Towards an Ontology for Representing a Student’s Profile in

Adaptive Gamified Learning System

Siwar Missaoui

, Souha Bennani

and Ahmed Maalel

2,3 c

University of Sousse, Higher Institute of Management, 4000, Sousse, Tunisia

University of Manouba, National School of Computer Sciences, RIADI Laboratory, 2010, Manouba, Tunisia

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

https://orcid.org/0000-0002-1755-9033

https://orcid.org/0000-0002-4092-8618

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

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)

(Rezgui et

al, 2014)

(Challco

et al,

2014)

(Abyaa et

al, 2017)

(Munassar

and Ali,

2019)

(Sarwar et

al, 2019)

(Bouihi

and Bahaj,

2019)

(Hassan et

al, 2019)

X X

(Dermeval

et al,

2019)

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

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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

Learner_

informat

ion

Learning_

style_valu

Player

type

hasPlayerType

Player_i

nformati

Player_ty

pe_value

Cognitive

hasDisabilities,

has Abilities,

hasCompetence

hasIntelligence

Learner_

informat

ion

Cognitive

_Type,

Cognitive

_Value

Interactio

preference

hasAestheticPre

ferences,

hasAestheticPre

ferences_Color,

hasAestheticPre

ferences_Font

Player_i

nformati

Color_val

ue,

Font_valu

Skills

hasCommunicat

ion_Skills,

hasCreativity_S

kills,

hasLeadership_

Skills

Learner_

informat

ion

Skills_typ

Skills_val

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

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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.

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Knowledge Representation.

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