Information Management in E-Learning System

2005 ◽  
pp. 275-283 ◽  
Author(s):  
Liu Jing ◽  
Zheng Li ◽  
Yang Fang
Keyword(s):  
Information Management ◽  
Learning System ◽  
E Learning

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.

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