[1]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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A collaborative filtering recommendation method for online learning resources incorporating the learner model

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 6 Page number: 1117-1125 Column: 学术论文—知识工程 Public date: 2021-11-05
Title:
A collaborative filtering recommendation method for online learning resources incorporating the learner model
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
Keywords:
learner models; online learning resources; collaborative filtering; personalized learning; learning resources recommendation; learning style characteristics; cognitive level characteristics; interest preference characteristics
CLC:
TP391;G434
DOI:
10.11992/tis.202009005
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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Last Update: 2021-12-25

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