The COVID-19 pandemic has caused tremendous loss starting from early this year. This article aims to investigate the change of anxiety severity and prevalence among non-graduating undergraduate students in the new semester of online learning during COVID-19 in China and also to evaluate a machine learning model based on the XGBoost model. A total of 1172 non-graduating undergraduate students aged between 18 and 22 from 34 provincial-level administrative units and 260 cities in China were enrolled onto this study and asked (...) to fill in a sociodemographic questionnaire and the Self-Rating Anxiety Scale twice, respectively, during February 15 to 17, 2020, before the new semester started, and March 15 to 17, 2020, 1 month after the new semester based on online learning had started. SPSS 22.0 was used to conduct t-test and single factor analysis. XGBoost models were implemented to predict the anxiety level of students 1 month after the start of the new semester. There were 184 and 221 students who met the cut-off of 50 and were screened as positive for anxiety, respectively, in the two investigations. The mean SAS scores in the second test was significantly higher than those in the first test. Significant differences were also found among all males, females, and students majoring in arts and sciences between the two studies. The results also showed students from Hubei province, where most cases of COVID-19 were confirmed, had a higher percentage of participants meeting the cut-off of being anxious. This article applied machine learning to establish XGBoost models to successfully predict the anxiety level and changes of anxiety levels 4 weeks later based on the SAS scores of the students in the first test. It was concluded that, during COVID-19, Chinese non-graduating undergraduate students showed higher anxiety in the new semester based on online learning than before the new semester started. More students from Hubei province had a different level of anxiety than other provinces. Families, universities, and society as a whole should pay attention to the psychological health of non-graduating undergraduate students and take measures accordingly. It also confirmed that the XGBoost model had better prediction accuracy compared to the traditional multiple stepwise regression model on the anxiety status of university students. (shrink)

Researching Lived Experience introduces an approach to qualitative research methodology in education and related fields that is distinct from traditional approaches derived from the behavioral or natural sciences—an approach rooted in the “everyday lived experience” of human beings in educational situations. Rather than relying on abstract generalizations and theories, van Manen offers an alternative that taps the unique nature of each human situation. The book offers detailed methodological explications and practical examples of hermeneutic-phenomenological inquiry. It shows how to orient oneself (...) to human experience in education and how to construct a textual question which evokes a fundamental sense of wonder, and it provides a broad and systematic set of approaches for gaining experiential material that forms the basis for textual reflections. Van Manen also discusses the part played by language in educational research, and the importance of pursuing human science research critically as a semiotic writing practice. He focuses on the methodological function of anecdotal narrative in human science research, and offers methods for structuring the research text in relation to the particular kinds of questions being studied. Finally, van Manen argues that the choice of research method is itself a pedagogic commitment and that it shows how one stands in life as an educator. (shrink)