1 / 33

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.

dorinda-urbina
Download Presentation

TRENDS IN TECHNOLOGY BASED LEARNING : TOWARDS TRULY INTELLIGENT TUTORING SYSTEMS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related