Modelling Student's Comprehension of Historical Text Using Fuzzy Case-Based Reasoning

This contribution presents the use of Fuzzy Case-Based Reasoning methods for modelling student’s Historical Text Comprehension (F-CBRDHTC). The proposed fuzzyCase-Based Reasoning synergism handles the uncertainty in the acquisition of expert’s knowledge regarding student’s observable behaviour during Historical Text Comprehension (HTC). It integrates the right balance between expert’s knowledge described in the form of fuzzy sets and previous experience-cases. A fuzzy k-nn retrieval and similarity-measuring algorithm helps the student model to handle case adaptation by facilitating the retrieval process from the case base. The F-CBRDHTC student model assesses the student’s cognitive profile and profile descriptor, which describe the student’s learning difficulties, and reflects assessment results back to the student. This open student model helps students to better understand their beliefs and instructors to target their teaching strategies to student groups with specific learning difficulties. Preliminary experiment with human experts indicates that the model is effective in handling uncertainty in student diagnosis.