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Intellectual Control Algorithm Interaction
improvement by the Users Education Process of the
Automation Education Systems
Uglev V. A.
Abstract – The paper considers an integrated
approach to trajectory learning control algorithms
for individualizing a person in the training systems
based on Artificial Intelligence methods, Learning
Computer Testing and Cognitive Maps of Diagnosis
Knowledge.
Index Terms – Intellectual Control, Automation
Educated System, Learning Computer Testing, Cognitive
Maps of Diagnosis Knowledge, Artificial Intelligence.
I. INTRODUCTION
Modern conditions of civilization require the
development of innovative, controlled and effective
forms of learning: the old forms of verbal-logical
learning start to show its limitations brighter, which
leads to the need for a transition to new forms of imagelogical Dialectical Learning [1]. On the one hand,
education clearly dominate the requirements of
standardization processes of control and mass training
of specialists, and on the other hand -the role of the
principles of individualization of trajectory learning, of
student-centered approach, transparency and openness
of the educational process. Let us turn to the topic in
terms of its automation, using a high-tech approach to
intellectual interaction Control process of the users with
Educated Systems.
Automated Educated Systems (AES), as the most
advanced interactive training tools, are designed to
solve complex pedagogical tasks: transition of
educational material, knowledge control, preparation of
consolidated financial statements and making decisions
on the control of the educational process. The
effectiveness of these operations each year is growing,
but it still can not be compared with the work of human
teachers. The main reason for difficulty - a small
amount of information about the user for control
algorithms. The strategy of the AES work may be based
on different approaches:
- as a frame - when the response of the system is
Manuscript received April 92, 2011.
V. A. Uglev is with the Center for Applied Research of Siberian
Federal University, Zheleznogorsk, 662971 Russia (corresponding
author to e-mail: [email protected]).
firmly described in a set pattern of operations;
- in the form of statistical and probabilistic model when the response of the system depends on the
hypothesis state about the level of training of the user
(based on a probabilistic automaton, Petri net, Markov
chain, etc.);
- as an intellectual system - when in the basis for
decision-making is laid one or more methods of
Artificial Intelligence (neural networks, expert systems,
etc.).
It is obvious that the choice of approach depends
significantly on the effectiveness of the AES. We
consider possible mechanisms of the advancement of the
AES by integrating into it the methods of Artificial
Intelligence (AI) as the most prospective one in the
automation of the study.
II. BASE MODEL OF ELECTRONIC EDUCATION COURSE
The composition of ATS, traditionally, includes such
subsystem as a transmitter of Electronic Education
Course (EEC), a system of Computer Testing (CT),
knowledge base and intellectual planner. controlling, in
accordance with the canons of the cybernetic approach
[2], is done by the scheduler, who should be responsible
for the effective adoption of the following decisions:
- about the composition of the teaching material
according to the user’ s objectives and the material
developers of EEC;
- about the sequence of presentation of teaching
material and adapt it to the current state of knowledge;
-about the importance of the evaluation score
successes of the user based on the results of the control
measure taking (the scoring for the solution of the tests
and tasks);
- about the contents of the recommendations, given to
the user in natural language form to control his
trajectory when working with EEC.
All it requires special rules in the storage and
processing of information, based on statistical methods,
data analysis and Artificial Intelligence.
Assume that the developer of EEC, working with
services AES, sets educational material in the hierarchy
of the type a tree on the following levels: subjects sections of the course - didactic units- a set of tests,
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tasks and terms. In addition, in the AES it should be
included some meta-information, which should form a
kind of ontology learning process, including statistics
data on the educational process and a number of
models:
- a model of organization of educational material;
- a model of a student;
- a model of learning process and control.
In order to allow a flexible layout of the course and
its intellectual analysis it will be necessary to enter the
assembly level, which forms the implementation course
for a particular user according to his goals. This can be
achieved by applying the notation of semantic networks
(see Fig. 1): each node is a unit of study (including the
characteristics of the type of material, its being wanted,
importance, complexity, developed competencies as
well as storing references to test items and terms), and
edge - its ties within the educational material (which
determines the relationships of the sequence of study,
the logic of presentation, the child and parent relation)
[3]. Thus, the original tree structure can be displayed in
a new structure having an individual configuration and
described significant for AES properties (metainformation).
