scholarly journals Prediction of the Academic Achievement of Pupils Using Data Mining Techniques

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 1355-1364
Author(s):  
Mokhalad Eesee Khudhur ◽  
Mohammed Shihab Ahmed ◽  
Saif Muhannad Maher
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Academic Success ◽  
Student Performance ◽  
Drop Out ◽  
Data Set ◽  
Perceptron Algorithm ◽  
Improve Student Performance ◽  
Using Data ◽  
Logistic Regression Algorithm

Introduction: During this epidemic, a problem in fundamental education affecting all globe is occurring, and we note that education and learning were online and conducted in students. Academic performance of students must be forecast, so that the instructor may better identify the missing pupils and offer teachers a proactive opportunity to develop additional resources for the student to maximize their chances of graduation. Students' academic achievement in higher learning (EH) has been extensively studied in addressing academic inadequacies, rising drop-out rates, graduation delays, and other difficult questions. Simply said, the performance of students refers to the amount to which short and long-term educational objectives are met. Academics nonetheless judge student achievement from different viewpoints, from grades, average grade points (GPAs) to prospective jobs. The literature encompasses numerous computing attempts to improve student performance in schools and colleges, primarily through data mining and analysis learning. However, the efficiency of current smart techniques and models is still unanimous. Method: This study employs multiple methods for machine learning to forecast student progress. With its accurate data sample prediction, five integrated classification algorithms have been created to forecast students' academic success (support vectors, decision-making trees algorithm and perceptron algorithm, logistic regression algorithm and a random forest algorithm). Results: Students' academic achievement has been reviewed and assessed. The performance of five learning machines mentioned in Section 4 is discussed here. First, we displayed the data after pre-processing by simply displaying distributions to form the data packet and then evaluated 5 important learning methods and described the variables in the data set. The entire series of 480 characteristics were examined.

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques

2021 ◽  
Vol 12 (1) ◽  
pp. 94-110
Author(s):  
Deepti Aggarwal ◽  
Sonu Mittal ◽  
Vikram Bali
Keyword(s):  
Data Mining ◽  
Uttar Pradesh ◽  
Drop Out ◽  
Primary Data ◽  
Educational Institutions ◽  
Technical Institute ◽  
Data Set ◽  
Data Mining Techniques ◽  
Preventive Actions ◽  
Using Data

The educational institutes are focusing on improving the performance of students by using several data mining techniques. Since there is an increase in the number of drop out students every year, if we are able to predict whether a student will complete the course or not, it is possible to take some preventive actions beforehand. The primary data set used for modelling has been taken from a reputed technical institute of Uttar Pradesh which consists of data of 6,807 students containing 20 academic and non-academic attributes. The most relevant attributes are extracted using CorrelationAttributeEval (in WEKA) technique using Ranker search method which ranks the attributes as per their evaluation. Synthetic minority oversampling technique (SMOTE) filter is applied to deal with the skewed data set. The models are built from eight classifiers that are analysed for predicting the most appropriate model to classify whether a student will complete the course or withdraw his/her admission.

scholarly journals Applying Deep Learning Neural Networks in Predicting Students' Cumulative Grades

2020 ◽  
Vol 10 (2) ◽  
pp. 293-297
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Academic Performance ◽  
Deep Learning ◽  
Student Performance ◽  
Quality Education ◽  
Data Set ◽  
Improve Student Performance ◽  
The Difference ◽  
Indirect Information

The distinguished universities aim to provide quality education to their students. One way to achieve the highest quality in university studies is to discover knowledge to predict student performance and grades in courses etc. Recently, the amount of data stored in educational databases is accumulating very quickly, as these databases contain indirect information that can be used to improve student performance. Academic performance is affected by many factors, so it is necessary to predict student performance to determine the difference between students who are excelling in studies and students who need to exert more effort to improve their performance and their level of achievement. Hidden or Indirect knowledge is part of the educational data set and can be extracted using various means, such as data mining techniques and the use of classification, and deep learning through neural networks. This paper has been designed to extract knowledge describing students' performance in the courses required for graduation, in a way that helps academic advisors in providing academic advice and guidance to students to improve their cumulative grades.

