DATA MINING TECHNIQUES USED IN ONLINE MILITARY TRAINING
«DATA MINING TECHNIQUES USED IN ONLINE MILITARY TRAINING»
by Elena ŞUŞNEA
Source: Conference proceedings of "eLearning and Software for Education" (eLSE) (Conference proceedings of "eLearning and Software for Education" (eLSE)), issue: 01 / 2011, pages: 201205, on www.ceeol.com. The following ad supports maintaining our C.E.E.O.L. service
Access via CEEOL NL Germany
DATA MINING TECHNIQUES USED IN ON-LINE MILITARY TRAINING Elena SUSNEA “Carol I” National Defense University, Panduri Street 68-72, Bucharest, Romania
[email protected]
Abstract: With this paper we intend to present the way in which data mining (DM) techniques can be used within the military educational system. The analyzed data are kept in the databases of the Learning Management System (LMS). Using the models and the patterns that have resulted, we will extract useful information necessary for projecting and allotting the educational resources for on-line military personal training. For the beginning we will emphasize the necessity of using DM techniques within the educational system and then we will present some tendencies in using these analyse techniques within the academic system. We will propose a model for the KDD (Knowledge Discovery in Database) process which is present in on-line training. At the end, we will use classifying trees for allotting the instances from the data set provided by LMS for predefined classes. Keywords: data mining, knowledge discovery in database, military field.
I.
INTRODUCTION
The wide use of Internet has led to major changes and possibly irreversible in all activity fields, including the military one due to the way in which information and knowledge are split and disseminated in real time. This led to appearance of some instruments known under the name of Learning Management Systems (LMS). Thus, on-line education developed during the last decade as a new way of distance teaching-learning. In this context, the low costs for data storage and the data processing from academic institutions have determined important raises regarding the volume of acquired data. Quantity, eterogenity and data gathering speed are a few features that make the data analyse more and more difficult by using the classical methods and this is why the use of some special methods of automatic processing is required. Gathered from several fields such as statistics, artificial intelligence, form recognition DM techniques help knowledge discovery and extraction from the educational data bases. The success of using these techniques in on-line educational system happens because of the opportunities offered by these methods regarding identification of patterns and/or systematic relationships between variables within structured data sets, and then to validate the findings by applying the detected patterns to new subsets of data [1] so as to generate implicit, previously unknown, and potentially useful information from data [2]. This information is subsequently interpreted in order to get the knowledge that will be used by the university in managerial decisions.
II.
CONCERNS REGARDING THE USE OF DM TECHNIQUES IN THE ACADEMIC SYSTEM
The use of XXI century technologies and of the advanced techniques of data analyse within the educational system has become a necessity, because they “will enable new modes of scholarship that complement centuries-old ways of conducting research” says Haym Hirsh [3].
Regarding the research activity, there are international discussions that bring together scientists and humanities scholars concerning exploitment of the educational resources available digitally (books, newspapers, photographs and countless other documents) by launching international contests [4] that have as objective the way how DM and data analysis tools currently used in the sciences can improve humanities and social science scholarship [5]. DM techniques used more and more frequently, will help give new insights to academic inquiry [6]. On-line education uses more and more LMS systems such as Blackboard, Moodle, ILIAS. They allow the successful use of DM methods in order to model different phases of the educational system starting with the user profiles to building the models based on which the educational contents can adapt and personalize learning to the needs of each student [7]. In this regard recommendations have been given in order to personalize the hyperlinks so as to achieve an adaptive surfing, the students using the best way to cover the learning material [8], [9]. Different studies have been realized in order to determine certain types of adapting in on-line systems starting from the analyze of the differences between personalizing a feedback for the user level and for the group level [10]. Also, the data provided by an LMS system has been analyzed using DM techniques in order to realize predictions regarding students drop out [11], and to identify certain classes of students according to certain pre-established similarities [12], [13].
III.
DM AND KDD IN ON-LINE MILITARY TRAINING
Development of military processes and phenomena is tightly linked to the technological evolution. Having a professional army also implies using an advanced system of instruments and training technologies [14]. Within an environment in which global security faces new threatens and weaknesses, it is a great priority to train the military that will go to the operation theaters and thus the use of on-line learning platforms has grown to be a necessity. This idea has been presented at The Fourth International Forum on Technology Assisted Training, for Defence, Security and Emergency Services where the advantages and the opportunities offered to the educational field by the new technologies have been emphasized [15], also the importance of using the e-learning technologies in creating an educational network and building a knowledge portal in order to help spreading the information [16]. LMS systems allow conceiving and developing the educational contents specially to meet the specific needs of the military field and alsoto give the possibility of being assimilated by the military personnel with its own rythm , regardless of space and time. The LMS ILIAS application, used for training the military at ”Carol I” National Defence University, allows course dissemination in different formats or material dissemination as learning and training support with the help of certain standardized instruments and models [17], [18].
Figure 1. The phases of the KDD process in order to analyse the data in on-line military training In this case, KDD process is a complex one (Figure 1) involving the existence of a data base store in the LMS ILIAS system, which contains the users’ (officer-students) data. Starting with this data and the a priori knowledge about the educational system, a relevant data set is selected taking into account the point of view of the reason why the analyse is being conducted. Then this data is transformed by using methods of characteristic extraction and selection in order to be used in modelling. By using DM methods and models, we can determine the models/patterns which help collecting the information used for extracting the knowledge that will be subsequently integrated in the knowledge base of the system. Within this process DM is an essential phase and this is why will discuss about it more detailed in the following section.