Part 1
Part 4
Part 3
Part 2
Fig. 1 Example of semantic links organization among the elements of
EEC in the course model
It is obvious that under certain assumptions, the
proposed structure can be reduced to any developed
standard in the field of e-learning (IMS, CMI, SCORM,
etc.). But, in our opinion, the closest in philosophy of
data is a standard Learning Objects Metadata [4]. In this
case, no restrictions on the presentation of material:
Therefore, we turn to a discussion the following points:
what methods of AI can be efficiently used to operate
the structure described above.
III. INTELLECTUAL ALGORITHM IN AES
The idea of a comprehensive user support during the
entire period of study in an environment AES involves
developing a set of solutions to optimize the interaction
between man and machine. Criteria for optimizing act
simultaneously as a goal of the authors of the course
(government or industry standards, such as [5]), and the
purpose of the student, expressed in a set of these
competencies - all are static parameters that define the
standard of education [6]. In addition, the data with the
work time of the user with the individual subsystems
AES (including module CT), expressed in the form of
quantitative and qualitative indicators are available for
the training system for fixation. Quantitative indicators
include the following: the test results, rates of change of
test scores in a cut section of the course, frequency of
treatment to the elements of EEC, the time of the test,
etc. The quality indicators are not so obvious: the
systematic work with EEC, the adequacy of user
response to prompts and recommendations of AES , the
reliability of testing results, etc. All this requires an
integrated intellectual analysis of information about the
learning process.
Integration into the AES of the following AI
technologies allows us to find a balanced solution of
many problems with automated path control of the
educational process [7]:
- Semantic networks - the basic element on which it is
necessary to build a model of the course, and the
algorithms of individualization and adaptation;
- Expert systems - the main instrument for integrated
multi-criteria analysis of quantitative and qualitative
information;
- Methods of Data Mining - mechanisms for
identifying and updating the expert system for
problematic moments in the learning process;
- Fuzzy logic - a basic method for transforming
quantitative data into qualitative.
Systematic coverage of the factors of the learning
process of the expert system is due to processing of
semantic relations elements EEC and statistics about the
learning process. This allows a reasonable approach to
implementing the algorithm of intellectual knowledge
test, then move to the algorithm to identify the problems
of developing training material and as a result of
formalizing a mechanism of synthesis of individual
trajectories of learning.
IV. INTELLECTUAL LEARNING CONTROL
Teachers test, as is known, can be conducted in the
learning process, and then he called an intermediate or a
boundary, and on its completion, and then it is called the
ultimate control. Current AES, based on this approach
to testing, is in deep crisis. This is due to the fact that
learning processes and controls are artificially isolated.
From systems theory and systems analysis it is known
that any subsystem must operate not only relying on its
local goal, but take into account the objectives of the
whole system. Final testing, as well as intermediate, is
created solely for the sake of control and it has limited
impact on the learning process (see, eg, [8]). Therefore,
having changed the purpose of testing, we must move to
a Learning Test, which functions as appreciation of the
level of knowledge and training assistance. For the first
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time the two approaches to the formation of an
individual trajectory of the material EEC (direct and
indirect), based on the results of Learning Computer
Testing have been proposed by the author in [9]. To do
this quiz, expert systems are entered, involved in the
implementation of three algorithms:
- the adaptation of the test on the thematic content;
- multicriteria evaluation of test results;
- developing a set of recommendations to the user for
further work with the material EEC.
When integrated in the AES of Learning Computer
Testing method it is assumed that the user himself and
repeatedly refers to the subsystem CT. It should be
emphasized that the adaptation mechanism operates
only in training mode, while in other cases,
implementing a standardized control. The process of
analyzing the semantic relationships and their
correlation to learning objectives and learner's current
rates provide broad opportunity for the development of
control information. This allows us to concentrate all
data on the learning process in a single result set of
information, having got a name the Cognitive Maps of
Diagnostic Knowledge.
