ITS and SCORM

1. ITS and SCORM Xiangen Hu, Andrew Olney, Eric Mathews, Art Graesser The University of Memphis

2. Agenda • AutoTutor • ADL and SCORM • ITS and SCORM2004 • Summary

3. AutoTutor

4. Tutoring • Effective Tutoring Involves the Student: (Graesser, Person, and Magliano 1995; Chi 1996) • Taking an active role in knowledge construction • Asking questions • Generating their own explanations • Recognizing their misconceptions • Synthesizing information

5. Tutoring • Untrained tutors are effective (Bloom 1984; Cohen, Kulik, and Kulik 1982; Graesser and Person 1994) • Possible explanations for tutoring effectiveness: (Graesser, Person, and Magliano 1995) • Interactive dialog • Collaborative construction of knowledge • Explanations are generated • Concrete examples • Deep reasoning questions

6. AutoTutor • A computer program that is designed to tutor students in computer literacy and conceptual physics. • Simulates the tutoring strategies used by human tutors. • AutoTutor interacts with students and covers topic material in the course of a conversation.

7. AutoTutor Language Extraction Topic/Problem Selection Speech act classifier Dialog Management Latent Semantic Analysis Curriculum Script Animated Agent

8. Simulation When a car without headrests on the seats is struck from behind, the passengers often suffer neck injuries. Why do passengers get neck injuries in this situation? Question Head Parameter Controls Describe what happens

9. ADL and SCORM

10. What is ADL? Advanced Distributed Learning

11. Advanced Distributed Learning:What’s New? • Advanced Technologies • Advanced delivering technology • Advanced assessment methodologies • Advanced Learning Paradigms • Cognitive theory of learning • Intelligent tutoring • Advanced Standards • Shareable learning objects • Anyone, anywhere, anytime, any system

12. Home Field Distributed Learning Distributed Simulation Performance Assessment School In Transit Office Embedded Training Digital Knowledge Libraries

13. ADL Co-Labs and Partnership Labs An open collaborative environment for sharing learning technology research, development, and assessments.

14. ADL Vision • Distributed Learning • web-based modular content • intelligent tutors • interoperability and reuse • wide spread collaboration Any Time Any Where Learning ADL STRATEGY • Distance Learning • web-based Classroom • video tele-training Right Time Right Place Learning • Electronic Classroom • CBT/CD-ROMs Classroom

15. Strategy • Exploit existing network-based technologies • Create platform-neutral, reusablecourseware and contentto lower costs • Promote wide spread collaboration to satisfy common needs • Enhance performance with emerging and next-generation learning technologies • Develop common framework that drives COTS product cycle • Establish a coordinated implementation process Explore the technology for “learning objects”

16. Functional Requirements for “learning objects” • Accessibility: access instructional components from one remote location and deliver them to many other locations • Interoperability: use instructional components developed in one location, with one set of tools or platform, in another location, with a different set of tools or platform • Adaptability: tailor instruction to individual and situational needs • Reusability: incorporate instructional components into multiple applications • Durability:operate instructional components when base technology changes, without redesign or recoding • Affordability:increase learning effectiveness significantly while reducing time and costs

17. ADL “SCORM” • Need of standards • Best practice in all other industries • Proven methodology • Object oriented • Cutting edge technology • xml based • “SCORM” • Sharable Content Object Reference Model • Innovated Strategy • ADL Co-Labs • PlugFest

18. ITS and SCORM2004

19. ITS basics • Two Levels of questions • What material does the tutor want to cover with the student? • Knowledge of the domain • Teacher’s model • Student’s Model • How does the tutor present the material during the session? • Teaching knowledge • Tactics • Theory driven

21. SCORM2004 SN • Sequencing and Navigation • Activity Tree • Smallest unit is SCO • Clustering • Allows structuring to implement knowledge structure • Rollup Rollover • Dynamic sequencing adaptive to the users • Capable of answering the first question of ITS • Organize knowledge • Not a standard for the second level question • Theory neutral

22. Formal Models • Need of formal model • Having a standard, one needs a model • An ITS model that model Teacher/Student Interaction • ITS contains • A set of items • A relation among the items • ITS • Presents a stimulus to students • Receive a response from students • ITS • Assign (numerical) values to any combination of stimulus/response pair • Store interaction history • Select next item as stimulus based on probability distribution that is conditional to the prior interaction history.

23. Formal Models • Two types of Models for ITS • knowledge based • REDEEM (Ainsworth and Wood 2004): Advanced Planning • uncertainty based • Markov Decision Processes (Matsuda & Van Lehn, 2001): Based on interaction between tutor and student

24. Summary

25. Summary • ITS are theory driven in nature • Teaching tactics emphasize effective micro dialog/interaction between tutor and student • Highly individualized, tailored to user learning style • Smallest unit may be simple feedback such as hint or prompt • Probability of selecting next item is determined by specific teaching tactics

26. Summary • SCORM SN emphasize higher level knowledge structure • Theory neutral • Provide basic selection mechanism at the level of knowledge structure • Have difficulty covering all teaching tactics as standard • The concept of assets allow ITS objects handle micro interactions, but not capable capture detailed interaction at the asset level

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