IDEAL: an Intelligent Distributed Experience-based Adaptive Learning Model
Tahani M. Alsubait, Mohamed M. Khamis
Faculty of Engineering, Computers and Systems Dept., Al- Azhar University.
Email: tmsubait@uqu.edu.sa, and mkhamis1@hotmail.com
Abstract
Intelligent Tutoring Systems increasingly show promise as a technology that will expand the
horizons of web-based education to anyone with an Internet connection. However, there are many
considerable challenges in making such systems more effective. This work brings different
dimensions together to form a proper foundation for E-Learning systems. It combines techniques
from web-based learning, intelligent tutoring systems, course management systems, adaptive
hypermedia systems, collaborative learning systems and traditional learning systems to present a
better web-based intelligent tutoring system that conforms to the E-Learning standards. Finally,
based on the experience-based approach, this work has investigated the technical feasibility of
applying a multi-agent technique to implement a prototype system for the proposed model. A webbased e-learning system called Intelligent Distributed Experience Based Adaptive Learning
(IDEAL) has been implemented to test the functionality of the model.
Keywords: ITS, adaptive learning, collaborative learning, learning objects, e-learning model,
agent-based systems.
1
Introduction
The role of the technology in education has been evolved and radical changes have occurred since
the emergence of the Internet and World Wide Web (WWW). The Internet has provided various
unexpected ways of learning where the web can be used as a medium for different new ways of
learning such as Adaptive Hypermedia Systems (AHS), Intelligent Tutoring Systems (ITS), and
Web-Based Learning (WBL) systems.
A large number of organizations have adopted e-learning programs, but far fewer have addressed
all the benefits of e-learning applications. More research should be devoted to assure the gain of the
most benefits of e-learning applications if organizations are to fully benefit from their investments.
In this paper, a model is presented for e-learning that supports reusability and collaboration and
provides intelligent and adaptive e-learning environment.
2
Background
2.1 Learning Styles
Learning performance depends on many factors such as motivational factors, learning styles, and
socio-economic factors [1]. This paper focuses on adapting the learning material according to
students’ learning styles. Learning style is defined as the characteristic behaviours of learners that
serve as relatively stable indicators of how they perceive, interact with and respond to the learning
environment. Over 80 models of learning styles are available. In this research, the work of Felder
and Silverman is adopted for many reasons. For example, it focuses on aspects significant in
engineering education. Felder and Soloman [2] have developed an Index for Learning Styles
instrument (ILS) which is a tool to measure the four dimensions of learning styles, Processing
(Active/ Reflective), Perception (Sensing/ Intuitive), Input (Visual/ Verbal) and Understanding
(Sequential/Global).
2.2 Intelligent Tutoring Systems
The intelligent tutoring system is broadly defined as educational software containing an artificial
intelligence component. This component tracks students' work, tailoring feedback and hints along
the way by collecting information on a particular student's performance; it makes inferences about
strengths and weaknesses, and can suggest additional work [3].
There are four main interconnected modules of an intelligent tutoring system as shown in Figure 1,
they are domain knowledge module, pedagogical or teacher module for guiding the teaching
process, student module that to keep specific information to each individual student, and a user
interface module to enable interaction among student, teacher and domain knowledge [4,5].
Figure 1: Intelligent Tutoring Systems Modules
2.3 Adaptive Learning Systems
Adaptive Hypermedia Systems (AHS) emerged as a new area of research at the crossroads of
hypermedia, adaptive systems and intelligent tutoring systems [6]. Adaptive hypermedia is
considered as a development of classic hypermedia systems that lacked adaptation according to
user knowledge and needs. According to Brusilovsky there are two general ways of adaptation in
adaptive hypermedia. They are adaptive presentation (content-level adaptation) and adaptive
navigation support (link-level adaptation) [7].
2.4 Collaborative Learning Systems
Many studies showed that collaboration enhances the effectiveness of learning and have positive
effects on students [8]. Collaborative Learning Systems (CLS) allow geographically distributed
learners to work together on common interests. In this paper, two different techniques for
collaboration are considered. The first is the collaboration among students using the e-learning
system, and the second is collaboration with learning companions (LC). The second technique
depends on using virtual companions as helpers or troublemakers for real students.
3
Knowledge Representation
Defining the requirements for knowledge representation is an important step on the road of
specifying the knowledge representation technique used in the proposed ITS. The requirements
should cover the requirements of all users involved in the system along with the requirements of the
system itself.
The following requirements should be possessed by a KR system:
 Representational Adequacy: the ability to represent the required knowledge.
 Inferential Adequacy: the ability to manipulate the knowledge represented to produce new
knowledge inferred from the original.
