Towards Adaptive Learning Systems Based
on Fuzzy-Logic
Soukaina Ennouamani(&) and Zouhir Mahani
Ibn Zohr University, Agadir, Morocco
soukaina.ennouamani@edu.uiz.ac.ma, z.mahani@uiz.ac.ma
Abstract. E-learning systems have the ability to facilitate the interaction
between learners and teachers without being limited by temporal and/or spatial
constraints. However, the high number of students at universities, the huge
number of available learning in the web, the differences between learners in term
of characteristics and needs make the traditional e-learning systems more limited.
For this purpose, adaptive learning has been recently explored in order to cope
with these limitations and to meet the individual needs of learner. In this context,
many artificial intelligence methods and approaches have been integrated in such
computer-based systems in order to create effective learner models, structured
domain models, adaptive learning paths, personalized learning format, etc. Such
methods are highly recommended for designing adaptive e-learning and mlearning systems with good quality. In this paper, we focus only on one of these
methods, called fuzzy logic, which is widely used in educational area. We present
the integration of fuzzy logic as a valuable approach that has the ability to deal
with the high level of uncertainties and imprecision related to learners’ characteristics and learning contexts.
Keywords: E-learning Adaptive learning Personalized education
Artificial intelligence Fuzzy logic Fuzzy inference systems
1 Introduction
In recent years, computer-based learning is considered as one of the research areas that
have attracted the attention of many researchers, and particularly e-learning that has
had an impressive revolution in the field of smart learning. In this context and based on
the use of Information and Communication Technologies (ICT), e-learning environments have the potential to be integrated in heterogeneous groups of learners in order to
enhance their experiences and to provide them with a myriad of learning resources. In
fact, smart learning platforms support the flexible teaching-learning process through
mitigating temporal and spatial constraints [1].
In this context, teachers who used to know everything become teachers who must
be continuously learning and reflective on their knowledge and skills [2]. It becomes
more and more difficult to satisfy every learner’s needs, and to achieve the desired
quality of teaching and learning which is continuously changing over time. This is
because of instructors who can not adjust their teaching strategies for every single
learner, especially at universities where the number of students is high. In addition to
© Springer Nature Switzerland AG 2019
K. Arai et al. (Eds.): CompCom 2019, AISC 997, pp. 625–640, 2019.
https://doi.org/10.1007/978-3-030-22871-2_42
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this, the total number of remote learners makes the diagnosing of their interests and the
determination of the best instructional actions, a difficult task for instructors [1].
In addition, e-learning is challenged by a considerable number of limitations related
to the technical aspects, viz. crowded learning platforms, various user profiles, huge
number of learning resources, etc. These limitations have encouraged researches in the
field of smart learning to move forward to adaptive e-learning, which is an appropriate
approach for heterogeneous learning groups [3]. Therefore, the integration of new
methods and tools becomes an obligation in order to map with the new learning
requirements. Artificial intelligence based methodologies are one of the considerable
techniques that have been used to design intelligent educational systems [4].
Various artificial intelligence approaches (Bayesian Network, Machine Learning,
Heuristic Methods, Ontology Construction, Fuzzy Logic) have been integrated to solve
an important number of problems related to e-learning. Each solution has been
designed regarding to the objective of each proposal, such as recommendation, content
adaptation, format personalization, learning path generation, learning collaboration, etc.
In this paper, we introduce the use of fuzzy logic in e-learning as a significant method
to create practical learner models and/or to design learning device parameters in order
to develop adaptive e-learning systems.
In order to help researchers to understand the artificial-intelligence-based learning
systems, this paper focuses only on fuzzy logic and describes its integration trends into
some adaptive educational systems. Furthermore, we aim to analyze the importance of
this integration to enhance the effectiveness of different adaptive e-learning solutions,
and to review up to date application developments of these solutions. To achieve this
objective, this paper is structured as follows: The second section draws on the concept
and the definition of smart learning as well as the adaptation in learning systems. The
third section presents the fuzzy logic by introducing Zadeh’s fuzzy theory, fuzzy IfThen rules as well as fuzzy inference systems. The next section discusses the fuzzybased adaptive learning systems by reviewing how the some previous related works
have integrated this logic to design adaptive e-learning systems. After all, we conclude
our paper and we present our future work and perspectives.
