Secure E Learning Using Data Mining Techniques

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SECURE E LEARNING USING DATA MINING TECHNIQUES

ABSTRACT

Educational data mining (EDM) is a field that concentrates on prediction and is known for its role in uncovering hidden information from large volume of data. EDM has seen an emergence of research leading to strategies that aim to address issues of higher education with primary focus on students’ performance. Educational Data Mining provides a set of techniques, which can help the educational system to overcome these issues. The objective of this research is to introduce Educational Data Mining, by describing a step-by-step process using a variety of techniques such as Attribute Weighting (Weighting by Information Gain, Relief, Hi-Squared, Uncertainty), Clustering (K-Means), Classification(Tree Induction), Association Mining (Apriori, FPGrowth, Create Association Rule, GSP) in order to achieve the goal to discover useful knowledge from the Moodle LMS. Analyzing mining results enables educational institutions to better allocate resources and organize the learning process in order to improve the learning experience of students as well as increase their profits. The experimental results have shown that the data mining model presented in this research was able to obtain comprehensible and logical feedback from the LMS data describing students’ learning behavior pattern.

 

 

CHAPTER ONE:

GENERAL INTRODUCTION

1.1 Background of the Study

The Internet has become a pervasive medium that has changed completely the education environment, and the way of knowledge that shared throughout the world. It provides an easy way to search and access to any information that you need. E- Learning has made knowledge accessible to a large number of people (Kumar, Bharadwaj and Pal 2012). E-Learning is the booming technology where anyone can learn everything from any part of the world. It is the digital way of learning the concepts and does not require the help of other persons to do so. It also requires the large space for data storage such as user information, course records and course details and so on. There are lot of learning applications available on the internet among which some might be subjected to frauds. So the security is the demanding thing every users looking for to protect their details. The users also seek for flexibility of using the applications.   So, people must find ways to automatically analyze, classify, summarize, discover data and characterize trends in it, and to automatically flag anomalies. This is one of the most active and exciting areas of the database research community. Researchers in areas such as statistics, visualization, artificial intelligence, and machine learning are contributing to this field through the application of data mining techniques in data management and security.

Data mining is the concept for extracting the appropriate data from the large set of database. In today’s world it is widely used for many applications where learning applications is one of the major part.Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization (Yong et al, 2014). The incessant advantages of data mining have landed its application in numerous fields including the educational research which is commonly known as Educational Data Mining (EDM).

EDM is defined as an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational setting, and using those methods to better understand students, and the settings which they learn in (International EDM Society, 2011). It is often stressed with the improvement of student models which denote the student’s current knowledge, motivation, metacognition, and attitudes.

It has been proposed that educational data mining methods are more often different from standard data mining methods, due to the need to explicitly account for (and the opportunities to exploit) the multi-level hierarchy and non-independence in educational data. EDM methods are drawn from a variety of literature, including data mining and machine learning, psychometrics and other areas of statistics, information visualization, and computational modelling (Baker & Yacef, 2009).

As a result this study intends to develop a secure e – learning using data mining techniques

1.2      Statement of Problem

Since the 1960s, database and information technology have been evolving systematically from primitive file processing systems to sophisticated database systems. The idea of gaining knowledge through specialized analysis of mass data is as old as the evolution of databases and steadily increased both in the amount of data processed and the sophistication of question people try to answer. The abundance of data, coupled with the need for powerful data analysis tools, has been described as a data rich but information poor situation (Jiawei & Micheline , 2006 ). Thus,  it is highly desirable that educational institutions in Nigeria  take steps to create information value out of this growing body of data.

1.3 Proposed System

This system proposes the development of secured e-learning application where it allows only the authorized people to access the services using the LMS Moodle. For the user to authorized the cryptographic algorithm is used where the secret key is send to the user’s email id. The user is allowed to access the platform only after he/she authenticates the secret code by entering in the website.

This system also includes various services. They include the following

  1. Discussion forum
  2. Online test
  3. Rating based review of the database contents
  4. Filtered web search

The feedback collected will be used for the development of the sites. This secured learning allows user to analyse their knowledge standard by taking online tests. The test results will be analysed by the admin authorization. The rating and reviews would act as an impression board for the users to understand the application well. So this application also welcomes the user’s ratings. The security has been implemented in order to eliminate the fake ratings by which people can be deceived easily and it is the modern technique employed widely in the market to make their apps popular. The filtered web provides the best outcome of search when the user enters the keyword by eliminating the unwanted page loads. The elimination of unwanted page loads also saves more space and time in the system.

