Emotional States Management for an Advanced Intelligent Tutoring
Domenico Redavid
1 a
, Stefano Ferilli
2 b
, Liza Loop
and Liudmyla Matviichuk
4 c
Economics and Finance Department, University of Bari, Largo Abbazia S. Scolastica, Bari, 70124, Italy
Computer Science Department, University of Bari, Via E. Orabona 4, Bari, 70125, Italy
LO*OP Center, Inc., 16511 Watson Rd, Guerneville, 95446, CA, U.S.A.
Department of Informatics and Computing Tools, T. H. Shevchenko National University ”Chernihiv Colehiu”,
53, Hetmana Polubotka Str., Chernihiv, Ukraine
Intelligent Tutoring System Framework, Ontologies, Emotional States.
One of the result of the application of Artificial Intelligence (AI) to e-learning environments are Intelligent
Tutoring Systems (ITSs). A crucial aspect in the field of e-learning concerns emotional states, which impor-
tance is increasingly felt also at the level of educational institutions. On the other side, the Management of
moods at Information Technology level is becoming more and more important because enable new scenarios
where new innovative applications can be proposed. In this paper is described a possible framework able to
manage emotional states that could be adopted as a solution to address the Personal, social and learning to
learn competence, one of the eight key competences lifelong learning indicated by EU COUNCIL.
Intelligent Tutoring Systems, or ITSs, are a conse-
quence of the application of Artificial Intelligence
(AI) to e-learning environments. The ITS vision can
be summarised as the creation of a personalised tutor
for each student able to provide them with exactly the
support they need to get the most out of their learning
experience. Unlike a dedicated human tutor, the ITS
would also be able to take into account many more
parameters and data and thus be fully compliant with
the goals and objectives (strict or otherwise) of each
individual supported user. Being able to also report
to the learner on the effectiveness and efficiency of
the learning activities would increase his or her ca-
pability to acquire skills and/or knowledge. Students
would also be empowered to share this information
with their teachers, thereby enhancing teacher effec-
tiveness. Recently, KEPLAIR (Knowledge-based En-
vironment for Personalised Learning using an Artifi-
cial Intelligence Recommender) (Ferilli et al., 2021;
Ferilli et al., 2022) has been proposed. It is an ITS
designed to make pervasive use of artificial intelli-
gence to perform its tasks. Although KEPLAIR was
designed for independent learners, due to its advanced
features it can also be used in contexts such as com-
pulsory schooling to involve students in the choice of
study materials and, as in the case proposed in this pa-
per, in the choice of learning paths. Indeed, for some
years, there has been an in-depth discussion at the Eu-
ropean level on the subject of the skills that individ-
uals must acquire in order to ensure their full devel-
opment. This is a pivotal issue, with cascading im-
plications that affect the issues of training, education
and orientation towards work and social welfare. The
result of this process was the elaboration of the eight
European key competences, which the Member States
of the European Union are called upon to transpose,
facilitating their acquisition by all citizens. The refer-
ence text is the Recommendation on key competences
for lifelong learning
approved by the European Par-
liament on 22 May 2018. The Reference Framework
sets out eight key competences:
Literacy competence,
Multilingual competence,
22 May 2018 on key competences for life-
long learning - https://eur-lex.europa.eu/legal-
Redavid, D., Ferilli, S., Loop, L. and Matviichuk, L.
Emotional States Management for an Advanced Intelligent Tutoring System.
DOI: 10.5220/0011590200003335
In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD, pages 253-259
ISBN: 978-989-758-614-9; ISSN: 2184-3228
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Mathematical competence and competence in sci-
ence, technology and engineering,
Digital competence,
Personal, social and learning to learn competence,
Citizenship competence,
Entrepreneurship competence,
Cultural awareness and expression competence.
In particular, Personal, social and learning to learn
competence is related to the ability to self-reflect,
to manage time and information effectively, to work
with others constructively, to remain resilient and to
manage one’s own learning and career. It includes
the ability to deal with uncertainty and complexity, to
learn to learn, to support one’s physical and emotional
well-being, to maintain physical and mental health,
and to be able to lead a health-conscious and future-
oriented life, to empathize and to manage conflict in
an inclusive and supportive context. Emotional well-
being is closely interconnected with the recognition
of emotional states.
The objective of this position paper is to outline
a possible IT framework able to manage emotional
states that could be exploited as a solution for a prac-
tical case of implementation of the EU recommenda-
tions on key competences, the Italian case on PCTO
In this section we report the related work and some
specific prototypes implementation that will be used
to implement the proposed ITS framework.