 Acquisitioned Efficiency: the ability to acquire new knowledge intelligently wherever possible
rather than reliance on human intervention.
In this context, we have to discuss the types of knowledge that may be represented in an ITS. Three
types of knowledge can be defined as learner model, domain knowledge, and teacher model.
3.1 Learner Model
Researchers in the ITS community have always considered it important to develop a model of the
learner. The adaptive and collaborative capabilities of the system are mainly based on this model.
Among many characteristics that define the student model, there are characteristics that have a
great influence on the learning process such as:
 Previous knowledge of the student that determines the concepts that he can learn.
 Student’s skills and learning styles that determines the teaching approach that best suites him.
 Student’s emotions and motivations is another important characteristic that is usually forgotten
in student modelling research.
Thus, the paper proposes a multi-layer comprehensive user model that is based on the IMS Learner
Information Package (LIP) standard. The proposed learner model consists of three layers as shown
in Figure 2.
 Personality layer: This layer consists of information related to IMS LIP and any extensions to
it, along with information about the learning style and skills of the learner.
 Behaviour Layer: This layer is responsible for recording information about user activities in the
system.
 Knowledge Layer: This layer includes information about learner current knowledge as opposed
to a Knowledge Interrelation Network (KIN).
User Model
Personality Layer
IMS LIP
Learning
Style
Motivatio
ns
Skills
Skills
Network
Behavior Layer
Learner Behaviors
Knowledge Layer
Learner
Knowledge
KIN
Figure 2: Learner Model
3.2 Teacher Model
In real life, teachers usually use their previous experiences when they teach certain subject. More
experienced teachers have a wider repository of teaching strategies that differ according to different
situations that they are facing (i.e. different students with different skills and learning styles). This
leads us to think that the most appropriate way to store and represent teacher strategies is through
using a case-based representation where each case represents an experience to teach a certain
subject in a certain strategy. Student’s previous knowledge, skills and learning styles can be used to
index the case-based repository of teaching cases. The teacher model will include other data such as
personal information about the teacher, average records of his previous students, field of interests,
and preferred teaching strategies.
3.3 Domain Knowledge Model
Knowledge of the domain can be seen as interconnection of two networks which are the network of
concepts (knowledge space) and the network of educational material (hyperspace or media space)
[9]. Knowledge space is a structured representation of the domain knowledge. It consists of
interconnected nodes where each node represents a small piece of knowledge (i.e. a concept). On
the other hand, the hyperspace or media space contains learning objects of different types, for
example, static files (like HTML, PDF or PowerPoint files), dynamic files (like HTML files
containing scripts and java applets), audio files, video clips, or flash animations. The standard way
for structuring the media space is through using learning object metadata (LOM) which specifies
the relationships between the learning objects [10].
3.3.1
Course Model
The adaptation capability of the proposed system highly depends on the past successful course
plans stored in the database. The course model is a structured case-based model, where each case
represents an experience of course delivery. The proposed course structure can be represented as in
Figure 3. Each course has its own objectives that can be determined by the teacher.
have
Achieved by
Consists of
Learning Objects
Teaching strategies
topics
Mandatory Concepts
Prerequisite
Objectives
courses
+
To meet these objectives, certain concepts must be added to the learning plan of the student; along
with any necessary prerequisite concepts not previously studied by the student. Each concept can
be learned by studying multiple topics and each topic can be taught by different teaching strategies
that correspond to the student mastered skills and learning style. A decision is made about the most
appropriate learning objects available in the LO repository that teach a certain topic and match the
student learning style. The selected LO is mapped to its associated topic in the course plan.
Can be taught
A LO is a topic taught by
by
certain strategy
Figure 3: Course Model
4
Elaborated Model
4.1 Agent-Based Architecture
IDEAL system architecture is shown in Figure 4. It is organized with different agents where each
agent is responsible for carrying out specific task. The architecture represents a distributed elearning system aiming at benefiting from the valuable distributed repositories of learning materials
available on the web. The adaptive nature of the system is highly depends on the student model
which is composed of three main components knowledge, skills, and learning styles. In addition,
the model supports students' collaboration and group working by providing a collaboration
manager agent that manages companions' selection and communications. To improve the
functionality of the system, some historical learning databases are added to provide learning from
experience capabilities to the system.
The LO Finder & Filter agent runs periodically to search through distributed LO’ repositories to
collect learning materials that coincide with the general requirements of the system and that is
related to the domain knowledge concepts. This process results in building a rich virtual LO
repository in the system that provides all the necessary learning materials in coordination with the
local LO repository.
