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PROCESS AND APPLICATION OF DATA MINING IN
INTERNET BASED LEARNING IN HIGHER
EDUCATION – A MODEL
P. Karthika1 ,V. Ravikumar2 and P. Karthikeyan3
1
Assistant Professor, Department of Management Studies,
MPNMJ Engineering College, Chennimalai, Erode- 638 112, Tamilnadu, India
1
Haed and Assistant professor, Department of Management Studies,
MPNMJ Engineering College, Chennimalai, Erode- 638 112, Tamilnadu, India
3
Assistant Professor (Sr.Grade), School of Management Studies,
Kongu Engineering College, Perundurai, Erode-638 052, Tamilnadu, India
ABSTRACT
Learning is a continuous and life long process. Convenient learning has become the need of the current
set-up. Information and communication technologies have brought about marvelous changes in the
teaching-learning process. Internet based learning system has started to play an indispensable role in a
lot of activities. Availability of right content at the time is the real need of the learner. This paper
provides an insight into data mining in internet based learning. The role of data mining in internet based
learning environment , data mining system components, interactive data mining are discussed together
with a process model for implementing data mining in internet based learning at higher education. The
process model includes conceptualization and identification, Selection and filtering, Pattern extraction
and evaluation and finally visualization.
Keywords: Data Mining, internet-based Learning, Conceptualization and identification, Selection
Pattern extraction and visualization
1.INTRODUCTION
Traditionally finding the useful patterns in data has been given a various of names
together with data mining, knowledge extraction, information finding, information collecting,
dataselection, and data pattern processing. The word data mining has been mostly used by
mathematicians, data forecasters, and the management information systems (MIS)
communities. It has also enlarged attractiveness in the database arena. The word KDD was
invented at the first KDD workshop in 1989 [1] to emphasize that "knowledge" is the end
product of a data discovery. It has been make popular in artificial intelligence and machine
learning. Data mining is a significant step in overall KDD process. Knowledge discovery from
database involves the application of specific algorithms for extracting required data based on
specific pattern [1]. The various additional activities in this process involve data gathering, data
assortment, data cleaning, integration of suitable previous knowledge, and correct
interpretation of the results of mining. All these activities ensure that useful knowledge is
derived from varied sectors such as marketing, Finance, Banking, Engineering,
Telecommunication and Teaching.
Knowledge Detection in database is the method of outcome of knowledge from immense
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amount of data where data mining is the important of this process. Data mining can be used to
mine comprehensible meaningful patterns from large databases and these patterns may then be
converted into knowledge. Data mining is the process of take out the information and patterns
derived by the KDD process which helps in vital administrative. Data mining works with data
warehouse and the whole process is divided into action plan to be performed on data:
Collection, transformation, mining and results interpretation. In this paper, reviewed
Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it [2]Big data is a term that describes the growth of the
amount of data organization and the potential to discover new insights when analysing the data.
Big data spans three various dimensions, which include volume, velocity, and variety [3]. There
is pressure in higher educational institutions to provide up institutional effectiveness [4] .Data
Mining (sometimes called data or knowledge discovery) has become the area of growing
significance because it helps in analysing data from different perspectives and summarizing it
into useful information. There are increasing research interests in using data mining in
teaching. This new emerging field, called Educational Data Mining, deal with developing
methods that discover knowledge from data originating from educational [5].
[6] It has evidently captured the multi-disciplinary aspect of internet based-learning. Fig. 1
illustrates how the various fields of study interact to constitute Internet based-learning.
Traning
Education
Learning
Managment
Information Technology
Knowledge
Internet Based
Learning
Fig.1 Internet based learning applied to various fileld
Data mining (DM) is the process of where data is scrutinized and summarized into useful
information. In short, data mining is process of developing patterns from large databases
[7].DM analyses large dataset to extract hidden patterns such as similar groups of data records
using clustering technique. The data is used for machine learning and projecting analysis.DM
works to analyse data stored in data warehouses and results in effective decision making [8].
Weiss and Indurkhya in [9] anticipated that, “DM is the search for valuable information in large
volumes of data”. According to Technology Forecast [10], and Piatetsky-Shapiro et al. [11], it
is the process of extracting before unknown, useful information which include knowledge,
association rules, pattern finding, numerical and statistical techniques. Query languages or
graphical user interface are required to express the DM requests and discovered information , so
that results obtained from the DM Engine become understandable and usable for end users.
Data mining can also be used in the areas of manufacture control, customer retention,
knowledge exploration, sports, astrology, and Internet Web Surf-Aid.
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Knowledge discovery forms an integral part of data mining towards solving problems in a
specific domain. Knowledge is valuable asset to most organizations as a substantial source to
enhance organizational competency. Researchers and practitioners in the area of knowledge
management, view knowledge in board sense as a condition of mind, an object, a process, an
access to information, or a capacity. Today’s knowledge world needs information at a faster
pace across the organization to survive in the midst of global competitive environment.