2.1 Related Works
The recognition of emotional states (also known as
moods) falls within the scope of affective computing,
an interdisciplinary research field spanning computer
science, psychology, and cognitive science. Mood
has a lower intensity but a longer duration than emo-
tion since it is an emotional state that affects the ex-
perience and behavior of a person. To the best of
our knowledge, unlike standard emotions where vari-
ous approaches have been proposed (Imani and Mon-
tazer, 2019), (Yadegaridehkordi et al., 2019), frame-
work proposal for the management of moods in ed-
ucation from IT perspective has not yet been inves-
Linee guida dei percorsi per le competenze
trasversali e per l’orientamento (PCTO), MIUR,
4/9/2019 - https://www.miur.gov.it/web/guest/-/linee-
tigated in depth. In (Abaalkhail et al., 2018) is pre-
sented a survey on ontologies for the representation
of affective states and their influences including pro-
posal for moods ontologies. Indeed, the paper denotes
that there are only few ontologies proposal that target
mood and human influence. In (Bolock et al., 2021)
is proposed an interesting proposal of a psychologi-
cally driven ontology for describing human behavior
based on character traits and states. The OWL on-
tology they propose highlights that taxonomic knowl-
edge representation is not sufficient to fulfill the pur-
pose. In fact, the use of Machine Learning methods to
predict mental state rather than a Semantic Web Rea-
soner is adopted. The ITS framework we propose will
work by applying multi-strategy reasoning rather than
Machine Learning methods.
KEPLAIR is an ITS designed to act as a personalised
tutor that helps learners find the tools, resources, ex-
periences, and connections they need to learn exactly
what they have chosen to learn. KEPLAIR’s artificial
intelligence engine will facilitates fine-grained per-
sonalisation for each user, taking into proper account
the many aspects involved: learner’s cognitive level,
pre-existing knowledge about the topic, and preferred
physical and social environments, needs, background,
abilities, aims, interests, tastes, preferences, attitudes,
behaviours, motivations, expectations, context, and
community. KEPLAIR’s recommendations might in-
clude books, audio files, videos, online courses, mem-
bership associations, community resources, or even
other people. KEPLAIR can help students find their
way both through predetermined curricula, and on
their own educational goals. The system will perva-
sively use symbolic AI to carry out its tasks. Dis-
tinguishing characteristics and contributions of KE-
PLAIR are the extensive set of features it proposes,
covering all aspects of the learning process. To en-
sure homogeneity and coordination among all of its
subsystems and components, a crucial role is played
by an ontology. It will act as a schema for the data that
informs all the internal representations and behavior,
so as to smoothly connect and orchestrate all the var-
ious functions and ensure both internal and external
interoperability. In particular, interoperability with
GraphBRAIN technology (detailed in the next sub-
section) is guaranteed since it has the same organiza-
tion of knowledge (Ferilli et al., 2022). KEPLAIR
was conceived with the aim of supporting students
independently of educational authorities, although it
can also be used to support teachers and education
managers. Since one of the PCTO objectives is the
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
involvement of learners in the definition of learning
path and in the choice of study materials, KEPLAIR
seems well suited for this purpose as we explain in the
proposed approach section.
2.3 GraphBRAIN
GraphBRAIN (Ferilli and Redavid, 2020a) is a
general-purpose tool that allows to design and col-
laboratively populate knowledge graphs, and provides
advanced solution for their fruition, consultation and
analysis. The functions provided by GraphBRAIN
bring to cooperation many different Artificial Intel-
ligence tasks, techniques, and approaches for im-
proving knowledge management and (personalized)
fruition by users, including: database technology, on-
tologies, data mining, machine learning, automated
reasoning, natural language processing, personaliza-
tion and recommendation, collaborative and social in-
teraction tools, and social network analysis. While
most of these items are investigated and exploited
separately in the state-of-the-art, the relevance of the
GraphBRAIN methodology is in their being really in-
tegrated, and not simply juxtaposed, so that each of
them takes directly or indirectly advantage from all
the others. This allows GraphBRAIN to find relevant,
personalized, and non-trivial information, e.g., a so-
cial approach is used to build and integrate ontologies;
user models are used to guide data mining; ontologies
are used to guide database interaction and interface
generation; data mining is used to filter a manageable
and relevant portion of a huge graph on which car-
rying out automated reasoning, etc. GraphBRAIN’s
functions can be used on-line, interactively by end
users or delivered as Web services to other applica-
tions for obtaining selective and personalized access
to the stored knowledge. For the formal representa-
tion of emotional states only a taxonomic representa-
tion model may not be sufficient, but more expressive
models are needed. For this reason, GraphBRAIN
was considered in this proposal. In fact, it supports
multiple formal knowledge representation languages
ranging from OWL (so it is possible to reuse existing
ontologies) to FOL (more flexible for the representa-
tion of variable knowledge structures such as context-
and situation-dependent ones).
