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Outline

Title
Abstract
FAQs
Figures
Introduction
Edm Objectives
Analysis of Student's Data Using Rapid Miner
Edm Data and Stakeholders
Analysis of Data
Implementation and Results
Future Scope
References

Analysis of student’s data using rapid miner

Profile image of Sheena AngraSheena Angra

Journal on Today's Ideas - Tomorrow's Technologies

https://doi.org/10.15415/JOTITT.2016.41004
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9 pages

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Abstract

Data mining offers a new advance to data analysis using techniques based on machine learning, together with the conventional methods collectively known as educational data mining (EDM). Educational Data Mining has turned up as an interesting and useful research area for finding methods to improve quality of education and to identify various patterns in educational settings. It is useful in extracting information of students, teachers, courses, administrators from educational institutes such as schools/ colleges/universities and helps to suggest interesting learning experiences to various stakeholders. This paper focuses on the applications of data mining in the field of education and implementation of three widely used data mining techniques using Rapid Miner on the data collected through a survey.

FAQs

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AI

What factors significantly affect student academic performance based on data analysis?add

The analysis identifies factors such as 10th percentage, 12th percentage, attendance, and father's income as influential on student performance.

How effective are clustering algorithms in educational data mining?add

The K-means clustering algorithm demonstrated improved performance by reducing intra-cluster variance while grouping similar students' data.

What data mining techniques were utilized in this educational study?add

The study applied WJ-48 decision tree algorithm, K-means clustering, and linear regression for data analysis.

What methodology was used to collect data for analyzing student performance?add

An online survey collected data on 40 factors, but only certain factors like father's occupation proved influential.

What is the main future aim for the developed Java framework in education?add

The framework aims to evaluate students based on only influential factors, enhancing decision-making processes in enrollment.

Figures (7)
Figure |: Implementing WJ-48 Algorithm
Figure |: Implementing WJ-48 Algorithm
ii) K-means Clustering Algorithm: Clustering is a process which groups simi- means that w performance. Figure 3 shows the implementation of K-means algorithm, figure shows the dis ribution of different items in different clusters and figure 5 describ he calculation of performance vector for all the 4 clusters. Performance of a clus depends on the number of items per cluster. ar objects into one cluster (12). It is also termed as unsupervised classification. It hile classifying data objects clustering does not depend on training tuples and predefined classes [13].K-means is a clustering algorithm which wor on Euclidian distance and is used to group similar objects in one cluster. It defines K centers for K clusters that is; one center is defined for each cluster. The main o jective of using K-means is to reduce intra cluster variance and to maximize t KS O-
ii) K-means Clustering Algorithm: Clustering is a process which groups simi- means that w performance. Figure 3 shows the implementation of K-means algorithm, figure shows the dis ribution of different items in different clusters and figure 5 describ he calculation of performance vector for all the 4 clusters. Performance of a clus depends on the number of items per cluster. ar objects into one cluster (12). It is also termed as unsupervised classification. It hile classifying data objects clustering does not depend on training tuples and predefined classes [13].K-means is a clustering algorithm which wor on Euclidian distance and is used to group similar objects in one cluster. It defines K centers for K clusters that is; one center is defined for each cluster. The main o jective of using K-means is to reduce intra cluster variance and to maximize t KS O-
Figure 3:Implementing K-means Clustering algorithm on collected data
Figure 3:Implementing K-means Clustering algorithm on collected data
iii) Linear Regression: Linear regression is a predictive analysis technique which is used to find the relationship between one dependent variable and other independent variable. Figure 6 shows the implementation of linear regression on the data and figure 7 shows the root mean squared error in the data which estimates the deviation of data from the expected mean.
iii) Linear Regression: Linear regression is a predictive analysis technique which is used to find the relationship between one dependent variable and other independent variable. Figure 6 shows the implementation of linear regression on the data and figure 7 shows the root mean squared error in the data which estimates the deviation of data from the expected mean.
Our main purpose is to predict the performance of students of next semester based on the results of previous semester.After analysis of data using the mentioned 3 techniques we are also working on the rules which will be generated after applying these techniques.In order to accomplish this we have collected some academic and demographic data of large number of students so that these techniques can be tested on wide range of students also.We have applied decision tree algorithm on this data and we have found hat there are some factors that are influencing the performance of students and these factors include 10" percentage, 12™ percentage,attendance,father’s income and 3™ sem SGPA.So we have decided to design a java framework using Netbeans IDE 8.1 to accomplish this task which includes only those factors which influences the student’s performance so that only these factors should be taken into consideration while enrolling or judging a student.Figure 8 gives the choice to select a particular algorithm and since we have worked only on decision tree for large amount of data so figure 9 describes the generated rules.
Our main purpose is to predict the performance of students of next semester based on the results of previous semester.After analysis of data using the mentioned 3 techniques we are also working on the rules which will be generated after applying these techniques.In order to accomplish this we have collected some academic and demographic data of large number of students so that these techniques can be tested on wide range of students also.We have applied decision tree algorithm on this data and we have found hat there are some factors that are influencing the performance of students and these factors include 10" percentage, 12™ percentage,attendance,father’s income and 3™ sem SGPA.So we have decided to design a java framework using Netbeans IDE 8.1 to accomplish this task which includes only those factors which influences the student’s performance so that only these factors should be taken into consideration while enrolling or judging a student.Figure 8 gives the choice to select a particular algorithm and since we have worked only on decision tree for large amount of data so figure 9 describes the generated rules.
Figure 9: Rules generated after applying Decision Tree Algorithm II. CONCLUSION This paper presentes implementation of 3 data mining techniques on the collected data. EDM is emerging as a great research area in the field of education.It uses various techniques to understand student’s behavior and performance.It helps to predict grades of students of one class by analysing the grades of previous classes.It analyses student’s offline and online activities and also suggests some methods to insructors/teachers to organise the course content according to the student’s needs and performance. The full integration of data mining in the educational environment is not yet witnessed but the future line of research in this area can be a full operational implementation of data mining in educational environment for all the stakeholders.Different techniques works on different parameters such as performance vector and root mean square error.
Figure 9: Rules generated after applying Decision Tree Algorithm II. CONCLUSION This paper presentes implementation of 3 data mining techniques on the collected data. EDM is emerging as a great research area in the field of education.It uses various techniques to understand student’s behavior and performance.It helps to predict grades of students of one class by analysing the grades of previous classes.It analyses student’s offline and online activities and also suggests some methods to insructors/teachers to organise the course content according to the student’s needs and performance. The full integration of data mining in the educational environment is not yet witnessed but the future line of research in this area can be a full operational implementation of data mining in educational environment for all the stakeholders.Different techniques works on different parameters such as performance vector and root mean square error.

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