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.

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 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%.

scholarly journals Aplicación de la minería de datos en la extracción de perfiles de deserción estudiantil [Application of data mining in extracting student dropout profiles]

2013 ◽  
Author(s):  
Ricardo Timarán Pereira ◽  
Andrés Calderón Romero ◽  
Javier Jiménez Toledo
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
Student Dropout ◽  
First Results ◽  
Toma De Decisiones ◽  
Academic Profiles ◽  
Effective Decision Making ◽  
Using Data ◽  
Undergraduate Programs ◽  
The University

Resumen En este artículo se presentan los primeros resultados del proyecto de investigación cuyo objetivo es detectar patrones de deserción estudiantil a partir de los datos socioeconómicos, académicos, disciplinares e institucionales de los estudiantes de los programas de pregrado de la Universidad de Nariño e Institución Universitaria IUCESMAG, dos instituciones de educación superior de la ciudad de Pasto (Colombia), utilizando técnicas de Minería de Datos. Los resultados obtenidos corresponden a la Universidad de Nariño. Se descubrieron perfiles socioeconómicos y académicos de los estudiantes que desertan utilizando la técnica de clasificación basada en árboles de decisión. El conocimiento generado permitirá soportar la toma de decisiones eficaces de las directivas universitarias enfocadas a formular políticas y estrategias relacionadas con los programas de retención estudiantil que actualmente se encuentran establecidos. Palabras claveExtracción de Perfiles, Deserción Estudiantil, Minería de Datos, Clasificación, Árboles de Decisión   Abstract The first results of the research project that aims to identify patterns of student dropout from socioeconomic, academic, disciplinary and institutional data of students from undergraduate programs at the University of Nariño and IUCESMAG University, two higher education institutions in the city of Pasto (Colombia), using data mining techniques are presented. The results correspond to the University of Nariño. Socioeconomic and academic profiles were discovered of students who drop using classification technique based on decision trees. The knowledge generated will support effective decision-making of university staff focused to develop policies and strategies related to student retention programs that are currently set.KeywordsExtraction of Profiles, Student Dropout, Data Mining, Classification, Decision Trees

scholarly journals Retraction: Rezig, S. et al. (2018). Using Data-Mining Methods for Predicting Sequential Maintenance Activities. Applied Sciences, 8(11), 2184

2019 ◽  
Vol 9 (4) ◽  
pp. 633
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Applied Sciences ◽  
Data Mining ◽  
Research Project ◽  
Mining Methods ◽  
Maintenance Activities ◽  
Using Data ◽  
The University ◽  
Sequential Maintenance

After the results from a research project with former students of the university were published in Applied Sciences [...]

scholarly journals A Student Performance Prediction Model Using Data Mining Technique

2018 ◽  
Vol 7 (2.15) ◽  
pp. 61
Author(s):  
Rohaila Abdul Razak ◽  
Mazni Omar ◽  
Mazida Ahmad
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Decision Tree ◽  
Student Performance ◽  
Prediction Accuracy ◽  
Significant Variable ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Predicting Performance ◽  
Using Data

Predicting performance is very significant in the education world nowadays. This paper will describe the process of doing a prediction of student performance by using data mining technique. 257 data sets were taken from the student of semester 6 KPTM that involved four (4) academic programs which are Diploma in Computer System and Networking, Diploma in Information Technology, Diploma in Business Management and Diploma in Accountancy. Knowledge Discovery in Database (KDD) was used as a guide to the process of finding and extracting a knowledge from the dataset. A decision tree and linear regression were used to analyze the dataset based on variables selected. The variables used are Gender, Financing, SPM, GPASem1, GPASem2, GPASem3, GPASem4, GPASem5 and CGPA as a dependent variable. The result from this indicate the significant variable that contribute most to the students’ performance. Based on the analysis, the decision tree shows that GPASem1 has a strong significant to the CGPA final semester of the student and the prediction accuracy is 82%. The linear regression shows that the GPA for each semester has a highly significant with the dependent variable with 96.2% prediction accuracy. By having this information, the management of KPTM can make a plan to ensure that the student can maintain a good result and at the same time to make a strategic plans for those without a good result.  

Sign in / Sign up

Export Citation Format

Share Document