scholarly journals A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhihao Zhang
Keyword(s):  
Collaborative Filtering ◽  
Dimensional Space ◽  
Learning System ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Resources ◽  
Search Methods ◽  
E Learning ◽  
Predicted Values ◽  
Collaborative Knowledge

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.

Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

2014 ◽  
Vol 4 (1) ◽  
pp. 1-14 ◽  
Author(s):  
M. Waseem Chughtai ◽  
Imran Ghani ◽  
Ali Selamat ◽  
Seung Ryul Jeong
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Traditional Education ◽  
Experimental Result ◽  
Personalized Recommendation ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Content Based Filtering

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

2020 ◽  
Vol 34 (10) ◽  
pp. 2059033
Author(s):  
Gang Huang ◽  
Man Yuan ◽  
Chun-Sheng Li ◽  
Yong-he Wei
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Cold Start ◽  
Petroleum Exploration ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

scholarly journals Multi-Agent Adaptive e-Learning System Based on Learning Styles

2021 ◽  
Vol 4 (1) ◽  
pp. 1-12
Author(s):  
Faith Ngami Kivuva ◽  
Elizaphan Maina ◽  
Rhoda Gitonga
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Intelligent Agents ◽  
Personalized Learning ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Information Agent ◽  
E Learning ◽  
Multi Agent

Most traditional e-learning system fails to provide the intelligence that a learner may require during their learning process. Different learners have different learning styles but the current e-learning systems are not able to provide personalized learning. In this paper, we discuss how intelligent agents can aid learners in their learning process. Three agents have been developed namely, learner agent, information agent, and tutor agents that will be integrated into a learning management system (Moodle). Learners are provided with a personalized recommendation based on the learning styles.

Personalized Recommendation Research in E-Learning Systems

2013 ◽  
Vol 433-435 ◽  
pp. 603-606
Author(s):  
Bing Wu ◽  
Ping Ping Chen
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Important Research ◽  
Research Areas ◽  
Communication Efficiency ◽  
E Learning ◽  
Explicit Study

The purpose of this paper is to review the literatures which have made an explicit study on personalized recommendation in E-Learning systems. By identifying the important research areas, which are in different perspectives, firstly, filtering recommendation is introduced before the illustration of how it has been developed in E-Learning systems. Then personalized recommendation is proposed for E-Learning system. Although social network is the basic way to improve the communication efficiency with others in E-Learning system, previous studies pay less attention on this. Therefore social network analysis should be taken into consideration for the recommendation in E-Learning system for further research.

An intelligent e-learning system based on learner profiling and learning resources adaptation

2008 ◽  
Vol 51 (1) ◽  
pp. 224-238 ◽  
Author(s):  
Paraskevi Tzouveli ◽  
Phivos Mylonas ◽  
Stefanos Kollias
Keyword(s):  
Learning System ◽  
Learning Resources ◽  
E Learning

scholarly journals SURVEI 2009: MUTU SITUS E-LEARNING SEKOLAH INDONESIA MASIH SANGAT MINIM

2012 ◽  
Vol 6 (1) ◽  
pp. 80
Author(s):  
Heru Suhartanto
Keyword(s):  
Survey Data ◽  
Learning Process ◽  
Learning System ◽  
Learning Resources ◽  
Time And Space ◽  
Indonesian Government ◽  
Internet Infrastructure ◽  
E Learning

Di beberapa negara yang infrastruktur internet telah memadai, banyak sekolah yang telah memanfaatkannya sebagai salah satu faktor pendukung kesuksesan proses pembelajaran. Pemerintah Indonesia pun telah memulai teknologi ini guna memperluas akses sumber daya pembelajaran sehingga tak terbatas oleh waktu dan ruang. Makalah ini melaporkan hasil survei yang menggambarkan seberapa jauh sistem e-learning telah dipakai di sekolah Indonesia. Data hasil survei menunjukkan bahwa kualitas pemanfaatan situs e-learning masih kurang dan perlu mendapat perhatian dan dukungan dari semua pihak terkait. In some countries that have adequate internet infrastructure, many schools are already using it as one of the factors supporting the success of the learning process. The Indonesian government also has initiated this technology to expand the access to learning resources that is unlimited by time and space. This paper reports the results of the survey that describe the extent to which e-learning system has been used in Indonesian schools. Survey data indicate that the quality of utilization of e-learning is still lacking and need attention and support from all parties concerned.

The Study of User Model of Personalized Recommendation System Based on Linked Course Data

2014 ◽  
Vol 519-520 ◽  
pp. 1609-1612
Author(s):  
Ji Yan Wu ◽  
Chan Le Wu
Keyword(s):  
Linked Data ◽  
Recommendation System ◽  
User Model ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Path ◽  
E Learning ◽  
Linked Courses ◽  
Efficient Learning ◽  
Massive Information

The massive information on Internet makes users obtain information become in efficient.Similarly,the users in the field of e-learning face with the problems that learning resources are inefficient and learning paths unreasonable,etc .Linked Courses data is a standard of W3C proposed will be organized the courses in the national excellent courses into a linked data database. On the basis the author design a user model to meet the user's learning requirements, which will be the most suitable learning Resources recommended to the user. Finally, the author validate the model with experimental ,the application shows that the model can indicate the user's learning requirements,and rational and efficient learning path is recommended.

scholarly journals Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm

2020 ◽  
Vol 214 ◽  
pp. 01051
Author(s):  
Baiqiang Gan ◽  
Chi Zhang
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Online Course ◽  
Learning Resources ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Open Courses ◽  
Online Learning Resources

In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.

scholarly journals Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

2022 ◽  
Vol 16 ◽  
pp. 122-131
Author(s):  
Mingxia Zhong ◽  
Rongtao Ding
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Behaviors ◽  
Filtering Algorithm ◽  
Learning Platform ◽  
Collaborative Filtering Algorithm ◽  
Learning Platforms

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

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