PBLOntology: A Domain Ontology with Context Elements for
Problem-based Learning
Adriana Silva Souza
, Adolfo Duran
and Vaninha Almeida
Bahia Federal Institute of Education, Science and Technology, Porto Seguro, Bahia, Brazil
Information Technology Superintendence, Federal University of Bahia, Salvador, Bahia, Brazil
Department of Computer Science, Federal University of Bahia, Salvador, Bahia, Brazil
Keywords: Problem-based Learning, Ontology, Context.
Abstract: In education, ontologies have been proved useful for structuring intelligent tutors, collaborative learning,
creation of learning models, semantic search for recommendation of learning material, personification and
adaption of educational content based on the student’s context. Problem-Based Learning (PBL) is a
pedagogical methodology that is regarded as an alternative to traditional learning for skills development.
However, the use of web-based technologies to support learning in the PBL methodology is still recent. A
systematic review was conducted and it has shown the lack of formal representation of the PBL concepts
based on ontology language. Thus, this paper proposes a reference ontology for PBL called PBLOntology,
which uses context elements of the methodology. For conception of the ontology, a research was conducted
in a computer-engineering course that adopts the PBL methodology. To assess the PBLOntology, we
defined relevant criteria regarded as fundamental for ontologies: testing activities and evaluation with
experts. Although most of the experts stated that the definitions satisfied or partially satisfied, their feedback
allowed us adjust some definitions, improving the ontology.
In the era of information, it is imperative that
students develop mechanisms to build their own
knowledge in an autonomous way, by developing
multiple abilities and skills (Álvarez et. al, 2005). A
pedagogical methodology suitable to this scenario is
the Problem-Based Learning (PBL). It aims to
encourage students to develop critical thinking,
problem solving skills, self-learning, collaboration
and communication skills, among others, by means
of problem solving, which may not have a unique
solution and are often complex (Ribeiro, 2005).
The Semantic Web has enabled the development
of personalized learning environments and new
forms of collaborative and interactive learning,
contributing to a more active and dynamic process
of teaching and learning (Kasimati and Zamani,
2011). In this scenario, ontologies have been
proposed for learning environments in order to
promote semantic interoperability, sharing, and
learning customization (Gaeta et. al, 2009).
The student learning behavior can change
according to the environment in which he/she
interacts. This learning context includes for instance,
the style and speed of learning, the time available,
the location, personal interests, among others that
can contribute to semantically enrich the process of
teaching and learning (Medeiros et. al, 2010).
The student context is useful to define the
student profile, recommend learning objects,
personalize and customize content. Context-sensitive
learning environments have also applied ontologies
to model context (Barbosa, 2009; Maran and
Bernardi, 2014).
The use of web-based technologies to support
learning in the PBL methodology is still recent
(Brush, 2013; Sobocan, 2017). Souza et. al (2014)
conducted a systematic review in order to search for
evidences of semantic web technologies for PBL. It
was not found in the literature a formal
representation of the PBL concepts based on
ontology language, which would result a common
and shared understanding, available for reuse.
The development of an ontology for PBL process
could be useful for: (i) interoperability among
systems using PBL; (ii) developing of intelligent
systems and agents to assist the tutoring stage of the
methodology; (iii) to assist the recommendation and
adaptation of content at the research phase of the
Souza, A., Duran, A. and Almeida, V.
PBLOntology: A Domain Ontology with Context Elements for Problem-based Learning.
DOI: 10.5220/0006804903760383
In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 376-383
ISBN: 978-989-758-298-1
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
process PBL; (iv) to assess the accomplishment of
the PBL process in distance education systems; (v)
inferences and analysis on the development of skills
and abilities proposed by the methodology.
This paper proposes a reference ontology for
PBL called PBLOntology, product of a shared and
consensual knowledge, bringing contextual elements
of the methodology into this ontology. For its
conception, we conducted a case study based on
observations, questionnaires and interviews within
the course of Computer Engineering in the State
University of Feira de Santana (UEFS), Brazil.
The PBLOntology was instantiated using actual
data. Its concepts were assessed in two ways:
through testing and by the evaluation with domain
experts. Essentially this ontology consists of a
computational artifact that can be shared, reused and
enriched both by researchers and professionals
willing to develop semantic-based applications for
The remainder of the paper is structured as
follows: Section 2 presents the PBL methodology;
Section 3 briefly describes some related works;
Section 4 presents the PBLOntology and the
development steps; Section 5 discusses the
evaluation process and results; finally, Section 6
draws some conclusions and points out future works.
