Redefining Learning Efficacy with Fuzzy Logic Algorithm: Cognitive Load Analysis and Decision Making in Special Education Classroom

The current work studies the application of fuzzy logic algorithms to enhance effectiveness of learning in special education classrooms using a combination of cognitive load analysis and adaptive decision-making. Traditional teaching methods, however, tend to struggle with the various learning needs of learning students with cognitive and developmental problems that cause added cognitive load and reduced involvement. This research proposes to define the fuzzy logic-based framework which describes the uncertainty and the variability in the learner's performance which leads to the individualization of the instructional strategies. The system utilizes student responses to determine cognitive load, student’s response to update make the system more appropriate in terms of difficulty and pacing of content activity. Experimental results have shown great levels of improvement in attention, understanding and task completion for students of different learning abilities. The study puts the potential of computational intelligence in aiding educators to make informed decisions in favor of inclusive and productive learning environments. Future work there can be done to integrate multimodal data and consider scale up to more diverse educational environments.

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