基于聚类算法的线上学习行为分析——以Java面向对象程序设计课程为例

Analysis of Online Learning Behavior Based on Clustering Algorithm:Take the Java Object-oriented Programming Course as an Example

为了有效利用线上学习平台记录的学生学习行为数据,从多方面刻画学习者画像,充分发挥线上教学的作用,以安徽科技学院物联网专业2017级80名学生为研究对象,对其开展了Java面向对象程序设计课程线上教学。在人工智能技术的驱动下,首先统计学生学习时间和视频内容数据,分析他们的观看习惯和对重点、难点内容的重视程度;然后在K-Means++聚类算法的基础上,分析课程视频、章节检测、学习次数、作业和签到等特征对学习效果的影响。结果表明:上述方法可以帮助总结学生的学习态度、偏好和习惯,将相似学习风格的学生聚为一类。老师可以通过线上学习行为的分析调整教学内容,改进教学方法,从而改善线上教学效果。

In order to effectively use the student behavior data recorded by the online learning platform, depict the portraits of learners from various aspects, and take full advantage of the online teaching and learning, 80 students of grade 2017 majoring in Internet of Things in Anhui University of Science and Technology are taken as the research objects, and the online teaching of Java object-oriented programming course is carried out. With the help of artificial intelligence,firstly,we count the students’ learning time and collect their video content data of Java objectoriented programming course,and analyze their viewing habits and their attention paid to key and difficult content.Secondly,we analyze the influence of course videos,chapter tests,study and sign-in times,and homework performance on students’ study effects based on the K-Means++ clustering algorithm. The results show that the above methods help summarize students’ learning attitudes,preferences,and habits to categorize students with similar learning styles. In conclusion,teachers can choose teaching contents and adjust teaching methods to improve online teaching effects based on the analysis of online learning behavior.