The PBL methodology emerged in the late 1960s in
the medicine program at McMaster University in
Canada, due to dissatisfaction and boredom of
students exposed to a large volume of knowledge
perceived as irrelevant to medical practice (Barrow,
1996). In Brazil, PBL has been adopted by medical
courses and has been also employed in many other
areas of higher education, such as pedagogy,
business administration, and engineering (Ribeiro,
A popular reference for PBL systematization is
the "seven steps" framework, proposed by the
Maastricht University (Deelman and Hoeberigs,
2009): (1) presentation of the problem and
enlightenment of unknown terms; (2) identification
of the problem posed by the statement; (3) problem
discussion and formulation of hypotheses to solve it;
(4) summarization of hypotheses; (5) formulation of
learning objectives. Based on prior knowledge, the
subjects to be studied for solving the problem are
identified; (6) self-study of the issues raised in the
previous step; (7) return to tutorial group to discuss
again the issue in the light of new knowledge
acquired in the self-study phase.
Figure 1: Conceptual Framework of the PBL
The PBL session begins with the presentation of
a problem to the group members, in which is raised
the problem scenario. After that, students perform
the collaborative whiteboard discussion. At this
stage, students identify ideas and facts, formulate
hypotheses, and define issues and learning goals.
After the whiteboard discussion, students perform
self-study to investigate the literature looking for
solutions to the issues identified in the previous step.
In the next step, students meet again in a tutorial
session to perform a new collaborative whiteboard
discussion, applying the new knowledge. New ideas,
facts, questions and goals can be identified within a
cycle of interactions until the problem resolution.
The Problem Solving stage consists of the
submission of a solution by means of the delivery of
a software, document, presentation, among other
deliverables. At the end of each problem, the
evaluation process is performed. Thus, the students
have the opportunity to reflect on the knowledge
built and to assess the problem, the tutor, the peers,
and themselves.
For the methodology to be effective, it is
necessary an active participation of the tutor
responsible for the group. The tutor acts on three
stages of the process: (i) Tutoring monitors the
group throughout the PBL process, (ii) Diagnostic
evaluation identifies students or tutorial group
weakness during the session and (iii) Formative
Assessment - evaluates the students development of
skills and competencies during the PBL process,
such as collaboration, communication, writing,
leadership, self-study, among others.
PBLOntology: A Domain Ontology with Context Elements for Problem-based Learning
Jacinto and Oliveira (2008) present an architecture
based on ontologies, where each component of the
architecture is structured by an ontology, helping the
understanding of the concepts of each component
and consequently the promotion of interoperability
between models of architecture.
Fontes et. al (2011) propose a domain ontology
for PBL to facilitate an effective access to
information about the area. It states that an
important aspect to be considered in PBL is the lack
of standardization and uniformity of the concepts
related to PBL, hampering the common and shared
understanding of the domain.
Our work differs from the related works in the
following aspects: (1) our ontology considers the
context of the PBL sessions in the formalization
process; (2) the ontology presented in Fontes et. al
(2011) can not be reused because the author has not
shared the ontology in any repository and
unfortunately it is no longer available, whereas our
proposed ontology is available in a repository for
reuse; (3) the ontology shown in Jacinto and
Oliveira (2008) is not specific for PBL, besides the
PBL formalized process does not conform to the
classical references of the methodology: the 7 steps
(Deelman and Hoeberigs, 2009) and the PBL cycle
proposed in Hmelo-Silver (2004); and (4) these
previous ontologies have not been evaluated.
Table 1: Comparison between PBLOntology and the
ontologies that formalize the PBL process.
Jacinto e
Fontes et
al. (2011)
Consider the
PBL Cycle
within an
4 THE PBLOntology
PBLOntology is available for download in Ontohub
(http://ontohub.org/repositories/pblontology), a web-
based repository for ontologies based on open source
software. The Protégé was chosen to develop the
PBLOntology because it is an open source code tool
and it provides a powerful editor of ontologies
including semantic web standard languages. We use
the language OWL (Web Ontology Language) due to
the fact that it is a robust language, recommended by
the W3C (World Wide Web Consortium) as a
standard for representing ontologies. The rule
language defined for the axioms creation is the
SWRL, it allows the combination of the developed
axioms with OWL. The query language is SPARQL,
because it is also supported by Protégé and
recommended by the W3C. The chosen inference
engine is the Pellet for supports SWRL rules and it
has a consistency check mechanism of ontology and
good performance. The 101 Method (Noy and
MCguiness, 2001) was adopted because it explicitly
describes the steps comprising the development of
an ontology.
