Analysis of online college teaching data before and after the COVID-19 epidemic
KONG Xiao1, LIU Naijia2, ZHANG Menghao1,3, XU Mingwei1,3
1. Institute for Network Science and Cyberspace & BNRist, Tsinghua University, Beijing 100084, China; 2. Information Technology Center, Tsinghua University, Beijing 100084, China; 3. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Abstract：The corona virus disease 2019 (COVID-19) pandemic has led Chinese universities to turn to online teaching. Several researchers have used surveys to study the characteristics and challenges of online teaching. However, these surveys inevitably included subjective bias and sample limitations. This study analyzed the server logs of the Tsinghua University course selection system during online teaching with comparisons with those of the past two years. Since all students must visit the course selection system every semester, the server logs track the students' learning and living habits. After data cleaning, visualization and analysis, the logs show that over 98% of the sessions came from outside the campus during the epidemic. The number of visits to the system decreased by 25%-47%, the number of visits originating from mobile devices decreased by 7%, and the average browsing time per visit declined by 8%. Thus, this study shows evidence that online teaching leads to a notable decline in student engagement with the system. This study then suggests that colleges pay more attention to the student's productivity and engagement, thereby improving the quality of teaching.
孔啸, 刘乃嘉, 张梦豪, 徐明伟. COVID-19疫情前后高校在线教学数据分析[J]. 清华大学学报（自然科学版）, 2021, 61(2): 104-116.
KONG Xiao, LIU Naijia, ZHANG Menghao, XU Mingwei. Analysis of online college teaching data before and after the COVID-19 epidemic. Journal of Tsinghua University(Science and Technology), 2021, 61(2): 104-116.
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