Towards an Ontology for Representing a Student’s Profile in
Adaptive Gamified Learning System
Siwar Missaoui
1a
, Souha Bennani
2b
and Ahmed Maalel
2,3 c
1
University of Sousse, Higher Institute of Management, 4000, Sousse, Tunisia
2
University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia
3
University of Sousse, Higher Institute of Applied Science and Technology, 4003, Sousse, Tunisia
Keywords: Ontology, Student Profile, Learning System, Adaptive Learning, Gamification.
Abstract: Learner and learning content are the key factors contributing towards the success of any adaptive learning
system. Each learner searches for an adequate environment to his needs which offers personalized and
adaptive content that provides a learning experience to be more successful and more useful to him. Moreover,
he likes to study in a fun and entertaining environment that gives them a sense of engagement and motivation.
Education research shows that considering student profile is effective in adapting courses and profile
modeling is an important process that aims to give as complete representation as possible of all the aspects
related to the user's features. With regard to motivation, some studies have approved that gamification is a
good solution to enhance student engagement and that there is a strong link between it and motivation.
Therefore, this article presents our contribution through a SPOnto ontology for representation of students
profile, by combining the two concepts “adaptive learning” and “gamification” to provide a personalized
gamified experience. We propose a student profile ontology, to benefit from semantic web technologies,
which presents a global model of the student based on many important characteristics in order to help decision-
making in the different academic contexts and to motivate him to achieve his learning goals.
1 INTRODUCTION
Online learning platforms, educational systems and
continuing education programs are all based on a
uniform approach that may be inappropriate to the
profile of the learners, where a large number of
students are educated without taking into account
their preferences, their cognitive abilities, their
learning style, their behaviors and their very
particular personalities typical to each one of them. In
fact, a new concept appeared called adaptive learning
connected to artificial intelligence and cognitive
science research, which provides learning appropriate
to a learner's ability and takes into account his profile
and his preferences. This concept has been introduced
by many researchers as a solution for students’
engagement but it is not as effective as anticipated.
The lack of motivation is one of the main reasons for
this issue. However, motivation is a decisive factor in
students’ learning that determines how much effort
a
https://orcid.org/0000-0002-1755-9033
b
https://orcid.org/0000-0002-4092-8618
c
https://orcid.org/0000-0001-7918-5957
and commitment they have put into learning a
particular topic (Brophy, 2013) and the human being
only carries out any activity taking into account the
reward obtained once this activity is successfully
completed. In this regard, many researchs have shown
a link between motivation and gamification. This
latter is the use of game metaphors, game elements
and ideas in a different context than games to increase
motivation and engagement, as well as to influence
user behavior (Marczewski, 2013). Several researchs
approved that the integration of gamification has
great potential to engage students and facilitate
learning but it is complicated to implement it properly
and it is not a simple process of adding game
elements. Most gamified systems follow the “one size
fits all” approach when integrating elements of the
game, without taking into account differences
between learners. So, to target these problems, we
have proposed “SPOnto”: an ontology of
representation of student profile where we combined
Missaoui, S., Bennani, S. and Maalel, A.
Towards an Ontology for Representing a Student’s Profile in Adaptive Gamified Learning System.
DOI: 10.5220/0010107301490156
In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 2: KEOD, pages 149-156
ISBN: 978-989-758-474-9
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
149
the two concepts “adaptive learning” and
“gamification” to provide an adaptive gamified
learning appropriate to student’s profile which is
based on many important characteristics to more
motivate and engage them.
The comparative study of (Hamim et al, 2019)
shows that the ontology approaches are the most
recently used to model the profile of student. This
latter was applied for the purpose of modeling the
student profile, as being a technique that can give as
complete a representation as possible of the student
profile. Ontology has been commonly used as a useful
knowledge engineering technique to reduce
ambiguity and help with information sharing. It
characterizes several domains’ purposes’ description
through structured and formalized languages.
2 RELATED WORK
The use of ontologies in student profiling has gained
considerable attention in recent years since it is
suitable to tackle interoperability and information
sharing challenges. Several ontological models have
been proposed in the literature to overcome these
issues.
