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Construction of a diagnostic system of deaf students' knowledge level using adaptive neurofuzzy inference systems (ANFIS)

This paper presents the construction of a system suitable for student diagnosis with the use of neurofuzzy techniques. To be more specific, the extent of usefulness of Adaptive NeuroFuzzy Inference Systems (ANFIS) is examined for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the specific methods is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process. The system was trained through data extracted from an educational project called ENFORA.