论文标题
结合调查和传感器来探索学生行为
Combining surveys and sensors to explore student behaviour
论文作者
论文摘要
学生的归属感对于成功的学习道路很重要，小组工作构成了现代大学物理教育的重要组成部分。为了研究赫尔辛基大学入门物理学生的小组动态，我们连续七个星期从七个实验室课程的七个实验室课程中收集了网络数据。数据是通过Sociopatterns平台收集的，并补充了学生的主要学科，学习年份和性别。我们还收集了力学基线测试，以衡量物理知识和科罗拉多州对科学调查的学习态度，以衡量态度。我们开发了通过使用助教的连接来研究实验室会议的小型网络的指标。在网络中，我们发现稳定的人口统计学均匀和异质群体。尽管有些学生始终与他们的网络有松散的联系，但我们无法确定风险因素。根据我们的结果，无论各个部分或成立的小组中的学生人口统计，物理实验室课程都同样成功地建立了牢固的联系组。因此，补充调查的社会模式为研究学生社交网络的动态提供了机会。
Student belongingness is important for successful study paths, and group work forms an important part of modern university physics education. To study the group dynamics of introductory physics students at the University of Helsinki, we collected network data from seven laboratory course sections of approximately 20 students each for seven consecutive weeks. The data was collected via the SocioPatterns platform, and supplemented with students' major subject, year of study and gender. We also collected the Mechanics Baseline Test to measure physics knowledge and the Colorado Learning Attitudes about Science Survey to measure attitudes. We developed metrics for studying the small networks of the laboratory sessions by using connections of the teaching assistant as a constant. In the network, we found both demographically homogeneous and heterogeneous groups that are stable. While some students are consistently loosely connected to their networks, we were not able to identify risk factors. Based on our results, the physics laboratory course is equally successful in building strongly connected groups regardless of student demographics in the sections or the formed small groups. SocioPatterns supplemented with surveys thus provides an opportunity to look into the dynamics of students' social networks.