(Munassar
and Ali, 2019) proposed a Framework
for adaptive e-learning (PALO) which is based on
semantic web technology and offers students personal
materials. They modeled learning objects and the
learner’s profiles according to different learning
styles and knowledge levels by integrating the OWL
ontology and the SWRL rules. The work presented by
(Abyaa et al, 2017) based on modeling the learner’s
knowledge using ontologies and rule reasoning. They
take into consideration in their adult learner’s
knowledge model the different knowledge types and
categories, learner’s prior knowledge, previously
learned but forgotten knowledge, misconception and
errors. (Ameen et al, 2012) proposed student profile
ontology to personalize the content learning, based on
the academic information. (Sarwar et al, 2019)
concentrated on different attributes to profile the
learner such as learning style, knowledge, age,
locale/origin, professional experience and
qualification. They proposed a semantic e-learning
framework not only for profiles learners through
ontology but also for categorizing them based on their
profiles for recommending the suitable learning
content. The work presented by (Bouihi and Bahaj,
2019) proposes a revised version of the classical 3-
tiers architecture for a semantic web based
recommender system, by adding a semantic layer
hosting on ontology and semantic rules. This layer
contains a learning management system ontology that
comprises two interdependent sub ontologies:
learning content ontology and learning context
ontology. (Hamim et al, 2019) presented, after
performing a comparative study under two main
criteria: the profile modeling approach and the
characteristics used taxonomy of student
characteristics that might be used for profile modeling
and that encompasses different point of view from the
student. Their study shows also that the machine
learning and ontology approaches are the most
recently used to model the profile of student. (Hassan
et al, 2019) proposed a framework in which each
student is presented with adaptive gamification
experience (activities and elements) according to his
learning dimensions that have been identified from
the interactions that a student performs with the
system using a mathematical formula. (Rezgui et al,
2014) proposed a learner profile ontology, which
presented a general view of the different learner’s
characteristics, to improve the learner model with
semantic in order to provide a personalized content
and learning paths according to specific student’s
needs. For the personalization of game design
elements in collaborative learning contexts, (Challco
et al, 2014) created gamification ontology,
representing some gamification concepts and they
focused on the definition of player roles and
gameplay strategies. They demonstrate its use
through a case study. (Dermeval et al, 2019) connect
theories of both concepts “gamification” and
“Intelligent Tutoring Systems (ITS)” using the
ontology to allow automated reasoning, to enable
interoperability, and create awareness about theories
and good practices for the designers of gamified ITS.
According of the description of existing studies
related to student profile ontology in the previous
section, we were allowed to highlight the major limit
that was identified is that the most studies didn’t give
a complete representation of student profile in all the
aspects related to the student features. The majority
has concentrated in one or some characteristic of the
student and didn’t cover all the different aspects in the
same time. According to the table1 which represents
a comparison of studies dealing with the concept of
student profile ontologies in the educational context,
we can notice that the academic features are the most
used. The majority of studies didn’t focus enough on
student motivation, engagement and how can use
gamification features to more attract them, but they
just focus of the improvement of the student academic
performance and his learning process. The source of
data varies from one study to another, and the
majority of studies use questionnaires, academic
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
150
databases, and the interactions of learners for the e-
learning systems.
Table 1: Comparison of related works.
Article
Use
academic
features
Use
psychologic
al features
Use
gamification
features
(Ameen et
al, 2012)
X
(Rezgui et
al, 2014)
X
(Challco
et al,
2014)
X
(Abyaa et
al, 2017)
X
(Munassar
and Ali,
2019)
X
(Sarwar et
al, 2019)
X
(Bouihi
and Bahaj,
2019)
X
(Hassan et
al, 2019)
X X
(Dermeval
et al,
2019)
X
The added value of our work in comparison with
the previous mentioned works is that we propose a
student profile ontology that captures the full details
and aspects related to the student features, to give as
complete a representation as possible of the student
profile, which containing both implicit and explicit
information about him. As shown in the figure1, we
defined a taxonomy inspired by (Hamim et al, 2019)
to represent the different characteristics and
categorize them in three categories: academic,
psychological and gamification features. This
taxonomy can aid the design of a student profile that
covered the different point of view from the student.
3 SPOnto ONTOLOGY
DEVELOPMENT
Ontologies can be built from scratch or can be reused
an existing ontologies. There are many
methodologies for ontology development. Among
them we were inspired by a METHONTOLOGY
(Fernández-López et al, 1997) to develop our SPOnto
ontology from scratch, simplifying the process of five
main steps that can be described as follow: (1) Scope
and requirements definition, (2) Knowledge
acquisition, (3) Conceptualization, (4) Design &
Implementation, (5) Evaluation.
