[1]刘芳,田枫,李欣,等.融入学习者模型在线学习资源协同过滤推荐方法[J].智能系统学报,2021,16(6):1117-1125.[doi:10.11992/tis.202009005]
 LIU Fang,TIAN Feng,LI Xin,et al.A collaborative filtering recommendation method for online learning resources incorporating the learner model[J].CAAI Transactions on Intelligent Systems,2021,16(6):1117-1125.[doi:10.11992/tis.202009005]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年6期
页码:
1117-1125
栏目:
学术论文—知识工程
出版日期:
2021-11-05

文章信息/Info

Title:
A collaborative filtering recommendation method for online learning resources incorporating the learner model
作者:
刘芳1 田枫1 李欣2 林琳1
1. 东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318;
2. 讷河市第一中学,黑龙江 讷河 161300
Author(s):
LIU Fang1 TIAN Feng1 LI Xin2 LIN Lin1
1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;
2. Nehe No. 1 Middle School, Nehe 161300,China
关键词:
学习者模型在线学习资源协同过滤个性化学习学习资源推荐学习风格特征认知水平特征兴趣偏好特征
Keywords:
learner modelsonline learning resourcescollaborative filteringpersonalized learninglearning resources recommendationlearning style characteristicscognitive level characteristicsinterest preference characteristics
分类号:
TP391;G434
DOI:
10.11992/tis.202009005
摘要:
在线教育存在“信息迷航”问题,而传统的信息推荐方法往往忽视教育的主体—学习者的特征。本文依据教育教学理论,根据在线教育平台中的学习者相关数据,研究构建了适用于在线学习资源个性化推荐的学习者模型。以协同过滤推荐方法为切入点,融合学习者模型中的静态特征和动态特征对协同过滤方法进行改进,建立融入学习者模型的在线学习资源协同过滤推荐方法。以2020年3~7月时间段的东北石油大学“C程序设计”课程学生的真实学习数据和行为数据为数据集,对本文提出的方法进行验证和对比,最后证明本文提出的方法在性能上均优于对比方法。
Abstract:
Online education exhibits the problem of “information loss”. At the same time, traditional information recommendation methods often ignore the characteristics of learners, i.e., the main body of education. Based on the theory of education and teaching as well as the relevant data of learners on the online education platform, this paper constructs a learner model suitable for personalized recommendations for online learning resources. Based on the collaborative filtering recommendation method, the static and dynamic features of the learner model are integrated, with the aim to improve the collaborative filtering method, thereby establishing a collaborative filtering recommendation method for online learning resources incorporating the learner model. The real learning and behavior records of students taking the C programming course in the Northeast Petroleum University starting from March 2020 to July 2020 were selected as the dataset to conduct experiments and evaluations on the proposed research method. The comparative test shows that the performance of the proposed method is better than that of the comparative method.

参考文献/References:

