=Paper=
{{Paper
|id=Vol-2589/Paper1
|storemode=property
|title=Adaptive and Personalized e/m-Learning : Approaches and Techniques
|pdfUrl=https://ceur-ws.org/Vol-2589/Paper1.pdf
|volume=Vol-2589
|authors=Ouissem Benmesbah,Lamia Mahnane,Mohamed Hafidi
|dblpUrl=https://dblp.org/rec/conf/citsc/BenmesbahLH19
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==Adaptive and Personalized e/m-Learning : Approaches and Techniques==
https://ceur-ws.org/Vol-2589/Paper1.pdf
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)
Adaptive and Personalized e/m-Learning :
Approaches and Techniques
Ouissem Benmesbah Mahnane Lamia Mohamed Hafidi
LRS Laboratory LRS Laboratory LRS Laboratory
Badji Mokhtar University Badji Mokhtar University Badji Mokhtar University
Annaba-Algeria Annaba-Algeria Annaba-Algeria
ouissa2007@yahoo.fr mahnane_lamia@yahoo.fr mhafidi@yahoo.fr
Abstract— Adaptive and personalized approaches within learning path. Finally, Section 4 presents the conclusion of
e/m learning systems enable adapting learning Objects (LOs) the work.
and process to the different needs and contexts, to help the
learners in improving their knowledge or skills. In this paper, II. OVERVIEW OF THE LEARNING ADAPTATION
we review the recent research on learning adaptation to pursue CLASSIFICATION
two goals: First is to unify the classification of adaptation
The first research question can be answered by presenting
types; the second is to study the different approaches and
a general classification of adaptation types used in the field
techniques used to implement the learning adaptation in its two
main types : adaptation of the LOs selection and adaptation of of e/m-learning. Several researchers have addressed the
the LOs sequencing. adaptation type classification applied in the field of adaptive
learning, but these classifications are slightly different. In
Keywords—Adaptive e/m Learning, Learning path this section we will study them and propose a new
adaptation, Adaptation by selection classification.
I. INTRODUCTION Sampson [2] identifies three main categories of
adaptation related to educational resources within adaptive
With the rapid development of information technology in and personalized learning systems: -Selection Adaptation:
education and learning field, researchers have created myriad This type of adaptation deals with selecting appropriate
learning resources. It has been a difficult task for learners to learning objects LO based on different selection criteria
find suitable learning resources from the Internet. Without derived from learners’ contextual elements. -Presentation
effective adaptation, irrelevant resources will lead to Adaptation : considers that LOs is adaptively structured for
learners’ cognitive overload and affect learning outcomes. access via mobile devices by taking into account parameters
Therefore, learning systems need to be adapted to the related to the learners’ type of mobile device, the learner’s
learners' context and needs. profile (including learner’s preferences and learning style),
An adaptive learning environment provides personalized Parameters related with learner’s location, physical
learning resources and processes to the learner through self- conditions and learner’s temporal information. -Sequencing
directed study. An adaptive learning model can be Adaptation : This type of adaptation rearranges or reorders
subdivided basically into a learner model, domain model, the navigation and sequencing possibilities of different LOs
and adaptive engine. In such environment, the adaptive e/m that are linked to each other towards creating personalized
learning system should adapt its services to a learner’s needs learning paths by taking into account different criteria
and context. The purpose of adaptation is to optimize the derived from learners’ contextual elements (previous
relationship between the learner context and learning knowledge, availability and current location, Time,…).
content; hence, the learning outcome could be obtained with Premlatha defines two adaptation types: adaptive
minimum time and interaction and could also increase the presentation at the content level and adaptive navigation
learner satisfaction [1]. support at the link level [1].
