Implementing the Hybrid Neuro-Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Abstract

Learning difficulty is a significant issue that comprehensively needs to be taken care of by university instructors with a focus on altering their ways of educating students since various degrees of learning difficulty could be observed among students. Hence, proper approaches should be adapted and utilized that provide chances for each student, securing his/her educational right. By doing so, it guarantees that each student has a right of learning with a different pace of conducting it. For example, the same educational and teaching materials cannot be adapted to each student. Hence, the training of students with learning difficulty should follow a model that is characterized as “from bottom to top,” namely, “normal to learning with difficulties.” The characterization of this approach is very similar to that of the treatment of human beings: biological malfunctions by medicine and mechanical malfunctions by psycho-sociology. This paper proposes a diagnostic model to determine the cognitive ability of students who deal with learning disabilities. The computerized model is proposed based on a combination of fuzzy logic and machine learning utilizing association rules and cellular automata. The advantage of the proposed method is that it helps find the needs of these types of students, so the widely implemented practices that shape his/her learning behavior are avoided. Therefore, practical recognition of each student with a different pace of learning should be realized.