Figure 1: Student’s characteristic taxonomy.
We choose this methodology because it is listed
as one of the most mature ontology engineering
methodologies existing in literature. Moreover, it
includes activities to support most activities of the
ontology development lifecycle (Dermeval et al,
2019). Our ontology’s process phases are described
in detail in the following subsection.
3.1 Scope and Objectives Specification
The scope of our ontology is the learner’s profiling.
The main users of our ontology are schools and e-
learning systems.
The main objectives of our ontology are:
Offering an efficient description of students in
various aspects (behavior, knowledge, learning
style etc.) in order to help decision-making in the
different academic context and to be to act in case
of problems such as failure, drop out. While,
ontology technique is the best technique that can
give as complete a profile representation as
possible.
Towards an Ontology for Representing a Student’s Profile in Adaptive Gamified Learning System
151
Define the most adaptive learning resources
depending on their profiles. Therefore, a profile
model allows the detection of profile patterns
which can be applied in similar cases which leads
to facilitate the research, accelerate the decision
and make it more efficient and make the system
centered on the user.
3.2 Knowledge Acquisition
Once we have established the scope and the
objectives of our ontology, we move to the second
step of the pre-development stage which is
“knowledge acquisition”. This step comprises
exploration, extraction and derivation of knowledge
from the domain of interest. Many resources have
been used to gather domain knowledge. In our case
learner’s profiling, we have acquired the domain
knowledge and key concepts and elements from
domain experts and researchers working in this field.
We used terminologies from prominent existing
ontologies from literature, as depicted in table 2.
Table 2: List of extracted student profile concepts from
literature.
Concept Description Source
Personal
Information
Describes biographic and
demographic data.
(Rezgui
et al,
2014)
Personality
As an internal factor that gives
consistency over time for an
individual’s behavior.
(Rezgui
et al,
2014)
Transcript
Describes an institutionally-
based summary of academic
achievement.
(Rezgui
et al,
2014)
Motivation
Is the desire to do something.
It could be intrinsic and
extrinsic.
(Challco
et al,
2014)
Performance
Describes the learner’s
measured performance, e.g.
grades.
(Rezgui
et al,
2014)
Competency
Describes the set of
knowledge, skills and abilities
the learner has acquired
during learning.
(Rezgui
et al,
2014)
Learning
style
Describes the learner’s
preferred method of learning
and acquiring knowledge, and
also of having physical and
sociological needs.
(Munass
ar and
Ali,
2019),
(Sarwar
et al,
2019)
Cognitive
Refers to a variety of mental
processes related to
information manipulation, it
includes: intelligence,
competence, experience,
abilities.
Hamim
et al,
2019)
Skills/
interest
Refers to the talents and
interests centers which
include talents that enabled
the student to succeed, it
encompasses: creativity,
interpersonal,
communication, leadership,
understanding (speed and
logic).
Hamim
et al,
2019)
Knowledge
Refers to the information
acquired through experience
or education, it can be:
general, theoretical, and
practical on a particular topic
(Rezgui
et al,
2014)
Qualification
Describes any qualifications,
certifications, licenses or
degrees awarded to the
learner.
(Rezgui
et al,
2014)
Player type
Refers to classify users and
identify the differences
between them as a player in
front of a gamified system
(Rezgui
et al,
2014)
Game
element
Refers to any kind of type
from games and apply them to
gamification as concept,
mechanics etc.
(Challco
et al,
2014)
3.3 Conceptualization
Next phase, following the METHONTOLOGY
process, is to perform the conceptualization of our
ontology. This step takes as input the list of concept
resulting from the knowledge acquisition. It includes
defining the core concepts, a glossary of terms, a tree
of concepts, and the relations between the concepts in
the ontology. Based on our sources of knowledge, we
defined the following core concepts, which are
represented in the class diagram in figure 2:
- Profile, which represents all the basic information
relating to student’s background, goals, interest
and preferences.
- Personal Information, which represents the
biographic and demographic data.
- Academic Information, which represents all
information related to the student learning, such
as the information acquired through experience or
education, thinking abilities and skills etc.
- Player Information, which represents all
information related to the player such as player
type and game elements preferences etc.
