Utilizing Multiagent-Based Conceptual Learning in STEM Education for Analytical Learners through a Random Classifier Model
Keywords:
conceptual learning, deductive, inductive, multiagent, random classifier, STEMAbstract
The study illustrates a multiagent-based conceptual learning framework for analytical learners using logical, linguistic, numerical, and abstract reasoning skills. This approach provides tailored and adaptable learning paths based on strengths and weaknesses to improve learning. Multiple agents provide individualized feedback and coaching to enhance analytical skills holistically. Engagement, learning style, and material availability are considered while calculating reasoning scores. This analysis helps the framework offer relevant learning materials and activities by understanding each learner's requirements and preferences. The multiagent-based strategy also encourages peer-to-peer learning and knowledge sharing, improving the learning experience. The proposed methodology uses a reasoning score and a random forest classifier to predict learners' learning styles. The model compares to inductive, deductive, and transductive machine learning models. The random forest classifier model beats other learning methods. The random forest classifier may be able to effectively predict learners' learning styles. In addition, multiagent-based learning improves collaboration and enables customized learning experiences.
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