This paper is published in Volume-4, Issue-3, 2018
Area
Big Data
Author
Rabia Ashrafi
Co-authors
Sharmila Sankar
Org/Univ
B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
Pub. Date
14 May, 2018
Paper ID
V4I3-1271
Publisher
Keywords
E-Learning, Web-based learning system, Application model.

Citationsacebook

IEEE
Rabia Ashrafi, Sharmila Sankar. E-learning: Distributed processing of large datasets with parallel algorithm, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Rabia Ashrafi, Sharmila Sankar (2018). E-learning: Distributed processing of large datasets with parallel algorithm. International Journal of Advance Research, Ideas and Innovations in Technology, 4(3) www.IJARIIT.com.

MLA
Rabia Ashrafi, Sharmila Sankar. "E-learning: Distributed processing of large datasets with parallel algorithm." International Journal of Advance Research, Ideas and Innovations in Technology 4.3 (2018). www.IJARIIT.com.

Abstract

In today’s lifestyle, every task has been executed by the help of internet. The online system or the internet facilities getting more widespread as well as its becoming part of the human lifestyle. Now in days, every individual recommends that learning should at any-place and any-time, and this recommendation is resolved by E-Learning system. There are multiple E-learning portals are available like javaTpoint. The aim of proposed e-learning platform was: • Course data materials must be secure. • Allowing learner to register and enter into the courses. • Learning should be easier, fluent and learner friendly. • Effective communication between learner and e-learn platform. The learner, using a web browser, interacts with the e-learning application. The learner can register to the system for particular course Next step is learner recommended for textual study material and video study material so that learner can refer notes or be learning the material as per choice. The learner can learn easily, flexible at any time, at anywhere we present a technical analysis of seven studies in the context of the application of data mining approach in e-learning. The results of our analysis support the use of data mining techniques for building a new generation of intelligent e-learning systems for different tasks and domains. E-learning course offerings are now plentiful, and many new e-learning platforms and systems have been developed and implemented with varying degrees of success. These systems generate a top increasing amount of data, and much of this information has the potential to become new knowledge to improve all instances of e-learning. Data mining processes should enable the extraction of this knowledge. Now implementing e-learning web interface can help to design courses more effectively, detect anomalies, inspire and guide further research, and help learners use resources more efficiently. The long-term objective is that to create fully featured learning system for the learning environment.