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USING DATA MINING IN E­LEARNING ­A GENERIC FRAMEWORK FORMILITARY EDUCATION

«USING DATA MINING IN E­LEARNING ­A GENERIC FRAMEWORK FOR MILITARYEDUCATION»

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 �U�NEA

"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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