scholarly journals Student Performance Evaluation Using Data Mining Techniques for Engineering Education

2018 ◽  
Vol 3 (6) ◽  
pp. 259-264
Author(s):  
Veena Deshmukh ◽  
Srinivas Mangalwede ◽  
Dandina Hulikunta Rao
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Engineering Education ◽  
Student Performance ◽  
Data Mining Techniques ◽  
Using Data

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%.

Performance Evaluation of Social Network Using Data Mining Techniques

2012 ◽  
pp. 25-49 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Ajith Abraham ◽  
Sachidananda Dehuri ◽  
Manas Ranjan Patra
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Social Network ◽  
Data Mining Techniques ◽  
Using Data

scholarly journals A Survey on Performance Evaluation and Prediction of Teachers’, Students’ Performance Using Data Mining Techniques

IJARCCE ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 281-285 ◽  
Author(s):  
Sudarshan B. Wadkar ◽  
Dr. Dharmadhikari S. C. ◽  
Santosh Kumar Dwivedi
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Data Mining Techniques ◽  
Using Data
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