Even though academic research on adaptive learning El jenati [3] adopts in his work three types of adaptation :
environments has increased, the field lacks a comprehensive -Adaptive content: The adaptation of the content is based on
literature analysis of the classification of the adaptation the selection of the adequate pedagogical content which take
types, and the most used approaches and algorithms used to into account the learner’s context. Some learners may wish
implement every type of adaptation. to get a simple version of the content and others may wish to
This paper presents a study of learning adaptation in e/m get a detailed version. - Adaptive navigation: The Adaptive
learning systems from 2008 up to 2019. It aims to specify: navigation allows to learners to find their paths by adapting
the presentation of links to the objectives, knowledge and the
Rq1- What should be adapted? preferences of the learner. -Adaptive presentation: The
Rq2- How it should be adapted? adaptive presentation is to adapt the visual presentation to the
preferences and needs of the learner. Some learner can easily
The rest of this paper is structured as follows: Section 2 read the presented music score and will know how it sounds,
describes the main types of learning adaptation. Section 3 but others will want an audio version.
collects the most used approaches and algorithms used to
implement the adaptation of the learning content and the The work presented in [4] focused on various e-learning
problems, from these problems, we can extract a set of
adaptation types: -Learning path generation (LPG): focused called a context model, this model must integrate information
on providing a sequence of learning object materials to the such as the location of the learner, time, the environmental
learners -Object recommendation (OR): Allows an adaptive characteristics (noise level, lighting level), device
selection of LOs and -Personalization of content (POC): characteristics…etc [5-7-8-10-11-12-15] .
specifies what learning objects are needed for the course
established for a specific learner requiring a specific subject. Arriving to the second research question:“How the
learning content should be adapted’. The answer to this
Based on the works presented above, we can define two question consists to find the most applied approaches and
main classes of adaptation: algorithms in the learning adaptation field.
Adaptation related to the content: It can be divided into As can be seen from Table 1, the majority of the works
two sub categories: depends on Ontologies enriched with Semantic Web Rule
Language (SWRL). Ontologies as a key and important
- Adaptation of the content selection: Proposes a set of component of semantic web technologies are used to
LOs adapted to the learner’s needs and contexts. represent knowledge about e-learning domain. SWRL is a
-Adaptation of the content presentation: Different strong mechanism for inferring new relations and knowledge
presentation forms of educational resources include [2]: which cannot be reached using ontologies [5-6-3-7-8-9-10-
Changing the format for the same type of educational 11].
resource (e.g. wav files to mp3 files), Changing the type of Another category of works is based on ontology
the educational resource (e.g. -Changing the dimensions of modeling of the context and the Leaning domain but it is not
the educational resource (e.g. scaling down or scaling the based on rules but rather on Algorithms [12-13-14-15]:
dimensions of the educational resource).
Erazo-Garzón in [12] used semantic search (keywords)
Adaptation related to the learning path: It aims to find the and route algorithms applied to ontological models due to
learners’ paths while learning in e-learning system by their expressiveness and extensible architecture, to determine
adapting the learner preferences, learning styles, and with precision the concepts and semantic relations that exist
other characteristics of an individual user. among academic and contextual information. In [13] and
In this work, we have excluded the adaptation of the [14], Semantic learning objects search is proposed, it is based
Content presentation, that’s because we are convinced that on the query expansion of the user query and uses the
the adaptation of the format and the scale can be avoided and semantic similarity to retrieve semantic matched learning
replaced by a selection adaptation which will include the objects. In [15], a novel context-aware mobile learning
Content selection with the most appropriate format and scale application is proposed to encourage and promote Hadith
according to the learner's learning style and / or learning, three dimensions (location, time and profile) of user
characteristics of his mobile while being based on the density context are implemented, context-filtering based regular
and high availability of resources pedagogical models in the expression and ontology matching-recommending techniques
LO Repositories, which store several format of the same are used to match appropriate hadith on learner’s context.
