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TRENDS IN TECHNOLOGY BASED LEARNING : TOWARDS TRULY INTELLIGENT TUTORING SYSTEMS

TRENDS IN TECHNOLOGY BASED LEARNING : TOWARDS TRULY INTELLIGENT TUTORING SYSTEMS. Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Department of Systems Theory and Design E-mail: Janis.Grundspenkis @cs.rtu.lv. AGENDA.

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TRENDS IN TECHNOLOGY BASED LEARNING : TOWARDS TRULY INTELLIGENT TUTORING SYSTEMS

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  1. TRENDS IN TECHNOLOGY BASED LEARNING: TOWARDS TRULY INTELLIGENT TUTORING SYSTEMS Janis Grundspenkis Riga Technical University Faculty of Computer ScienceandInformation Technology Department of Systems Theory and Design E-mail: Janis.Grundspenkis@cs.rtu.lv

  2. AGENDA • TRADITIONAL vs. TECHNOLOGY BASED LEARNING • VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS • E-LEARNING • M-LEARNING • INTELLIGENT TUTORING SYSTEMS • HYBRID SYSTEMS FOR LEARNING • CONCLUSIONS

  3. TRADITIONAL LEARNING (1) FACE-TO-FACE (“TALK AND CHALK”) • “+” • Explanation • Communication • Between the teacher and students • Among students • Adaptation to individual students (in case of small number of students)

  4. TRADITIONAL LEARNING (2) FACE-TO-FACE (“TALK AND CHALK”) • “-” • Different teaching quality depending on teacher (pace dependent) • Strict schedule (time and place dependent) • Weak adaptation to individual students (in case of large number of students)

  5. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (1) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  6. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (2) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  7. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (3) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  8. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (4) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  9. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (5) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  10. VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS* (6) * Anohina A. Clarification of the Terminology Used in the Field of Virtual Learning. In: Scientific Proceeding of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 17, RTU Publishing, Riga, 2003, pp. 94-102.

  11. Technology Based Distance Online ComputerBased InternetBased WebBased VIRTUAL LEARNING: DIFFERENT TERMS AND VIEWS (7) RELATIONSHIPS OF TERMS

  12. E-LEARNING (1) • “+” • Teaching and self-pacedlearning of anyone, at anytime, anywhere • Substantial cost savings due to elimination of travel expenses • Just-in-time access to timely information • Modularity of presentation (facilitates different construction of learning events)

  13. E-LEARNING (2) • “+” • Improved collaboration and interactivity among students • Content can be updated and delivered in real-time • Higher retention of content through personalized learning • Online training is less intimidating than instructor-led courses

  14. E-LEARNING (3) • “-” • Learning materials cost quite a lot more than textbooks • Requires more time, dedication, and time management skills • Weak motivation (absence of teacher) • Lack of real time communication • Weak support from the e-learning environment

  15. M-LEARNING • Mobile devices open the possibility of collaborative and independent learning • Cellular phones • Smart phones • Personal digital assistants (PDA)

  16. INTELLIGENT TUTORING SYSTEMS (1) AIM • To provide sophisticated instructions on one-to-one basis adapting the learning process to the strength, weaknesses and the level of knowledge and skills of each particular learner

  17. INTELLIGENT TUTORING SYSTEMS (2) TASKS • Monitoring of actions of the learner in the learning environment • Appropriate responding to them • Assessment of learner’s knowledge • Choice and presentation of learning material • Presentation of feedback and help • Adaptation of teaching strategy

  18. INTELLIGENT TUTORING SYSTEMS (3) • Incorporation of a new concept Web semantics thanks to the development of “more expressive” mark-up languages and mainly to the use of ontologies • Convergence of Artificial Intelligence and Learning Environments • Convergence of Knowledge Management Systems and Multi-Agent Systems

  19. AGENT BASED INTELLIGENT TUTORING SYSTEMS* (1) STRUCTURE • Expert module (the domain knowledge concerns objects and their relationships taught by the system) • Tutoring module (holds teaching strategies and instructions needed to implement the learning process) • Student diagnosis module (infers the student model for each individual) • Communication module (responsible for the interaction between the system and the learner) * Grundspenkis J. and Anohina A. Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 22, RTU Publishing, Riga, 2005, pp. 110-120.

