Improving Decision-making in Virtual Learning Environments using
a Tracing Tutor Agent
Aluizio Haendchen Filho
, Jeferson Miguel Thalheimer
, Rudimar L. S. Dazzi
, Vinícius Santos
and Paulo Ivo Koehntopp
Laboratory of Applied Intelligence, University of the Itajaí Valley (UNIVALI), Rua Uruguay, 458, Itajaí, Brazil
Univertity Center of Brusque (UNIFEBE), Brusque, Brazil
Catarinense Association of Educational Foundations (ACAFE), Florianópolis, Brazil
Keywords: Intelligent Tutor, Software Agents, Decision-making, Virtual Learning Environment.
Abstract: Quality of care in the Virtual Learning Environment is often compromised by large numbers of students. This
presents a difficult task for human tutors. On the other hand, Intelligent Tutoring Systems are evolving
towards a decision support system. One vision of Artificial Intelligence and education is to produce a tutor
for every student or a “community of tutors for every student”. Here we present a model of intelligent tracing
tutor agent responsible for tracking students in the virtual learning environment. We have designed the
Tracing Tutor Agent as one of the agents of a collaborative organization of intelligent tutor agents. Each agent
has his role, responsibilities and permissions. The main focus of this work is to present a model of Tracing
Tutor Agent (TTA), which is one of the organization’s agents. It has the following responsibilities: (i) monitor
students’ actions in VLE; (ii) to monitor the actions of the human tutor in the VLE; and (iii) collaborate and
interact with the other organization’s agents to supply the human tutor with information in order to improve
decision making and performance, increasing attendance and avoiding evasion.
The possibility of investing in universities and private
courses have made the educational market very
attractive and profitable for national and international
groups that have been constantly involved in large,
million-dollars negotiations. With the technological
advancements and the spread of distance learning
courses, the number of students entering this
environment has significantly increased every year.
Despite all the technological advances and
concerns to ensure courses quality, Virtual Learning
Environments (VLE) continue to present significant
problems (Tomelin, 2016). The quality of care in the
VLE is often compromised by large numbers of
students. Consequently, problems including
demotivation and sentiments of isolation arise,
causing low use and high evasion rate.
According to Vicari and Giraffa (2003), with the
emergence of Artificial Intelligence (AI), the
developers began to use Computed Assisted
Instruction (CAI) techniques to make the software
more active in the process of student interaction.
From that initial experience emerged the Intelligent
Computer-assisted Instruction (ICAI) method and
following that Intelligent Tutoring Systems (ITSs).
Shifting from a problematic Expert System to an ITS
appeared to be a natural course.
In the search for automation and trying to
reproduce the teacher pedagogy, the tutors were
initially developed with the purpose of replacing the
teacher. With the evolution of virtual learning
environments and the emergence of active learning
methodologies, ITSs are evolving towards a decision
support system. One vision of Artificial Intelligence
and education is to produce a “tutor for every
student”, or a “community of tutors for every
student.” This vision includes the process of learning
in social activity, accepting multimodal input from
students (Woolf, 2010). Thus, the role of intelligent
tutors needs to be adapted to this new reality.
We have designed the Tracing Tutor Agent as a
collaborative agent of an intelligent tutor from the
organization. Each agent has his own role, responsibi-
lities and permissions. Many of the agents' activities
are executed in real time. When their lifecycle starts,
they remain in constant monitoring of the virtual
learning environment. In order to properly fulfill their
Filho, A., Thalheimer, J., Dazzi, R., Santos, V. and Koehntopp, P.
Improving Decision-making in Virtual Learning Environments using a Tracing Tutor Agent.
DOI: 10.5220/0007744006000607
In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 600-607
ISBN: 978-989-758-372-8
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
roles, the collaboration and interaction between them
are two fundamental properties.