To support the development of the PBLontology,
it was performed a field research in the Computing
Engineering course of the State University of Feira
de Santana. The field research consisted of
observations, questionnaires and interviews, during
the tutorial sections and it has been performed in a
period of six months.
4.1 The PBLOntology Development
A systematic mapping conducted to verify if there
was some ontology that could be reused. It has
shown a lack of formal representation of the PBL
concepts based on ontology language (Souza et. al,
The development of the PBLOntology
comprises of two phases: first we formalized the
knowledge of the essential elements that should be
considered in PBL. The second phase focused on the
formalization of contextual elements that emerged
on the collaborative discussions of tutorial sessions.
The PBLOntology is structured as follows: (i) a
classes and subclasses hierarchy representing the
PBL taxonomy; (ii) Object properties that qualify or
relate classes/individuals; (iii) data type properties,
representing the attributes of the classes; (iv)
instances, representing individuals of the classes; (v)
SWRL axioms, which are rules to represent the
knowledge domain; and; (vi) SPARQL queries.
During the PBLOntology development, 28
competency questions (CQ) were created. These
questions indicate what our ontology must be able to
answer and they are important to help testing the
ontology, evaluating the constraints, semantic rules,
and checking its consistency and competence. The
following are some of our competency questions:
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
CQ1: Which students of a group have not been
CQ2: What are the learning questions of a
CQ3: What is the problem solving ability of a
CQ4: What were the strategies created by the
tutor in a tutorial session?
The above competency questions can be useful
in many scenarios: the CQ1, for instance, can help
the coordinator indicating which student would be
the next coordinator. In a distance learning course
that uses PBL, the system itself can suggest a
coordinator. The CQ4 can help beginner tutors in the
creation of strategies according situations other
tutors had experienced.
4.2 Classes
To represent the PBL process twelve main classes
were defined (ActionPlan, Evaluation, Fact, Idea,
LearningIssue, Person, Problem, Process, SelfStudy,
TutorialGroup, TutorialSession and WhiteBoard)
and one main class (Context) to represent context of
the collaborative discussions during sessions. Figure
2 shows the main classes of the PBLOntology.
Figure 2. Main classes of the PBLOntology.
The Process class consists of the following
stages: presentation of the problem; discussion of the
problem by the group in the tutorial session;
individual study phase that occurs after the tutorial
sessions; and the evaluation phase. According to the
PBL phases, the Process class was defined as a
disjoint union of Evaluation, Problem, SelfStudy,
TutorialGroup and TutorialSession classes.
The Problem class represents the problem
addressed within the PBL process. This problem
must be a real or potentially real situation. It is
intended to cover a particular content, aiming at the
construction of knowledge and the development of
skills and competencies.
The Evaluation class represents the assessment
of both the student and the methodology. It
considers the following aspects: compliance with the
targets; attendance; collaboration and behavior.
These represent the properties of the class. It also
includes assessing the assignments of the
coordinator, whiteboard secretary, meeting
secretary, and the evaluation concerning the delivery
of the final product as well as the final report of the
problem resolution.
The TutorialSession class represents the meeting
with the group of students mediated by the tutor. It is
intended to discuss the proposed problem, record the
discussion progress on the whiteboard while the
problem is being approached.
The WhiteBoard class represents the whiteboard
where students record discussions about the problem
during tutorial session. A whiteboard is, therefore,
an aggregation of concepts ideas, facts, goals and
learning issues. This class is a disjoint union of the
following classes: Idea, Fact, LearningIssue and
The SelfStudy class represents the individual
study phase on the PBL process. Here students
research the contents to respond to the learning
issues and the achievement of the established
The TutorialGroup class represents the PBL
tutorial group, which consists of a tutor and students.
The Tutor class and the Student class are disjoint.
The Context class comprehends two main
subclasses: GroupContext and SessionContext. The
GroupContext class represents the group level and
the people who form the group. To define the group's
context we have considered the group structure
representing the maximum and minimum number of
students, and if the group had to be split into other
groups or if it had to join another group. The context
of people who are part of the tutorial group was also
considered: i. e., skills of students in the group, areas
of computing interest, previous experience in PBL,
and reprobation or desertion of the student. The
SessionContext class captures the context of tutorial
sessions. This class considers the temporal context
of the tutorial sessions, such as start time, end time,
days of the week, the order of the session, the
strategies to enable a productive tutorial session, and
the interaction context yielded by the group of the
tutorial session.
