To Improve the E-learning System using Data Mining Technique with Internet of Thing Exposure

Author(s):  
R. Jayakumar
Author(s):  
Songsakda Chayanukro ◽  
Massudi Mahmuddin ◽  
Husniza Husni

Current study on e-Learning user’s behaviour model obtained the specific models. In many cases, the e-Learning user’s behaviour model for open source e-Learning system such as Moodle, which can predict learning outcome or learning performance is still defi cient and cannot generally apply in many institutions due to the fact that the majority of prediction models were developed particularly for certain institutions. This study proposes to produce a general model that can make a prediction of learning outcome inspired by Skinner’s theory, which explains the relationship between learner, achievement, and learner reinforcement. This study proposes similar patterns in e-Learning user’s behaviour models of different institutions by the data-mining technique based on the learning environment theory. Therefore, this research is conducted in three main phases; include data preparation from weblog of different institutions with the same e-Learning system, data extraction by the accurate classifi er model fi nding process and model verification for generating a verification pattern. The research outcome will be a similar pattern that could be used as a direction for creating a more appropriate e-Learning users’ behaviour model and could be used broadly in other higher institutions.  


2015 ◽  
Vol 21 (2) ◽  
pp. 95
Author(s):  
Hyo Soung Cha ◽  
Tae Sik Yoon ◽  
Ki Chung Ryu ◽  
Il Won Shin ◽  
Yang Hyo Choe ◽  
...  

2016 ◽  
Vol 139 (6) ◽  
pp. 46-47
Author(s):  
M. Ashrafa ◽  
D. Asha ◽  
D. Radha ◽  
M. Sangeetha ◽  
R. Jayaparvathy

Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.


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