content. So in our study, we will focus only on two major The third category of works applied Evolutionary
types of adaptation that are: adaptation by selection and computing algorithms (EC) to implement the adaptation by
adaptation of the learning path. selection:
In the following sections, we will move to the second Latha in [16] presents an evolutionary approach for
research question which is "How pedagogical content should tuning the parameters required for personalizing the learning
be adapted? And this, by studying the different approaches, content delivery. The compatibility level of the LO are tuned
techniques or algorithms used to achieve each type of with respect to the learning style of the learner; the
adaptation. complexity levels of the learner are tuned based on the
III. OVERVIEW OF THE LEARNING ADAPTATION APPROCHES feedbacks from similar learners and the knowledge levels of
the learners are tuned with respect to the complexity level of
AND ALGORITHMS
the learning objects. The interactivity levels of the learners
A. Adaptation of LOs Selection: are tuned based on the behavior of the learners during the
Table 1 presents a collection of works that propose an learning process. For that purpose Compatible Genetic
adaptation by selection, these works can be divided into two Algorithm (CGA) is applied.
categories: the first implements only the adaptation by Yang [17] in his work, proposed an Attributes-based Ant
selection, which consists of selecting LOs adapted to the Colony Optimisation System (AACS) to help learners find
needs and/or context of the user. The second category aims at an adaptive learning object more effectively by considering
performing adaptation by selection as the first phase the relationship between learner attributes (e.g. learning
followed by a second phase which is the adaptation of the style, domain knowledge) and LO’s attributes. For that
LO s sequencing. In this second case, we are limited to study AACS algorithm is proposed, it is derived from an extension
the technique or the algorithm used in the first phase of the of the Ant colony system that updates the trails’ pheromones
adaptation. from different knowledge levels and different styles of a
Among 16 works collected in Table 1, 9 works perform group’s learners to create a powerful and dynamic learning
the adaptation by selection based on the learner model [6-3- object search mechanism.
9-13-14-16-17-18-19]. This model includes information such In Dwivedi [18], the author develops learning path
as learner profile, learning needs or objective, learning style, recommendation framework by employing course
knowledge level, ..., this means that these works have not generator’s advice and evolutionary approach namely a
integrated the notion of the context which constitutes what is
variable length genetic algorithm (VLGA) after generating [14] Semantic Ontology - Semantic Learning style,
learner profiles through registration process. Algorithm search (Query expension teaching methods,
learning activities
Discrete Particle Swarm Optimization (PSO) was [15] Semantic Ontology -context- Location-Time-
employed by Wang and Tsai [19] to choose the material Algorithm filtering based regular Profile
suitable for a review course based on the material relevance expression + Ontology
degree, difficulty level and the number of available learning matching
resources. [16] Evolutionar GA - Compatible Learning style
y Algorithm Genetic Algorithm Knowledge level
Based on the related works analysis presented above, we (CGA) Feedback
notice that the adaptation algorithms used to select a set of [17] Evolutionar ACO - attribute-based Learning style-
LOs adapted to the profile/Context of the learner can be y Algorithm ant colony system Knowledge level
classified into two main categories (see Fig. 1): (AACS)
[18] Evolutionar GA - Variable lentgh Learning Style –
Rule-based techniques: y Algorithm genetic algo Knowledge level –
Goal
Ontology combined with SWRL adaptation rules is the
[19] Evolutionar PSO for selecting LO Difficultly level,
most used Rule-based technique. Ontology helps in y Algorithm relevance of material
improving adaptive learning by providing a suitable
vocabulary for learners to describe the course materials and
represent the learner’s belief, expectation and context being
used for the recommendation of LOs which are dependent on
the domain ontology [4].
Algorithms-based techniques :
- Semantic Algorithms: based on Ontology modeling
and reasoning based on different types of algorithms
- Evolutionary computing Algorithms that includes
genetic algorithm (GA), Swarm optimization techniques
(PSO and ACO techniques).
