在线学习参与度的Markov模型及其长期预测  

Markov Modeling of the Participation Level in Online Learning and Its Long-Term Prediction

作　　者：钱学明[1] 

机构地区：[1]无锡科技职业学院物联网与人工智能学院,江苏 无锡

出　　处：《应用数学进展》2023年第11期4702-4707,共6页Advances in Applied Mathematics

摘　　要：在线学习参与度是评估线学习教学质量的重要指标。本文拟借助Markov链,通过引入激活函数,构建一种新的更新矩阵,从而获得在线学习参与度的数学模型,以便在不同阶段对在线学习参与度进行评估。进一步,利用Z变换获得转移矩阵的极限向量,实现对教学活动参与度的长期预测。该研究是实现在线教学的诊断与干预的有效途径之一。最后,通过实例分析,说明了理论模型的有效性。The participation level of online learning is an important index for evaluating the teaching quality of online learning. In this paper, we develop a mathematical model of online learning participation level based on Markov chain. By introducing an activation function, a new update matrix is con-structed in order to evaluate the online learning participation level at different stages. Moreover, the limit vector of the transfer matrix is obtained by using Z transform to realize the long-term pre-diction of participation level in teaching activities. This study is one of the effective ways to realize the diagnosis and intervention of online teaching. Finally, the effectiveness of the theoretical model is illustrated through example analysis.

关 键 词：在线学习参与度 MARKOV模型 长期预测 更新矩阵 Z变换 

分 类 号：G63[文化科学—教育学]