To simplify the task of the Instructional Planner agent, some databases were added to the model. If
necessary background knowledge is absent from a student’s profile then, it is the job of the Concept
Selector agent to search the KIN to find the knowledge nodes that may compensate the missing
piece of knowledge which is present in the student profile. In the same way, if the student lacks a
necessary skill then the Teaching Strategy Selector agent tries to find some skills mastered by the
student that may compensate the missing skill.
The journey of building a course plan begins when the Instructional Planner agent tries to find a
perfect plan in the previous successful plans database that match the objectives, background
knowledge, skills and learning style of the current student. The perfect plan would be the one that
has the largest scoring value according to the scoring mechanism which is explained later in this
section. If a suitable plan is found, then it can be adopted by the Instructional Planner and no
further work should be done. Otherwise, the Instructional Planner has to synthesize a new improved
plan in cooperation with the Concept Selector and Strategy Selector agents.
The Session Manger presents the learning material to the student according to the synthesized plan.
The student behaviour database records all the information related to the delivery of the course.
Student-teacher interactions are managed by the Session Manager. Every progress made by the
student during the session is reflected in the student profile by the Updater agent. Moreover, the
student behaviour database is regularly analyzed by the Scheduled Analyzer to verify and resolve
any weak points in the plan.
The collaboration manager agent is responsible for detecting the conditions necessary to establish
collaboration between students. Then, it defines the set of students capable to interact with the
current student (those who have the background knowledge needed to establish a conversation in
the current topic and have the suitable learning style). The roles of each student in a collaboration
session must be defined before establishing the connection. At the end of the conversation, the
collaboration manager evaluates student satisfaction and stores all the information about the
collaboration session in the CE database. The collaboration manager has to decide whether to
provide the student with a real companion or virtual companion. The virtual collaborator agent
facilitates the interaction with virtual companions.
The Registrar agent, as the name suggests, is responsible for registering new students and collecting
all the necessary information about them either internally from the student himself or externally by
importing the student profile from another e-learning system. Authentication and access control are
essential issues in multi-users e-learning systems to provide users with privacy. Access Controller
and Authenticator agent is in charge of this job.
General
Distributed LO
Requirement
Domain Knowledge
Repositories
s
Ontology
Knowledge Interrelation Network
KIN
L
L
O
Skills Network
LO
O
L
L
Finder &
O
Filter
O
SN
Agent
Local LO
Virtual LO
Repository
Repository
Knowledge
Teacher Model
Concept
Selector
L
L
O
O
Teaching
Virtual
LO
Instruction
Strategy
Companion
Selector
al Planner
Selector
s
Previous
Successful
L
Successfu
Conversatio
C
l Plans
ns
K
S
Updat
P
S
S
L
Virtual
er
Scheduled
Session
learning
Collaborato
Knowledge , Skills
r
& Learning Style
Manager
Analyzer
C
Student Model
Scheduled
Collaborati
on Analyzer
Collaborati
Users
on
E
Access
Student
Controller
Behaviour
&
Data Base
Authenticat
or
Databa
se
C
Manager
Registra
E
Collaboration
r
Experience
Store
Web
LIP
LIP
Remote Student
Teacher
Student
Figure 4: IDEAL System
Architecture
Profiles
Real Companion
4.2 Operational Model
The IDEAL operational model goes through the following stages:
 Study the student profile: The student previous knowledge and his mastered skills are
evaluated besides determining the student learning style.
 Select a suitable course plan: According the student profile and the goals of the course
specified by the teacher, the plan is determined. This process is accomplished in three
steps as follow:
 Select the best plan from the previous plans database.
 Study the knowledge base in order to add any additional concepts to the plan.
 Build an improved course plan.
 Deliver the course: After a course plan is determined, the delivery for the course becomes
available. This delivery process takes place on one or multiple sessions based on student
progress and capabilities. At the end of each session, student assessment takes place.
 Update the student profile: Based on assessment results of the previous step, student profile
should be updated to accommodate the current student response.
 Analyze student behaviour during the course: Student assessment results must be analyzed
to identify any breaches in the course plan. If these results are under the expected rate then
the course plan must be improved.
 Update the course plans: The question which presents itself at this stage is what the possible
solutions to the identified course problems are. The typical answer would be that some
prerequisite knowledge concepts were not included in the plan. So, KIN is traced to infer
the missing concept.
 Study collaboration options: According to the result of the assessment provided at the end
of the session, a collaboration situation can be defined. This process includes specifying
the role of the student in the collaboration session and the identity of his participant.
 Start collaboration session: After defining all the necessary parameters, the collaboration
session can start immediately.
 Evaluate and analyze collaboration: At the end of the collaboration session, the student is
asked about the benefit of collaboration in order to evaluate his received responses.
