Cognitive and Fuzzy Models to Understand Learning Behaviors: AI for Enhanced Learning Pedagogy and Decision Making
Abstract
The reason why it is important to know the behavior of the learners is so that adaptive and efficient learning systems will be developed. The conventional learning systems are largely learning platforms that are founded on the outcome measures and have no way of modeling the mental states and uncertainty that prevail in human learning. The current paper suggests the use of the framework steered by artificial intelligence which would integrate cognitive modeling and fuzzy logic to understand the learning behaviors and assist the pedagogical decision making. The cognitive models, that involve the knowledge acquisition, engagement, and the psychological burden of learners and the fuzzy models that address the ambiguity in behavioral and emotional indicators by relying on linguistic reasoning. The proposed system to be discussed involves a fuzzy inference-based mechanism of cognitive states to adaptive instructional decisions viz. content personalization, modification of feedback and intervention strategies. The use of experimental evaluation has demonstrated higher predictability of learner performance, high levels of engagement and decision making, as opposed to the traditional rule-based and machine learning methods. These findings justify the appropriateness of using the combination of cognitive and fuzzy models in improving the pedagogy of learning in the intelligent educational system.