An Adaptive Learning System based on Tracking
Insaf Tnazefti-Kerkeni
, Henda Belaïd
and Bénédicte Talon
LISIC, Main, Université du Littoral Côte d’Opale, Calais, France
LIPAH, Faculté des Sciences de Tunis, Le Belvédère, Tunisie
Keywords: Adaptive Learning, LMS, Personalization, Traces, Indicators, Agents, SMA.
Abstract: Success in training is an opportunity that must be offered to each student. However, many universities are
experiencing high rates of failure and dropout, especially during the first year of higher studies. We believe
that creating a process based on personalization of teaching can contribute to the decrease of failure rate
during undergraduate studies. To achieve this goal, we are specifically interested in online learning
supported by a Learning Management System (LMS). We have integrated, in a previous works, new tools
using traces of learners’ activities during collaborative works on an LMS. We therefore propose a system
based on intelligent agents. We are designing smart dashboards, automating detection of specific learners'
difficulties in order to offer alternatives or solutions to their problems.
According to the study published by INSEE
(Dardier et al., 2013), learners' failure and
professional trajectories consisting of unemployment
and limited-term employment are often linked to
three main factors: history, educational level and
socio-demographic background, orientation during
the educational path.
The first factor – history - is linked to the nature
of the learning path (difficulties encountered,
changes in orientation, absenteeism, etc.). The
second factor – educational level and socio-
demographic - is dependant of the environment in
which the learner operates outside of study hours:
parental education, family income, etc. Finally, the
third factor – orientation during the educational path
- concerns specialties choosen during the school
career. This study also notices that "the Baccalauréat
holders registered in technological degrees drop out
less often than those registered in generalist
formation" (27% against 29%). According to the
study, individualized follow-up offered in
technological formations benefits to students.
Several reforms and actions have been
undertaken by the French state and other members
INSEE: Institut National de la Statistique et des Etudes
of the OECD (eg: Multi-year plan against poverty
and for social inclusion launched). A slight drop in
the rates mentioned above is observed (ie: -3 points
on the rate of leavers without a diploma in 2015, -
0.8 points of the rate of young people without a job,
or training at the OECD level in 2015) . This
progress is due to a more regular and personalized
monitoring and to the integration of new
technologies in training and education services
(interactive platform, MooC for employment for
The objective of our work is to design and
develop an effective model of adaptive learning. Our
model is designed to detect the learner profile and to
measure, in real time, the evolution of the learner's
skills. The aim is to permanently adapt the flow and
form of resources and to offer methods fitting the
needs and profile.
Two levels of individualization will therefore be
dealt with by the designed model:
1 - Recommendation on resources and
disciplinary contents.
2 - Recommendation on the type of support
(peer-learning, tutoring, etc.) adequate for the
detected profile.
The tool should offer monitoring functionalities
for teachers and training managers. These
functionalities are necessary to follow evolutions of
learners' skills and eventually prevent the risk of
dropping out or not completing the training. Alerts
Tnazefti-Kerkeni, I., Belaïd, H. and Talon, B.
An Adaptive Learning System based on Tracking.
DOI: 10.5220/0009571604550460
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 455-460
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
will notify actors (tutor, teacher, training manager,
etc.). Detailed situation reports (blocking points,
type of assistance needed, risk of dropping out,
recommendation for a solution, etc.) will allow
regular and individualized monitoring.
Researchers have been largely interested in
adaptation of educational systems. However, most
systems are used for specific context such as the
ELM-ART system (Brusilovsky et al., 1996). ELM-
ART is an adaptive hypermedia system designed to
learn the LIPS language.
More recently, we notice that ontologies are
largely used, as a basis for modeling in adaptive
systems. For example, in (Henze et al., 2004),
authors use semantic web to create learning
scenarios and to structure the courses. In
(Muruganandam et al., 2017), ontology is used to
model the learner profile.
A system called Manhali is presented in (El
Haddioui, 2015). It allows an adaptation of
educational strategies according to the behavior and
the learning style of the student. Based on the
learner's profile, adaptation is made on three levels:
graphic aspect of the platform according to the
configuration of the learner's machine, adaptation of
scientific content taking into account the skills of the
learner and adaptation of teaching strategies
according to behavior and style of the learner.
(Chachoua et al., 2016) uses traces left by
learners when they are active on an elearning
system. Duration of an activity and the number of
attempts to solve a problem are the traces that
interest the authors. These traces are used for
building an evaluation model. This model is
integrated in an adaptation model based on
ontological rules and an adaptation algorithm. The
result is adapted resources and learning strategy.
