A Data Mining Based Pervasive User Requests Prediction Method in e-Learning Systems

2006 ◽  
Author(s):  
Tao Ye ◽  
Li Xue-Qing ◽  
Du Ping ◽  
Liang Kan
Keyword(s):  
Data Mining ◽  
Prediction Method ◽  
Learning Systems ◽  
E Learning

scholarly journals Research on Personalized and Secured E-Learning Systems using Data Mining Technique

2019 ◽  
Vol 8 (11S) ◽  
pp. 664-667
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Learning Systems ◽  
Large Set ◽  
Data Mining Technique ◽  
Large Space ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
The Web

Data mining is the concept for extracting the appropriate data from the large set of database. In today’s world it is widely used for many applications where learning applications is one of the major part. The e-Learning is the booming technology where anyone can learn everything from any part of the world. It is the digital way of learning the concepts and does not require the help of other persons to do so. It also requires the large space for data storage such as user information, course records and course details and so on. There are lot of learning applications available on the internet among which some might be subjected to frauds. So the security is the demanding thing every users looking for to protect their details. The users also seek for flexibility of using the applications. In perspective of distributed world, the complexity and interoperability of the data brings challenges in e-learning domain.Depends upon learner’s choice, the web based learning modules were developed for the students. Thus, a holistic approach is required for achieving the personalized content since the student groups are heterogeneous in nature. In addition to, the personalized content has to be protected in order to maintain the data integrity and privacy of the users. In this work, we survey about the present scenario of the web-based e-learning systems. Initially, we present the services oriented architecture of the e-learning systems and also clearly explain the different elearning layers.Then, we portray the existing studies processed in web based e-learning systems. Finally, we discuss about the challenges still persists in web-based learning systems. This paper will guide the upcoming researchers in e-learning fields.

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

2017 ◽  
pp. 1-23 ◽  
Author(s):  
Renuka Mahajan
Keyword(s):  
Data Mining ◽  
Recommender System ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Comprehensive List ◽  
Research Areas ◽  
New Models ◽  
E Learning ◽  
Research Intensity

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

scholarly journals Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals Adjusting Felder-Silverman learning styles model for application in adaptive e-learning

Psihologija ◽  
2012 ◽  
Vol 45 (1) ◽  
pp. 43-58 ◽  
Author(s):  
Djordje Mihailovic ◽  
Marijana Despotovic-Zrakic ◽  
Zorica Bogdanovic ◽  
Dusan Barac ◽  
Vladimir Vujin
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Learning Styles ◽  
Learning Systems ◽  
Questionnaire Data ◽  
Data Mining Techniques ◽  
Significant Difference ◽  
E Learning ◽  
Using Data ◽  
Experimental Group

This paper presents an approach for adjusting Felder-Silverman learning styles model for application in development of adaptive e-learning systems. Main goal of the paper is to improve the existing e-learning courses by developing a method for adaptation based on learning styles. The proposed method includes analysis of data related to students characteristics and applying the concept of personalization in creating e-learning courses. The research has been conducted at Faculty of organizational sciences, University of Belgrade, during winter semester of 2009/10, on sample of 318 students. The students from the experimental group were divided in three clusters, based on data about their styles identified using adjusted Felder-Silverman questionnaire. Data about learning styles collected during the research were used to determine typical groups of students and then to classify students into these groups. The classification was performed using data mining techniques. Adaptation of the e-learning courses was implemented according to results of data analysis. Evaluation showed that there was statistically significant difference in the results of students who attended the course adapted by using the described method, in comparison with results of students who attended course that was not adapted.

scholarly journals ПОШУК, ОТРИМАННЯ Й АНАЛІЗ ДАНИХ В ОСВІТІ: СУЧАСНИЙ СТАН І ПЕРСПЕКТИВИ РОЗВИТКУ

2016 ◽  
Vol 50 (6) ◽  
pp. 152
Author(s):  
Yurii O. Kovalchuk
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Learning Systems ◽  
Basic Principles ◽  
Data Mining Algorithms ◽  
Independent Direction ◽  
E Learning ◽  
The Subject ◽  
Classification And Regression ◽  
Mining Algorithms

The main tasks (classification and regression, association rules, clustering) and the basic principles of the Data Mining algorithms in the context of their use for a variety of research in the field of education which are the subject of a relatively new independent direction Educational Data Mining are considered. The findings about the most popular topics of research within this area as well as the perspectives of its development are presented. Presentation of the material is illustrated by simple examples. This article is intended for readers who are engaged in research in the field of education at various levels, especially those involved in the use of e-learning systems, but little familiar with this area of data analysis.

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