Knowledge tracing: Modeling the acquisition of procedural knowledge

@article{Corbett2005KnowledgeTM,
  title={Knowledge tracing: Modeling the acquisition of procedural knowledge},
  author={Albert T. Corbett and John R. Anderson},
  journal={User Modeling and User-Adapted Interaction},
  year={2005},
  volume={4},
  pages={253-278}
}
This paper describes an effort to model students' changing knowledge state during skill acquisition. [] Key Method As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule.
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1,588 Citations

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References

SHOWING 1-10 OF 33 REFERENCES

Student Modeling in an Intelligent Programming Tutor

The predictive validity of this modeling process is assessed, the implications for the rules in the ideal student model are examined, and the recommendations for remediation are examined.

Cognitive Tutors: Lessons Learned

The 10-year history of tutor development based on the advanced computer tutoring (ACT) theory is reviewed, finding that a new system for developing and deploying tutors is being built to achieve the National Council of Teachers of Mathematics (NCTM) standards for high-school mathematics in an urban setting.

Cognitive Modeling and Intelligent Tutoring

General Principles for an Intelligent Tutoring Architecture.

The major outcome of the research project has been a set of ideas for developing intelligent tutoring systems and an architecture for implementing these ideas which has been used so far to teach LISP, prolog, and pascal at CMU, and NYNEX has adapted it to teach COBOL.

Skill Acquisition and the LISP Tutor

An analysis of student learning with the LISP tutor indicates that while LISP is complex, learning it is simple. The key to factoring out the complexity of LISP is to monitor the learning of the 500…

Transfer of programming skills in novice lisp learners

This research addresses novices' learning of the programming language LISP and transfer between coding, debugging, and evaluation and supports an identical elements theory of transfer based on ACT* learning mechanisms.

The Acquisition of Procedures from Text: A Production-System Analysis of Transfer of Training. Technical Report No. 16.

Mind Bugs: The Origins of Procedural Misconceptions

From the Publisher: As children acquire arithmetic skills, they often develop "bugs" - small, local misconceptions that cause systematic errors. Mind Bugs combines a novel cognitive simulation…

Bypassing the intractable problem of student modelling

Practical guidelines and changes in philosophical approach are suggested which may help in building effective student models within intelligent tutoring systems.

Rules of the Mind

Production Systems and the ACT-R Theory and the Identical Elements Theory of Transfer.