scholarly journals Evaluating the Performance of Engineering’s Students in Mathematic Subject based on Academic Decision-Making Techniques

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 154-165
Author(s):  
Abbas Atwan Mhawes ◽  
Ahmed Yousif Falih Saedi ◽  
Ali Talib Qasim Al-Aqbi ◽  
Lamees Abdalhasan Salman
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Cognitive Skills ◽  
Data Extraction ◽  
Learning System ◽  
Factors Affecting ◽  
Data Mining Techniques ◽  
Sequence Modeling ◽  
Wide Range ◽  
Estimation Sequence

Data mining is characterized as a quest for useful knowledge via large quantities of data. Some basic and most common techniques for data extraction are association rules, grouping, clustering, estimation, sequence modeling. For a wide range of applications, data mining techniques are used. Techniques of data analysis are essential to the preparation and implementation of the administration of the learning system, including behavioral guidance and personal behavior appraisal. The article applies data analytical methods to the role of student classification. Several tests are used for the interpretation of the findings. In keeping with the methodology proposed in the paper, the classification using cognitive skills provides more detailed results than the findings of other study published. Five algorithms were used (J48, Naïve Bayes, Multilayer Perception, K Star and SMO). This essay discusses and measures the application of the various algorithms so that factors affecting the success and failure of students can be identified, student performance can be estimated, and the significant consequences of the mathematics system for the second university year can be identified. However the number of exams can be minimized using data mining techniques. In terms of time and consequences, this shortened analysis plays a key role.

scholarly journals Survey Paper on Information based Learning System using Educational Data Mining

2021 ◽  
Vol 9 (VI) ◽  
pp. 1868-1873
Author(s):  
Dhanendra Kumar
Keyword(s):  
Data Mining ◽  
Student Performance ◽  
Data Warehouse ◽  
Educational Data Mining ◽  
Learning System ◽  
Essential Information ◽  
Educational Method ◽  
Data Mining Techniques ◽  
Survey Paper ◽  
Level Of Understanding

Educational Data Mining (EDM) is a platform for learning and exploring from data to get essential information and generate the unique pattern which will help study, analyse and skill student performance in academic. Various data mining methods can be apply to filter the data from data warehouse to implement data mining techniques which helps student for taking decisions for better outcome. The model which can be use in Educational data mining must be a constructive and descriptive model applied on data warehouse and must gather very accurate data for enhance the performance of study. Regression analysis can also be used to develop a model to use as study tool; it can be used dependent or independent variables. If the model is enough perfect for using as study tool then every cluster of data must be use that model to fetch the resultant data. Sometimes educational data mining is considered as overall performance of students, but each student has its own level of understanding the contents so that method must also be enough flexible for every one ; for fulfilling this requirement educational method can be complex, but once it is constructed then it will helpful for every one. This paper is describing various data mining techniques and their proper uses.

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.

Data Mining

2011 ◽  
pp. 921-926
Author(s):  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Electronic Commerce ◽  
New Technologies ◽  
Research Area ◽  
Future Research ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Degree Of Confidence ◽  
Common Application ◽  
Existing Data

There is an explosion in the amount of data that organizations generate, collect, and store. Organizations are gradually relying more on new technologies to access, analyze, summarize, and interpret information intelligently. Data mining, therefore, has become a research area with increased importance (Amaratunga & Cabrera, 2004). Data mining is the search for valuable information in large volumes of data (Hand, Mannila, & Smyth, 2001). It can discover hidden relationships, patterns, and interdependencies and generate rules to predict the correlations, which can help the organizations make critical decisions faster or with a greater degree of confidence (Gargano & Ragged, 1999). There is a wide range of data mining techniques, which has been successfully used in many applications. This article is an attempt to provide an overview of existing data mining applications. The article begins by explaining the key tasks that data mining can achieve. It then moves to discuss applications domains that data mining can support. The article identifies three common application domains, including bioinformatics, electronic commerce, and search engines. For each domain, how data mining can enhance the functions will be described. Subsequently, the limitations of current research will be addressed, followed by a discussion of directions for future research.

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 Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq

2021 ◽  
Vol 11 (6) ◽  
pp. 5229
Author(s):  
Rana Riad K. AL-Taie ◽  
Basma Jumaa Saleh ◽  
Ahmed Yousif Falih Saedi ◽  
Lamees Abdalhasan Salman
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Random Forest ◽  
Multilayer Perceptron ◽  
Data Extraction ◽  
Sequential Minimal Optimization ◽  
Health Issues ◽  
Sequence Modeling ◽  
Data Mining Algorithms ◽  
Bayes Net

Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

scholarly journals A Comprehensive Assessment on IOT Devices with Data Mining Techniques

2021 ◽  
Author(s):  
G. Vijay Kumar ◽  
M. Sreedevi ◽  
Arvind Yadav ◽  
B. Aruna
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Comprehensive Assessment ◽  
Smart Devices ◽  
Heterogeneous Environments ◽  
Data Mining Techniques ◽  
Wide Range ◽  
Entire World ◽  
Iot Devices ◽  
Cloud Technologies

Now at present development the entire world using vast variety of smart devices associated among sensors & handful of actuators. There is an enormous progress within the field of electronic communication; processing the data through devices and the bandwidth in internet technologies makes very easy to access and to interact with the variety of devices all over the whole world. There is a wide range research in the area of Internet of Things (IoT) along Cloud Technologies making to build incredible data which are creating from this type of heterogeneous environments and can be able to transform into a valuable knowledge with the help of data mining techniques. The knowledge that is generated will takes a crucial role in making intellectual decisions and also be a best possible resource management and services. In this paper we organized a comprehensive assessment on various data mining techniques engaged with small and large scale IoT applications to make the environment smart.

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