The main focus of this work is to present a model of
Tracing Tutor Agent (TTA). It has the following respon-
sibilities: (i) monitor the actions of students in VLE; (ii)
monitor the actions of the human tutor in the VLE; and
(iii) collaborate and interact with the other organiza-
tion’s agents to supply the human tutor with information
in order to improve decision making and performance,
increasing attendance and avoiding evasion.
To substantiate the approach, this section presents the
following concepts: (i) intelligent tutors; (ii) data
webhouse and (iii) agent development platform.
2.1 Intelligent Tutors
Traditionally, ITSs are educational computational
systems that aim to promote immediate and
customized instruction to provide students with
individualized learning (Vos, 1995, Van Lehn, 2006,
Bernacki et al., 2014, Latham et al. Lin et al., 2014).
These instructions are performed without human
intervention, making the system simulate the
behaviour of a human tutor. In this way, tips are
introduced to help the student to correctly develop the
problem and identify errors (Wenger, 1987; Azevedo
et al., 1999; Curilem (2007), and Adams et al. (2014).
ITSs have been investigated by several authors,
including Raabe (2005), Giraffa (1999), San Pedro,
Baker and Rodrigo (2014), Rissoli, et al. (2006), and
more recently Mayer et al. (2014).
According to Rissoli, Giraffa and Martins (2006)
ITSs have the ability to learn and teach. In this way,
ITSs allow adaptation of teaching strategies to the
learning of each student's needs. The dynamic and
coherent combination of information and the content
of pedagogical aspects are related to every student.
According to Dazzi (2007), ITSs are computer
systems that tutor a student in a given domain. ITSs
models the students' understanding of a topic. As it
performs certain tasks in the system, it compares the
student's knowledge to the model they have of an
expert in that domain. If there is a difference, the
system can use its domain model to generate an
explanation to help the student to identify the mistake.
The system can also adjust student learning levels and
styles and present information, tests and more
appropriate feedback.
Ausubel (1980) proposes the Significant Learning
Theory (SLT) method, which helps to clarify the
construction of new knowledge based on the learner's
cognitive structure. In the educational context, this
theory promotes respect for the characteristics of
students. This demands more from their own education-
nal processes rather than their sociocultural particulari-
ties. It requires a personalized and interactive education,
which allows the advancement of each student consider-
ing their particular needs. This personalized education is
not feasible in traditional teaching and learning, where a
large number of students are under the pedagogical
responsibility of a single tutor (Souza et al., 2013).
Research conducted by Azevedo et al. (2016) and
Mudrick, et al. (2017) support the idea that assistance
and feedback of virtual pedagogical agents’ favour
self-regulated and complex student learning. This
depends on tracking, modelling and incentive to the
intelligent and precise feedback (apud Barcenes et al.,
2018). Other authors like (Grawemeyer et al., 2017)
developed a system to improve commitment and
learning through formative feedback based on the
emotional state of the student, called iTalk2Learn.
In this sense, we observe the great potential of the
systems that offer personalized education and
tracking of students’ actions.
2.2 Data Webhouse
According to Kimball and Merz (2000), the Web
allows the possibility of recording practically all
behavioural actions of the user in a single click. In
terms of behavioural actions, it must be understood
that one can capture not only the page accessed but
also weather and navigability information.
To make their proposal feasible a technique called
clickstream is used for the exploitation of Web access
information. The recording of all interactions made
by anyone via an application or web site, is literally
called a clickstream. Activities carried out by the user
such as click capturing, form filling, and others,
create conditions for analysis, profile identifications,
preferences and trends of each particular user.
Figure 1 shows a very simple example of a
dimensional model for DWH.
Figure 1: Simple dimensional DWH example.
Improving Decision-making in Virtual Learning Environments using a Tracing Tutor Agent
As for Data Warehouse, Data Webhouse (DWH)
is built with an architecture called Dimensional
Model. Dimensional modelling is a discipline that
seeks to model data for the purposes of
understandability and performance.
