A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration

2016 ◽  
Author(s):  
Anoopkumar M ◽  
A. M. J. Md. Zubair Rahman
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Data Mining Techniques

A Systematic Review About Prediction of Academic Behavior Through Data Mining Techniques

2020 ◽  
Vol 17 (11) ◽  
pp. 5162-5166
Author(s):  
Puninder Kaur ◽  
Amandeep Kaur ◽  
Rajwinder Kaur
Keyword(s):  
Systematic Review ◽  
Data Mining ◽  
Data Analysis ◽  
Educational Data Mining ◽  
Prediction Methods ◽  
Time Data ◽  
Academic Behavior ◽  
Data Mining Techniques ◽  
Real Time Data ◽  
Extract Information

In the IT world, predicting the academic performance of the huge student population poses a big challenge. Educational data mining techniques significantly contribute in providing solution to this problem. There are several prediction methods available for data classification and clustering, to extract information and provide accurate results. In this paper, different prediction methodologies are highlighted for the prediction of real-time data analysis of dynamic academic behavior of the students. The main focus is to provide brief knowledge about all data mining techniques and highlight dissimilarities among various methods in order to provide the best results for the students.

Mining Learners’ Behaviors: An Approach Based on Educational Data Mining Techniques

2020 ◽  
pp. 655-670 ◽  
Author(s):  
Ouafae El Aissaoui ◽  
Yasser El Alami El Madani ◽  
Lahcen Oughdir ◽  
Ahmed Dakkak ◽  
Youssouf El Allioui
Keyword(s):  
Data Mining ◽  
Educational Data Mining ◽  
Data Mining Techniques

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 Data Mining Techniques Applying on Educational Dataset to Evaluate Learner Performance Using Cluster Analysis

2018 ◽  
Vol 3 (11) ◽  
pp. 25-31 ◽  
Author(s):  
Minimol Anil Job
Keyword(s):  
Data Mining ◽  
Clustering Algorithms ◽  
Educational Data Mining ◽  
Large Datasets ◽  
Business Value ◽  
Hidden Information ◽  
Data Mining Techniques ◽  
Learner Performance ◽  
Digital Era ◽  
Hidden Patterns

Due to the advancement of technology in this digital era, academic institutions are bringing out graduates as well as generating enormous amounts of data from their systems. Hidden information and hidden patterns in large datasets can be efficiently analyzed with data mining techniques. Application of data mining techniques improves the performance of many organizational domains and the concept can be applied in the education sectors for their performance evaluation and improvement. Understanding the business value of the collected data it can be used for classifying and predicting the students’ behavior, academic performance, dropout rates, and monitoring progression and retention. This paper discusses how application of data mining can help the higher education institutions by enabling better understanding of the student data and focuses to consolidate clustering algorithms as applied in the context of educational data mining.

scholarly journals Educational data mining using cluster analysis and decision tree technique: A case study

2020 ◽  
Vol 12 ◽  
pp. 184797902090867
Author(s):  
Snježana Križanić
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Decision Tree ◽  
Student Behavior ◽  
Educational Data Mining ◽  
Education Institution ◽  
Data Mining Techniques ◽  
Event Logs ◽  
Hidden Knowledge ◽  
E Learning

Data mining refers to the application of data analysis techniques with the aim of extracting hidden knowledge from data by performing the tasks of pattern recognition and predictive modeling. This article describes the application of data mining techniques on educational data of a higher education institution in Croatia. Data used for the analysis are event logs downloaded from an e-learning environment of a real e-course. Data mining techniques applied for the research are cluster analysis and decision tree. The cluster analysis was performed by organizing collections of patterns into groups based on student behavior similarity in using course materials. Decision tree was the method of interest for generating a representation of decision-making that allowed defining classes of objects for the purpose of deeper analysis about how students learned.

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