scholarly journals Investigation of the Use of Navigation Tools in Web-Based Learning: A Data Mining Approach

2008 ◽  
Vol 24 (1) ◽  
pp. 48-67 ◽  
Author(s):  
Christine G. Minetou ◽  
Sherry Y. Chen ◽  
Xiaohui Liu
2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.


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.


Author(s):  
Sabyasachi Pattnaik ◽  
Jui Pattnayak ◽  
Priyaranjan Dash

Data Mining and Data Warehousing are two most important techniques for pattern discovery and centralized data management in today’s technology. ELearning is one of the most significant applications of data mining. The main objective is to provide a proposal for a functional model and service architecture. The standards and system architecture are analyzed here. This paper gives importance to the integration of Web Services on the e-Learning application domain, because Web Service is the most advanced choice for distance education now. The process of e-Learning can be possible more effectively with the help of Web usage mining. More advanced tools are developed for online customer’s behaviour to increase sales, and profit, but no such tools are developed to understand learner’s behaviour in e-Learning. In this paper, some data mining techniques are discussed that could be used to enhance web-based learning environments.


2020 ◽  
pp. 841-866
Author(s):  
Dineshkumar B. Vaghela ◽  
Priyanka Sharma ◽  
Kalpdrum Passi

The explosive growth in the amount of data in the field of biology, education, environmental research, sensor network, stock market, weather forecasting and many more due to vast use of internet in distributed environment has generated an urgent need for new techniques and tools that can intelligently automatically transform the processed data into useful information and knowledge. Hence data mining has become a research are with increasing importance. Since continuation in collection of more data at this scale, formalizing the process of big data analysis will become paramount. Given the vast amount of data are geographically spread across the globe, this means a very large number of models is generated, which raises problems on how to generalize knowledge in order to have a global view of the phenomena across the organization. This is applicable to web-based educational data. In this chapter, the new dynamic and scalable data mining approach has been discussed with educational data.


Author(s):  
W. Villegas-Ch ◽  
S. Lujan-Mora ◽  
Diego Buenano-Fernandez ◽  
M. Roman-Canizares

2018 ◽  
Vol 12 (8) ◽  
pp. 116
Author(s):  
Yazan Alshamaila ◽  
Ibrahim Aljarah ◽  
Ala’ M. Al-Zoubi

With the use of Web 2.0 technology, e-commerce is undergoing a radical change that enriches consumer involvement and enables a better understanding of economic value. This emerging phenomenon is known as social commerce. Social commerce (s-commerce) presents a new alternative for consumers to search for and find information about products they are seeking to buy. In spite of its universality, the adoption of this burgeoning technology is affected by several factors. This research project is an initial attempt to explore individuals’ intention of s-commerce usage through the data mining approach. The data was collected via a web-based questionnaire survey of 360 social network site (SNS) users in Jordan. Data mining techniques were then used to analyze the collected data in order to figure out what group of features is best for predicting s-commerce adoption among SNS users. The results showed that data characteristics related to gender, monthly income, civil status, number of connections, and prior online shopping experience are key factors in the classification process. The findings may assist researchers in investigating social commerce issues and aid practitioners in developing new s-commerce strategies. 


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