Fig. 1. Approaches and techniques to implement Adaptation by Selection
TABLE I. ALGORITHMS USED TO IMPLEMENT THE ADAPTATION BY
LOS SELECTION
B. Adaptation of LOs sequencing (Learning path
Work Approach Technique Learner’s context
dimensions adaptation):
[05] Rule-based Ontology + SWRL Rules Network and Battery Curriculum sequencing, learning path adaptation,
level, learner profile adaptive learning path generation and adaptive learning
(Age,Knowledge
level) schema generation, designate all the same purpose, which is
the personalization and the adaptation of the learning
[06] Rule-based Ontology + SWRL Rules Learning Style – material sequence called learning path. Providing an optimal
Knowledge level-
learning path tailoring to the context of the learners is a
[03] Rule-based Ontology + SWRL Rules Learner profile crucial issue in online learning adaptation. An optimal
Learning Style – learning path could reduce the student’s cognitive overload
Knowledge level-
Language and disorientation; consequently, this process would improve
preferences) the learner’s learning outcome and efficiency of the
adaptation in the online learning systems [5].
[07] Rule-based Ontology + SWRL Rules Location, time
[08] Rule-based Ontology + SWRL Rules Location time This section presents a survey on learning path adaptation
kewords knowledge efforts in the m/e learning environment from 2008 up to
level device 2019. The survey highlights two points:
[09] Rule-based Ontology + SWRL + LO Pre-requisite- The different approaches to formulate the learning
weighting Algo) Knowledge level-
LO history- Social path problem.
relation
The algorithms applied to solve the learning path
[10] Rule-based Ontology + SWRL Rules Learning Style- adaptation problem.
Location-
TimeTechnology- 1) Approaches (Problem formulation):
objective- mobility
The main objective in learning path adaptation is to
[11] Rule-based Ontology + SWRL rules Location, time,
+ Greedy algo device, profile minimize the path or route of individual learning. According
to [18], to formulate the issue of learning path adaptation,
[12] Semantic Ontology – Route Context model various approaches have emerged. Among these approaches:
Algorithm algo+Semantic search (Location, time)
(Keywords) Constraints Satisfaction Problem (CSP): It’s a single
[13] Semantic Ontology - Semantic LO Learner profile objective with several constraints. The problem in CSP
Algorithm search (Query expansion is defined as the state of the variable definition. The
and Semantic similarity solution space of the problem comprises all possible
Algo)
sequences and the objective function is to minimize or
maximized the penalty function designed to evaluate evolutionary Optimizer for optimal search, is used for
the sequencing [5-20]. Many works in the literature mathematical optimization problems in the field of Artificial
proposed evolutionary algorithm, such as Genetic Intelligence. Given a large set of inputs and a good heuristic
Algorithm (GA) [28-29], Particle Swarm Optimization function, it tries to find a sufficiently good solution to the
(PSO) [34] or Ant Colony Optimization (ACO) [32] problem. This solution may not be the global optimal
and scheduling and planning Problem [38]. maximum.
Multi-Objective Optimization Problem (MOOP): The According to [4], Meta-heuristic algorithms like
concern in multi-objective optimization problem is to Evolutionary computation approaches (EC), have great
satisfy multiple objectives simultaneously [5]. In impact in the solution of the learning path adaptation
MOOP approach several algorithms are used, among problem by providing appropriate learning paths to learners.
them GA[25-27], PSO[35], Heuristic algorithms [25- Genetic algorithms [25-27-28-29], Ant colony algorithms
26] and Planning and Scheduling Technique [39]. [30-31-32-33] and Particle swarm optimization [34-35] are
widely used techniques in the construction of learning path
Domain modeling: This approach is implanted Through: sequence.
directed graph, concept map and ontology [3-9-21-22-
11], however, there has been no formal model for Machine learning techniques are widely present in the
discussing learning path problems based on Domain learning path adaptation field; Through the use of
modeling []. Reinforcement learning (RL) and Bayesian Network (BN).
