“电机与拖动”在线课程中学习风格研究

Research on Learning Styles in Motor and Drive Online Course

针对大学生在线学习中存在问题,分析其原因之一,是缺乏对网络环境下学生的行为数据的分析,从而无法判断学生的学习风格。因而基于数据挖掘技术,利用网络学习平台收集“电机与拖动”课程学习行为数据,建立了基于灰狼算法优化支持向量机构建多维度的学习风格模型,处理和预测其可能的学习风格。有助于教师及时掌握学生动态,调整线上线下教学方案,实现因材施教的个性化教学。

In view of the problems existing in college students′online learning,one of the reasons analyzed in this paper is the lack of analysis of students′behavior data in the Internet environment,for which students′learning style can not be judged.Therefore,based on data mining technology,this paper uses the Internet learning platform to collect the learning behavior data of Motor and Drive course,establishes a multi-dimensional learning style model based on Gray Wolf algorithm to optimize support vector mechanism to process and predict students′possible learning style.It is helpful for teachers to timely grasp students′dynamics,adjust online and offline teaching schemes,and realize personalized teaching according to their aptitude.