2 Adaptation in Smart Learning Environments
2.1
Smart Learning
Authors in [5], claimed that “there is no clear and unified definition of smart learning so
far”. However, and in spite of the difficulty of forming a definition of smart learning
that has a high usage in our daily life, many researches attempt to define it. In [6],
authors reported that smart learning environments not only employ digital technologies
in supporting learning, education as well as training but also provide a significant
design of the future learning environments. In fact, the word “smart” is now continuously used in the field of educational research in order to create new concepts, say,
Smart Education, Smart University, Smart Learning, Smart Classroom, and Smart
Learning Environment [7].
This drives us to mention the meaningful definition suggested by [8] who stated
that: “the essence of smarter education is to create intelligent environments by using
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smart technologies, so that smart pedagogies can be facilitated to provide personalized
learning services and enable learners to develop talents of wisdom that have better
value orientation, higher thinking quality, and stronger conduct ability.”
One of the important instances of smart learning is e-learning that utilizes computer, internet and network technologies as an integral part to facilitate learning anytime and anywhere. In this perspective, authors in [9] indicated that internet has
become a central core of the educative environment experienced by learners, and that
explains the quick adoption of e-learning all over the world. Furthermore, e-learning
becomes a prominent alternative to traditional learning paradigms and the predominant
method of delivering teaching materials to a learner supported by an instructor with the
aim of being evaluated [6, 10].
“E-Learning is an umbrella term that describes learning done at a computer, usually
connected to a network, giving us the opportunity to learn almost anytime and anywhere”
[11]. It is known that e-learning takes the advantages of computer networks to provide
leaning resources at/outside classrooms. It also operates internet technologies to insure the
storage as well as the information sharing. Therefore, e-learning can be considered as a rich
experience that generates new skills, understanding and knowledge for any teacher,
instructor, trainer, or manager hoping to offer a suitable learning for their learners.
On the other hand, and to be accurate, we have observed a fast transition in learning
methodologies. It has been started with d-learning (distanced learning), then the progress of Information and Communication Technologies has led to e-learning (electronic
learning) and recently, m-learning (mobile learning) has become the latest progress in
this area. M-learning is usually associated to the use of handheld devices, namely,
mobile phones, game consoles, laptops, and tablet computers in order to perform
training and teaching activities in a dynamic learning environment [12].
Mobile technologies are becoming more ubiquitous, pervasive and networked, with
important capabilities for context awareness and internet connectivity [13]. As a result,
learning has jumped from classrooms environment to real and virtual learning environments, and has become more personal, adapted and collaborative. Thus, the integration of mobile learning is seen, by the majority of researches, as an important
alternative to enhance learner’s interest and motivation [14].
Authors in [15] and [16] indicated that m-learning cross multiple contexts including
space and time borders, and involve social and effective interactions through using
personal electronic devices. These devices enable the mobile users to take the
advantage of virtual learning activities in order to improve their learning abilities
without being tethered to a specific location, say, university environment [17].
Other researchers [18, 19] pointed out that m-learning has the strength of ubiquity,
availability as well as technical functions such as geospatial technologies, social networking, sensors detection, multimedia tools, visual identification and audio recorder.
These features have promoted e-learning to m-learning as a wireless learning that
combines learning resources from the current real environment with the numerical one.
The above definitions of e-learning and m-learning have been frequently cited by
the majority of academic researches. This allow us to describe how ICT can change the
traditional paradigms of teaching and learning, and consequently to discover how to
use these technologies to transform learning from a limited academic activity into a
flexible and enjoyable daily experience.
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2.2
S. Ennouamani and Z. Mahani
Adaptation in Learning Systems
At the turn of the century, the considerable growth in the field of computer-based
learning including e-learning and m-learning have performed further concepts, viz.
adaptive/personalized learning systems, recommendation in learning systems, collaborative learning systems and so on. The main core of this progress is the integration of
artificial intelligence techniques to obtain the desirable solution regarding the users’
needs, objectives and motivations. In this paper, we pay a considerable attention to
adaptive and personalized learning systems only.
