Using data mining in e learning-a generic framework for military education

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USING DATA MINING IN ELEARNING A GENERIC FRAMEWORK FOR
MILITARY EDUCATION
«USING DATA MINING IN ELEARNING A GENERIC FRAMEWORK FOR MILITARY
EDUCATION»
by Elena ŞUŞNEA
Source:
Conference proceedings of "eLearning and Software for Education" (eLSE) (Conference proceedings of
"eLearning and Software for Education" (eLSE)), issue: 01 / 2013, pages: 411415, on www.ceeol.com.

The 9th
International Scientific Conference
eLearning and software for Education
Bucharest, April 25-26, 2013
10.12753/2066-026X-13-066
USING DATA MINING IN E-LEARNING -
A GENERIC FRAMEWORK FOR MILITARY EDUCATION
Elena ùUùNEA
"Carol I" National Defence University, Bucharest, Romania
esusnea@yahoo.com
Abstract: In the last years, the development of interactive learning environments, learning management
systems (LMS), and intelligent support systems, has allowed the collection of huge amounts of data.
However, e-learning databases often are large, heterogeneous and complex. In this context, one of the
biggest challenges that e-learning systems face today is to extract knowledge from e-learning database
through the analysis of the information available in the form of data generated by their users (students,
teachers, other persons). Educational institutions can use data mining to extracts the relevant, useful,
valid and actionable information from e-learning databases. Data mining can analyze educational data
from different perspectives and summarize it into useful information for learners, teachers and their
educational institutions. Thereby, it will become a powerful means to improve performance of the
education system. In this paper, we study the capabilities of data mining in the context of military
educational system, by proposing an analytical guideline for students, teachers, and decision-makers to
enhance their current activities. The managerial decision making process becomes more complex as the
complexity of educational entities increase and international security environment. Educational
institute seeks more efficient technology to better manage and support decision making procedures or
assist them to set new strategies and plan for a better management of the current processes. One way to
effectively address the challenges for improving the quality is to provide new knowledge related to the
educational processes and entities to the managerial system. This knowledge can be extracted from
historical and operational data that reside in the educational organization's databases using the
techniques of data mining technology.
Keywords: e-learning, data mining
I. INTRODUCTION
The educational web systems were created as direct result of students needs to access large
information databases for their studies and also teachers needs of disseminating and sharing their study
materials in different forms corresponding to the disciplines from the educational plan and to
communicate with the students on the basis of submitted themes or to exchange knowledge. Most of
student judgment processes and decisions are influenced by web-based information easily available
online [1].
These web systems confer freedom of movement to the students and teachers who are not tide
in by certain locations. These systems gather huge quantity of useful information for students’
behaviour analysis and for assisting the authors in detecting possible errors, shortcuts and means to
improve the didactic materials. Daily, these educational management systems deliver huge amounts of
data and information which, in order to be transformed into knowledge, must be analyzed, but their
analysis it is physically impossible for human to do manually. Here intervenes the need to introduce
some instruments to assist the authors to solve the pop-up problems. The data mining techniques are
extremely useful as this issue is regarded.
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Educational data mining is an emerging discipline concerned with developing methods for
exploring the unique types of data that come from educational settings, and using those methods to
better understand students, and the settings which they learn in.
II. WHAT IS DATA MINING?
At first glance, data mining is a content knowledge management tool which became „an
innovative and powerful research tool in business for knowledge discovery and the development of
predictive models from large volumes of historical data” [2].
In its simplest form, data mining defines the iterative process of extracting the knowledge
hidden in large database. Data mining process involves a circuit wherein undergo many phases among
which there are: data acquisition from students, feature selection and extraction from database of
learning management system, discovery of the models and patterns using data mining techniques,
models interpretation and knowledge generation [3] .
Once with the expansion of Internet and text type electronic format, it also appeared the need
for automated extraction of knowledge from a text and therefore data mining had a new baby
specialization: text mining. Differently from the data mining, text mining presumes a software
addressing to the large public consumer of network solutions the reasons for that being the universality
of acquisition demand of information in real time and low costs for information acquiring (the
connection’s price), comparatively to the data mining. Text mining has as main goal the automated
extraction of novel, valid and operational knowledge.
III. DATA MINING TECHNIQUES
The data mining techniques allow the extraction of information and the fulfilment of forecasts
starting from historical data.
Education is an essential element for the betterment and progress of a country. It enables the
people of a country civilized and well mannered. Mining in educational environment is called
educational data mining, concern with developing new methods to discover knowledge from
educational database in order to analyze student’s trends and behaviors towards education. Lack of
deep and enough knowledge in higher educational system may prevent system management to achieve
quality objectives, data mining methodology can help bridging this knowledge gaps in higher
education system [4].
In the late years, the researchers investigated a series of data mining techniques in order to
help the teachers to improve the e-learning systems. These techniques help the teachers to discover
new knowledge grounded on data provided by students and were grouped in three categories in regard
to the types of problems they can model:
- classification and regression represents the wider category of applications consisting in the
construction of patterns to forecast the appurtenance to a set of classes or values. There
exist certain techniques dedicated to solve the classification and regression issues but the
decisional trees, Naive-Bayes technique, neuronal networks and k-NN are widely
recognized;
- analysis of associations and succession, as well called the „market basket” analysis is a
technique generating descriptive patterns emphasizing the rules of correlation among the
attributes of a data set;
- cluster type analysis is a descriptive technique used to put into group the similar entities
from a data set and also to underline the entities with substantial differences in relation to
a group. The cluster group techniques grounds on algorithms from the neuronal networks
area, demographical algorithms, k-NN etc.
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These techniques can be succesfully used to discover many kinds of knowledge such as
association rules, classifications and clustering. The discovered knowledge can be used 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 and so on [5]. Thus, appeared the
learning analytics concept defined to be „the measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of understanding and optimising learning and the
environments in which it occurs” [6].