Technology plays a vital role in all walks of life. It has created a great impact in various sectors.
Literature revels that education sector is benefited by the usage of different technology and
modern database principles. Data mining techniques have emerged as successful technique in
internet based learning system. The rest of this paper is arranged as follows: Section 2 describes
the survey of papers. Section 3 introduces the Data Mining process. Section 3.1 describes the
data mining system component. Section 3.2 descripe the intercative data mining. Section 4
shows the experimental results and comparison made with other methods. Section 5 describes
the conclusion. This paper discusses the various benefits of data mining in internet based
learning environment together with an analysis of various models for data mining in internet
based learning. This paper concludes with a theoretical model arrived based on literature study
that could be implemented in internet based learning.
2. RELATED WORK
Data mining determine knowledge assets from the data store/ warehouse repository used
mainly for business intelligence and education. For example, data mining is used by e-learner to
extract the desired content so as to facilitate timely learning. There are several groundwork to
develop data mining in internet-based learning [12] proposed a model to systematically mine
and manage useful knowledge in R&D organizations.
Internet-based learning supports the computer-based and computer supported education and
training system exploiting web as the representation and delivery medium. A web-based
learning system is a high-tech in the field of e-learning due to the advantages of usability,
interoperability and accessibility provided by the internet technology. Reusability,
interoperability and accessibility are major concepts in the development of modern learning
environments and extensively proposed by many researchers. [13]. designed internet-based
education systems as a muti-agent architecture working with intelligent reusable learning
objects. [14] made pioneering efforts in data mining that mined the knowledge of page contents
and student navigation pattern. [15] focused on student enrolments in community colleges and
reports a case study in which data mining was used to monitor and predict community college
students’ transfer to four-year institutions. [16] presented a real life application of data mining
to find weak students. [15] studied the impact of data mining on higher education. This study
aided to gain insights about the exiting higher education worldwide and its improvement from
data mining perspective. [17] discussed a new model for using data mining in higher
educational system. [18] proposed a framework for effective educational process using data
mining techniques to uncover the hidden trends and patterns making accuracy based predictions
through higher level of analytical sophistication in students counselling process. [19] proposed
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to shape the analysis problem as data mining task.
Educational data mining (EDM) is an emerging tools and techniques to educationally related
data. The discipline focuses on analysing educational data to develop models for improving
learning experiences and improving Institutional effectiveness. Data mining is a significant tool
for helping organizations enhance decision making and analysing new patterns and
relationships among a large amount of data. A in EDM was presented, from applying data
mining for understanding student retention and attrition to finding new ways of making
individual student. Many opportunities be existent to study EDM from an organizational unit of
analysis to individual course-levels of analysis. Some work is strategic in nature and some of
the research is extremely technical.
[16] visualized that the education domain offers many interesting and challenging applications
for data mining. First, an educational institute often has many diverse and varied sources of
information. There are the traditional database (e.g students’ information, educators’
information, class and schedule information, alumni information) online information (online
web pages and course content pages) in recent times, multimedia databases. Second, there are
many diverse internet groups in the educational domain that give rise to many interesting
mining requirement. For example, the managers may wish to find out information such as the
admission requirements and to predict the class enrolment size for timetabling. The students are
wish to know how best to select courses based on prediction of how well they will perform in
the courses selected. The alumni office need to know how best to perform target mailing so as
to achieve the best effort in reaching out to those alumni that are likely to respond. All these
application not only contribute an educational institute in delivering a better quality teaching
experience, but also aid the institution in running its administrative tasks effectively. With so
abundant information and so many varied needs, it is foreseeable that an integrated data mining
system that is able to cater to the special needs of an educational institution will be in great
demand. The literature survey had enabled us to study various papers which made significant
impact on our findings from Indian perspective. A small percentage of the studies were based
on traditional theories of learning, but without anchoring aspects of web-based learning [20]
3. DATA MINING PROCESS
The data mining process obtain five steps is represented in Fig.2. Data repository is the general
term used to refer a destination designated for data storage. The data is extracted from the data
repositories is the first step of data mining process. Data cleaning and loading into data mining
database is the second step in the data mining. Clustering is the task of discovering groups and
structures in the data that are in some way or another "similar", without using known structures
in the data. Regression is attempts to find a function which models the data with the least error.
Classification is the task of generalizing known structure to new data. For example, an e-mail
program might attempt to classify an e-mail as "legitimate" or as "spam".
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Raw
Data
Data Mine
Database
Raw
Data
(b)
(a)
T
A
B
L
E
(c)
(d)
(e)
Fig. 2. Data Mining Process
In the Fig. 2
(a) Extraction of data from repositories
(b) Data cleansing and loading into data mining database
(c) Data transformation
(d) Pattern duscovery using algorithms such as clustering, regression and
classification.