Researches in adaptive systems can be traced back to the early 1990s. At that period
of time, teachers started to make considerable efforts to evaluate the ability of their
students, including their knowledge level, skills, background, learning styles, interests,
etc. The principal objective was to take these characteristics into consideration in order
to provide courses in a way that fits every student’s needs. This adjustment can be done
inside classrooms with smaller number of participants [20] or for one-to-one teaching
[21]. However, and since the majority of traditional classrooms consist of a huge
number of students, teachers definitely can not consider each learner’s needs to afford
the tailored learning.
In order to solve the above problems, and since the ultimate goal of any smart
learning system is to maximize the student’s quality of learning, several researchers
have begun to study the different possibilities to adapt the educational systems and to
facilitate the learning process in an individual way. In this context, learner modeling
techniques that have been introduced in Intelligent Tutoring Systems (ITS) are also
integrated into the learning applications aiming to be adaptive and personalized [22].
As indicated in [23], adaptive e-learning systems have the power to provide students with personalized online learning materials and services. Adaptive educational
systems attempt to enhance student learning experiences by modeling the ideal learning
environment based on their specific needs [24]. Therefore, this kind of systems are
getting more attention due to their potential of making learning available, more flexible
and in a dynamic design.
Generally, the target of any adaptive learning system is to personalize the different
learning approaches in order to achieve the students’ satisfaction, and to facilitate the
learning process [25]. In other words, learners must be modelled in a way to assimilate
not only their needs but also their individual characteristics that can be classified in
different categories. Thus, and through this kind of software, many connections and
links are created between learner characteristics and available learning resources.
As mentioned in [10], the development of any adaptive learning system involves
three components that dynamically interact with each other, namely, learner model,
domain model and adaptation model (Fig. 1). The learner model is the core of these
systems that provides a structured presentation of the learner characteristics [26]. The
domain model is a structured design of the existing knowledge based on an abstract
representation [27]. Finally, the adaptation model is the bridge between the learner and
domain models by combining the learners’ needs and characteristics with the relevant
learning materials [28].
Towards Adaptive Learning Systems Based on Fuzzy-Logic
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Fig. 1. Adaptive e-learning system components [10]
According to [22] and [29], an adaptive system must be able to manage a structured
learning navigation in order to generate suitable learning resources, to control each
learner activity, to estimate learner’s behavior and interactions, to deduce user’s needs
and preferences and to create better links between learner model and domain model. In
fact, every adaptive e-learning or m-learning system has a specific target, namely,
adapting learning path, contents, support/instruction, and presentation. The target can
not be reached without having a basic source of adaptation that can be classified as
learner characteristics and learner interactions. A predetermined pathway that can be
followed to achieve that target is also required in such educational systems (Fig. 2).
These three elements are considered to determine what information about the learner
should be collected, and how it will be used in order to provide the desired adaptation
[30, 31].
In addition, m-learning systems can be considered more adaptive than e-learning
systems. This is due to the available sensor detectors in mobile devices that allow to
provide more information about the learner’s context. In other words, mobile sensors
can determine the learner’s motions via the accelerometer, the distance between the
learner and the handled device using the proximity sensor, the environmental noise
through the microphone, etc. Consequently, the user’s context can be predicted with
more accuracy allowing the system to build a conception about the appropriate presentation of the learning content.
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S. Ennouamani and Z. Mahani
Fig. 2. The tripartite structure of adaptive learning systems [38]
3 Fuzzy Logic Theory and Fuzzy Inference Systems (FIS)
3.1
Fuzzy Logic Theory
Obviously, our brains have the capacity to deal with the facts which have a lack of
exactness. Thus, we can answer a question about how much a car is near to another,
using the expressions which are open to more than one interpretation, for example,
very near, not very near, far away, quite away, etc. In contrast, machines in general and
computers in particular can not give such precision, because it is based on binary set
(0,1) membership. Therefore, and based on this issue, fuzzy logic was introduced by
Zadeh [32] as a further theory to the classical set theory. The fuzzy set theory was
developed to generalize the classical crisp sets, and to answer such uncertain questions
using the concept of partial membership.