[1] 中国互联网络信息中心(CNNIC). 第45次中国互联网络发展状况统计报告[R]. 北京: 中国互联网络信息中心(CNNIC), 2020: 4.
[2] AL-SHAMRI M Y H. Power coefficient as a similarity measure for memory-based collaborative recommender systems[J]. Expert systems with applications, 2014, 41(13): 5680-5688.
[3] NAJAFABADI M K, MOHAMED A, ONN C W. Animpact of time and item influencer in collaborative filtering recommendations using graph-based model[J]. Information processing & management, 2019, 56(3): 526-540.
[4] WANG Yong, DENG Jiangzhou, GAO J, et al. A hybrid user similarity model for collaborative filtering[J]. Information sciences, 2017, 418-419: 102-118.
[5] JIANG Shan, FANG S C, AN Qi, et al. A sub-one quasi-norm-based similarity measure for collaborative filtering in recommender systems[J]. Information sciences, 2019, 487: 142-155.
[6] MU Yi, XIAO Nianhao, TANG Ruichun, et al. An efficient similarity measure for collaborative filtering[J]. Procedia computer science, 2019, 147: 416-421.
[7] WANG T I, TSAI K H, LEE M C, et al. Personalized learning objects recommendation based on the semantic-aware discovery and the learner preference pattern[J]. Educational technology & society, 2007, 10(3): 84-105.
[8] SEGAL A, KATZIR Z, GAL Y, et al. EduRank: a collaborative filtering approach to personalization in E-learning[C]//Proceedings of the 7th International Conference on Educational Data Mining. London, UK, 2014: 68-74.
[9] ZHANG Fuzheng, YUAN N J, LIAN Defu, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, USA, 2016: 353-362.
[10] KLA?NJA-MILI?EVI? A, IVANOVI? M, VESIN B, et al. Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques[J]. Applied intelligence, 2018, 48(6): 1519-1535.
[11] BAKER R S J D, CORBETT A T, ALEVEN V. More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing[C]//Proceedings of the 9th International Conference on Intelligent Tutoring Systems. Montreal, Canada, 2008.
[12] SALEHI M. Application of implicit and explicit attribute based collaborative filtering and BIDE for learning resource recommendation[J]. Data & knowledge engineering, 2013, 87: 130-145.
[13] KURILOVAS E, SERIKOVIENE S, VUORIKARI R. Expert centred vs learner centred approach for evaluating quality and reusability of learning objects[J]. Computers in human behavior, 2014, 30: 526-534.
[14] 现代远程教育技术标准化委员会. CELTS-11, 学习者模型规范[S]. 现代远程教育技术标准化委员会, 2000: 11.
[15] COSTA R D, SOUZA G F, VALENTIM R A M, et al. The theory of learning styles applied to distance learning[J]. Cognitive systems research, 2020, 64: 134-145.
[16] ARIEVITCH I M. Reprint of: the vision of Developmental Teaching and Learning and Bloom’s Taxonomy of educational objectives[J]. Learning, culture and social interaction, 2020, 27: 100473.
[17] ZLATKOVIC D, DENIC N, PETROVIC M, et al. Analysis of adaptive e-learning systems with adjustment of Felder-Silverman model in a Moodle DLS[J]. Computer applications in engineering education, 2020, 28(4): 803-813.
[18] DASCALU M I, BODEA C N, MOLDOVEANU A, et al. A recommender agent based on learning styles for better virtual collaborative learning experiences[J]. Computers in human behavior, 2015, 45: 243-253.
[19] GONZáLEZ G, LóPEZ B, DE LA ROSA J L. A multi-agent smart user model for cross-domain recommender systems[C]//Proceedings of Beyond Personalization 2005: The Next Stage of Recommender Systems Research, International Conference on Intelligent User Interfaces IUI 2005. San Diego, USA, 2005.
[20] 谢修娟, 陈永, 李香菊, 等. 融入信任的变权重相似度模型在线学习协同推荐算法[J]. 小型微型计算机系统, 2018, 39(3): 525-528
XIE Xiujuan, CHEN Yong, LI Xiangju, et al. Collaborative recommendation algorithm of online learning based on trust-combined simi-larity model with variable weight[J]. Journal of Chinese computer systems, 2018, 39(3): 525-528
[21] 陈秀明, 刘业政. 多粒度犹豫模糊语言环境下未知权重的多属性群推荐方法[J]. 控制与决策, 2016, 31(9): 1631-1637
CHEN Xiuming, LIU Yezheng. Method of group recommender systems with unknown attribute weights in a multi-granular hesitant fuzzy linguistic term environment[J]. Control and decision, 2016, 31(9): 1631-1637
[22] KOREN Y. Collaborative filtering with temporal dynamics[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France, 2009: 89-97.
[23] 孙歆, 王永固, 邱飞岳. 基于协同过滤技术的在线学习资源个性化推荐系统研究[J]. 中国远程教育, 2012(8): 78-82
[24] 郑洁, 钱育蓉, 杨兴耀, 等. 基于信任和项目偏好的协调过滤算法[J]. 计算机应用, 2016, 36(10): 2784-2788, 2798
ZHENG Jie, QIAN Yurong, YANG Xingyao, et al. Collaborative filtering algorithm based on trust and item preference[J]. Journal of computer applications, 2016, 36(10): 2784-2788, 2798
[25] 丁永刚, 张馨, 桑秋侠, 等. 融合学习者社交网络的协同过滤学习资源推荐[J]. 现代教育技术, 2016, 26(2): 108-114
DING Yonggang, ZHANG Xin, SANG Qiuxia, et al. The collaborative filtering recommendation of learning resources combined with learners’ social network[J]. Modern educational technology, 2016, 26(2): 108-114
[26] 刘忠宝, 宋文爱, 孔祥艳, 等. 云环境下学习者建模与学习资源推荐方法研究[J]. 电化教育研究, 2017, 38(7): 58-63
LIU Zhongbao, SONG Wenai, KONG Xiangyan, et al. Research on learner modeling and learning resources recommendation in cloud environment[J]. E-education research, 2017, 38(7): 58-63

备注/Memo

备注/Memo:
收稿日期:2020-09-07。
基金项目:国家自然科学基金项目(61502094);黑龙江省教育科学规划重点课题(GJB1421113);黑龙江省优秀青年科学基金项目(YQ2020D001);黑龙江省高等教育教学改革研究项目(SJGY20190098);东北石油大学引导性创新基金项目(2020YDL-11);东北石油大学优秀中青年科研创新团队项目(KYCXTD201903) ;东北石油大学研究生教育创新工程项目(JYCX_11_2020)
作者简介:刘芳,副教授,博士,主要研究方向为智慧教育、多媒体与现代教育技术、计算机视觉、智能数据分析处理。获黑龙江省科技进步二等奖1项、大庆市科技进步二等奖1项。主持和参与国家自然科学基金项目、黑龙江省自然科学基金项目6项。发表学术论文21篇;田枫,教授,博士,主要研究方向为计算机视觉、智能数据分析处理。主持和参与国家自然科学基金项目、国家科技重大专项项目8项。获发明专利授权16项。发表学术论文31篇;李欣,助教,主要研究方向为智慧教育
通讯作者:刘芳.E-mail:lfliufang1983@126.com
更新日期/Last Update: 2021-12-25