 Update the successful collaborations database: This database is essential to the virtual
companions of the IDEAL system since it can help them how to interact intelligently with
real students.
4.3
4.3.1
IDEAL Adaptation Model
Retrieving a Case –Calculating Similarity Measures
Each teaching case was designed with specific issues in mind. The evaluation of past teaching cases
includes calculating the similarity measures between the case of current user and the previous
teaching cases. This process should consider all the issues that affect the construction of the
previous teaching case. These issues include the teaching objectives, student’s knowledge
mastering levels, his skills appraisal levels, his learning style along with any possible effective
environmental issues. Moreover, looking at past students’ achieved results may be helpful in
choosing the best previous teaching experience.
It is worth noting that each of the above issues has its own relative impact on the teaching
experience. The relative importance of each issue can be set by the system administrator.
The problem is how to evaluate previous teaching cases in order to choose the teaching case that
has the highest degree of similarity to learner l. First, let us suppose that E = {e1, e2,… ., en} is a set
of n-effective issues. Second, assume that L = {l1, l2, …., lk} is a set of k-previous learning cases,
while l by itself will be used to indicate the current learning case. Moreover, the similarity between
the effect of ei on the current learning case and its effect on previous learning case lp is Si(l, lp).
R={r1, r2, …., rn} is a set of relative importance values corresponding to different effective issues in
n
E; so that rj is the relative importance of issue ej; where (rj < 1) and
r
j1
j
 1 . Based on the above
assumptions, the following formula can be used to evaluate the similarity between the current
learning case l and previous learning case lp.
n
S (l , l p )   ri * S i (l , l p )
… [ 1]
i 1
Definition 1: N(A) represents the number of elements of the set A.
Next, we will shed lights on some practical ways to calculate Si(l, lp). Putting in mind that Sj(l, lp)
≤1 which implies that S(l, lp) ≤ 1. First, recall that the cornerstone in building a course is the
course objectives. So, we will start by proposing a way to calculate So(l, lp) which is the similarity
between objectives of learner l and objectives in the learning case lp. To do so, assume that the set
of objectives required by the current learning case is O={o1, o2, ….. , om} and the set of objectives in
the learning case lp is Op ={op1, op2, ….. , opf} then:
SO (l,l p ) 
N(O  Op )
N(O) ... [ 2]
Assuming that every learner has at least one learning objective, then we can derive that So(l, lp) ≤1.
Next, So(l, lp) can be substituted in formula 1 for Sh(l, lp) where h {1, .., n} and eh is the label for
the objectives issue which is a element in E.
Secondly, let us suppose that the set of topics needed to be added to the course plan due to lack in
student l background knowledge or skills is T = {t1, t2, ….. , tm} and the set of topics in the learning
case lp that has no direct links to the objectives of that course (rather, it was added to compensate a
lack of student’s background knowledge or skills) is Tp ={tp1, tp2, ….. , tpf} . Then:
if
N(T)  N(Tp )  0
 1

 1 N(Tp ) if N(T)  0 & N(Tp )  0
St (l,l p )  
N(T  T ) N(T) otherwise
p


Note that if the current student needs no topics to be added to his teaching plan (i.e. N(T)= 0) then
the most suitable previous plan could be the least expensive (i.e. with the least number of added
topics); meaning that 1/N(Tp) will yield smaller values if N(Tp) is large. In the same way, St(l, lp)
can be substituted in formula 1.
Thirdly, referring to the four dimensions of Felder’s learning styles model, we can define fd  {-1 ,
0, +1}; Where Fld is the learning style of learner l in dimension d which can take one of three
values as illustrated in Figure 5. Similarly, fpd  {-1 , 0, +1} is the learning style of learner lp in
dimension d.
Figure 5: Calculating Similarity Measures for Learning Styles
The learning style of learner l is compared to the learning style that was considered in learning case
lp. Then, the following formula can be used to calculate the similarity between the two learning
styles:
4
SF (l,l p )   Md / 4 ; Where Md is the similarity between the learning style of the two
d 1
learners in dimension d and can be computed as follows.
1

Md  1 2
0

;f d  f pd
;f d  f pd ;f d  0 or f pd  0
;otherwise
Finally, some of the environmental issues that have an impact on the teaching experience are:
geographic location, connection speed, processing power …etc. These issues may be considered to
choose the best-fit past teaching experience.
