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 Impact of Data Mining Technique in Education Institutions

2015 ◽  
Vol 4 (02) ◽  
pp. 01-07
Author(s):  
Mr. Bhushan Bandre, Ms. Rashmi Khalatkar
Keyword(s):  
Data Mining ◽  
Educational Institution ◽  
Educational Institutions ◽  
Data Mining Technique ◽  
Admission Process ◽  
Data Mining Techniques ◽  
Mining Technique ◽  
Major Parameter ◽  
Using Data ◽  
Major Decision

Major decision making process using large amount of data can be done by various techniques using data mining. In education sectors various data mining techniques are implemented to analyze the student’s data from the admission process itself. Due to large number of educational institution in India, excellence becomes a major parameter for the institutions to grow and with stand. Nowadays education institutions use data mining techniques to show their excellence. The main objective of this work to present an analysis of individual semester wise results of engineering college students using different techniques of data mining. Here we used different classification algorithms like decision tree, rule based, function based and Bayesian algorithms to analyze the semester results and comparison is made by considering parameters like accuracy and error rate. Our output shows the most suited algorithm for analyzing data in educational institutions.

scholarly journals Predicting Student Drop-Out in Higher Institution Using Data Mining Techniques

2020 ◽  
Vol 1496 ◽  
pp. 012005 ◽  
Author(s):  
W F Wan Yaacob ◽  
N Mohd Sobri ◽  
S A Md Nasir ◽  
W F Wan Yaacob ◽  
N D Norshahidi ◽  
...  
Keyword(s):  
Data Mining ◽  
Drop Out ◽  
Data Mining Techniques ◽  
Higher Institution ◽  
Using Data

scholarly journals DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

scholarly journals An Analysis of Students’ Academic Record Using Data Mining Techniques and Identification of Key Factors to Aid Students’ Performance

2018 ◽  
Vol 5 (1) ◽  
pp. 45-50
Author(s):  
Md Ashaduzzaman ◽  
Shihabuzzaman ◽  
Md Hasanur Rahman Sagor ◽  
Md Mizanur Rahman ◽  
Ahmed Iqbal Pritom
Keyword(s):  
Data Mining ◽  
Educational Institutions ◽  
Classification Algorithms ◽  
Key Factors ◽  
Science And Engineering ◽  
Data Mining Techniques ◽  
Using Data ◽  
The University ◽  
Academic Record ◽  
The Impact

With the improvement of information technology, presently educational institutions generally store and compile a huge volume of students’ data. This huge volume of data can be analyzed using different data mining techniques and extract hidden relation between students’ result with other academic attributes. The main objective of this paper is to evaluate the impact of different academic attributes on the students’ final result using data mining techniques. We used different data mining techniques to analyze students data collected from Green University of Bangladesh. We applied three well-known classification algorithms namely Decision Tree, Naïve Bayes, and SVM to develop a prediction model that can suggest probable grade by analyzing parameters like the midterm, attendance, assignment, presentation, class test, final, and CT marks. Our goal is to find out the key factors playing as a catalyst for getting good or bad CGPA. Through this research, the university authority will get the knowledge about key factors playing significant role in students’ result that will help them to take proper decisions to improve students’ grade that in turns will reduce students’ dropout. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 45-50

Exploration of Healthcare Using Data Mining Techniques

2018 ◽  
pp. 243-259 ◽  
Author(s):  
Anindita Desarkar ◽  
Ajanta Das
Keyword(s):  
Data Mining ◽  
Patient Care ◽  
Knowledge Discovery ◽  
Healthcare Cost ◽  
Healthcare Sector ◽  
Huge Amount ◽  
Data Set ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques

Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initially, this chapter explores on the various types of eHealth data and its characteristics. Subsequently it explores various domains in healthcare sector and shows how data mining plays a major role in those domains. Finally, it describes few common data mining techniques and their applications in eHealth domain.

scholarly journals A Study of the Applications of Data Mining Techniques in Higher Education

2015 ◽  
pp. 1-4
Author(s):  
SUSHIL VERMA ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Business Processes ◽  
Large Data ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Higher Education System ◽  
Student’S Performance ◽  
Quality In Higher Education

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set. Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions and student’s performance. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of Unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance. The paper aims to purpose the use of Data mining techniques to improve the efficiency of higher educational institutions. If data mining techniques such as clustering, dicision tree and association can be applied to higher education processes, it can help improve student’s performance.

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.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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