematically analyzes existing research from multiple dimensions and levels,guided by the research questions of “how to achieve personalized learning resource recommendations” and “how to evaluate recommendation results”.Specifically,the paper firstly categorizes and summarizes the per-sonalized recommendation process of learning resources from five dimensions,including characteristics,recommendation objectives,deep learning technologies,integration methods of side information,and recommendation patterns,to answer the question of how to realize personalized recommendation of learning resources.Second,this paper inductively compares the evaluation process of recommendation results from three aspects,including datasets,evaluation metrics,and experimental methods,and provides unified download links for all open-source datasets,to answer the question of how to evaluate the recommendation results.Finally,this paper explores future research trends of learning resource recommendation from two perspectives:overcoming the inherent limitations of current recommendation methods as well as integrating and utilizing external emerging technologies.

Key words: Smart education, Learning resource recommendation, Personalization, Deep learning, Knowledge graph

中图分类号: 

  • TP391
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