V. COGNITIVE MAPS OF DIAGNOSTIC KNOWLEDGE
Cognitive Maps of Diagnostic Knowledge (CMDK)
are designed for solution the intermediate data mining
tasks on the learning process in the AES. CMDK
should not be equated with the theory of Mind Maps
[10]: although this method is the concentration of
information, but it relies on theiry of statistical analysis,
cognitive computer graphics [11] and Data Mining.
The main problem to be solved by CMDK can be
expressed as follows [12]:
- to concentrate the information about the learning
process within a single service;
- visualize the current state of the learning process,
taking into account the history of education and the
outcome standards (dialectic);
- to implement a centralized preprocessing of data for
submission to the expert system for estimating the next
session CT or in the synthesis of complex prompt.
As a result of CMDK expert system receives a vector
of the following questions, important to control:
- Does the user get recommendations of AES?
- Is training progressing successfully?
- Does the user work with the AES enough?
- What is most important to study at the current level
of knowledge and error?
Thus, the cognitive maps of diagnostic knowledge
form the basis for the control decisions in the algorithms
for adaptation and personalization.
VI. PROVIDING INDIVIDUALISATION IN AES
Processing of information about all the parameters of
educational process, as was shown above, is as follows:
learning – control – analysis of the results – control –
training... Therefore, in all phases of AES it should be a
process of gathering information about the user's
learning activities and, where it is possible, to
implement intellectual control. The trajectory of
learning, which was originally defined in the synthesis
of course, depends on the speed and efficiency of the
process of development of material, estimated largely by
the tests and training problems. But in the process of
learning from time to time a situation arises that requires
a return to the previously studied materials, which
requires individualized providing of material or to do
the next stage of controlling [13].
The idea of individualization in automated learning is
expressed in the concept of learner-centered approach.
Even with the work of B.F. Skinner, N.A. Crowder,
W.P. Bespalko of programmed instruction
was
established such a strategy knowledge test, when a
wrong decision of individual test items the user was
directed to the teaching material, which contains their
theoretical basis [14]. On this principle now work
almost all the systems of pedagogical CT and AES. It
gives the results only when in front of student there is a
simple course with a consistent presentation of
educational material, in order to "learn everything"
(emphasis on short-term memory). When learning a
modern engineering disciplines work with the material
becomes more complicated: there is a non-linear
movement of educational materials, especially when the
individual elements of knowledge were previously
obtained outside of the courses, misunderstanding and /
or partially forgotten. Moreover, the adaptation of the
EEC and test control with the user's purpose can
significantly modify the initial tree structure display of
educational material (see Section II). On this basis, at
the present level of technology development of AI and
EEC it is not allowed to take each training unit in
isolation. Consequently, each element of EEC should be
analyzed in the context of educational material, goals,
and the user's knowledge, not forgetting about the
standardization [15].
In the new generation of AES process of
individuation should cover all stages of learning, which
requires the use of AI techniques in the analysis of
available factors, revealing the features of the learning
process. Manifestation of individualization should be
rationally organized to:
- Selection of logic and layout of EEC, when the
goals of the authors of course do not match with the
goals of the student (there selected list of individual
competencies and indicates the depth of learning
material);
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VII. SOME RESULTS
Approbation of the control ideas outlined in the
learning process (special courses for senior students)on
the experienced patterns of intellectual software has
demonstrated a positive effect .There greatly increased
self-restraint of students to independent learning,
reduced the number of calls to the tutor for help, selfassessment of learning outcomes has become realistic.
However, it was possible to increase the degree of
personalization algorithms work with the AES of the
user. To illustrate this, we give a generalized diagram of
achievement of students using an experimental learning
system (Fig. 2): averages show a steady positive
dynamics in the training of user interaction with the
automated system. This confirms the promise of this
research in improving the efficiency of learning control
algorithms of interaction of AES with human beings.
CONCLUSION
Learning human-computer interaction should be
effective and controlled as a temporary aspect and the
aspect of development of required information amounts.
