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Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228(4698), 456-462.
Background
Intelligent tutoring systems are computer-assisted technology which has “programs that simulate an understanding of the domain they teach and can respond specifically to the student’s problem-solving strategies” (Anderson et al, 1985, 456). The authors are interested in exploring the aspects that make a successful intelligent tutoring system. Published in 1986, this is one of the earlier writings regarding ITSs.
The article provides a framework for designing intelligent tutoring systems according to the ACT, adaptive control of thought, theory. The ACT theory refers to cognitive psychology and the role of the types of memory in cognition. The focus of the ACT approach is on procedural memory and how that could be used by intelligent tutoring systems. The four aspects of the ACT theory approach that the authors highlight are knowledge compilation, use of production, working memory limits, and goal structures(Anderson et al, 1985, 457). By programming tutoring systems to address these aspects, the developers can provide personalized instruction for users.
The authors also discuss model-tracing as a framework for ITSs. Model-tracing involves programming an ideal model for problem-solving and possible paths a user could take to solve a problem. The model-tracing paradigm is based on ACT practices. The tutor does not provide instruction unless it sees the user has deviated from the model for solving the problem. Based on the type of error and what step the user is on in solving the problem the computer gives feedback on the user’s work(Anderson et al, 1986, 458). The authors provide two examples of tutoring systems, a geometry tutor and a LISP programming tutor, they evaluated. The two examples showcase the ACT theory components and the model-tracing paradigm within intelligent tutoring systems.
Key Points:
- ACT theory approach to intelligent tutoring systems
- Knowledge compilation
- Use of production
- Working memory limits
- Goal structure
- Implications
- Explicit goal structure
- Minimizing working memory loads
- Instruction in problem-solving context
- Immediate feedback on errors
- Model-tracing paradigm
- Specific production for the solution
- Production of errors a student could make
- Geometry tutor
- Cannot move to new concept without demonstrating mastery
- User noted liking the subject after use
- LISP tutor
- Attention directed to conceptual issues
- Goals of the problem being solved displayed throughout process
Design Principles
- One prominent feature of successful education is immediate feedback on errors. Both described tutoring systems employ immediate feedback. The immediate feedback allows users to adjust their problem-solving strategy and understanding of the problem.
- An important design consideration for ITSs is to include instruction in a problem-solving context. This allows users to put concepts in