4.3.2
Modifying a Case
As a result of the evaluation process discussed above, the best-fit teaching experience is determined
and its evaluation score is obtained. If this score is within an acceptable range (1> score > ½) then
the case is considered to be worth reusing. The adopted case must be modified to accommodate the
new settings of the current student. First, any extra objectives that are not required by the current
student are eliminated along with any topics linked with them. Second, the existing learning
materials associated with the current set of topics are adjusted to correspond to the learning style of
the current student. Third, new required objectives are added to the plan in addition to all the topics
which are needed to achieve these objectives. Each topic can then be mapped to a suitable learning
material.
4.3.3
Synthesizing a New Teaching Case
In some cases, no teaching experience can be found to match the current settings of the student; the
model proposed by IDEAL handles this issue by providing the capability to synthesize a new
teaching plan. This is done in three stages:
 Concept Sequencing.
 Material Selection.
 Final Tuning.
These steps were discussed thoroughly in the previous chapter. Note that the time and effort spent
to synthesize a new teaching case are supposed to be larger than the time and effort spent to reuse
an existing teaching case; putting in mind that the new case will need further dynamic adjustments
as discussed in the next section.
4.3.4
Dynamic Adaptation of Teaching Case
Dynamic adaptability of the IDEAL model depends on predicting student‘s expected results and
comparing those results with student’s achieved results. The results are analysed to infer the weak
points in the plan that need adaptation. In doing so, the tree of topics included in the plan is traced
to calculate the expected student’s results according to his background knowledge mastering levels.
The topics are weighted by the instructional designer to determine their relevance importance
during the course intake. This is similar to the process of calculating the GPA of a student in which
the weight is an integer value (usually 2 or 3) that represents the importance of the topic. First, the
set of prerequisite topics that must be mastered before learning the current topic is determined
along with their relevance importance value and the mastering level value. The sigma of
multiplying each topic mastering level by its weight is then calculated. Finally, the calculated result
is multiplied by a special factor that determines the difficulty level of the current topic. This factor
can be calculated by referring to the results achieved by all the students that studied this topic
before. The better the achieved results the higher the difficulty factor value is. This process is
illustrated through the following formula:
ET =
 W
p
pPT
* Rp 
W
pPT
* DT
P
Where ET is the expected result of topic T, PT is the set of all the prerequisites of topic T, WP is the
relevance importance value of topic P, RP is the mastering level of topic P and DT is the difficulty
factor of topic T. The scheduled-analyzer’ agent has to compare the expected result ET to the
achieved result R and modify the plan accordingly.
Assessment Result
Current Topic
of the
Assessment Result of the
Prerequisite Topics
Learning
Style
Collaboration Situation
Below the average
Any
Any
Student needs help
Within the average
Below the average
Any
Student needs help
Within the average
Above/within average
Not Ref
Student needs encouragement
Above the average
Above/within average
Act/Ver
Student can help other students
Above the average
Above the average
Act/Ver
Student can encourage others
Above/within average
Above/within average
Act/Ver
Student can discuss with others
Table 1 : Defining Collaboration Situation
4.4 IDEAL Collaboration Model
The collaboration model depends mainly on two issues: student assessment results for the current
topic and his learning style compared to other students’ assessment results and other students’
learning styles respectively. The most important job of the Collaboration Manager is to detect the
current collaboration situation based on the above issues. This situation can be defined by referring
to Table 1. The collaboration session can be summarized in the following steps:
 Define the current topic studied by the student along with its prerequisites.
 Fetch the student assessment results in regard to the defined topics.
 Define the student role in the collaboration session based on his assessment results and learning
styles.
 Define collaborator type (i.e. real or virtual).
 Establish collaboration.
 Evaluate collaboration and analyze the evaluation results to update the DB accordingly.
5
Implementation
A prototype for the IDEAL model was implemented using agent technology. This technology was
adopted as the agents allow reusability, flexibility, modularity and maintainability. The prototype
system was developed by employing different promising technologies: JavaServer Pages (JSP),
Java Database Connectivity (JDBC), MySQL Database, eXtensible Mark-Up Language (XML),
Java Agent DEvelopment Framework (JADE), and Apache Tomcat web server under Windows
operating system. The system provides many functions such as:
 Users’ management.
 Learning plan management.
 Adaptive learning management.
 Collaborative learning management.
6
Conclusion
This paper provides the e-learning community with an experience-based model for web-based
intelligent tutoring systems. The model combines different ideas from intelligent tutoring systems,
adaptive hypermedia systems, collaborative learning systems, personalized learning, and distributed
repositories into a web-based distance learning environment. It includes a standardized user and
knowledge models, allowing interoperability and sharing of user profiles and learning materials
between different e-learning systems. Finally, a prototype for the presented model is implemented
based on mobile agent technology to provide the system with the flexibility for more development
in the future. The implemented model called IDEAL.
7
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