2.4 WoMan
WoMan (Workflow Manager) (Ferilli, 2014) is a
framework for workflow learning and management,
based on First-Order Logic representations. Its pro-
cess mining engine, applicable to activity logs coming
from actual process executions, is able to learn mod-
els involving concurrent, repeated, optional and dupli-
cate tasks in any combination, weighted elements. Its
full incrementality avoids the need for having all the
examples available from the beginning, still allowing
the learning to start from scratch. Correct models can
be learned using very few examples (in principle, any
set of examples including at least one representative
of each allowed process). It can also handle noise in a
very straightforward, intuitive way. Finally, the repre-
sentation language allows the description of not just
the flow of events, but also the context in which the
activities take place, and hence the learning of com-
plex (and human- readable) pre- and post-conditions
for the workflow elements. A relevant issue in Pro-
cess Management in general, and in Process Mining
in particular, is to assess how well can a model pro-
vide hints about what is going on in the process exe-
cution, and what will happen next. Indeed, given an
intermediate status of a process execution, knowing
how the execution will proceed might allow the (hu-
man or automatic) supervisor to take suitable actions
that facilitate the next activities. The task of activity
prediction may be stated as follows: given a process
model and the current (partial) status of a new process
execution, guess which will be the next activity that
will take place in the execution (Ferilli et al., 2017).
WoMan models can be used for the monitoring and
supervision of processes and, when applicable, can be
translated into standard representations (Petri nets).
Both controlled and real-world experiments show that
WoMan outperforms existing process mining systems
in accuracy, effectiveness and efficiency. It ensures
quick, correct convergence towards the correct model,
using much less training examples than would be re-
quired by statistical techniques, even in the presence
of noise. WoMan is currently being wrapped in a Web
service that can be exploited by external applications
for learning, simulation and checking of workflows.
3.1 EU Recommendation on
Transversal Competences
The European Union has defined transversal compe-
tences as those skills that enable citizens to act con-
sciously in a profoundly complex social context and
to meet the challenges posed by increasingly digi-
tised and interconnected organisational models. The
European Council (with the Recommendation of 22
May 2018) summarised the transversal competences
by specifying a comprehensive framework structured
according to the specific competence elements. This
Emotional States Management for an Advanced Intelligent Tutoring System
framework is organised according to four semantic ar-
eas and one in particular is related to emotional states:
Personal, Social and Learning to Learn. Com-
petence refers to the ability to manage one’s own
learning, to lead a physically and mentally healthy
life, to create the right conditions to work well in a
group, to act in complex situations and to manage
interpersonal dynamics in an inclusive and con-
structive perspective.
Transversal competences are placed at the centre
of the learning pathway because they improve the
learner’s degree of awareness of his or her own per-
sonal growth. At the same time, they activate reflec-
tive and behavioural skills that are essential for mov-
ing around in social and work contexts; in fact, they
involve processes of thought and cognition, but also
of behaviour. They are key competences in the per-
spective of lifelong learning because they are char-
acterised by a high degree of transferability to differ-
ent tasks and environments, thus equipping the learner
with skills that enable him/her to improve the quality
of his/her own behaviour and to implement effective
strategies for the different contexts in which he/she
will be acting. Furthermore, it is important to con-
sider the importance of these soft skills also in a self-
orientative function: the student must be able to ob-
tain feed-back on his strategies and use them to re-
organise his ability to orientate himself in different
areas. In short, transversal competences enable the
student to enrich his personal assets with knowledge,
skills and attitudes that enable him to behave ade-
quately and effectively in the complexity of the sit-
uations in which he finds himself.
The peculiar nature of transversal competences
implies for the school an innovation in teaching
methodology, oriented towards strengthening the con-
nection between formal, informal and non-formal
contexts in which learning takes place. The emo-
tional and relational aspect is placed at the centre
of the educational process and becomes a substan-
tial element of lifelong learning. In the same way
as the teaching methodology, the monitoring of the
learning process, and thus the assessment tools, must
also be adapted to the characteristics of transversal
competences. Among other things, this also means
organising and prioritising individual and group in-
terviews, simulations and other active methodolo-
gies (role playing, project work, etc.) over ’tradi-
tional’ forms of assessment. Assessment, in fact, no
longer concerns only the goals and skills acquired, but
also the degree of awareness acquired by the student,
first and foremost in knowing how to judge and en-
hance his or her abilities in terms of transversal skills.
