![](bg2.png)
One of the ways to overcome these obstacles may be
the use of adaptation technology (Kla
ˇ
snja-Mili
´
cevi
´
c
et al., 2017).
Thus, the state of elaboration of this problem and
current trends in the development of management sys-
tems for educational environments for e-learning are
indicative of its theoretical and practical significance,
and determine the urgency of the chosen theme. The
goal of the research is to develop a functional archi-
tecture that supports the above goals of e-learning us-
ing mobile agent technology.
The introduction of multi-agent systems is one of
the most promising areas for building virtual educa-
tional environments for distance education systems.
The goal of this article is the possibility of illustrat-
ing the advantages of using intelligent agents to op-
timize the location and configuration of appropriate
resources for distance learning courses and organiz-
ing collective collaboration in the e-learning environ-
ment.
The main objectives of the research are to develop
the structure of the training service based on the use of
a personal learning environment and intelligent agent-
managers, which may be used to ensure individual
learning. It uses a set of agents that may personal-
ize learning based on previous requests from students
(or groups of students), and improve learning and col-
laboration based on previous knowledge and learning
styles.
2 RESULTS
As of today, the SCORM (Shareable Content Object
Reference Model) standard that is a standard for shar-
ing learning materials based on the IEEE 1484.12.1
standard model (IEEE, 2020) has been developed, and
is currently being used. SCORM has been devel-
oped to ensure the multiple use of learning materi-
als, support for and adaptation of training courses, in-
troduction of information of individual training ma-
terials into training courses or disciplines in accor-
dance with individual user requests. In June 2006, the
United States Department of Defense established that
all developments in the field of e-learning should meet
the SCORM requirements. A promising direction for
e-learning standardization has become the successor
of SCORM – Tin Can API model (Romero, 2015),
which enables to consider the types of learning activi-
ties that are not available in SCORM: mobile learning,
simulations, informal learning, games; tracks events
without using the Internet, and has a reliable system
for maintaining the required level of security and user
authentication.
When creating complicated and distributed sys-
tems, multi-agent systems (MAS) can offer a vari-
ety of solutions, especially in the field of distance
learning. The combining of agent technology with
other methods such as the Educational Data Mining
(EDM) and Case-Based Reasoning (CBR), which in
turn are based on cloud technology, is important in
taking the learning process to the next level. The
three-level multi-agent management architecture for
distance learning in the e-learning system, which con-
tains the following set of intelligent agents, is pro-
posed to meet the above functional requirements (fig-
ure 1):
• Tutor Agent is a set of tools for creating rules
that enable tutors to adapt the selection of learning
material, define appropriate search terms for find-
ing learning materials based on certain learning
styles, and to communicate with other agents for
collaboration and establish interaction between
tutors and students in a distance learning system.
• Lesson Planning Agent is designed to collect in-
formation and complicated reasoning required for
defining and developing a curriculum (Woolf and
Eliot, 2005).
• Learner Agents are required to organize the ef-
fective interaction of students with the e-learning
environment, and enable to unite various learning
resources into a single whole and constantly mon-
itor learning outcomes.
• Personalization Agents are responsible for cus-
tomizing training materials based on the preferred
learning style of each individual student or work-
group (Wilson, 2000).
The greatest interest for implementing LMS is
represented by learning agents, which in some liter-
ature are also referred to as autonomous intelligent
agents that determines their independence and ability
to learn. Figure 2 shows the flow of work of an agent-
manager as part of LMS, which meets the following
requirements: to work in real-time mode; learn based
on a large amount of data; analyze oneself in terms
of behavior, mistakes and success; contain a database
of examples with the possibility of replenishing it, as
well as learn and develop in the process of interaction
with the environment.
The objective of formalized description of modu-
lar e-learning systems to ensure the ergonomic qual-
ity of human-machine interaction has been solved. As
a result, a complex of component and morphological
models, which is the basis for the formation of infor-
mation support to adaptive e-learning as the “man –
technology – environment” classical systems and con-
tribute to the search for ergonomic reserves of com-
Using Intelligent Agent-managers to Build Personal Learning Environments in the E-Learning System
293