ABSTRACT

Educational data mining (EDM) is a field that concentrates on prediction and is known for its role in uncovering hidden information from large volume of data. EDM has seen an emergence of research leading to strategies that aim to address issues of higher education with primary focus on students’ performance. Educational Data Mining provides a set of techniques, which can help the educational system to overcome these issues. The objective of this research is to introduce Educational Data Mining, by describing a step-by-step process using a variety of techniques such as Attribute Weighting (Weighting by Information Gain, Relief, Hi-Squared, Uncertainty), Clustering (K-Means), Classification(Tree Induction), Association Mining (Apriori, FPGrowth, Create Association Rule, GSP) in order to achieve the goal to discover useful knowledge from the Moodle LMS. Analyzing mining results enables educational institutions to better allocate resources and organize the learning process in order to improve the learning experience of students as well as increase their profits. The experimental results have shown that the data mining model presented in this research was able to obtain comprehensible and logical feedback from the LMS data describing students’ learning behavior pattern.

 

 

CHAPTER ONE:

GENERAL INTRODUCTION

1.1 Background of the Study

The Internet has become a pervasive medium that has changed completely the education environment, and the way of knowledge that shared throughout the world. It provides an easy way to search and access to any information that you need. E- Learning has made knowledge accessible to a large number of people (Kumar, Bharadwaj and Pal 2012). E-Learning is the booming technology where anyone can learn everything from any part of the world. It is the digital way of learning the concepts and does not require the help of other persons to do so. It also requires the large space for data storage such as user information, course records and course details and so on. There are lot of learning applications available on the internet among which some might be subjected to frauds. So the security is the demanding thing every users looking for to protect their details. The users also seek for flexibility of using the applications.   So, people must find ways to automatically analyze, classify, summarize, discover data and characterize trends in it, and to automatically flag anomalies. This is one of the most active and exciting areas of the database research community. Researchers in areas such as statistics, visualization, artificial intelligence, and machine learning are contributing to this field through the application of data mining techniques in data management and security.

Data mining is the concept for extracting the appropriate data from the large set of database. In today’s world it is widely used for many applications where learning applications is one of the major part.Data mining is a multidisciplinary field, drawing work from areas including database technology, machine learning, statistics, pattern recognition, information retrieval, neural networks, knowledge-based systems, artificial intelligence, high-performance computing, and data visualization (Yong et al, 2014). The incessant advantages of data mining have landed its application in numerous fields including the educational research which is commonly known as Educational Data Mining (EDM).

EDM is defined as an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational setting, and using those methods to better understand students, and the settings which they learn in (International EDM Society, 2011). It is often stressed with the improvement of student models which denote the student’s current knowledge, motivation, metacognition, and attitudes.

It has been proposed that educational data mining methods are more often different from standard data mining methods, due to the need to explicitly account for (and the opportunities to exploit) the multi-level hierarchy and non-independence in educational data. EDM methods are drawn from a variety of literature, including data mining and machine learning, psychometrics and other areas of statistics, information visualization, and computational modelling (Baker & Yacef, 2009).

As a result this study intends to develop a secure e – learning using data mining techniques

1.2      Statement of Problem

Since the 1960s, database and information technology have been evolving systematically from primitive file processing systems to sophisticated database systems. The idea of gaining knowledge through specialized analysis of mass data is as old as the evolution of databases and steadily increased both in the amount of data processed and the sophistication of question people try to answer. The abundance of data, coupled with the need for powerful data analysis tools, has been described as a data rich but information poor situation (Jiawei & Micheline , 2006 ). Thus,  it is highly desirable that educational institutions in Nigeria  take steps to create information value out of this growing body of data.

1.3 Proposed System

This system proposes the development of secured e-learning application where it allows only the authorized people to access the services using the LMS Moodle. For the user to authorized the cryptographic algorithm is used where the secret key is send to the user’s email id. The user is allowed to access the platform only after he/she authenticates the secret code by entering in the website.

This system also includes various services. They include the following

  1. Discussion forum
  2. Online test
  3. Rating based review of the database contents
  4. Filtered web search

The feedback collected will be used for the development of the sites. This secured learning allows user to analyse their knowledge standard by taking online tests. The test results will be analysed by the admin authorization. The rating and reviews would act as an impression board for the users to understand the application well. So this application also welcomes the user’s ratings. The security has been implemented in order to eliminate the fake ratings by which people can be deceived easily and it is the modern technique employed widely in the market to make their apps popular. The filtered web provides the best outcome of search when the user enters the keyword by eliminating the unwanted page loads. The elimination of unwanted page loads also saves more space and time in the system.

1.4      Aim of the study

The overall aim of this study is to develop a secure e – learning using data mining techniques

1.5      Significance of the Study

Data mining techniques have been successfully applied in many different fields and several contributions have been made in the field of EDM. This study will greatly assist educational institutions in developing a secure model for managing student information.

The overall aim of this study is to develop a secure e – learning using data mining techniques

1.5      Significance of the Study

Data mining techniques have been successfully applied in many different fields and several contributions have been made in the field of EDM. This study will greatly assist educational institutions in developing a secure model for managing student information.

SECURE E LEARNING USING DATA MINING TECHNIQUES

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