Educational Data Mining: an application of data mining techniques with the educational web-based system for predicting student performance

2014 ◽  
Author(s):  
Ji Hoon Seo ◽  
Irfan Ajamal Khan ◽  
Jin Tak Choi
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Web Based ◽  
Data Mining Techniques ◽  
Predicting Student Performance ◽  
Web Based System

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%.

scholarly journals Enhancing the Quality Education using Predictive and Descriptive Data Mining Model

2019 ◽  
Vol 8 (3) ◽  
pp. 6843-6847
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Educational Data Mining ◽  
Strong Relationship ◽  
Data Mining Techniques ◽  
Huge Data ◽  
Student’S Performance ◽  
Predictive Data Mining ◽  
Mining Model ◽  
The Given

Data mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student's performance to enhance the study level of the students in the organization.

scholarly journals Prediction of Student Performance System using Machine Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 32-37
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Naive Bayes ◽  
Educational Data Mining ◽  
Estimation Method ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Prediction Algorithm ◽  
Data Mining Techniques ◽  
Bayes Algorithm

Educational organizations are unique and play the utmost significant role in the development of any country. In the Educational database, due to the enormous volume of data for predicting student's achievement becomes more complicated. To upgrade a student's performance and triumph is more efficient in a practical way using Educational Data Mining Techniques. Data Mining Techniques could deliver favor and brunt to educators and academic institutions. The student's data ((i.e.) Name,10th %,12th cut off, CGPA, No of arrears, etc.) are gathered. Then, the datasets are imported into the Anaconda Navigator. Then, analysis and classification based on attributes of the students and the schemes are performed. Then using the prediction algorithm Naïve Bayes what are all the features the particular student is eligible for are predicted as placed. The student's input that has disparate data about their past and present academics report and then apply the Naïve Bayes algorithm using Anaconda Navigator to search the student's achievement for placement. A proposed methodology based on a classification approach to finding an improved estimation method for predicting the placement for students. This project can find the association for academic achievement of each particular student and their placement achievement in campus selection.

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