All dimensional models are built around the
concept of measured facts. The Facts table,
represented by the entity Clickstreams, stores users'
clicks on the VLE. The dimensions relate to the
entities that serve as perspectives of analysis in any
subject of the model. In the example, Student,
Discipline, Time and Tutor are the dimensions
connected to the fact table. Dimensions are rich in
descriptions. For example, the Student dimension
stores all the student profile data.
2.3 Agent Development Platform
The inherent difficulties of agent-based systems
development induce the search for tools in order to
minimize complexity and reduce time and cost. For
the development of the solution, we use the Midas
Platform (Haendchen Filho, Prado, and Lucena,
2007). Midas offers a flexible, extensible, adaptive,
loosely coupled, and service-oriented architecture in
order to interoperate in the Web. The platform
provides a complete runtime environment where
agents can execute concurrently within the same host.
Figure 2 shows the Midas generic architecture
detailing the Agent Container (AC). AC is a web
container where application agents and components
are hosted, providing a platform and a framework to
simplify the Multi Agent System development. The
mechanisms embedded in Midas work in two
abstraction levels: (i) the generic architectural level,
where four middleware agents are responsible for
providing infrastructure services; and (ii) in the
agents’ design level, providing abstract classes that
define hot-spots from which concrete agents and/or
components can be developed. The abstract template
already empowers the agents with communication
facilities, including event-based listeners and a
The reference architecture is composed of five
models: (i) Message-Oriented Model (MOM):
dedicated to the architectural issues about the
structure and transport of messages; (ii) Service-
Oriented Model (SOM): focussed in the services and
actions executed by requesters and providers; (iii)
Resource-Oriented Model (ROM): focussed in the
architectural aspects about the resources handling;
and (iv) Management Model (MGM): devoted to the
management of resources.
Blackboard is one of the most used information
exchange techniques in symbolic cognitive Multi-
Agent System. Its structure follows the basic
blackboard pattern: the knowledge sources represent
the agents, the data structure is visible to all agents,
and the controller is responsible for notifying the
agents about the changes in the environment.
Figure 2: Agent Platform (Haendchen Filho et al. 2007).
Agents are autonomous entities that have their
own thread execution and can implement adaptive or
intelligent behaviour. The abstract class provides the
interfaces and triggers the agent execution thread and
the signatures for lifecycle management. The
component is an abstract class that represents purely
reactive entities, typically used to encapsulate the
domain-specific rules of the application, including
business processes, data access objects, and legacy
application functionalities.
This section is intended to present the main
specifications of the system. The structure is divided
in (i) Generic Model; (ii) Learning Model; (iii) VLE
Webhouse Model; and (iv) Tracing Tutor Agent Role,
presented below.
3.1 Generic Model
The generic model is composed of an organization of
intelligent tutors, data sources, VLE and two main
actors: the human tutor and the student, as shown in
Figure 3.
Intelligent Tutor System is a software agent
organization composed of the following agents: (i)
TTA (Tracing Tutor Agent), which represents the
tracer tutor, which is the main object of this paper; (ii)
ITA (Interface Tutor Agent), which represents an
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
interface tutor; its role is to provide information and
notification adaptive graphical interfaces in VLE; (iii)
PTA (Pedagogical Tutor Agent), that follows students’
interaction with the VLE, collect the information
required for the modelling of students’ profile used to
customise the environment assist and guide students
during the construction of their learning (Dos Santos et
al., 2002); (iv) AITA (Artificial Intelligence Tutor
Agent), which manipulates artificial intelligence
techniques like machine learning and data mining
techniques that perform predictions and prescriptions;
(v) Student representing a virtual student agent, (vi)
Tutor representing a virtual Tutor, and (vii)
Component, representing reactive objects used by
agents to obtain information stored in the database. The
databases are represented in the figure by the DWH
and Academic Information System (AIS).
Figure 3: Generic model.