Artificial Intelligence techniques, in conjunction with a
set of innovative teaching methods (the Dialectical
Learning, the Learning Computer Testing, the Cognitive
Maps of Diagnostic Knowledge) should accelerate the
emergence of Automated Education Systems of the new
generation.
Mark (%)
- Selection of problems for the consolidation of
knowledge;
- Selection of tests for the subsystem Learning
Computer Testing in training mode;
- Grading based on the results of the test;
- Synthesis in prompts calling for help in the training
mode bye Learning Computer Testing;
- Synthesis of complex recommendations to the user
on the result of performing the next stage of controlling
(or a random request);
- Engaging in dialogue with the electronic assistant in
natural language form.
All these aspects of individualization , as it was
shown in Sections III-V, could be realized by combining
Artificial Intelligence techniques and Learning
Computer Testing methods and CMDK. An important
feature of this combination of technologies is that at any
stage of training, you can get not only a comprehensive
diagnosis of the level of studying of the material, but
also a set of specific recommendations. At the same
time the proposals that haves the ability to synthesize
EEC, will not only define current difficulties of the user
but they will explain their reason taking an information
from CMDK and a structure of database of expert
system. The unit of explanation is responsible for it
given to any expert system with production output
knowledge). Information provided to the user will be
synthesized from the fact that the current appreciation of
knowledge, identified the cause-effect relationships and
the facts supporting the assessment.
Number of test
Fig. 2 Generalization of statistics marks in the student group on the
results of work with intellectual learning system
REFERENCES
[1] M. Botov, “Aboute the Dialectical Learning method”, Vocational
education (application to journal), no. 7, 2007, pp. 27-37.
[2] N. Wiener, Cybernetics or Control and Communication in the
Animal and the Machine, New York: The Technology Press and
John Wiley & Sons, Inc. – Paris: Hermann et Cie, 1948.
[3] V. Uglev, V. Ustinov, F. Samrina, “Approach to the Semantic
Links organization about the education material structure in
Automated Education Systems”, Neuroinformatic, her
application and data analysis, Krasnoyarsk: IVM SO RAN,
2010, pp. 171-175.
[4] IEEE 1484.12.1-2002/ Learning Objects Metadata standard. –
New York: IEEE, 2002.
[5] Software Engineering 2004: Curriculum Guidelines for
Undergraduate Degree Programs in Software Engineering, New
York: ACM and IEEE, 2006.
[6] V. Uglev, V Ustinov, B. Dobronets, “The System Analysis of the
Learn Computer Testing”, Information Technology, no. 4, 2008,
pp. 81-87.
[7] V. Uglev, “The Methods of Artificial Intellect as Basis of
Learning Computer Testing in Remote Leaning”, Interactive
Systems and Technologies: the Problem of Human-Computer
Interaction, vol. 3, Ulyanovsk: UlSTU, 2009. pp. 420-422.
[8] E. Triantafillou, “Computerized Adaptive Test – Adapting to
What?”, Informatics Education Europe II Conference, SEERC,
2007, pp. 379–385.
[9] V. Uglev, F. Samrina, “Using of possibilities in learning tests for
individualization of displaying material in electronic education
courses”, Modern Techniques and Technologies, IEEE ED-S
Translated S.Sladkov. Tomsk: TPU Press, 2008. pp. 96-100.
[10] T. Buzan, B. Buzan, The Mind Map Book, London: BBC, 2000.
[11] A. Zenkin,Cognitive Computer Graphics,Moscow:Science,1991
[12] V. Uglev, “The use of the Cognitive Maps of Diagnostic
Knowledge for learning process optimization in Automated
Education Systems”, Modern Techniques and Technologies,
Minsk: BGU, 2010, pp. 506-510.
[13] E. Georgiadou, E. Triantafillou, A. Economides, “Evaluation
Parameters for computer adaptive testing”, British Journal of
Educational Technology, 2006. vol. 37, no. 2, pp. 261–278.
[14] V. Bespalko, Education and Learning with computers
participation, Moscow: MODEC, 2002.
[15] V. Uglev, “On the specificity of individualization of training in
automated training systems”, Philosophy of Education, no. 2,
2010, pp. 68-74.