Figure 2 represents the class diagram, which is a
graphical representation of the concepts of our
student profile ontology and the relationships
between them. The glossary of terms contains the
definitions of all terms related to the domain
(concepts, attributes, relations). Table 3 provides a
list of some concepts used in our ontology which we
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
152
specify, for every concept, the following information:
the properties, the parent, and the relationships.
Table 3: List of concepts of the SPOnto ontology.
Concept Role Parent Attributes
Profile - Thing -
Learning
style
hasLearning_St
yle,
hasInput_Learni
ngStyle,
hasPerception_
LearningStyle,
hasProcessing_
LearningStyle,
hasUnderstandi
ng_LearningSty
le
Learner_
informat
ion
Learning_
style_valu
e
Player
type
hasPlayerType
Player_i
nformati
on
Player_ty
pe_value
Cognitive
hasDisabilities,
has Abilities,
hasCompetence
s,
hasIntelligence
Learner_
informat
ion
Cognitive
_Type,
Cognitive
_Value
Interactio
n
preference
hasAestheticPre
ferences,
hasAestheticPre
ferences_Color,
hasAestheticPre
ferences_Font
Player_i
nformati
on
Color_val
ue,
Font_valu
e
Skills
hasCommunicat
ion_Skills,
hasCreativity_S
kills,
hasLeadership_
Skills
Learner_
informat
ion
Skills_typ
e,
Skills_val
ue
Physical
condition
hasPhysicalCon
dition
Personal
_inform
ation
Physical_
Condition
_type
(…) (…) (…) (…)
3.4 Design and Implementation
Our ontology is implemented with protégé 5.2
ontology editor, which is by far the most widely used
ontology editing environment, and is saved as an
OWL (Web Ontology Language) file (Protégé, 2013).
OWL is a common ontology language which defines
and describes classes, subclasses and properties i.e.,
object properties, datatype properties and annotation
properties. In the following subsections, we present
our ontology model with regard to the main aspects
of profiling domain knowledge.
The student profile is created using all students’
information. Student information was commonly
divided into three major categories:
Learner Information, which represents the
academic information. It concerns all information
related to the student learning, such as the information
acquired through experience or education, thinking
abilities and skills etc. Students’ academic details are
collected in “Learner_information” class which is in
turn divided into several subclasses such as
Learning_style”,“Mental_capacity”,“Knowledge”,
Skills”, “Cognitive” and “Learning_goals”. Among
these subclasses we find:
Learning style, which describes the learner’s
preferred method of learning and acquiring
knowledge. There are several models of learning
style, but we used in our work “Felder Silverman
Learning Style Model which contains a four-
dimensional: Dimensions Perception (sensing /
intuitive), Dimensions Processing (active /
reflective), Input measurement (visual/ verbal),
and Understanding (sequential / global)
(Munassar and Ali, 2019).
Mental capacity, which represents a hierarchy of
educational objectives such as cognitive, sensory
and affective domains. For this criterion we used
the bloom taxonomy, which helps the instructors
to analyze the level of each student under six
categories of cognitive domains such as
remembering, understanding, applying,
analyzing, evaluating and creating (Sami &
Arumugam, 2019). This taxonomy not only helps
student to evolve thinking abilities but also to
identify the skills they are lacking with.
Figure 3 shows the instances of the “Profile”
class, among these we found the instance “Profile 1
which has some properties related to learner
information. An example, “Profile1” has some
abilities like Reading and writing, has video as
learning style, has some disabilities in math
calculation but has a good memory as competence.
Personal Information, which represents the
biographic and demographic data. This includes data
such as: name, age and address etc. All of these data
are collected in “Personal_information” class which
is in turn divided into several subclasses such as
Physical_Condition”, “Personality” and
Demographic_data”. For the personality criteria, we
followed the FFM model (Denden et al, 2018), which
categorizes personality traits in five dimensions: (1)
Extraversion; (2) Agreeableness; (3)
Conscientiousness; (4) Neuroticism; and, (5)
Openness.
Player Information, which represents all
information related to the player such as player type,
game elements preferences etc. These information are
collected
in Player_information class which is in
Towards an Ontology for Representing a Student’s Profile in Adaptive Gamified Learning System
153
Figure 2: Conceptual Model of SPOnto ontology.