PBLOntology: A Domain Ontology with Context Elements for Problem-based Learning
We consider as interaction context the student
actions during a tutorial sessions, such as: the
collaboration with the discussion, the achievement
of the goals, the sharing of new knowledge, the
questions formulated during the tutorial session, and
the clarification of doubts raised by other students.
The tutor interaction was also considered: the
assistance in defining the whiteboard questions, the
questions raised by the tutor, the clarification of
pertinent questions, the assistance in defining the
whiteboard functions, and the encouragement of
student participation.
4.3 Properties
Aiming to represent relationships between
individuals of classes, 37 Object Properties were
defined in the first phase, whereas 23 Object
Properties were established in the second phase.
Table 2 shows two Object Properties defined in the
Table 2: Object Properties.
The hasPerson property represents the
relationship between the individuals of the
TutorialGroup and the Person classes that are part
of a tutorial group. The TutorialGroup class was,
therefore, established as the domain of this property,
and as the range the Person class. The hasFact
property is the inverse of isFactPartOf property and
represents the relationship between individuals of
the class WhiteBoard and Fact defined in a tutorial
session. The domain of this property is the
WhiteBoard class and the range is the Fact class.
To the representation of DataType Properties, 26
properties were established in the first stage of the
PBLOntology development, and 29 properties in the
second. As an example, Table 3 shows two Datatype
Properties defined in the PBLOntology.
Table 3: Datatype Properties.
The DataType Properties learningQuestion
represents a learning question defined on the
whiteboard during a tutorial session. The domain of
this property is the LearningIssue class and the
range is string. The DataType Properties
actionStrategy property represents actions related to
the strategies, in other words, what the tutor needs to
do during the tutorial session for its proper
functioning. The domain of this property is the class
Strategy and the range is the string type.
4.4 Restrictors and Axioms
The semantics of the terms belonging to the
ontology proposed in the first phase was defined
with twelve restrictions. Those restrictions involve
existential, universal,quantifiers and cardinality.
The restriction defined in the Process class,
shown in Figure 3, establishes that the individuals of
the Process class are formed by the union of the
classes Evaluation, Problem, SelfStudy,
TutorialGroup and TutorialSession. Besides, it states
that this union is a sufficient and necessary condition
to establish the Process class as an aggregation.
Figure 3: Restriction that determines the formation of the
Process class.
We have specified five axioms in the language
SWRL (Semantic Web Rule Language). The axiom
shown in Figure 4 is designed to make inferences
about the PBL experience of a student. The rule
states that if there is a person within a tutorial group,
within a PBL process, then this person has a context
The context holds the PBL experience of a person.
This axiom can be useful to recommend students as
a tutor based on their PBL experience.
Figure 4: SWRL axiom that infers PBL experience of a
4.5 Instances
We have created a consistent database populated
with actual instances suitable to support the
development of future research. These instances
Were collected during the field research. In the first
ICEIS 2018 - 20th International Conference on Enterprise Information Systems
stage, 266 individuals were instantiated and among
the classes defined in ontologies. In the second
stage, over 97 individuals were instantiated.
Figure 5 presents one instances of the Fact class
where we can observe some data type properties
(PrirKnowledge) instantiated in that class and an
inferred object property (isFactPartOf).
Figure 5: Instance defined in PBLOntology.
In the literature, there are two evaluation methods
for ontologies: test activities and evaluation with
specialists (Santos, 2013; Silva, 2013). According to
Vrandecic (2009) it is important to define some
criteria to evaluate ontologies. Based on Vrandecic
(2009), we defined the following criteria for
evaluation of the PBLOntology: accuracy,
adaptability, clarity, completeness and competence,
consistency and coherence, and conciseness.
To evaluate the consistency, coherence and
competence criteria, we carried out testing activities
in both ontology development stages. These tests
consisted of running the reasoner Pellet of protégé
and observing the inferences made in the ontology,
checking its behavior, consistency and formalization
The competency assessment was performed by
the SPARQL queries that were created to answer
competence questions.
For CQ1: Which students of a group have not
been coordinators? The SPARQL query is presented
SELECT ?Group ?Student WHERE{
?Grouppbl:hasPerson ?Student.