(Nafea et al., 2017) proposes an adaptive engine
that can be integrated into any LMS. It is based on
rules written in a rule-based reasoning algorithm.
To illustrate our approach, a case study is presented
hereafter. It is adopted from a real situation we
encountered: G. is a first-year student at the
information Technology department of the IUT. He
starts his formation with 80 other students (each one
being "unique"). The training manager sends an
email to the educative team to inform about the
situation of this student. Here is a summary of this
G. can only read with the help of his audio
device (and very hardly without). He hardly knows
how to write and has difficulties memorizing
information. He regularly forgets the meaning of
simple words and is not able to organize his ideas
and to express them. He has trouble doing
calculations and he can’t focus for long time. He
can’t stop moving or walking for a long time also.
Finally, G. may, under significant stress, be unable
to speak and to hear what is said to him.
So far, G. has benefited from a support service.
He is worried because he finds it hard to imagine
studying without this system that had allowed him to
have a certain balance in high school. However, G.
is courageous and of good will.
Although it is very complicated to make the
necessary adaptations for this student, the training
manager recommends that each teacher consider
this question and thinks about what to do for this
student in his teaching
G. will obtain (among other things) the right to
use his computer during exams as well as the right
to have the exams adapted (with reading and / or
This case perfectly illustrates the specific
conditions in which adaptation is needed to offer
ways to reduce the risk of failure of a student.
We focus the Moodle LMS. We are working on
adaption of scenarios and contents in order to detect
specific learning difficulties of the audiences and to
best meet their specific needs (offering the adequate
learning scenario, the adequate resources in the right
form and a correct learning pace).
4.1 System Functionalities
A smart tracking system should then offer:
Identification of learners who have a risk of
learning difficulties;
Identification of the nature of problem (poor
assimilation of knowledge, unsuitable
resources - in content and / or format, etc.);
Adaptation of the learning scenario, the
resources offered, the learning pace, the
support, etc. to each of these learners having
CSEDU 2020 - 12th International Conference on Computer Supported Education
So, the proposed system is based on two steps:
Tracking Learners: creation and real-time
updating of a learner model to identify
students or learners who have problems or
risks. Detection of the nature of these
Adaptation: presentation of alternatives or
specific solutions to these problems.
In a previous work (Talon et al., 2013), we have
developed a multi-agent system for tracking
students’ activities and calculating indicators. This
system collects various traces on the ILIAS learning
platform in the context of collaborative projects.
These traces are aggregated in the form of indicators
(of different categories). A dashboard allows the
teacher to assess activities of the students. It allows,
for example, detection of inactive students called
“sleeping students”. Dashboards form a space
where teachers can appreciate activity and
participation of each student but also offer to
students a way to be aware of their real activity. All
the developed indicators are purely informative.
Now, we go further by adding:
“Traces on demand”. The teacher will choose
data he wants to collect and combine in order
to personalize a learning path.
“Indicators formulation on demand”. The
teacher will create his/her own and specific
indicators. He/She will select elements in the
traces database and will determine the rules
that, according to him/her, should be applied
to formulate them.
4.2 A Multi-agents Platform
This tracking system uses agents to collect traces, to
update the learner model and to develop indicators
enabling the personalization of pedagogical
Agents are responsible for collecting traces
during the training of a student: his success, the
number of time he takes the tests to succeed, his
presence time on the LMS, the time he needed to
resolve a problem, etc. They also generate indicators
according to the teacher’s will, present them in the
teacher's dashboard and they are always looking for
the presence of conditions requiring the adaptation
of the scenario.
This system implies that the teacher:
Defines and implements a "standard" scenario
in which he offers different resources
according to each cognitive ability (level 1 of
adaptability). Example: a common process to
develop the meta-competence VARIABLE
(knowing and understanding the concept of
variable, declaring a variable, different type of
data, etc.).
Defines the indicators (on demand) that will
allow differentiating between students. For
example abstraction_level, is an indicator that
increases when a student fails in exercises
related to the notion of understanding what a
variable is. These indicators allow the teacher
to define rules. Thus, if the student has an
abstraction_level indicator less than 8, then he
should watch some specific videos before
passing the same scenario again.
If the adaptation of scenarios proves to be
unsuccessful, the system notifies the teacher who
can decide to modify the again or to deactivate them.