The student accesses the VLE, having its clicks
captured and stored in a Data Webhouse during its
navigation. After obtaining the data and performing
the analyses, the TTA provides the information to the
human tutor. With this information, the human tutor
can make decisions and send notifications or keep in
touch with students detected on alert. Also, the TTA
may obtain permissions from the human tutor to send
notifications and help students with difficulty.
In VLE, listeners are placed in relevant spot,
waiting for the clicks to trigger the script to store the
information. Locations do not necessarily have to be
on the links since with dynamic page load, data can
be stored with simple interactions. Clickstreams,
about the click data to the user ideally, should be sent
to the webhouse data for storage.
All actions the user take can disclose knowledge
about the use of the system. Obtain such knowledge
the data webhouse is widely used to process analyses,
obtaining information from two main sources: (i)
communication protocol data, stored in the web
services logs; and (ii) behaviour seized with site
scripts after establishing a session. User’s behaviour
on pages is a critical part because it is not so simple
to change a site to capture the information.
There are two approaches to deploy scripts on site
pages: (i) placing a script within each existing clickable
element; and (ii) create a script to listen to data on all
pages and filter the relevant ones. By placing a script
on the clickable elements, it is possible to ensure that
all important information is correctly captured. The
disadvantage of this approach is to generate much work
on site maintenance. On the other hand, using a script
to listen to all page data is a simpler approach, but it
has the disadvantage of losing relevant information
about what is being clicked on.
It is important to clarify that a significant part of
student clicks, especially internal clicks in VLE, can
be collected in the log files generated by some
platforms, such as Moodle (Dougiamas, 2001) and
others. In addition to the clicks performed on the
VLE, the system may also capture clicks from other
browser open windows during the study. To enable
this, the system will prompt users for permission to
use cookies on their computer. With cookies, it is
possible to capture various preferences and interests
of users, and especially the external pages visited
during the learning process, such as collecting articles
and materials related to the topics of the classes.
3.2 Domain Learning Model
VLEs usually have a model called the domain model,
where the contents practices and learning pathways
are defined. In the domain model, the schedules are
defined to allow the contents’ presentation to the
students in the diverse pedagogical strategies of the
system. Figure 4 adapted from (Positivo, 2017)
presents a learning pathway model. The example
shows the activities of a weekly learning schedule.
Figure 4: Model of learning pathway.
In the learning process the student navigates in the
VLE looking to follow the pathways defined in the
domain model. During navigation, the student
performs several actions, such as the reading of
teaching material, participate in challenge cases,
attends video conference explanation, performs the
practical activities requested by the teacher, and
participates in virtual meetings like forums, chats or
others. One of the roles of TTA is to monitor students'
actions in the learning pathway, as will be shown in
the role model.
Improving Decision-making in Virtual Learning Environments using a Tracing Tutor Agent
3.3 VLE Webhouse Model
As presented in Section 2, in a Data Webhouse
(DWH), data is stored in dimensional models.
Dimensional modelling can begin by dividing it into
two parts: measurement (facts) and context
(dimensions). Measurements are usually numeric and
repeated. Facts are usually surrounded by a large
textual context (dimensions). Figure 5 shows the
DWH dimensional model.
Figure 5: VLE Webhouse Model.
The template contains a central Fact table
connected with the dimensions relevant to the context
of the VLE. The relationships among Facts table and
dimensions are made by their primary keys.
Users clickstreams and/or collected logs are
stored in the Fact Table, represented by the
click_count attribute. This attribute is an array type,
and contains the data collected in the clicks or log
files, such as the page id, the start time (second /
minute / hour / month / day / year) of the event, event
name, description of what happened in the event, the
previous source or page, the user's IP address, and so
The summary descriptions of dimensions are
given below:
Calendar date: attributes may include days of
the week, seasons, holiday, workday, tax
period, among others.
Time_of_day: is a complement to the calendar
date, where are recorded the time slots during
the day, including hours, minutes, and time
slots like lunchtime, class time, and so forth.
