Design and Implementation of Intelligent Teaching System Based on Dynamic Weight Adjustment

Abstract

With the rapid advancement of Internet technology, online education has increasingly become a vital method for acquiring knowledge and skills. However, the conventional education and teaching systems often employ a random selection of test questions, overlooking the
individual differences in learners abilities and their progress in learning proficiency, which results in a sluggish enhancement of learning efficiency. To address this issue, this paper introduces an intelligent teaching system grounded in a linear regression model featuring bidirectional
dynamic weight adjustment. This system sets initial weights based on the difficulty, significance, relevance, and scope of knowledge covered
by each question, and subsequently adjusts these weights dynamically through a linear regression model, taking into account users real-time
responses and additional factors.