Prior to the coronavirus disease 2019 (COVID-19) pandemic, due to the rarity of pandemics in recent centuries, suitable conditions did not exist in educational institutions for the implementation of asynchronous distance teaching. No empirical studies have been conducted on whether the considerable environmental changes caused by COVID-19 have affected students’ online learning behaviors. Therefore, this study collected information on students’ online learning behaviors during the COVID-19 pandemic and other periods to examine whether pandemic-caused environmental changes affected students’ online learning behaviors. This study focuses on the 60-day transmission after the beginning of the second semester of the 2019 academic year. The data source was from a comparative assessment between the pandemic group (331 students) and the control group (101 students). The Spearman Rank Correlation Test and the Wilcoxon signed-rank test were used as our statistical methods. This paper presents preliminary results on how COVID-19 has affected students’ online learning behaviors and proposes asynchronous online learning as a method for maintaining university students’ learning during the COVID-19 pandemic.

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Early warning systems (EWSs) have been successfully used in online classes, especially in massive open online courses, where it is nearly impossible for students to interact face-to-face with their teachers. Although teachers in higher education institutions typically have smaller class sizes, they also face the challenge of being unable to have direct contact with their students during distance teaching. In this research, we examined the online learning trajectories of students participating in four small private online courses that were all taught by one teacher. We collected relevant data of 1,307 students from the campus learning management system. Subsequently, we constructed 18 prediction models, one for each week of the course, to develop an EWS for identifying students in online asynchronous learning at risk of failing (i.e., students who fail their final examination). Our results indicated that the fifth-week model successfully predicted student performance, with an accuracy exceeding 83% from the eighth week onward.

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