Academic date: associated with different
structures that differ on the number of modules
(semesters, four-month periods and
trimesters). These structures have a different
hierarchy, each with five levels: Year, Module
(6, 4 or 3 months), Period (classes or
examination period), Week (variable length,
defined internally by each institution) and
Page: among the attributes of this dimension
can be cited: the page source (e.g., static,
dynamic), function (content, exercise, video,
forum) and so forth.
Session: session is the collection of actions
taken by a visitor to a site while it navigates
through it without leaving this site.
Causal: describes the conditions of the current
progress of the subject, such as beginning of
the subject, period of tests, etc.
Student: information about the student profile.
Academic records: provides information on
the student’s trajectory, like courses taken,
historical grading, and so forth.
Discipline: the attributes can include class
hours, credits, opening and closing dates, and
so forth.
Teacher: information about teacher's profile.
Tutor: information about the tutor including
the degree of training, number of tutored
disciplines, etc.
Domain_model: describes the course schedule
of the learning path in the VLE.
Referrer: brings information about the URL
from where the user came from.
In the dimensional model, the student's historical
dimension is connected to the student dimension,
characterizing a variant of the dimensional model
named snowflakes. The main source of information
for obtaining dimension data is the institution's
Academic Information System.
3.4 Tracing Tutor Role Model
The role model has been used by many approaches
(Gonçalves, 2009, Haendchen Filho, 2017) to provide
a summary of software agents. According to the
theory, a role can be described by two basic attributes:
(i) responsibilities: they are role obligations and
indicate functionality, and (ii) permissions: they are
the rights associated with the role and indicate the
resources that the agents can use. Intuitively,
responsibilities are associated with services that an
agent must provide, while permissions are associated
with the services the agent must fulfil their
TTA does not communicate directly with the
human tutor or student. Communication tasks with
human actors are carried out by ITA. TTA's main
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
responsibilities are to collect data on learning
pathways and collaborate with other intelligent tutors
(ITA, AITA and PTA).
It provides information for ITA (detailed in the
TTA Role Model) that makes it available for VLE
managers, human tutors, and students. The data
collected in the clicks and/or log files are stored and
made available to AITA, which can use this data to
feed the machine learning and data mining
algorithms. TTA also collaborates with the PTA,
which can suggest new pathways and check which
trails the students most successfully use. As
mentioned, all information for decision making
comes to human administrators and tutors via ITA.
TTA's responsibilities are divided into four
groups: (i) student monitoring; (ii) tutor monitoring;
(iii) Provides Information for ITA and (iv) Statistical
Reports for ITA. Figure 6 shows the role model of the
agent, with its Responsibilities, Permissions and
Figure 6: TTA Role Model.
The services of the Student Monitoring group
comprise tasks related to information about the
participation and interactions of the students in the
VLE, according to the domain learning model:
Time spent by the student on VLE: identifies
students who are not entering the VLE, those
who are entering infrequently, and those
who are frequent attendees.
Chapter download: verifies if the student
has downloaded the materials made
available for reading.
Time spent on cases challenge: monitors the
amount of time the student spent on the
challenges assigned by the teacher.
Student time on explanation of the scene:
monitors whether the student has attended
and participated in the video conferences.
Student time in quiz/tests/exercises: they are
synthesized as Stop for Practice in the
learning pathway, verifies if the student is
participating in these activities and how
much time he spends in each one.
Time spent on virtual meetings: verifies if
the student participates in the forums, chats
and online activities carried out in the VLE.
The related services in the Tutor Monitoring
group comprise activities associated with the
activities performed by the tutor in the VLE:
Average response time for doubts: the
amount of time that the human tutor or
teacher takes on average to answer students'
Time spent by the human tutor VLE: the
amount of time the human tutor remains in
the VLE.
