Discovering the behavior of the students using data mining techniques

Abstract

The real issue of numerous online sites is the introduction of numerous decisions for the different users at once. This normally comes about into tedious undertaking in discovering the correct item or data on the site. The user present intrigue relies on the navigational conduct which causes the associations to control users in their perusing exercises and acquire some applicable data in a limited ability to focus time. Since, the subsequent examples, which are acquired through data mining systems, did not perform well in the forecast of future temples designs due to the low coordinating rate of coming about tenets and of user's perusing conduct. This paper centers around the investigation of the pro-grammed web use data mining and proposal framework, which depends on current user conduct through his/her, snap stream information. In this paper, we attempt to show signs of improvement understanding on how Internet utilization of understudy's conduct in Engineering College can influence on their everyday scholarly exercises additionally it thinks about the use examples of various department' understudies. What's more, we endeavor to discover similitudes and dissimilarities of use examples of understudies on different branches and discovering connections between Internet utilization examples of understudies and their student performance CPI (Cumulative Performance Index). This paper displays the consequences of an investigation for a time of three months, in regards to the behavior mining of understudies identified with their Internet use designs with examining access log documents.

Learning Behavior Based on Data Mining Technology

“…It was able to "predict and process students' learning behavioral trajectories with data and models" [5]. Siements, an expert in learning analysis, argued that learning analysis adopts intelligent data, student-generated data, and analysis models to tap the connection between information and the society [6]. It is a good way to predict learning behavior and give suggestions for adaptive learning.…”