In literature, it has been proved that the fuzzy set theory offers a considerable rang
of methods to clarify the ambiguous and uncertain situations. It is a convenient solution
to manage the precision of information as well as the fuzziness of different states [33].
Fuzzy sets deal with vague terminologies, viz. young, rich, tall, and others. It represents
a transition from the rigid membership of a class of objects inside the crisp set (1 if it
belongs to the set, and 0 otherwise), to the fuzzy set that gives the opportunity to have a
normalized partial grade of membership (a value between 0 and 1) [34]. Figure 3
represents an example of the degree of membership of three fuzzy sets related to the
determination of the age.
Towards Adaptive Learning Systems Based on Fuzzy-Logic
631
Fig. 3. Hierarchical structure of a linguistic variable [39]
Zadeh introduced fuzzy set theory to solve our daily problems of uncertainty
caused by incomplete data as well as human subjectivity [35]. It can be considered as
the most compatible method with the human-being decision-making process [36],
using linguistic variables as well as the degrees of membership which symbolize the
grade of stability of an item inside a fuzzy set [37]. A fuzzy set is defined as an ordered
set (x, lA(x)), where x belongs to X and lA ðxÞ belongs to [0, 1], equipped with a
membership function lA ðxÞ : X ! [0, 1], where:
lA ðxÞ ¼
3.2
8
>
<
>
:
1
½0; 1
0
; x belongs to A
; x is partially in A
ð1Þ
; x does not belongs to A
Fuzzy If-Then Rules and Fuzzy Inference Systems (FIS)
– Fuzzy If-Then rules:
Zadeh’s theorem has become more useful when Mamdani [40] employed it in a
practical application to control an automatic steam engine. Mamdani’s method is a
relational model known as a linguistic method because the premise and the outcome are
computed with words. It is a manually developed method that uses defined rules in
order to generate the adequate results via a fuzzy membership function [41].
Mamdani’s fuzzy If-Then rules are the expressions of the form “IF A THEN B”,
where A and B are labels of fuzzy sets [32] associated with convenient membership
functions. Considering the flowing example:
If the speed is high; then apply the brake a little
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S. Ennouamani and Z. Mahani
In this example, “speed” is a linguistic variable, and “brake” is also a linguistic term
characterized by a membership function. This example represents a reasoning situation
in an environment of imprecision guided by the human ability of making decisions.
On the other hand, Takagi and Sugeno [42] introduced another form of if-then rules
that follow a data driven model. In contrast to Mamdani’s rules, this method is based on
historical data computed with weighted average [41]. Another example suggested by
[43] describes the resistant force on a moving object:
If velocity is high; then force ¼ k ðvelocityÞ2
Where, “high” is a linguistic proposition described by a membership function. After then,
the inferred conclusion is described by a non-fuzzy equation of the input (velocity).
The above types of fuzzy if-then rules have been employed in a myriad number of
application domains. They represent an essential block in the Fuzzy Inference Systems
(FISs) that provides a description of the system’s workflow. FIS are explained in the
following paragraphs.
There are many application domains of fuzzy logic and fuzzy sets, namely, Fuzzy
Inference Systems(FIS), Fuzzy Decision Trees (FDT) and so on. In this paper, we focus
on FIS because it has been integrated in the majority of adaptive computer-based
educational systems that we reviewed in this paper (Sect. 4).
– Fuzzy Inference Systems (FIS):
FIS or fuzzy-rule-based systems introduced by Mamdani consist of four blocks,
namely, Fuzzifier, Fuzzy Rule Base, Inference Engine, and Defuzzifier (Fig. 4). These
blocks are also known as the basic stages of any FIS, described as follows:
Fig. 4. The Mamdani’s fuzzy inference system
• Fuzzifier: Based on the crisp sets of input data, the Fuzzifier converts this input to
fuzzy sets using membership functions as well as fuzzy linguistic variables. In this
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block, the performed process is known as Fuzzification (crisp to fuzzy) [41]. Linear
triangular and linear trapezoid membership functions are frequently used in FIS
[44].