Therefore, in line with the general teaching approach,
the activation and participation of the student is also
a central element for the monitoring and evaluation
system of the training pathway.
3.2 Transversal and Orientation
The EU recommendations have been implemented
in Italy through Transversal and orientation compe-
tences (denoted as PCTO) guidelines. The main pur-
pose of the PTCO is to make sure the student acquires
the functional competences for the study pathway un-
dertaken and the transversal competences aimed at
orientation in the world of work or at subsequent
higher education. This means covering the curricular,
the experiential and the orientation dimensions.
Starting from this fundamental premise, the PT-
COs can be develop with different organisational
forms, not only according to the course of study or
to the territorial specificity of the school, but also ac-
cording to the personal needs of each student. Person-
alisation of the study pathway is an essential aspect
because it allows the student to become aware and
self-directed in defining his or her personal growth
project. This is why it is possible to develop differ-
ent types of PTCO within the same class group. Fur-
thermore, it must be considered that the possibility
of realising the pathway abroad is also envisaged, al-
ways as a function of an activity that is as coherent
and functional as possible in relation to the student’s
specific pathway. For this reason, the programme
presents multiple options with respect to the organisa-
tion with which the school can enable collaborations.
In addition to public and private bodies, third sec-
tor and entrepreneurial entities are becoming increas-
ingly important. Therefore, the design of a PTCO in-
stance must have flexibility as a fundamental organi-
sational criterion, but within a well-defined regulatory
3.3 PCTO Roles and Evaluation
Fundamental to the success of a PTCO is the role of
the disciplinary departments, whose task is to ensure
consistency with the three-year Education and Train-
ing Offer Plan as prescribed in Italy. However, it is
the Class Councils that design (alone or in collabo-
ration with the external body) the pathway, manage
the activities and carry out the final assessment. In
fact, first the Class Council selects the competences
for the class group, then each individual teacher must
identify (from among these selected competences) the
specific ones that he or she considers functional to his
or her teaching. The careful selection of the com-
KEOD 2022 - 14th International Conference on Knowledge Engineering and Ontology Development
petences to be developed has a fundamental impor-
tance. It must allow for student self-direction, in-
volving them already in the planning of activities and
stimulating their reflection and active participation.
Similarly, communication with families, documenta-
tion of all the stages of the pathway and sharing the
results of the experience are critical for the outcome
of the PTCO. Finally, it is essential that the school in-
stitution moves towards co-design involving the exter-
nal parties in defining the objectives and educational
methods in that cases when the project is carried out
in collaboration with a third party.
Coordination between the parties involved is per-
formed by the tutor, who is appointed by the educa-
tional institution to perform certain functions that are
fundamental for the implementation of the pathway.
In addition to the coordination between the school in-
stitution, the third parties involved and the family, the
tutor constantly monitors the development of the ac-
tivities, provides assistance to the student and informs
the school institution of any critical issues. He/she is
a crucial figure because he/she plays a managerial and
supportive role, which fosters the creation of the right
context for achieving the planned goals. If provided
for in the PTCO project, the internal tutor may be sup-
ported by an external tutor selected by the host organ-
isation. This figure is the student’s referent within the
organisation where the training activity takes place,
but also acts as a link between it and the educational
institution. He/she is therefore expected to maintain
a constant liaison with the internal tutor. The interac-
tion between the two tutors, who must be selected on
the basis of appropriate training skills, is a key factor
for the success of the course.
The evaluation of the PTCO should assess the pro-
cess and the end result. In this way, it is not only the
objectives achieved that are assessed. Through the
structured observation of the entire process, the ac-
quisition of transversal competences is also assessed,
giving importance also to character and motivational
The most frequently used tools for participative
observation are rubrics, diaries, digital portfolios and
observation sheets. The final results, on the other
hand, are evaluated in several stages, ranging from
the identification of objectives to the verification of
the content learnt during the course. Obviously, the
observation of the entire process (carried out by the
tutors) influences the evaluation of the final results,
which is, however, done by the teachers of the class
council and affects behaviour and the final score.
