The Enhancement and Application of Collaborative Filtering in e-Learning System

2014 ◽  
pp. 188-195 ◽  
Author(s):  
Bo Song ◽  
Jie Gao
Keyword(s):  
Collaborative Filtering ◽  
Learning System ◽  
E Learning

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.

Career Goal-based E-Learning Recommendation Using Enhanced Collaborative Filtering and PrefixSpan

2018 ◽  
Vol 10 (3) ◽  
pp. 23-37 ◽  
Author(s):  
Xueying Ma ◽  
Lu Ye
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Personalized Learning ◽  
Learning System ◽  
Career Goals ◽  
Learning Goals ◽  
Background Information ◽  
Learning Path ◽  
E Learning ◽  
Cold Start Problem

This article describes how e-learning recommender systems nowadays have applied different kinds of techniques to recommend personalized learning content for users based on their preference, goals, interests and background information. However, the cold-start problem which exists in traditional recommendation algorithms are still left over in e-learning systems and a few of them have seriously affected the learning goals of users. Thus, an intelligent e-learning system have been developed which can recommend professional and targeted courses according to their career goals. First, an enhanced collaborative filtering (CF) approach is proposed considering users' career goals and background information. Then, the relevance between career goals and courses are calculated to alleviate the cold-start problem and recommend specialized courses for users. Finally, a PrefixSpan algorithm is combined with the above methods to generate a personalized learning path step by step. Some experiments are carried out with real users of different professions to test the performance of the hybrid algorithm.

E-learning experience in schools and colleges with 1C:Education platform

2020 ◽  
pp. 17-24
Author(s):  
T. A. Chernetskaya ◽  
N. A. Lebedeva
Keyword(s):  
Distance Learning ◽  
General Education ◽  
Learning Experience ◽  
Educational Process ◽  
Learning System ◽  
Full Time ◽  
Distance Learning System ◽  
Teachers And Students ◽  
E Learning ◽  
Mass Organization

The article presents the experience of mass organization of distance learning in organizations of secondary general and vocational education in March—May 2020 in connection with the difficult epidemiological situation in Russia. The possibilities of the 1C:Education system for organizing the educational process in a distance format, the peculiarities of organizing distance interaction in schools and colleges are considered, the results of using the system are summarized, examples of the successful use of the system in specific educational organizations are given. Based on the questionnaire survey of users, a number of capabilities of the 1C:Education system have been identified, which are essential for the full-fledged transfer of the educational process from full-time to distance learning. The nature and frequency of the use of electronic educational resources in various general education subjects in schools and colleges are analyzed, the importance of the presence in the distance learning system not only of a digital library of ready-made educational materials, but also of tools for creating author’s content is assessed. On the basis of an impersonal analysis of user actions in the system, a number of problems were identified that teachers and students faced in the process of an emergency transition to distance learning.

An E-learning System in Malaysia based on Green Computing and Energy Level

2017 ◽  
Vol 1 (4-2) ◽  
pp. 184 ◽  
Author(s):  
Arif Ullah ◽  
Nazri Mohd Nawi ◽  
Asim Shahzad ◽  
Sundas Naqeeb Khan ◽  
Muhammad Aamir
Keyword(s):  
Environmental Concern ◽  
Specific Area ◽  
Green Computing ◽  
Learning System ◽  
Main Step ◽  
Primary Concern ◽  
Green Is ◽  
E Learning ◽  
Energy Consuming ◽  
Power And Energy

The increasing of energy cost and also environmental concern on green computing gaining more and more attention. Power and energy are a primary concern in the design and implementing green computing. Green is of the main step to make the computing world friendly with the environment.  In this paper, an analysis on the comparison of green computer with other computing in E-learning environment had been done. The results show that green computing is friendly and less energy consuming. Therefore, this paper provide some suggestions in overcoming one of main challenging problems in environment problems which need to convert normally computing into green computing. In this paper also, we try to find out some specific area which consumes energy as compared to green computing in E –learning centre in Malaysia. The simulation results show that more than 30% of energy reduction by using green computing.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

PEMBELAJARAN E-LEARNING

2019 ◽  
Vol 2 ◽  
Author(s):  
Nuke Lulu Ul Chusna
Keyword(s):  
Subject Matter ◽  
Technological Progress ◽  
Learning Culture ◽  
Learning System ◽  
Internet Technology ◽  
The Internet ◽  
Digital Format ◽  
E Learning ◽  
Context Of Learning ◽  
Present Subject

The internet can be used as a way to transfer knowledge from teachers to students. Learning that utilizes the development of technology and information, namely the internet, one of which is the e-learning learning system. E-learning is a form of conventional learning that is transferred in digital format through internet technology, not only to present subject matter on the internet but also must be in accordance with the principles of learning.The e-learning learning model results in changes in learning culture in the context of learning. Learning becomes very flexible, because it can be adjusted to the availability of time from students in learning the material provided by the teacher.The teacher determines the success of students in learning, therefore teachers are required to have the ability to adapt to technological progress. Keywords: ICT,e-learning, e-learning learning

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