IV.
DM TECHNIQUES USED IN ON-LINE MILITARY TRAINING
The architecture is s modular one composed of several subsystems that work together so as to provide quality services for the students, containing the following subsystems: the evaluation subsystem, learning adminitration system, data base management system, interfaces characteristic subsystem. In order to have an efficient administration of the educational resources and to monitor the students’ performances, it is necessary to include a module for analysing the data stored on the platform, the statistic data provided by LMS not being enough so as to state decisions regarding the development of the educational process (Figure 2).
Figure 2. The statistic results provided by ILIAS system when testing a new series of students The main purpose of the analyzing procedure consists of including some facilities to identify certain models and/or patterns that characterize the data set provided by LMS platform, to efficiently allot the educational resources according to the student’s profile, to identify concrete problems that the student faces during the training activity and also to realize predictions regarding their performances. The data base used in the analyse contains data gathered from the final test from a number of 57 officer students enlisted for a military course for whom ILIAS platform provides information regarding the logging-in/logging-out moment, the acquired score for each item, test etc. Num. of non-terminal nodes: 2, Num. of terminal nodes: 3
ID=1
N=57 Medium
Test Results (in points) 60,000000 N=34
ID=3
Medium
N=23 High
Test Results (in points) 50,500000 N=9
Low
ID=5
N=25 Medium
Low Medium High
Figure 3. The decisional tree generated based on the results acquired at the test The histogram of test score distribution shows that 87 % from the students have acquired more 50 points, the maximum score being of 90 points. Also, the medium score acquired by that series of students is of 60 points. The medium time necessary to solve the test is 63 minutes. In order to improve the students’ performances we will classify the instances by using decisional trees. The predictors that are used are Test Results and Time Spent, and the labels of the classes realised according to the students’ performances are: Low, Medium, High. The decisional tree uses for classifying an hierarchic approach or a layered one. The root of the tree contains all the instances used to identify the classes. Each node of the tree represents a test, in our case the Test Results variable is used because it influences the classification by 88%.
Therefore classifying an instance involves a testing sequence, in this respect the decisional trees can be represented as sets of rules if-then-else [19]. The validation technique that we have used is k-fold (k=3). According to the chart (Figure 3) 38% from the students have acquired medium level results while 50% have very good results at the test.
V.
CONCLUSIONS
Having at the base the above graphic representation in the following training phase the students will be divided into three classes so that the teacher can work differently with the students according to their training level. The analyze can be done up for each item, meaning that those items which present a high degree of difficulty for the students can be identified and also the causes that have led to those problems can be discovered. Also, the items with a lower degree of difficulty can be conceived so that to increase the complexity degree. This DM technique can be successfully used in classifying the tests and the items.
References [1] [2] [3] [4] [5] [6] [7] [8]
[9] [10]
[11] [12] [13] [14]
[15] [16] [17] [18] [19]
http://www.statsoft.com W. Frawley and G. Piatetsky-Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, pg. 213-228, 1992. http://www.nsf.gov/news/ http://www.diggingintodata.org/ http://www.nsf.gov/news/ http://www.jisc.ac.uk/ Mobasher, B., Tuzhilin, A.: Special Issue on Data Mining for Personalization. In User Modeling and User-Adapted Interaction. Springer Netherlands, pg. 1-46, 2008. Romero, C., Ventura, S., Delgado, J.A. and De Bra, P., Personalized Links Recom-mendation Based on Data Mining in Adaptive Educational Hypermedia Systems in Creating New Learning Experiences on a Global Scale, pp. 293-306. Berlin Heidelberg, Springer, 2007. Brusilovsky, P., Peylo, C.: Adaptive and Intelligent Web-based Educational Systems, International Journal of Artificial Intelligence in Education, 156–169, 2003. Vasilyeva, E., Pechenizkiy, M. and De Bra, P., Adaptation of Feedback in e-learning System at Individual and Group Level. PING2007 Workshop on Personalisation in E-Learning Environments at Individual and Group Level, In User Modeling Conference, Greece pp. 49-56, 2007; Dekker, G., Pechenizkiy, M. & Vleeshouwers, J., Predicting Students Drop Out: a Case Study, In Proceedings of the 2nd International Conference on Educational Data Mining (EDM'09), pp. 41-50, 2009. Susnea, E., Classification techniques used in Educational System, In The 4 nd International Conference on Virtual Learning ICVL, pp. 376-379, 2009. Susnea, E., Using data mining techniques in higher education, In The 4 nd International Conference on Virtual Learning ICVL, pp. 376-379, 2009. Willems, C., Dawoud W., Klingbeil, T., Meinel, C., Security in Tele-Lab – Protecting an Online Virtual Lab for Security Training, Proc. Workshop ELS'09 (in conjunction with 4 nd ICITST), IEEE Press, London, UK, pp. 1-7, 2009. http://www.newsecurityfoundation.org/press/postreport.pdf Roceanu, I., Citizens` security education based on e-learning technology, In Online Educa. 13th International Conference on Technology Supported Learning & Training, Berlin: ICWE, 2007. http://www.ilias.de Barbieru, D., Radu, C., Beligan, D., SCORM 2004 and Proper Use of the Didactical Objectives, In The 6th International Scientific Conference eLearning and Software for Education, Bucharest, 2010. Mitchell, T., Machine Learning, McGraw-Hill, 1997.
