| 融入学习者模型在线学习资源协同过滤推荐方法 |
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| 引用本文： | 刘芳,田枫,李欣,林琳.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. |
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| 作者姓名： | 刘芳 田枫 李欣 林琳 |
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| 作者单位： | 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 |
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| 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。
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| 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 |
A collaborative filtering recommendation method for online learning resources incorporating the learner model |
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| LIU Fang,TIAN Feng,LI Xin,LIN Lin.A collaborative filtering recommendation method for online learning resources incorporating the learner model[J].CAAL Transactions on Intelligent Systems,2021,16(6):1117-1125. |
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| Authors: | LIU Fang TIAN Feng LI Xin LIN Lin |
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| Affiliation: | 1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, China;2. Nehe No. 1 Middle School, Nehe 161300,China |
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| 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. |
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| Keywords: | learner models online learning resources collaborative filtering personalized learning learning resources recommendation learning style characteristics cognitive level characteristics interest preference characteristics |
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