Evaluating an intelligent diagnosis system of historical text comprehension

This work aims to present and evaluate a Fuzzy-Case Based Reasoning Diagnosis system of Historical Text Comprehension (F-CBR-DHTC). The synergism of fuzzy logic and case based reasoning techniques handles the uncertainty in the acquisition of human expert’s knowledge regarding learner’s observable behavior and integrates the right balance between expert’s knowledge described in the form of fuzzy sets and previous experiences documented in the form of cases. The formative evaluation focused on the comparison of the system’s performance to the performance of human experts concerning the diagnosis accuracy. The system was also evaluated for its behavior when using two different historical texts. Empirical evaluation conducted with human experts and real students indicated the need for revision of the diagnosis model. The evaluation results are encouraging for the system’s educational impact on learners and for future work concerning an intelligent educational system for individualized learning.