J. Today’s Ideas - Tomorrow’s Technol.

Analysis of Student's Data using Rapid Miner

Sheena Angra, Sachin Ahuja


Educational Data Mining; Data Mining; EDM Objectives;Rapid Miner; EDM data and Stakeholders.

PUBLISHED DATE December 2016
PUBLISHER The Author(s) 2016. This article is published with open access at www.chitkara.edu.in/publications

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


Data Mining is an analytical process used to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. Data Mining has attracted many researchers and scientists to work in this area due to presence of large amount of data which is available in various formats like records,texts,files,sounds,images and many other formats[1].The data which is collected from various applications and repositories requires various data mining techniques to extract useful and novel information from them in order to give accurate results and for the purpose of future prediction. There are various steps which are used to extract information from data. The main purpose of using data mining techniques is to use various algorithms and methods to extract and discover some patterns. Data mining is used in various areas such as data visualization, statistics, machine learning, database systems and information retrieval[1].

Data mining covers multitude of application areas including Businesses, banking, Insurance, Scientific, Medical, Weather forecasting etc. which involves huge amount of data for processing. Educational Data Mining is an upcoming area in the field of data mining as educational settings are experiencing the phenomenon of data explosion.Computerized systems collect data about a multitude of everyday transactions in academic institution. Data is related to students’ attendance, performance, historical data, demographic data, admissions, accounts, internet usage and many more. The goal of educational data mining is to apply data mining techniques on the available data in terms of educational context and come up with a model using data mining techniques that help in decision making.

Page(s) 109-117
URL http://dspace.chitkara.edu.in/jspui/bitstream/1/780/1/2%20-%20Analysis%20of%20Students%20Data%20using%20Rapid%20Miner%20-%20Sheena%20Angar.pdf
ISSN Print : 2321-3906, Online : 2321-7146
DOI 10.15415/jotitt.2016.42007

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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