Implementasi Data Mining dengan Algoritma C4.5 untuk Memprediksi Tingkat Kelulusan Mahasiswa

2014 ◽  
Vol 6 (1) ◽  
pp. 15-20 ◽  
Author(s):  
David Hartanto Kamagi ◽  
Seng Hansun
Keyword(s):  
Data Mining ◽  
Drop Out ◽  
Time Pass ◽  
Academic Fields ◽  
C4.5 Algorithm ◽  
Index Terms ◽  
Using Data ◽  
Academic Year ◽  
Source Of Information ◽  
Test Result

Graduation Information is important for Universitas Multimedia Nusantara  which engaged in education. The data of graduated students from each academic year is an important part as a source of information to make a decision for BAAK (Bureau of Academic and Student Administration). With this information, a prediction can be made for students who are still active whether they can graduate on time, fast, late or drop out with the implementation of data mining. The purpose of this study is to make a prediction of students’ graduation with C4.5 algorithm as a reference for making policies and actions of academic fields (BAAK) in reducing students who graduated late and did not pass. From the research, the category of IPS semester one to semester six, gender, origin of high school, and number of credits, can predict the graduation of students with conditions quickly pass, pass on time, pass late and drop out, using data mining with C4.5 algorithm. Category of semester six is the highly influential on the predicted outcome of graduation. With the application test result, accuracy of the graduation prediction acquired is 87.5%. Index Terms-Data mining, C4.5 algorithm, Universitas Multimedia Nusantara, prediction.

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.

Using Data Mining for Forecasting Data Management Needs

2008 ◽  
pp. 2088-2104
Author(s):  
Qingyu Zhang ◽  
Richard S. Segall
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Forest Cover ◽  
Human Lung Cancer ◽  
Production Environment ◽  
Data Set ◽  
Biotechnology Research ◽  
Use Of Data ◽  
Using Data ◽  
Tools And Techniques

This chapter illustrates the use of data mining as a computational intelligence methodology for forecasting data management needs. Specifically, this chapter discusses the use of data mining with multidimensional databases for determining data management needs for the selected biotechnology data of forest cover data (63,377 rows and 54 attributes) and human lung cancer data set (12,600 rows of transcript sequences and 156 columns of gene types). The data mining is performed using four selected software of SAS® Enterprise MinerTM, Megaputer PolyAnalyst® 5.0, NeuralWare Predict®, and Bio- Discovery GeneSight®. The analysis and results will be used to enhance the intelligence capabilities of biotechnology research by improving data visualization and forecasting for organizations. The tools and techniques discussed here can be representative of those applicable in a typical manufacturing and production environment. Screen shots of each of the four selected software are presented, as are conclusions and future directions.

Observation of Success Status of Employees in E-Learning Courses in Organizations with Data Mining

2017 ◽  
Vol 9 (1) ◽  
pp. 38-49
Author(s):  
Fatma Önay Koçoğlu ◽  
İlkim Ecem Emre ◽  
Çiğdem Selçukcan Erol
Keyword(s):  
Data Mining ◽  
Evaluation Criteria ◽  
Data Set ◽  
Completion Status ◽  
Completion Date ◽  
E Learning ◽  
Pharmaceutical Industries ◽  
Mining Methods ◽  
Using Data ◽  
Performance Results

The aim of this study is to analyze success in e-learning with data mining methods and find out potential patterns. In this context, 374.073 data of 2013-14 period taken from an institution serving in e-learning field in Turkey are used. Data set, which is collected from information technology, banking and pharmaceutical industries, includes success and industry of employees', trainings which they complete, whether the trainings are completed, first login and last logout dates, training completion date and duration of experience in training. Using this data set, success status of participants is observed by using data mining methods (C5.0, Random Forest and Gini). By observing using accuracy, error rate, specificity and f- score from performance evaluation criteria, C5.0 has chosen the algorithm which gives the best performance results. According to the results of the study, it has been determined that the sectors of the employees are not important, on the contrary the ones that are important are the completion status, the duration of experience and training.

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