[1]李乡儒,梁惠雯,冯隽怡,等.在线教育平台中个性化学习资源推荐系统设计[J].计算机技术与发展,2021,31(02):143-149.[doi:10. 3969 / j. issn. 1673-629X. 2021. 02. 027]
　LI Xiang-ru,LIANG Hui-wen,FENG Jun-yi,et al.Design of Personalized Learning Resource Recommendation System for Online Education Platform[J].,2021,31(02):143-149.[doi:10. 3969 / j. issn. 1673-629X. 2021. 02. 027]
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在线教育平台中个性化学习资源推荐系统设计()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年02期
页码:
143-149
栏目:
应用前沿与综合
出版日期:
2021-02-10

文章信息/Info

Title:
Design of Personalized Learning Resource Recommendation System for Online Education Platform
文章编号:
1673-629X(2021)02-0143-07
作者:
李乡儒1 ; 梁惠雯2 ; 冯隽怡2 ; 肖江平2 ; 彭婉芬3
1. 华南师范大学 计算机科学学院,广东 广州 510631;?
2. 华南师范大学 数学科学学院,广东 广州 510631;?
3. 广州犀灵信息科技有限责任公司,广东 广州 511458
Author(s):
LI Xiang-ru1 ; LIANG Hui-wen2 ; FENG Jun-yi2 ; XIAO Jiang-ping2 ; PENG Wan-fen3
1. School of Computer Science,South China Normal University,Guangzhou 510631,China;?
2. School of Mathematical Sciences,South China Normal University,Guangzhou 510631,China;?
3. Guangzhou Xiling Information Technology Co. ,Ltd. ,Guangzhou 511458,China
关键词:
在线教育; 个性化推荐系统; 用户画像与反馈; 学习风格; 行为序列分析; 资源偏好
Keywords:
online education; personalized recommendation system; user portrait and feedback; learning style; behavior sequence analysis;  resource preference
分类号:
TP311
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 02. 027
摘要:
为了通过充分挖掘和分析用户的学习行为规律及认知特点, 借助互联网和人工智能技术提升个性化教育的深度和广度, 设计了一个包含用户画像的个性化学习资源推荐系统。该系统由数据层、数据分析层和推荐计算层构成。 数据层由用户数据以及包含知识资料、学习资料和标签集的资源库组成;数据分析层融合了以基础信息、学习行为等为代表的静态数据和动态数据,据此为用户生成个性化画像、提供直观形象的学习反馈;推荐计算层则通过相似性分析和聚类算法发现用户的学习行为规律,使用 TF-IDF 方法挖掘用户的资源偏好,并据此给出个性化的学习建议。 该系统已应用于一个以人工智能类课程为主的在线教育平台,为师生提供个性化画像、学习反馈与资料推荐的服务,当前处于第二个学期的试用阶段。
Abstract:
To improve the depth and breadth of personalized education by fully exploring and analyzing the learning behaviors andcognitive characteristics of users with the help of the Internet and artificial intelligence technology,a personalized learning resource recom-mendation system with user portrait is designed,which consists of data layer,data analysis layer and recommendation calculating layer.The data layer includes user information and a repository containing knowledge materials, learning materials and tag sets.? The data analysis layer integrates static data and dynamic data represented by basic information and learning behavior, so as to generate personalized portrait and provide intuitive learning feedback for users. The recommendation calculating layer finds out the patterns of learning behavior for users by similarity analysis and cluster analysis, mines the resource preference of users by using the TF-IDFmethod,and gives personalized learning suggestions accordingly. This system has been used in an online platform for artificial intelligence education tested its capabilities of providing teachers and students with personalized portrait,learning feedback and resource recommendation for the second semester.

相似文献/References:

[1]杜定宇 王茜.一种基于中间代理的个性化推荐系统[J].计算机技术与发展,2011,(09):66.
　DU Ding-yu,WANG Qian.An Agent-Based Personalized Recommendation System[J].,2011,(02):66.

更新日期/Last Update: 2020-02-10