(e) Data visualization and intrepretation of results
3.1 Data mining System Components
The Fig.3 denotes the classification of data mining system. Which is classifies based on
database technology, statistics, machine learning, information science, visualization and other
discipline. Data mining applied to the internet has the potential to be quite beneficial. Online
data mining is mining of data related to the online.
Statistics
Database
Technology
Machine
Learning
Data
Mining
Information
Science
Other
Discipline
Visualization
Fig. 3 Data Mining System Components
Statistics is at the core of data mining - helping to distinguish between random noise and
significant findings, and providing a theory for estimating probabilities of predictions, etc.
However Data Mining is more than stats. DM covers the entire process of data analysis,
containing data cleaning and preparation and visualization of the results, and how to produce
forecasts in real-time. Machine learning explores the construction and study of algorithms that
can learn from and make predictions on data [21] .The process of machine learning is similar to
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the data mining. Data Mining and machine learning both are systems search through data to
look for patterns. However, instead of take out data for human comprehension - as is the case in
data mining applications - machine learning uses that data to improve the program's own
understanding. Machine learning programs spot patterns in data and adjust program actions
accordingly.
There are more applications identified that can benefit from data mining. These include: Life
Sciences (LS), Customer Relationship Management (CRM), Web Applications,
Manufacturing, Competitive Intelligence, Retail/ Finance/ Banking, and Computer/ Network/
Security, Monitoring/ Surveillance applications, education Support, Climate modelling,
Astronomy, and Behavioural Ecology. Indeed, most scientific disciplines are becoming
data-intensive and turning to data mining as a tool. There are no of numbers of applications
identified that can benefit from data mining. Indeed, most scientific disciplines are becoming
data-intensive and turning to data mining as a tool.
As information technology transforms their role from passive data collection to a more active
exploration and exploitation of information, they face a severe challenge: how can they handle
a massive amount of data that generate, collect and store. There is a need to have a technology
that can access, analyse, summarize, and interpret information intelligently and automatically
[22].
3.2 Interactive data mining
Data has become a critical resource in many organizations and effectively managing the data
has become a major need. Various types of data base management systems have been
developed to manage the multimedia data types such as text, voice, video and the increasing no
of data base system are now inter operating with one another. Extracting useful information
from the data has also become urgent requirement. Data by itself will not be of much use unless
one can assign means to the data. Data mining techniques enable uses to post queries and
extract information from the data often previously unknown. Visualization techniques have
helped the user to graphically capture the data in the data base as well as help the user in the
mining process. There four data base management, visualization and the mining are three
technologies that have to be integrated to provide an interactive data mining. Environment that
can help the users a great deal to uncover previously unknown information. The World Wide
Web also adds another dimension to interactive data mining.
During recent years the explosion of the users and the internet and the increasing the no of
World Wide Web services are rapidly advancing digital library technology. Numerous data
bases are now scattered across the various sites. The goal is for users to access these databases
in a transparent manner. Furthermore, it’s also desirable for the users to extract information
from this data sources through the data mining. The data and information are extracted from the
data bases have to be formatted in a way that can be understood by the users. Therefore,
visualization techniques play a major role for a data management and mining on the internet.
Data mining is the process of extracting information and patterns from the data often previously
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unknown. Various tools are now available for data mining. Many of the data mining techniques
need to be guided by the user. That is, the user steers the data mining tools so that useful results
are obtained. This process is called interactive data mining as there is interaction between the
user and the data mining tool. Fig.4 illustrates interactive data mining showing the relationship
between the database management system, the visualization tool, and the data mining tool.
Visualization
Tool
Visualize
DBMS
Response
Data Mining
Tool
DBMS
Direct Data
Mining
Response
Fig. 4 Data Mining on Visualized Results
Visualization plays a major role in interactive data mining. For example, the visualization tool
may be used directly to graphically display the results extracted from the database possibly via
a data base management system. The graphical display can then be used by data mining tool to
extract patterns. For example, suppose an automobile company wants to sell automobiles. It
then queries the database to obtain information about the various neighbourhoods in the town.
The data base management system may graphically display the map of town and information
about the people in the town. From this picture the data mining tool cloud determine the types
of cars to sell to the people in various neighbourhoods.
