基于在线学习行为数据的学习效果模型与影响因素分析

为了探究学生在线学习情况与学习效果之间的关系,采用数据标注的方式解决学生学习行为表示的问题.以S大学在线教学平台数据为研究对象,通过数据挖掘技术探寻学生在线学习行为与学习效果之间的关系.对比多种机器学习算法后,选定随机森林算法作为学习效果预测模型的基本算法.研究发现：最能影响学习效果的因素是文档学习总时长,最终构建的学习效果预测模型对整体数据集的分类准确率达到84.69%.

Influence factor analysis and learning effect model based on online learning behavior data

In order to explore the relationship between students'' online learning situation and learning effect, students'' learning behavior representation was solved by adopting data annotation. Based on the data from the online teaching platform of S University, the relationship between students'' online learning behavior and learning effects was explored through data mining technology. By comparing a variety of machine learning algorithms, the random forest algorithm was selected as the basic algorithm of the learning effect prediction model. It was found that the total duration of document learning could affect the learning effect greatly. The final learning effect prediction model was constructed which provided an accuracy of 84.69% for the classification of the overall data set.