大学生在线学习行为投入评测的研究

Research on the Evaluation of College Students'Online Learning Behavior Investment

由于在线教学中师生时空分离,教师很难实时掌握学生的学习状态。为了帮助教师了解学生的学习情况并对学习状态差的学生进行预警,构建了大学生在线学习投入评测模型,并以信息与网络安全技术课程为例,测试其评测效果。首先利用网络爬虫软件抓取了中国大学MOOC和华为应用商城的实时数据,其次利用数据建表,最后利用Python编程对数据进行分类、统计和聚类分析,并将分析结果进行可视化展示,帮助学生了解自身的学习投入状况,同时也为教师和教学管理者提供个性化的干预与支持服务。

Due to the separation of time and space between teachers and students in online teaching,it is difficult to grasp students'learning status in real time.In order to help teachers understand students'input and give early warning to students who are in poor condition,an online learning evaluation model for college students is constructed.Taking the course"information and network security technology"as an example,the evaluation effect is tested.First,the crawler is used to crawl the real-time data of the University MOOC network and Huawei application mall,and then build a table for the data.Finally,the python programming is used to classify,count and cluster the data,and the analysis results are displayed in a visual interface to help learners understand their own learning investment,and teachers and teaching managers provide personalized intervention and support services.