  20. AGENT BASED INTELLIGENT TUTORING SYSTEMS* (2) • Agents comprising the student diagnosis module of intelligent tutoring system * Grundspenkis J. and Anohina A. Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 22, RTU Publishing, Riga, 2005, pp. 110-120.

  21. AGENT BASED INTELLIGENT TUTORING SYSTEMS* (3) • Agents comprising the tutoring module of intelligent tutoring system * Grundspenkis J. and Anohina A. Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 22, RTU Publishing, Riga, 2005, pp. 110-120.

  22. AGENT BASED INTELLIGENT TUTORING SYSTEMS* (4) • Agents comprising the expert module of intelligent tutoring system * Grundspenkis J. and Anohina A. Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 22, RTU Publishing, Riga, 2005, pp. 110-120.

  23. AGENT BASED INTELLIGENT TUTORING SYSTEMS* (5) • A set of agents comprising the architecture of an intelligent tutoring system (gray boxesare managing agent in a given component) * Grundspenkis J. and Anohina A. Agents in Intelligent Tutoring Systems: State of the Art. In: Scientific Proceedings of Riga Technical University, 5th Series, Computer Science, Applied Computer Systems, Vol. 22, RTU Publishing, Riga, 2005, pp. 110-120.

  24. ANIMATED PEDAGOGICAL AGENTS • Animated pedagogical agents emulate aspects of dialogue between the teacher and the learner • Roles of animated pedagogical agents • Agent as an expert (it is similar to human expert and exhibits mastery of extensive knowledge and performs better than the average within a domain • Agent as a motivator (it suggests its own ideas and encourages the learner) • Agent as a mentor (it incorporates characteristics of both the expert and the motivator

  25. HYBRID COURSES (1) • Hybrid courses offer a blend of in-class teaching and online learning and is an attempt to combine the best elements of traditional face-to-face teaching with the best aspects of distance education • Hybrid courses combine traditional lecture, seminar or lab sections with online and other technology based learning

  26. HYBRID COURSES (2) • A significant part of the course learning is online, and as a result, the amount of classroom seat-time is reduced • Hybrid courses encourage active, independent study and reduce the amount of time students spend in the classroom

  27. HYBRID COURSES (3) • Students spend more time working individually and collaboratively on assignments, projects, and activities • Students who successfully complete hybrid courses are typically self-motivated learners who possess a working knowledge of computers and the Internet

  28. HYBRID COURSES (4) • Faculty spend less time lecturing and more time reviewing and evaluating student work and guiding and interacting with students • Allow students much more flexible scheduling, while maintaining the face-to-face contact with the teacher

  29. HYBRID COURSES (5) • “+” • More learning, understanding, and retention • More interaction and discussion • Students are more engaged • More student and learning centered • Less listening and more active learning • Students are more accountable for own learning

  30. HYBRID COURSES (6) • “+” • Teachers can document & examine student work more thoroughly online than face-to-face • Faculty can teach in new ways • Accomplish new learning goals and objectives • More hands on student involvement with learning • Provides opportunities to learn in different ways

  31. HYBRID COURSES (7) • “-” • Involves an extensive course redesign • Difficult to define optimal proportion between traditional face-to-face teaching and online learning • Difficult to select which topics include in traditional face-to-face teaching and which topics left for online learning

  32. RESOURCES FOR HYBRID COURSES • UWM Hybrid Course Web Site • http://www.uwm.edu/Dept/LTC/hybrid.html • UWM Student Hybrid Course Web Site • http://www.uwm.edu/Dept/LTC/hybridcourses.html • Teaching With Technology Today – Hybrid issue • http://www.uwsa.edu/ttt/browse/hybrid.htm

  33. CONCLUSIONS • A lot of work has been done in technology based learning but many problems still exist • New technologies offer new opportunities and new challenges • Intelligent tutoring systems and animated pedagogical agents provide more adaptive support for learning • Hybrid courses offer a good balance between traditional face-to-face teaching and distance learning

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