Conclusion
The presented methods allow the modeling of student skills by a history of how well a student has performed on a series of tutoring cases. By this means, the explicit representation of a student’s knowledge, which is especially difficult for case oriented systems, can be avoided. Moreover, case histories make it possible to model the improvement of student’s skills and thus can be used for retrieving and adapting appropriate tutoring plans for further tutoring cases.
On the other hand, the acquisition of tutoring cases is made easier by the methods described. The author of a tutoring case builds a set of test configurations for each association step that has to be performed within the tutoring case. The elements of the obtained sets just have to be ordered according to their difficulty. An explicit assignment of user stereotypes or difficulty levels to association tests is not necessary. Only a small set of model configurations for given student stereotypes must be defined.
Further cooperative work within the scope of the project Docs’n Drugs will be dedicated to the implementation of thread configurations as corresponding networks of information and decision for testing and evaluating the proposed methods.
Additionally, research will focus on more sophisticated similarity measures between thread configurations and explicit weightings of threads that take their different importance for the tutoring process into account.
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Seitz, A. (1999). A Case-Based Methodology for Planning Individualized Case Oriented Tutoring. In: Althoff, KD., Bergmann, R., Branting, L. (eds) Case-Based Reasoning Research and Development. ICCBR 1999. Lecture Notes in Computer Science, vol 1650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48508-2_23
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