• Fuzzy Rule Base: It represents the key block in any FIS by containing a number of
fuzzy if-then rules (Mamdani and/or Sugeno types). As claimed by [37] and [41],
these rules are determined through training and/or historical data, or defined by
experts. Each fuzzy if-then rule can be considered as a native description of the
scheduled system that applies the membership values for prospective inferences.
• Inference Engine: It applies the fuzzy rules on the fuzzy input generated by the
Fuzzifier in order to establish the desired results. This engine represents the kernel
of decision making process [45] which is designed to dynamically integrate the
different system resources in a way to run a series of fuzzy-based outputs.
• Defuzzifier: This block is responsible of transforming the fuzzy output generated by
the inference engine into a crisp output. It allows to map each fuzzy set with one
crisp set in order to create a transition from linguistic values to numerical values
using, again, the membership functions. This process is called Defuzzification, it
consists of performing the inverse transformation (fuzzy to crisp) and it has the most
computational complexity [37]. Different Defuzzification methods and approaches
have been introduced in the literature, namely, the center of area method, bisector of
area method, mean of maximum method, smallest of maximum method, and the
largest of maximum method [46].
As concluded by [43], a fuzzy inference system that involves fuzzy if-then rules can
model the qualitative human knowledge aspects and reasoning processes, without using
accurate quantitative values as well as numerical sources and analyses.
4 Fuzzy-Based Adaptive Learning Systems: Related Works
Due to its popularity, fuzzy logic has become successfully applied in a variety of
complicated systems and applications that are involving a lack of exactness [47, 48]. In
recent years, fuzzy logic has been highly employed in computer-based learning in order
to cope with imprecision as well as the variation of the users’ characteristics, device
features, and domain knowledge structure. Fuzzy logic does not need a lot of data to be
executed, it has predictable processes [49], and these are the main reasons why it has
been and still used for developing e-learning systems. Fuzzy logic has been used for
achieving various goals in computer-based educational systems, namely, for creating
suitable assessment tests [50], predicting the learning styles in web environments [51],
evaluating a web based LMS [52], diagnosing students’ cognitive profiles and comprehension [53], creating a system that delivers personalized tips to students [54], and
so on. In this paper, we focus only on the use of fuzzy logic in adaptive learning
systems through presenting and studying a list of related works. Moreover, we present
the different techniques used in this area as well as how FIS is employed to create such
kind of learning systems.
In [45], the authors introduced and designed an online educational module based on
fuzzy set theory. They aim, through this proposal, to generate adaptive learning
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activities in a virtual environment. The basic motivation of this solution is that a learner
model is known by the uncertainty of knowledge acquisitions as well as decision
making process. Thus, fuzzy logic has been used to model learner profiles in order to
present the suitable learning activity in an individual way. This system starts with a
collection of all data that establish a learner profile as a crisp input for the FIS. After
that, the fuzzification step transforms the crisp input to a fuzzy input using the membership functions. Half trapezoidal, two triangular and half right trapezoidal functions
are used in this model. Always regarding the FIS stages, the inference engine starts
working in the third stage by the use of rules base related to the learner’s age, gender,
level of knowledge, level of difficulty of the learning activity as well as the learning
session duration. The inference engine uses if-then rules that allow to deduce the
appropriate learning activity. At the end, the defuzzification is performed via the
extraction of the suitable learning activity from the related data base, based on the value
given by the inference engine.
