Usage of XG Booster Classifier in Implementation of STEM Education Among Different Types of Learners for Multiagent System.
Keywords:
Multiagent system, STEM, XGBoost, Honey and Mumford Learning stylesAbstract
This study uses the Honey and Mumford Learning Styles Questionnaire to investigate how a multiagent system (MAS) can be integrated into STEM (Science, Technology, Engineering, and Mathematics) curricula to accommodate a variety of learner profiles. To improve individualized learning, the MAS uses an XGBoost classifier to identify several learning styles, including active, reflective, theoretical, and pragmatic. The system modifies its resource allocation, collaborative activities, and teaching tactics to accommodate individual preferences and cognitive processes. Student, teacher/tuition, collaborative learning, assessment and evaluation, adaptation and recommendation, resource management, and system coordination agents are the agents that make up the MAS. The goal of the study is to determine how well MAS supports engagement, comprehension, and skill across a variety of learning styles, therefore catering to the needs of diverse students in STEM education. It accomplishes this by fusing instructional strategies with technology.
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