scholarly journals Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55462-55470 ◽  
Author(s):  
Hanan Abdullah Mengash
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Student Performance ◽  
Data Mining Techniques ◽  
University Admission ◽  
Using Data ◽  
Support Decision Making

scholarly journals Predicting Student Performance by Using Data Mining Methods for Classification

2013 ◽  
Vol 13 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Dorina Kabakchieva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Student Performance ◽  
High Potential ◽  
University Management ◽  
Mining Methods ◽  
Predicting Student Performance ◽  
Using Data ◽  
Initial Results ◽  
Support Decision Making

Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

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%.

Using Data Mining Techniques to Discover Patterns in an Airline's Flight Hours Assignments

2021 ◽  
pp. 107-126
Author(s):  
Francisco Javier Villar Martín ◽  
Jose Luis Castillo Sequera ◽  
Miguel Angel Navarro Huerga
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information System ◽  
Predictive Modeling ◽  
Data Model ◽  
Free Software ◽  
Data Mining Techniques ◽  
Visualization Tools ◽  
Using Data

The quality of a company's information system is essential and also its physical data model. In this article, the authors apply data mining techniques in order to generate knowledge from the information system's data model, and also to discover and understand hidden patterns within data that regulate the planning of flight hours of pilots and copilots in an airline. With this objective, they use Weka free software which offers a set of algorithms and visualization tools geared to data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information system and analyze data model; secondly, they apply association rules to discover connection patterns in data; and finally, they generate a decision tree to classify and extract more specific patterns. The authors suggest conclusions according these information system's data to improve future decision making in an airline's flight hours assignments.

scholarly journals Knowledge-based model to support decision-making when choosing between two association data mining techniques

2017 ◽  
Vol 14 (2) ◽  
pp. 41-50
Author(s):  
Juan Camilo Giraldo Mejía ◽  
◽  
Diana María Montoya Quintero ◽  
Jovani Alberto Jiménez Builes ◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Mining Techniques ◽  
Knowledge Based ◽  
Association Data ◽  
Support Decision Making

The Application of Data Mining in Customer Management of Magazine Editing System

2013 ◽  
Vol 846-847 ◽  
pp. 1048-1051
Author(s):  
Xiao Qian Zhang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Customer Management ◽  
Large Database ◽  
Data Mining Techniques ◽  
Fierce Competition ◽  
Support Decision Making

China's commercial magazine faces of increasingly fierce competition in the customer, so it must improve its management and marketing method to enhance competitiveness. It is the key point to strengthening customer relationship management. The study in this paper uses data mining techniques to enhance the management of the customer to explore new customers, maintain overall customers and accelerate the development of the magazine. Through the establishment of large database and data mining, we find useful data and the relevance to support decision-making and better improve the competitiveness of the magazine.

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