4.3 Learner Model
To face the needed adaptation of systems, data are
collected about learners and their activities in a
specific model called a learner model (Tack et al.,
Indeed, a learner model “consists of meta-
knowledge which includes the instructional
decisions about a learner” (Kaya et al., 2011). As
M. A. Tadlaoui and al. said in (Tadlaoui et al.,
2016), “the main objective of learner model is to
modify the interaction between the system and the
learner in a dynamic way to address the needs of
each learner on an individual basis”. So, the learner
model is necessary to adapt the learning process to
individual learning needs. As it is said in (Gong,
2014), “the student model is the core component in
an ITS”. In (Tmimi et al., 2017), it is judged to be
useful in the phase of learning and adaptation in an
adaptive hypermedia.
Y. Gong (Gong, 2014) developed the idea that a
learner model should integrate two elements: the
learner behavior when using the system and personal
properties such as the knowledge of the learner,
performance, etc.
To build a learner model, different methods
exist: cognitive science method, machine learning
method or both at the same time.
The data contained in a learner model are of
different types:
Learner Data, which are personal information:
identity, gender, age, etc. gathered during the
registration process. It is a static view of the
learner model.
An Adaptive Learning System based on Tracking
Figure 1: System architecture.
Pedagogical Data: Competency, knowledge,
program and subjects sequence.
Personality Data: learning style, learner
features and preferences in learning. They can
be collected through tests conducted in LMS.
LMS Experience: used to identify whether the
learner is familiar with a certain LMS.
Cognitive Data: defines the preferences of the
learner. They can be obtained through tests
conducted in LMS.
We integrate all these information in the learner
4.4 System Architecture
The personalization of learning is now initiated on
the multi-agent platform presented in the paragraph
4.2. In such a system, acting in a virtual environment
requires that the agents can make a representation of
the learning situation.
Agents must be able to have a representation of
the environment, the learner's task, the educational
actions to be carried out.
A first set of questions concerns the user’s
environment. What are the objects that constitute the
universe of the learner and where are they located?
What are their properties? What are the possibilities
of action on these objects? What behaviors can they
have? What interactions exist between these objects?
Concerning the representation of the learner's
task, it is necessary to know which actions the
learner is supposed to perform (and if there are
chained constraints) and which actions have been
performed (with success or not). An agent must be
able to explain what to do, possibly to do for or with
the learner. In a context of collective work, where
responsibilities are shared and defined by rules, it is
moreover necessary to know if it is the right person
who carries out an action.
The last type of questions concerns educational
interventions. An educational agent must be able to
give information on the learning situation
(accessible objects, task progress), but it must also to
modify the environment for an educational purpose:
in order to adapt to the learning, it may be necessary
to simplify the problem, by masking some elements
or by inhibiting some interactions.
Figure 1 presents the system architecture. The
different agents present in the system are the
a-observer: It tracks every action done by the
student when using the LMS. The raw traces
are cleaned and treated to get modeled traces;
a-learner: It presents some indicators on the
dashboard of the student. These indicators
help the student to see his difficulties and to
get feedbacks regarding his learning;
a-expert: It is a smart agent. It analyzes the
modeled traces to update the learner model. It
suggests adaptations to the teacher. Four
adaptation levels can be proposed:
o Navigational level proposes an order of
educational sequences
CSEDU 2020 - 12th International Conference on Computer Supported Education
o Content level proposes adequate
resources according to the student’s
knowledge and in relation with the
corresponding sequences
o Presentation level determines the better
form and nature of the resources
o Learning process level which defines
specific learning methods to adopt
during sequences
a-teacher: It presents to the teacher tables and
graphs offering a monitoring space and
calculate indicators that will be displayed on.
In this paper, we proposed an agent-based
Personalized Learning Architecture. The system is
characterized by the following properties:
A learner model to store and permanently
update learner’s profile.
Learning strategies according to the learner's
Scenarios chosen by the course manager based
on prerequisites and learner's profile.
Ontologies play an increasing role in the new
generation of information or knowledge-based
systems. It is also a keystone of multi-agent systems
using high-level communication (Freitas et al.,
Our work is in progress. It consists firstly in
finalizing the ontology of the learner model.
Secondly agent integration and personalization of
scenarios will be dealt in the Moodle environment.
Our challenge is to identify, from traces and
questionnaires deployed throughout learning
processes on Moodle, the common trajectories
leading in achievement of objectives, and in
academic and professional success.
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