Learner analytics loosely joins a variety of data gathering tools and analytic techniques to
study student engagement, performance, and progress in practice, with the goal of what is learned to
revise curricula, assessment and teaching in real time.
Network analysis tools are also emerging as powerful ways for teachers to monitor learning
groups and identify potential or emergent problems among learners. For example, the popular LMS
Moodle has both built in, general and special purpose plugins that help teachers and other group
members understand individual and group behaviours [7] Standard Moodle analytics allow teachers to
view contributions or activities of individual learners [8]. One freeware tool used by learner analytics
is Google Analytics with the support of which, and other similar tools, aim to mobilize the power of
data-mining tools in the service of learning, and embracing the complexity, diversity and abundance of
information that dynamic learning environments can generate.
The data mining techniques help to the creation of conceiving and developing of educational
contents specially to meet the specific needs of the military field and also to give the possibility of
knowledge to be assimilated by the military personnel in each individual rythm, regardless of space
and time.
Data mining and learning analytics are not only used to support independent study but are
being utilized to support and enhance group work. For example a system that creates student groups
based upon individual learning styles and preferences.
IV. MILITARY E-LEARNING DATA MINING
The classical warfare is only part of leading the war. Nevertheless, the military e-learning is a
direct consequence of military action dynamics and complexity following the trend of security
environment in a continuous reconfiguration and resizing under the impact of globalization [9]. The
methods of leading military actions are rapidly changing, as well as the used weapons and the actors
involved in them. The military conflict got a pronounced non-military dimension while the threats and
risks are diversifying. Now we speak about psychological weapons, media weapons, WMD weapons,
UAVs and so on. For all these is needed a different education of militaries which can be enhanced by
e-learning tools.
The new realities of the international security environment are represented by the impact of
informational dominance in the battlespace, the exercitation of command-control and decision-making
under the conditions of informational flows movements in quasi-real time and the need to fulfil
command-control also under conditions when informational flows are interrupted etc [10]. Therefore,
the modern armed forces try to train their military personnel in a computing standardized manner by
using the network communication and information educational systems. In fact, this need in military
personnel education came on from the huge waste of resources in real time and space dimensions of
military training. Accessibility is another matter counting in this equation of transferring part of
military education and training from the real field to the virtual field. Having a professional armed
forces implies the use of advanced system of instruments and training technologies.
Data mining is already used in military purpose to provide security in societies. An example is
its use in “singling out people as suspected terrorists or criminals” [11]. This is possible because data
mining is a technique for extracting knowledge from large sets of data and therefore “scientists,
marketers and other researchers use it successfully to identify patterns and accurate generalizations
when they do not have or do not need specific leads” [11]. But this is kind of passive result.
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The educational designers seek to develop learning materials that the soldiers users seek to
‘pull’ the information from. Very simply, what a user ‘pulls’ from a self-paced eLearning package as a
consequence of their own endeavours they will learn more deeply and profoundly. The aim, then, is to
create an active learning environment as opposed to a passive learning environment where the
information is forced upon the user. Adhering to sound instructional design principles develops active
learning. Part of this active learning is fostered by the use of simulations. There are a number of
simulations very efective for the militaries training activity in the eLearning packages: siting claymore
mines in a section defence, using a team to construct a CAT1 wire fence, scoring/marking in the butts,
or making decisions as a platoon sergeant in a tactical scenario are a few of the simulation activities
used to confirm learning.
This kind of learning tool is used in the Australian Armed Forces where, for example, a user is
immersed into an operational environment whereby they are forced to make over 30 decisions as a
platoon sergeant. The user is placed in a position that requires a decision. This becomes a trigger to
branch off and acquire the information needed to help make the correct decision [12] [13]. Having
gleaned the requisite knowledge, the user drops back into the tactical scenario to make a decision
(from three choices). Having made a choice, the user is given feedback and moves to the next part of
the decision tree - noting that the adverse consequences of that decision impact upon future decisions
[14]. The result is a highly interactive and engaging simulated learning environment [15]. This is
possible to be accomplished through the use of video, audio, photos, operational radio traffic,
telephone, maps, intelligence, documentation, background noise, choice of problems and the
sequencing and timing of simulation, in order to recreate incidents the trained militaries can be
involved in a vivid and realistic way.
V. CONCLUSIONS
E-learning becomes more and more the generic background of education no matter it concerns
the civil or military fields. The classical blackboard and the piece of white chalk it cannot remain the
single manner to share the learning knowledge as long the technologies are performing and invade our
daily space. The high tech spread in all social life dimensions. All runs faster. Therefore, it is needed
rapid adjustment to change and in a matter of consequence to learn.
Data mining is an ongoing field, still in its infancy form, and even academic references are
scarce on the ground, although some leading education-related publications are already beginning to
pay attention to this new field. But, even in this incipient form it represents a powerful analysis
instrument offering to the educational institutions the possibility to better share their resources and
personnel on activities and to better accomplish the management of students’ results in order to
improve their educational and professional becoming.
The actual armed conflict goes out from the pure war sphere and is more a knowledge war.
Therefore, the soldiers must be trained to think situation not only to execute orders, but for this they
must have the proper knowledge acquired. As this concerns, military e-learning data mining should
become a more used tool because it already shown to be an incremental outfit.
Also, for the military e-learning data mining to be successful there is needed a
interdisciplinary collaboration among participants in the creation and exploitation of this technique in
order to improve military education quality. In this regard, there are needed military specialists, IT
specialists, pedagogues, trainers, communicational and marketing specialists. Each must come with its
own expertise in order to create better knowledge for military e-learning able to help to the
enhancement of militaries education.
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References
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