Visualization tools may be applied directly to the results obtained by the data mining tools. In
this case the data mining tool extracts information from the database via the DBMS and this
information is visualized by the visualization tool. Based on this information the data mining
tools may pose additional queries to DBMS to get more data so that it can deduce additional
patterns. Note that while the first approach applies data mining techniques to visualization, the
second approach applies visualization techniques to data mining. That is, in the first approach,
the results from the DBMS are visualized and the data mining tool is applied on the graphical
displays. In the second approach visualization techniques are used in the result of the data
mining tool so that the results can be graphically displayed. In both cases visualization tools
guide the data mining process. In an earlier paper we have identified a third approach where
visualization techniques compliment the data mining process. That is the data mining tool is
used to uncover patterns; but visualization tools may still be used on the data to have a better
understanding of the data [23]
4. PROCESS MODEL FOR DATA MINING IN INTERNET BASED
LEARNING
A study [24] states that conventional teaching methods provide the least effective method of
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learning. The author compares students’ scores on achievement tests using three forms of
instruction; conventional teaching, mastery teaching, and individualized tutoring. Mastery
teaching is an instructional technique whereby a teaching supplements a lecture with diagnostic
tests to determine where students are having problems, and adjust the instruction accordingly.
The proposed model support the internet-based learning with several scheme of learning
including adaptive, autonomous and collaborative learning.
Leaner
Instructor
Communication
Medium
Web-Based
Learning
Transformed
Dataset
Selecting and
Filtering
Pattern
Extraction
Visualisation
Conceptualization
Data Mining
Process
DB
Fig. 5 Process Model for Interent-based Learning
There is no specific model followed by internet-based learning system. Four components
emerge from the literature as part of a internet-based leaning system. They include the
following: student model, the pedagogical module, the expert model, and the communication
module or interface [25]. Based on the compontents and their work the proposed model which
is suitable for the internet based learning encironment is deveopled whish is illustrated in fig 5.
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Internet based technologies have led to great innovation in teaching and learning. Internet based
education system constitute one of the fastest growing areas in educational technology,
research and development. Internet based education is one of the innovation of technological
changes and it mainly overcomes the conventional boundaries of traditional learning systems. It
facilities a leaner-centric approach, 24X7 services to all activities concerning the teaching
learning process are possible through internet based learning.The instructor stores and delivers
information of each individual learner. For example, the model tracks how well a student is
performing on the material being taught or records incorrect responses that may indicate
misconceptions. Since the purpose of the student model is to provide data for the pedagogical
module of the system, all of the information collected should be usable by tutor.The
communication medium provides a model of the education process. For example, information
about when to review, when to present new topic, and which topic to be present is controlled by
this module. As mentioned earlier, the learners and instructor are used as input to this
component, so the instructive decisions reflect the differing needs of each student.The subject
experts contain the domain knowledge, which is the information being taught to the leaner.
However it is more than just a representation of the data; it is a model of how someone skilled in
a particular domain represents the knowledge. By using an expert model, the tutor can compare
the leaner’s solution to the expert’s solution, pinpointing the places and is the most important
since in the absebce of it, there would be nothing to teach the student. Generally, significant
knowledge engineering to represent a domain so that other parts of the tutor can access it.
The communication module controls interactions with a student, including the dialogue and the
screen layouts. For example, it determines how the material should be presented to the student
in the most effective way by providing an understanding of the application domain, the goals of
the system and its users, and the relevant prior background and prior knowledge. Selecting a
data set, or focusing on a subset of variables or data samples, on which discovery is to be
accomplish. Pre-processing and data cleaning, removing the noise, collecting the necessary
information for modelling, selecting methods for handling missing data fields, accounting for
period sequence information and changes. Data reduction and projection, finding appropriate
feature to represent data, using dimensionality reduction or transformation methods to reduce
the number of variables to find invariant representations for data. Choosing the data mining task
depends on the goal of KDD. Selecting methods and algorithms are to be used for searching the
patterns in the data. Mining the knowledge: searching for pattern of interest; evaluating or
interpreting the mined patterns, with a possible return to any previous steps; using this
knowledge for promoting the performance of the system and resolving any potential conflicts
with previously-held beliefs or extracted knowledge. These are the steps that all the data mining
task process through Selecting and filtering has been presented in jain [26]. He presented
taxonomy of available selection algorithms based on pattern recognition or ANN, sub-optimal
or optimal, single solution or multi-solution, and deterministic or stochastic. The relevant
classification result is based on prior knowledge of the classification task and useful review on
relevance and selection.
Visualization is useful for project in high dimensional data down to two or three dimensions.
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The main concern is protecting the distance information and deployment of the original data in
two or three dimensions. The traditional approach in data visualization is linear projection. A
more suitable choice is projecting the data onto a graph.
5. CONCLUSION
The research work in this area confirmed that data mining enhances teaching and learning
process in a internet-based environment. From this study evident that internet-based learning is
in practice in the higher education scanario. Data mining helps in extracting the required
information for the teaching-learning process and its saves a lot of time. The theoretical model
proposed in this article could serve as a basis for implementing data-mining techniques in
internet-based learning scenario.
ACKNOWLEDGEMENT
The author P.Karthikeyan would like to thank the University Grants Commission, Hyderabad,
India for its financial support to Minor Research Project No.F.MRP-4885/14 (SERO/UGC)
dated March 2014.
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