A personalized system for learning English proposed by [55] is based on learner
profiles and aims to help students improve their English language competency through
an extensive reading environment. The proposal uses learner preferences, fuzzy
inference process, memory cycle updates, and analytic hierarchy process in order to
provide learners with the most suitable English articles. The techniques of fuzzy
inferences and personal memory cycle updates allow the system under consideration to
extract the appropriate articles for the targeted learner’s ability as well as the desired
vocabulary. The authors employed fuzzy inference mechanism respecting the four
blocks described in the previous section, including the Input, the Fuzzifier, the Inference Engine and the Defuzzifier in order to determine the suitable articles from the
available database for every single learner. In accordance with this objective, the
considerable characteristics in this proposal are related to the article’s content and the
learner’s language ability, namely, the Average Difficulty of Vocabulary (ADV), the
Average Length of Sentence (ALS), the Total Length of Article (TLA), the Average
Ability of Vocabulary of the learner (AAV), and the Article Correlation (AC). These
characteristics represent the input of the FIS that are used in the following stage of
Fuzzification in order to calculate the degree of membership using a trapezoidal type
for each linguistic variable (low, medium, high). The inference step in this algorithm is
fulfilled via 243 rules based on combining three linguistic terms and five fuzzy input
items. In the final step, the Defuzzification involves the discrete center of area method
in order to generate an output that represents how appropriate an article is for a
determined learner based on a value between 0 and 1. The final decision is taken based
on the maximum value that describes the most convenient article for a learner. This
proposal represents a significant illustration of the integration of fuzzy logic in an
adaptive e-learning system.
Another work was suggested by [56] aims to use fuzzy sets to represent the learner’s knowledge level as a subset of the domain knowledge. The proposed approach is
implemented and evaluated in a Mental Architecture Digitized model which is based on
Fuzzy Cognitive Maps (FCM) in order to represent the dependence among the domain
concepts. The use of fuzzy logic in this proposal performs the user modeling through
the identification and the update of each learner’s knowledge level related to the
concepts of the domain knowledge under consideration. Consequently, the system
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provides users with the significant adaptive tips through an automatic modeling of the
learning or forgetting process of each learner regarding the FCMs.
Fuzzy logic was also used to model the student profile presented in [57] for personalizing online educational systems. In order to meet with this objective, the authors
have developed a fuzzy epistemic logic to present the learner’s knowledge state
through a multi-agent based student profiling system. This system stores the learning
activities and interaction history of each learner into the student profile database, and
then the profiling data is abstracted into a learner model. Afterward, dynamic learning
plans for each learner are established based on the student model and content model.
Therefore, every single learner gets personalized learning materials, personalized quiz,
and personalized advice. This fuzzy-based educational system have proven that the
personalization has the strength of increasing learning motivation, and consequently
enhancing learning effectiveness.
In [58], a personalized competence-based instructional system called “InterMediActor” has been introduced. The system tends to generate an individualized
navigation path for each learner based on the graph of dependencies between competences and student model. This proposal adopts fuzzy logic to deal with uncertainty
of the student’s assessment, including marks as well as prerequisite knowledge. Like all
fuzzy-logic-based systems, the first step of FIS is the Fuzzification where the system
transforms the student’s mark value (crisp value between 0 and 1) into a linguistic
value (negative, positive, or no mark) using the membership functions. Concerning
prerequisite knowledge level, it has five linguistic terms: not, little, enough, well, and
very well. The estimation of this characteristic is based on the marks obtained during
the final-assessment tests. The Fuzzification of the level of difficulty is calculated for
each competence of the course, and it is unchangeable during the session. Three
membership functions are used to describe the level of difficulty, namely, easy, normal,
and difficult. After Fuzzification, fuzzy if-then rules of the Inference Engine are applied
for describing relations between the level of difficulty of the competence, the marks
obtained in the final-assessment test for the competence, and the estimated prerequisites
of this competence. At the end and regarding FIS stages, the linguistic values of the
level of recommendation are defuzzified in order to be transformed into crisp values
(step 4). To be accurate, more recommended, recommended, less recommended, and
forbidden become 4, 3, 2, 1, and 0, respectively.
A fuzzy-based framework for learning path personalization was suggested by [59].