Figure 3: An example of properties and instances to
Learner_information” and “Profile” classes.
turn divided into several sub classes such as
Interaction_preferences”, “Motivation” and
Player_type”, as shown in figure 4. For the player
type classification, we used a Hexad typology (Lopez
& Tucker, 2019), which contains six types of players:
(i) Philanthropists, (ii) disruptors, (iii) Socializers,
(iv) Free Spirits, (v) Achievers and (vi) Players. This
typology user preferences for game elements in
gamified applications.
3.5 Evaluation
In the literature, many approaches of ontology
evaluation have been suggested to assess whether
ontology accurately and properly represented such
domain information. We have reviewed the most
common evaluation approaches and we have chosen
the criteria-based evaluation approach (Yu et al,
2005).
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
154
Figure 4: OwlViz view of “Player_information
subclasses.
We selected a list of criteria among those such as
clarity, consistency, conciseness and correctness.
Each criterion is explained in the following
subsection.
Clarity, Gruber (Gruber, 1993) sets out clarity
requirements which include: The described
ontology terms "should communicate effectively
the intended meaning" removing any aspects of
subjectivity or ambiguity. The ontology should
be documented with a natural language. In our
context, the feedback of the interviewed domain
experts has helped us to verify ontology clarity
and more specifically to omit or replace any
ambiguous term. For instance, we had
Student_information” as a concept which
represents all information related to the student
learning. During an interview with experts of the
domain, they agreed that the term “Student” does
not deliver its intended meaning because it’s very
general term which can represent any detail of
student and should be replaced with the term
“Learner” that is more significant and refers
specifically to learning information of the
student. As for the formal description of ontology
terms, most SPOnto ontology terms are formally
defined as they have been extracted from
literature and government manuals. For example,
the term “Profile” is defined as representation of
all the basic information relating to the student
features such as student’s background, goals,
interest and preferences.
Consistency, or coherence (Haghighi et al,
2013) requires the logical coherence of ontology
concepts and elements and avoids contradiction
or ambiguities. Any contradiction between
explicit or inferred axioms and their definitions
makes the ontology incoherent. As an example,
the class “verbal” and the class “visual” were
subclasses of both “Learning_style” and
Physical_condition”. In one hand, these classes
are defined as a learning style and in another
hand; these are defined as a type of physical
condition. However, the inferences were
inconsistent. So, we removed these classes from
Physical_condition”.
Conciseness, means that there should be no
unnecessary concepts or explicit redundancies
between definitions in the ontology. In our
ontological development and validation this
aspect has been carefully considered. For
instance, regarding SPOnto ontology, it included
2 classes “Mental_capacity” and “Cognitive” as
subclasses of the class “Learner_information”.
Mental_capacity” represented educational
objectives such as cognitive, sensory and
affective domains, based on the six cognitive
categories of bloom taxonomy and “Cognitive”
described some of mental processes such as
intelligence and competence etc. Since we
realized that categorizing cognitive is
unnecessary and useless for our ontology, we
have melted “Mental_capacity” and “Cognitive
classes into one class “Cognitive_capacity”.
Correctness, means that the ontology
represents the correct modeling of the real-world
concepts (Yu et al, 2005). This criterion has been
carefully considered in SPOnto ontology
development and validation. As an example,
domain experts’ feedback has led us to add the
concepts “Teacher feedback” and “Parent
feedback” since they constitute a key factor in
student profile identification and validation of his
predicted characteristics.
4 CONCLUSIONS
In the learning system, the major problem facing
students is that they cannot obtain pertinent
information based on their requirements which in turn
produces a feeling of boredom and reluctance in the
students and decreases their sense of motivation and
Towards an Ontology for Representing a Student’s Profile in Adaptive Gamified Learning System
155
engagement. Among the solutions associated with
content personalization in learning systems, dealing
with the heterogeneity of student profile in different
aspects is a complex task. But modeling an efficient
student profile describes the best way a student
prefers to learn and reflects his true needs which in
turn would enhance the usage. Besides, in order to
deal with the problem of motivation, we used
gamification techniques. The paper proposes
“SPOnto”: an ontology of representation of student
profile in a learning system which connects two
concepts “gamification” and “adaptive learning”. The
study is carried out under two main criteria: the
profile modeling approach and the characteristics
used. The representation of a student profile is
achieved using ontology. Our ontology allows to
build a global model of the student based on many
important characteristics in order to help to predict
their intentions and preferences and the decision-
making to personalize the learning scenario. The
resulting ontology was evaluated by virtue of a
criteria-based approach to check its design and
content.