?Student}} ORDER BY (?Group)
For CQ4: What were the strategies created by the
tutor in a tutorial session? The SPARQL query is
presented below.
SELECT ?TutorialSession ?Event ?Action
xt ?TutorialSession.
?Action } ORDER BY (?TutorialSession)
SPARQL queries were designed to verify if the
ontology was able to respond each of the defined
competence questions.. The competence questions
can support the systematization of the methodology
and help in decision-making. The PBLOntology
answered satisfactorily all defined competence
questions, hence, it meets the competence criteria
within the defined scope.
To verify if the ontology correctly captures and
represents aspects of the real world, a questionnaire
with 24 questions was developed. It was answered
by 4 experts, who are lecturers with PBL experience
in the course of Computer Engineering at the State
University of Feira de Santana. The questions aimed
to evaluate concepts and class names (conciseness
and clarity), relationships and constraints defined in
the ontology (accuracy), coverage and completeness.
Figure 6 presents the evaluation of the concepts and
class names.
Figure 6: Evaluation of the concepts and class names.
Analyzing the experts opinions, 59% of the
answers stated that the concepts and names of the
classes defined in the ontology are satisfactory, 30%
partially satisfy and 11% do not satisfy. We also
asked the experts to inform the most appropriate
concept and name, when the answers were not
totally satisfactory and this feedback was used to
improve the ontology.
Regarding the evaluation of accuracy, in two
questions the experts suggested changes in the
ontology relationships and constraints, these changes
were implemented. When assessing the coverage
and completeness, it was not possible to draw any
PBLOntology: A Domain Ontology with Context Elements for Problem-based Learning
conclusions, since 50% evaluated the ontology as
complete and 50% reported a lack of concepts.
The evaluation of the adaptability criterion was
subjective, accomplished through the extension of
the domain ontology incorporating context elements
that had emerged during the tutorial sessions.
Table 4 presents a general overview of the
questions and the evaluated criteria.
Table 4: Analysis and assessment of result.
Correction of
inference errors and
It complies with
the coherence
and consistency
The executed
queries brought the
expected replies.
It satisfies the
Concepts and names
of classes: most
questions satisfied
or partially satisfied,
and suggestions
were accepted.
It meets the
clarity criteria.
Coverage: 50% said
yes and 50% said
It is not possible
to ensure that
criteria is
Relationship and
restrictions: the
majority is correct.
It complies with
the accuracy
It was possible to
expand the domain
ontology including
contextual elements
observed during
tutorial sessions
It satisfies the
The assessment allowed us to make
improvements to the ontology according to the
defined evaluation criteria. A small number of
inconclusive issues would have been further
clarified had more experts participated in the
This paper presented an ontology for Problem-Based
Learning that provides a semantic formalization of
the PBL process, as well as the contextual elements
of collaborative discussions that appeared on tutorial
To mitigate potential problems, two approaches
were defined for the evaluation: testing activities and
expert evaluation. That approach towards the
assessment of the results allowed us to improve the
ontology. Thus, we provide strong evidence to
conclude that PBLOntology meets the defined
evaluation criteria satisfactorily, and displays
pertinent applicability to the field of Computer
The proposed ontology advances on current work
by providing a formalization of the PBL domain
based on ontology. It also establishes an innovative
contribution by identifying contextual elements for
the collaborative tutorial sessions.
Researchers and those who work with ontologies
and development of semantic applications for the
PBL domain can benefit from PBLOntology. This
ontology can serve as a basis for the knowledge of
the domain; It can also graphically illustrate the
concepts and relationships of the PBL and therefore
enable the understanding of the methodology for
beginners in the subject. Besides, it can be reused for
the creation of other ontologies or applications.
Another advantage of PBLOntology is that it has a
database with more than 300 instantiated
individuals, which can facilitate the testing of new
applications that use the ontology.
Although the ontology can be reused, shared and
expanded, contributing to research and studies in the
area, PBLOntology has some limitations: it was
evaluated only in a computation course, the scope of
contextual elements was very reduced and the
amount of specialists was not enough to evaluate
coverage and completeness of the ontology. Future
works should address these limitations.
Authors thank the expert professors and
undergraduate students of Computer Engineering at
The State University of Feira de Santana (UEFS) for
their collaboration in the experiments of this
research and Bahia Federal Institute of Education,
Science and Technology (IFBA) for granting a
scholarship to exclusive dedication to this research.
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PBLOntology: A Domain Ontology with Context Elements for Problem-based Learning