Ph.D. Registeration seminar


Published on

1 Comment
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • Notes about Points:============Constructivistic Pedagogical Approach:-----------------------------------------------Motivational Plan:----------------------Instructional planning can be divided into two streams, a content planning for selecting the next topic to teach and a delivery planning for determining how to teach the selected topic. Motivational components should be considered within delivery planning.
  • Ph.D. Registeration seminar

    1. 1. OPTIMIZING SERVICE ORIENTED ARCHITECTURE TO SUPPORT E-LEARNING WITH ADAPTIVE AND INTELLIGENT FEATURESPh.D. Proposal and Registration SeminarSubmitted To: Information Systems DepartmentFaculty of Computers and Information Sciences, Mansoura UniversityJune 2008 Submitted By:
    2. 2. SupervisorsProf. 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. 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. 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. 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. 6. Adaptive e-Learning Approaches Macro Adaptive Approach Aptitude Treatment Interactive Approach Micro Adaptive Approach Constructivistic - Collaborative Approach
    7. 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. 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. 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. 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. 11. What can we Do to be Adaptive? e-Learning Portal Personalization Dynamic Content Generation Dynamically Choosing Instructional Method Adaptive Questions Collaborative Facilities
    12. 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. 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. 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. 15. Source: Google TrendsFigure depicts the interest in Intelligent, adaptive, SOA, and e-Learningwithin the last 12 months.
    16. 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. 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
    18. 18. Adaptive QuestionIMS 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.” BACK