RL is an area of Machine Learning. It is about taking suitable
2) Techniques: action to maximize reward in a particular situation. It is
Once the learning path model has been formulated, the employed to find the best possible behavior or path it should
methods to build the approach are chosen according to the take in a specific situation. In the field of adaptive learning,
problem [5]. Table 2 presents the most used Algorithms in RL is used in [36]. The proposed approach consists of the
the field of learning path adaptation. following steps. Firstly, the learner’s state is determined.
Secondly, a learning material or path is suggested through a
Reasoning based On ontology is present in several works set of actions. Thirdly, based on RL, the learner state is
[3-9-21-22-11]. In such approach, reasoning techniques are updated, in addition, the rewards received by recommended
usually applied on metadata derived from an ontology model learning paths or materials are updated.
[8]. The reasoning is performed in terms of SWRL (Semantic
Web Rule Language) rules that are applied on knowledge Bayesian Network BN (also known as Bayesian
represented in the OWL-DL (Description Logics) ontology. probability theory) is also used for finding the adaptive
Problem of concern with this approach is their learning path [37]. BN is a directed graph whose nodes
inappropriateness to reasoning with uncertainty. It should be represent the uncertain variables of interest and edges are
noted that some of the context elements are quantized with influential links between the variables. Node probability
uncertainty leading to certain ambiguity while defining and table contains conditional probability (CP) values which are
reasoning with context, [8]. This problem can be dealt with assigned on the basis of the level of expertise, learning style
by integrating various reasoning models that may combine and learning pace of the learner. In the second step, BN is
probabilistic, Fuzzy reasoning techniques [8]. For that, we constructed to calculate CP value for each knowledge unit in
highlight in this survey other works, which propose a hybrid the learning path. Finally, the shortest path is selected to
solution by combining Ontology-based reasoning with: provide appropriate learning path for the learner [4].
Fuzzy logic [9] and with a Greedy algo [11]. Planning& scheduling techniques [38-39-40], as an
Case based reasoning technique CBR as another Artificial intelligence (AI) techniques, are also proposed to
reasoning technique [23-24] is also used to implement the generate sequences of e-learning routes which are tailored to
learning path adaptation problem. CBR is the process of the students’ profiles.
solving new problems based on the solutions of similar past Employing Data mining (DM) in intelligent learning
problems. CBR has both the capacity to represent knowledge systems has become a trend in developing learning systems,
and to reason about it. However, CBR suffers from the which makes educational data mining the focus of a new and
inexistence of genericity in knowledge representation; growing research community. Such a technique has the
specific requirements for CBR are usually processed as they following strengths: (1) it reduces the constraints on the scale
come. There are also some limitations like as knowledge of the database quality and the variable types; (2) it can
acquisition problems for unavailable or limited cases, analyze both a continuous variable and discontinuous
Inference efficiency is not always good as desired, straight variables efficiently; and (3) its results with graphical or rule
forward provision of explanation is missing [5]. expressions can be understood easily and can be explained.
Since Learning path problem is NP-Hard problem, Lin in [41] suggested that learning materials based on the
heuristic and meta-heuristics are usually used to approximate tree mechanism can meet individual requirements and can
its solutions. Heuristic search optimization algorithms are enhance learning efficiency in a learning environment.
used to implement a solution for the learning path adaptation; Other algorithms derived from the graph theory approach
among these algorithms we have Greedy algorithm and Hill are used for the same purpose, among these algorithms we
Clumbing algorithm. Greedy algorithm [25, 20, 11] is an can mention: the first-search depth (DFS) [42], binary integer
algorithmic paradigm that builds up a solution piece by programming and Adaptive Shortest path algorithm [44]
piece, always choosing the next piece that offers the most (See Fig. 2).