In future work, we intend to apply our model in an
existing e-learning system, called “class-quiz”, to
analyze it in a real system and approve the efficiency
of the student profile model on the basis of all these
characteristics.
REFERENCES
Brophy, J. E. (2013). Motivating students to learn.
Routledge.
Marczewski, A. (2013). User types in gamification-Part 2:
Players and balance. Gamified UK.
Hamim, T., Benabbou, F., & Sael, N. (2019, October).
Toward a Generic Student Profile Model. In The
Proceedings of the Third International Conference on
Smart City Applications (pp. 200-214). Springer,
Cham.
Munassar, W. A., & Ali, A. F. (2019). Semantic Web
Technology and Ontology for E-Learning
Environment. Egyptian Computer Science Journal,
43(2), 88-100.
Abyaa, A., Idrissi, M. K., & Bennani, S. (2017, October).
An adult learner's knowledge model based on
ontologies and rule reasoning. In Proceedings of the
Mediterranean Symposium on Smart City Application
(pp. 1-6).
Ameen, A., Khan, K. U. R., & Rani, B. P. (2012, October).
Ontological student profile. In Proceedings of the
Second International Conference on Computational
Science, Engineering and Information Technology (pp.
466-471).
Sarwar, S., Qayyum, Z. U., García-Castro, R., Safyan, M.,
& Munir, R. F. (2019). Ontology based E-learning
framework: A personalized, adaptive and context aware
model. Multimedia Tools and Applications, 78(24),
34745-34771.
Bouihi, B., & Bahaj, M. (2019). Ontology and Rule-Based
Recommender System for E-learning Applications.
International Journal of Emerging Technologies in
Learning (iJET), 14(15), 4-13.
Hassan, M. A., Habiba, U., Majeed, F., & Shoaib, M.
(2019). Adaptive gamification in e-learning based on
students’ learning styles. Interactive Learning
Environments, 1-21.
Rezgui, K., Mhiri, H., & Ghédira, K. (2014). An Ontology-
based Profile for Learner Representation in Learning
Networks. International Journal of Emerging
Technologies in Learning, 9(3).
Challco, G. C., Moreira, D. A., Mizoguchi, R., & Isotani, S.
(2014, September). An ontology engineering approach
to gamify collaborative learning scenarios. In CYTED-
RITOS International Workshop on Groupware (pp.
185-198). Springer, Cham.
Dermeval, D., Albuquerque, J., Bittencourt, I. I., Isotani, S.,
da Silva, A. P., & Vassileva, J. (2019). GaTO: an
Ontological Model to Apply Gamification in Intelligent
Tutoring Systems. Frontiers in Artificial Intelligence, 2,
13.
Fernández-López, M., Gómez-Pérez, A., & Juristo, N.
(1997). Methontology: from ontological art towards
ontological engineering.
Haghighi, P. D., Burstein, F., Zaslavsky, A., & Arbon, P.
(2013). Development and evaluation of ontology for
intelligent decision support in medical emergency
management for mass gatherings. Decision Support
Systems, 54(2), 1192-1204.
Protégé, A. (2013). A free, opensource ontology editor
and framework for building intelligent systems.
Sami, J. C. A., & Arumugam, U. (2019). A Descriptive
Analysis of Students Learning Skills Using Bloom’s
Revised Taxonomy.
Denden, M., Tlili, A., Essalmi, F., & Jemni, M. (2018,
July). Does personality affect students' perceived
preferences for game elements in gamified learning
environments?. In 2018 IEEE 18th International
Conference on Advanced Learning Technologies
(ICALT) (pp. 111-115). IEEE.
Lopez, C. E., & Tucker, C. S. (2019). The effects of player
type on performance: A gamification case study.
Computers in Human Behavior, 91, 333-345.
Yu, J., Thom, J. A., & Tam, A. (2005, October). Evaluating
ontology criteria for requirements in a geographic travel
domain. In OTM Confederated International
Conferences" On the Move to Meaningful Internet
Systems" (pp. 1517-1534). Springer, Berlin,
Heidelberg.
Gruber, T. R. (1993). Formal ontology in conceptual
analysis and knowledge representation. Chapter"
Towards principles for the design of ontologies used for
knowledge sharing" in Conceptual Analysis and
Knowledge Representation.
KEOD 2020 - 12th International Conference on Knowledge Engineering and Ontology Development
156