This document proposes optimizing service oriented architecture to support e-learning with adaptive and intelligent features. It discusses how adaptive learning refers to technologies that can dynamically recognize the role and profile of each learner and respond accordingly. It also discusses different adaptive learning approaches like macro, micro, and constructivist-collaborative approaches. Intelligent tutoring systems that utilize artificial intelligence techniques are also discussed. The proposal aims to make adaptive and intelligent e-learning features available as standard reusable services via an optimized service oriented architecture to enhance the overall system.

1. OPTIMIZING
SERVICE ORIENTED ARCHITECTURE
TO SUPPORT E-LEARNING WITH
ADAPTIVE AND INTELLIGENT
FEATURES
Ph.D. Proposal and Registration Seminar
Submitted To: Information Systems Department
Faculty of Computers and Information Sciences, Mansoura University
June 2008
Submitted By:

2. Supervisors
Prof. Dr. Alaa El-Din Riad
Head of Information Systems Department
Faculty of Computers and Information Sciences
Mansoura University
Egypt
Dr. Hamdy K. El-Minir
National Research Institute of Astronomy
Department of Solar and Space Research
Egypt

3. Agenda
 Adaptive Features in e-Learning
 Intelligent Features in e-Learning
 Optimizing SOA to support e-Learning with
Adaptive and Intelligent Features
 Feed Back, Suggestions, and Recommendations

4. Adaptive Features in e-Learning
 Adaptive behavior is a type of behavior that is
used to adapt to another type of behavior or
situation.
 Adaptive Learning refer to technologies that
can dynamically recognize the role and profile
of each learner, and respond accordingly.

5. Adaptive Learning Examples
 When a learner logs on, a learner-centric
system can immediately identify that person
and "understand" whether they are an
employee, a partner or a customer, and deliver
content accordingly.
 Adaptive Questions (Definition)

6. Adaptive e-Learning Approaches
 Macro Adaptive Approach
 Aptitude Treatment Interactive Approach
 Micro Adaptive Approach
 Constructivistic - Collaborative Approach

7. Macro Adaptive Approach
 Addresses adaptation of instructions by allowing different
alternatives in selecting a few main components such as:
 learning objectives
 levels of detail
 delivery system, etc.
 On basis of the student’s:
 learning goals
 general abilities
 and achievement levels in the curriculum structure.

8. Adaptive Treatment Interaction
 Treats adaptation of instructional strategies to specific student’s
characteristics.
 Proposes different types of instructions or even different media
types for different students.
 One aspect is the user’s control over the learning process according
to the abilities of the students by giving them full or partial control
over the style of the instruction or the way through the course.
 Levels of control,
 complete independence
 partial control within a given task scenario,
 and fixed tasks with control of pace.

9. Micro Adaptive Approach
 Addresses adaptation of instructions by diagnosing the
student’s specific learning needs during instruction and
providing instructional prescriptions for these needs.
 Researchers have attempted to establish micro-adaptive
instructional models using on-task rather than pre-task
measures. Monitoring the user’s behavior and performance,
such as response errors, response latencies, emotional
states, etc. can be used for optimizing instructional
treatments and sequences on a very refined scale.
 Uses the temporal nature of learner abilities and
characteristics, especially the dynamically changing ones.

10. Constructivistic - Collaborative Approach
 Focuses on modern aspects about how an e-learning
system can be used within the learning process and
follows the constructivistic pedagogical approach.
 An important element of this approach is the usage of
collaborative technologies which are considered often
as essential component of e-learning.
 Can take account of students’ motivational factors
combining the instructional plan with a “motivational”
plan.

11. What can we Do to be Adaptive?
 e-Learning Portal Personalization
 Dynamic Content Generation
 Dynamically Choosing Instructional Method
 Adaptive Questions
 Collaborative Facilities

12. Intelligent Features in e-Learning
 Intelligent Tutoring Systems (ITSs) have been
developed and evaluated for many years
 Objective is to provide highly structured lessons that
are to a large extent under automated control
 Include utilizing artificial intelligence techniques such
as decision making, machine learning, planning,
scheduling, and cognitive science
 Include utilizing artificial intelligence tools such as:
data mining, neural networks, and fuzzy logic.

13. What can we do to be Intelligent?
 Adaptive sequencing or personalization of the
course material
 Adaptive guidance for navigation
 Interactive problem solving support

14. Optimizing SOA
 Optimization here means:
 Overcoming previous shortages, limitations, and
challenges
 Enhancing overall system architecture by presenting
architectural modifications based on evaluation of
previous architecture
 Enhancing Security features
 Presenting Parallelism

15. Source: Google Trends
Figure depicts the interest in Intelligent, adaptive, SOA, and e-Learning
within the last 12 months.

16. Aim of Study
Support e-Learning with Adaptive and
Intelligent Features in the form of standard
reusable services available online via
Optimizing Service Oriented Architecture
Utilization in e-Learning

17. Research Activities
 Surveying currently available intelligent and adaptive features
presented to e-Learning
 Working on making use, modifying, and enhancing current adaptive
and intelligent features of e-Learning systems
 Optimizing SOA via enhancing Security and Parallelism
 Working on presenting modified and enhanced features in a
standard interface services to make them reusable in many aspects
 Evaluating proposed adaptive and intelligent features regarding
different evaluation aspects to determine the efficiency and
effectiveness of proposed features
 Working on providing real world scenarios, solutions, and reusable
components to real world institutions and organizations

20. Adaptive Question
IMS QTI defines adaptive questions (items) as:
“An adaptive item is an item that adapts its appearance, its scoring
(Response Processing) or both in response to each of the
candidate’s attempts. For example, an adaptive item may start by
prompting the candidate with a box for free-text entry but, on
receiving an unsatisfactory answer, present a simple choice
interaction instead and award fewer marks for subsequently
identifying the correct response. Adaptivity allows authors to create
items for use in formative situations which both help to guide
candidates through a given task while also providing an outcome
that takes into consideration their path.”
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