It takes into consideration the most preferred learning material and the least preferred
learning material. This by using the learning characteristics and pedagogical models
that are based on the learner personality factors determined through Myers-Briggs Type
Indicator (MBTI). Since Fuzzy logic is most suitable for working on imprecise input
data and for supporting natural description of knowledge, fuzzy logic techniques are
used in this proposal for enabling the system to classify learning material structure, and
then to adapt the selection of possible learning which are suitable for the student’s
fuzzy learning styles membership. For this purpose, the algorithm under consideration
runs in a way to combine and mix two approaches, namely, learning style approach and
fuzzy logic theory. It consists of three divisions; learning style (MBTI) approach, fuzzy
logic approach and dynamic course adaptation. The four stages of FIS are respected,
starting by Fuzzification stage that transforms the crisp learner’s personality
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characteristics into the appropriate linguistic values. On the one hand, the input linguistic variables are the learner’s personality scores: extrovert, introvert, sensing and
intuitive. On the other hand, the fuzzy outputs illustrate the learner’s acceptance level
of the learning material: theory, example, exercise and activities. Afterward, the
inference system based on 76 rules (if-then statements) and various inputs combined
with AND operator, generate multiple consequents. At the end, every student receives
the appropriate structure of learning materials which match his/her learning style and
personality.
The authors in [60] present the design and implementation of a fuzzy-logic expert
system for adaptation in e-learning. This model aims to introduce an adaptive system
for learning English as a second language, based on the student’s knowledge and
characteristics. The proposed approach follows the process of detecting the learner’s
sensory preferences in order to decide the appropriate learning content form. This is by
the use of an expert system which detects the learner’s knowledge level, helps to decide
the needed part of courses, adapts and presents the tailored learning materials based on
the previous characteristics. Regarding FIS, the Fuzzification of the student’s knowledge, assessment tests as well as his/her preferences is always followed by the performance of the Inference Engine. At this level, if-then rules determined by a language
education expert are employed to deal with the input related to the number of correct
answers within the category (V1), weight of correct answers within the category (V2),
importance of the category for further studies (V3), and time spent over answers of the
category (V4). The output linguistic variable from the fuzzy expert system represents
the need of further studies of the given category (V5). At the end, appropriate learning
materials are selected for each category through an algorithm that calculates its
importance (ISM). As a result, based on the length of the study time, the study variant
is adapted and a reduced one is generated when the study time is less. It includes only
the necessary learning materials.
In this section, we presented how fuzzy-logic can be integrated and involved in
adaptive learning systems. The related works described above follow the steps of
creating a Fuzzy Inference System in order to generate adaptive and personalized elearning platforms. This is by operating Fuzzification, Inference Engine based on rule
base, and Defuzzification. These stages are the core of the adaptation process in any
fuzzy-based adaptive learning system.
5 Conclusion and Future Work
The adaptation of learning has a positive effect on the learning process, leading to an
increased efficiency, effectiveness as well as learner satisfaction [61]. In this context,
the integration of pedagogical approaches as well as artificial intelligence methodologies becomes necessary to meet with the learner’s need and to deliver a more
interactive learning environment. For this purposes, we presented in this paper one of
the artificial intelligence methods called fuzzy logic. As claimed Mamdani, fuzzy logic
allows to “compute with words” which cannot be accomplished using other methods,
and it has the ability to represent human conceptualizations in a realistic way.
Towards Adaptive Learning Systems Based on Fuzzy-Logic
637
Therefore, fuzzy logic can contribute to create an big number of useful solutions in the
modelling of intelligent systems [57] including computer-based educational systems.
In this perspective, we studied the integration of fuzzy logic in adaptive learning
systems by reviewing some previous research in this field. We can deduce that fuzzy
logic is one of the significant ways to improve the system performance and to make it
able to generate serious decisions about the adaptation of learning materials that must
be delivered. This is because of the learner characteristics’ fuzziness that can be
handled with such method of resolving uncertainty problems. Consequently, this
integration increases the learner’s performance, comprehension, skills as well as his/her
satisfaction. In addition to this, authors in [62] share the same opinion by indicating
that an algorithm based on fuzzy logic helps to select the ideal model based on a set of
inputs and model specifications.
The main motivation of our next research is to develop a new fuzzy-based adaptive
learning system for higher education. We aim to incorporate the fuzzy logic in our
previous model of m-learning systems [29] in order to deal with the uncertainty of the
learner characteristics as well as context parameters. This will enable the adaptation
engine to run in a dynamic way allowing the system to be more effective. It will also
enhance the learning content adaptation as well as the format adaptation which is more
and more required especially in mobile environments. Therefore, the learning experiences of every single student in higher education will be more individual in the
transmission of the maximum crucial knowledge.
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