obvious and immediate benefit. So the problem where
choosing locally optimal also leads to global solution are best
fit for Greedy. Hill Climbing algo [26], which is another
[27] Evolutionary GA Context model (Learner
Computing motivation)
[25] Evolutionary GA Knolwledge level,
Computing
[29] Evolutionary GA Knowledge level,
Computing Concept, learning
objective, Time
[36] Machine RL Context model
learning (environmental context,
social, cognitive,
Feedback)
[37] Machine BN preferences
learning
[38] Planning & Case based Learner model
Scheduling planning
Fig. 2. Approaches and techniques to implement Learning Path
Adaptation [39] Planning & Planning Learner model
Scheduling technique +
ontology +
TABLE II. ALGORITHMS USED TO IMPLEMENT THE LEARNING PATH
ADAPTATION
Intelligent
agents (Hybrid
Work Approach Technique Learner’s context technique)
dimensions
[40] Planning & AI Planning and Knowledge level,
[3] Ontology Ontology + Learner context Scheduling scheduling Metadata LO, learning
SWRL Rules technique style, learning objective
[9] Ontology Ontology + Location time kewords Time
SWRL Rules+ knowledge level device
[44] Graph based Adaptive Learning objective,
Fuzzy Logic Shortest path Learning background,
[21] Ontology Ontology + Knowledge level- algo Preferences
SWRL Rules Prerequisites -satisfaction
Prior knowledge,
[22] Ontology Ontology + Objective, personality learning style
SWRL Rules type, Academic state [42] Graph based the depth First- Previous knowledge,
search (DFS) time restriction
[11] Ontology Ontology + Context model (Location,
Rules + Time, device,
Greedy environment)
algorithm IV. SUMMARY AND CONCLUSION
[24] CBR CBR + Not Mentioned
Ontology for Learning adaptation and personalization is an important
indexing cases research field in e/m-learning environment. It is quite
[23] Context model (Location,
necessary to discover the most efficient approach to realize
CBR CBR + Distance
heuclédienne time, technology) it. This paper presents a literature review of personalized and
adaptive learning algorithms from the two sides of
[25] Heuristic based Greedy Prequisites and
approach competencies
adaptation: the adaptation of the LOs selection and the
Algorithm for
shortest path
adaptation of the learning path or the LOs sequencing.
[20] Heuristic based Greedy Learner preferences,
approach Algorithm previous knowledge,
Through the statistical analysis of the current
availibility of time individualized learning algorithms, the different approaches
that are applied to construct them vary between semantic
[26] Heuristic based hill-climbing Learner model
approach
algorithms based on the ontology modeling of the domain
heuristic
learning path) model and the learner’s context model and evolutionary
[30] Evolutionary Shortest path Context (Mobility, computing techniques that includes genetic algorithms and
Computing Ant Colony Luminosity level, noise swarm optimization techniques. An additional set of
Optimization level algorithms are used for the purpose of the learning path
(ACO) Leraning style, knowlege adaptation, in this category we can find machine learning-
algorithms level)
Evolutionary
based algorithms like BN and RL, graph-based algorithms
[31] ACO Learner
Computing profile(Knowledge level) and planning and scheduling techniques.
[32] Evolutionary ACO Learner model (Cognitive In future work we will focus on the adaptation within m-
Computing level s, LO complexity Learning environment, which offer adapted learning
level metadata)
services in mobility, according to the nature of this kind of
[33] Evolutionary ACO from data Knowledge level – learning systems, we need to integrate on the one hand more
Computing mining based prerequisites
frequent pattern
learners contextual data like, location, time, mobility state,
graph model device characteristics, environment characteristics…and in
the other hand information that characterizes the learning
[34] Evolutionary PSO Learning objective,
Computing previous knowledge task like learner’s Learning style, knowledge level,
preferences,… As adaptation type, we are interested in the
[35] Evolutionary PSO Learning objective, well-known learning path adaptation problem which plays a
Computing learner knowledge level, central role in intelligent learning systems and it is
time for learning,
weights of concepts considered as one of the most challenging problems. Since
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