Discovering the behavior of the students using data mining techniques

2018 ◽  
Vol 7 (3.3) ◽  
pp. 518
Author(s):  
S Kamalakkannan ◽  
S Prasanna
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Performance Index ◽  
Current User ◽  
Correct Item ◽  
Limited Ability ◽  
Data Mining Techniques ◽  
Real Issue ◽  
Behavior Mining ◽  
Using Data

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. 

scholarly journals Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

2019 ◽  
Vol 3 (2) ◽  
pp. 10
Author(s):  
Ardalan Husin Awlla
Keyword(s):  
Data Mining ◽  
Performance Analysis ◽  
Student Performance ◽  
Fraud Detection ◽  
Classification Model ◽  
Data Mining Techniques ◽  
Incipient Stage ◽  
The Everyday ◽  
Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

Automatic Student Performance Prediction System Using Data Mining Techniques

2021 ◽  
Author(s):  
Jyoti Chowdhery ◽  
Aadila Jasmin ◽  
Anukriti Jaiswal ◽  
J. Angel Arul Jothi
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction System ◽  
Data Mining Techniques ◽  
Using Data

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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