The average number of feedbacks: counts
the average number of feedbacks given to
The services listed in the Provides Information for
ITA group comprise a sequence of alerts sent from
Students with delayed tasks: notify the
human tutor about students with overdue
Students with no attendance at VLE: notify
the tutor about students not entering the
Students with little frequency in VLE: notify
the tutor about students entering low on
The Statistical Reports group represents reports
that can be available for ITA with information
obtained from the monitoring tasks. For each
monitoring service, reports and graphs can be
generated to facilitate visualization for analysis and
decision-making. With this information, the ITA and
the human tutor can give individual attention to the
students, taking preventive measures to avoid low
achievement or even avoidance.
Role Model Permissions, as well as its
Collaborations are placed in the right-side column of
Figure 6. As mentioned, the permissions include data
and information that the tutor agent can use to fulfil
their responsibilities. TTA uses services provided
from ITS components in order to obtain data from the
database, as well as to generate reports and organize
Based on information provided by TTA on
interactions, students' course in learning pathways,
Improving Decision-making in Virtual Learning Environments using a Tracing Tutor Agent
and assessment, PTA can identify points of difficulty
and suggest course corrections in learning paths. This
knowledge could hardly be obtained by human tutors
in virtual environments with large numbers of
students. Moreover, intelligent tutors can be assigned
tasks to assistance students in the navigation process,
and the best course of learning paths through
recommendation systems, optimizing their
accomplishment and achievement.
Furthermore, tracking clickstreams will enable
the tutor to understand why certain groups to follow
a sequence of steps, while others follow different
ones. This will allow verification of the track defined
in the domain model. The expected quality will
hopefully permit continuous improvements in
learning pathways and GUIs.
The monitoring of the actions of the human tutor
in the VLE allows analysis and self-assessment of
their participation in the learning process.
The approach presented in this paper meets the
operational needs requested by the distance learning
managers of the University Center of Brusque
(UNIFEBE). Specialists in this area point out the
importance of information obtained by student
tracking. In this proposal, this information is made
available by the Tracing Tutor Agent. It provides
information for other collaborative agents in order to
improve the decision-making of the human tutors and
managers in the virtual learning environment.
As before mentioned, Intelligent Tutoring
Systems are characterized for incorporating Artificial
Intelligence techniques into their design and
development, acting as assistants in the teaching-
learning process (De Souza et al 2002). Currently,
Intelligent Agents concepts have been applied to
these systems as a way to improve them.
We chose the Multi-Agent Systems platform to
implement our proposal, considering that intelligent
software agents act dynamically and not only
reactively. They can act in a collaborative way to play
their roles more easily. In addition, intelligent agents
may learn from the knowledge engendered in the
environment, using this learning proactively for the
benefit of managers, human tutors and students.
We understand that in an Intelligent Tutors
organization each agent will have a specific role
model, acting collaboratively. For example, the
Interface Tutor Agent can use the information
provided by Tracing Tutor Agent to send notifications
and create dynamic graphical interfaces, inducing the
student to fill gaps in their learning path. PTA collect
the information required for the modelling of
students’ profile used to customize the environment
assist and guide students during the construction of
their learning. The Artificial Intelligence Tutor Agent
(AITA) can identify student clusters that have
succeeded in learning. It also provides subsidies for
the human tutor and to the managers virtual learning
environments, supporting the task of improving the
domain model. The AITA may also use machine-
learning techniques to identify potential student
evasion and prescribe actions to avoid it.
The main contribution of this work is to define a
model for intelligent tutors best adapted to the current
management needs of virtual learning environments.
The focus of this work was to define the role of
Tracing Tutor Agent, which is one of the
collaborative agents of the organization. Besides we
have introduced a Data Webhouse model in the
context of intelligent tutor models. We did not find
similar works in the literature, which makes the
approach innovative.
Future work will involve the implementation of
the Tracing Tutor Agent and the development of the
specifications of other intelligent tutors presented in
the model.
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