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Inside Multi-Media Case Based Instruction

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advanced case-based teaching architecture
animal
Animal Adaptation
artificial intelligence education
Case Library
case retrieval systems
Category=UYF
Category=UYQ
cognitive modeling
Computer Based Tutoring Systems
Diagnosis Procedure
Diagnosis Process
Diagnosis Task
dialogue
Dialogue Manager
environment
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
explanation
Explanation Questions
graduate level AI
Incidental Learning Task
indexing
Indexing Vocabulary
library
Likelihood Weight
manager
multimedia instructional design
Parasitic Rule
Pasta Salad
Procedural Tasks
questions
RP
Simulated Customer
social simulation learning
Social Transactions
Storytelling Opportunity
Storytelling Strategies
Student's Diagnosis
Student’s Diagnosis
task
Task Environment
Task Model
Teaching Executive
Tutorial Opportunity
Tutorial Response
vocabulary

Product details

  • ISBN 9780805825374
  • Weight: 1030g
  • Dimensions: 152 x 229mm
  • Publication Date: 01 Jun 1998
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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Description

The fourth in the Inside series, this volume includes four theses completed under the editor's direction at the Institute for the Learning Sciences at Northwestern University. This series bridges the gap between Schank's books introducing (for a popular audience) the theories behind his work in artificial intelligence (AI) and the many articles and books written by Schank and other AI researchers for their colleagues and students. The series will be of interest to graduate students in AI and professionals in other academic fields who seek the retraining necessary to join the AI effort or to understand it at the professional level.

This volume elaborates the Case-Based Teaching Architecture. A central tenet of this architecture is the importance of acquiring cases, and being able to retrieve and use those cases to solve new problems. The theses address the problems of building case bases, indexing large amounts of data contained within those case bases, and retrieving information on a need-to-know basis. They also reflect the work of researchers at the Institute to design tools that enable software programs to be built more effectively and efficiently.