Lastly, it should be emphasised that PTCO activities
must be included in the student’s curriculum, the doc-
ument that is attached to the final diploma to certify
the skills acquired by the student along the course of
4.1 Approach Steps
In this section, we report how the proposed frame-
work works identifying how the prototypes imple-
mentation will be used. In detail, three phases can
be identified:
Uploading content into KEPLAIR. In this phase
the school institution uploads, if not pre-existent,
the content that it considers to be useful for the
specific transversal competences pertaining to the
institution itself. This content will enrich the
material contained in KEPLAIR and will remain
available to the entire community that will use it.
Creation of the PCTO project. As prescribed by
the PCTO guidelines, a learning pathway that will
be assessed by the school institution must be cre-
ated. In this phase, the active involvement of the
student is explicitly stated in the EU recommen-
dations. The student will then use KEPLAIR in-
dependently obtaining the possible learning paths.
The material to be studied suggested in a learn-
ing path may or may not include the material up-
loaded in the previous phase by the institution.
In fact, KEPLAIR will determine to recommend
or not the institution contents by applying its AI
approaches on the whole material available in its
knowledge base. Finally, the student will dis-
cuss with the tutor the choice of pathway to be
submitted for approval by the educational institu-
tion from among those suggested by KEPLAIR.
The approved learning pathway will be seman-
tically formalised through a process specified in
the WoMan formalism. In this process there will
be the basic workflow represented by the chosen
learning pathway and some features chosen by the
institution useful to monitor learning progress in
accordance with PCTO guidelines. In particular,
these features will include those related to per-
sonal, social and learning to learn skills specified
in the section 1.
Course delivery. During the delivery of the
content, physical or virtual sensors, part of the
WoMan process, are used to detect values useful
to establish the mood of the learners. These val-
ues are stored in the graph DB managed by Graph-
BRAIN. This tool is enabled to manage these data
semantics by means of an ontological representa-
Emotional States Management for an Advanced Intelligent Tutoring System
tion that allows either taxonomic (e.g., OWL) or
more generic (e.g., FOL) representations. As stip-
ulated in the PCTO guidelines, the learning path
is evaluated at precise points of the activities to be
performed and also takes into account the moods
of the learners useful for monitoring the learning
progress. Since the semantics of all this informa-
tion can be interpreted by GraphBRAIN, anoma-
lous situations can be detected applying the multi-
strategic reasoning (Ferilli and Redavid, 2020b).
In this case particular information will be com-
municated to KEPLAIR that will modify the sug-
gested learning path. As consequence, the ma-
terial that was suggested to the learners and ap-
proved by the teachers can be modified in order
to improve the learning outcome (e.g. if Graph-
BRAIN detects a certain discomfort in the student
since supplementary material in Maths has been
assigned than KEPLAIR suggests alternative sup-
plementary material). In addition, the supervi-
sion functionality offered by WoMan will make
it possible to assess whether the specific student
is correctly following the process formalised for
him/her in the previous phase. Through this mon-
itoring, corrective actions can be taken in accor-
dance with the tutor in order to have a better result
of the student’s learning performance.
The ITS meta-analytic review presented in (Kulik and
Fletcher, 2016) has proved their efficiency with re-
spect to other forms of tutoring. As the authors them-
selves indicate, it is not easy to determine what the
next generation of ITS will look like, but they will cer-
tainly be influenced by three factors: computer hard-
ware, software, networking, and cognitive science. In
this paper we have outlined a possible framework that
can also handle the moods represented by learning
processes formalised in the WoMan formalism. Fur-
thermore, through GraphBRAIN it will be possible
to handle different knowledge representations and ap-
ply multi-strategic reasoning in order to improve stu-
dents’ learning performance. In addition, by using
KEPLAIR we are able to cover one of the fundamen-
tal requirements of PCTO: empowering students by
allowing them to create proposals for learning paths.
As future work, a comparison with realities outside
the European community is desirable. In particular,
we have planned to test the platform in the Ukrainian
context with the support of the T.H. Shevchenko Na-
tional University ”Chernihiv Colehium”. On the one
hand, the sharing of EU recommendations can be
an opportunity to bring these two realities closer to-
gether; on the other hand, the current difficult situa-
tion requires a massive use of online tools for school
education. Also if any e-learning has different goals
(Matviichuk et al., 2017), the deeper use of modern
technologies, namely elements of the artificial intelli-
gence (AI) proposed in this work, could be reveal new
modality to use an intelligent learning system (ITS).
This work is partially founded by the Apulia Region
through the ‘Research for Innovation—REFIN’ ini-
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