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Proceedings of 2020 Ieee International Conference on Teaching, Assessment, and Learning for Engineering ; : 31-38, 2020.
Article in English | Web of Science | ID: covidwho-1312298


Many universities around the world arc now providing online courses on platforms of MOOC (Massive Open Online Course) related to the COVID-19 outbreak. Therefore, it is important for universities to quantify student-learning effectiveness based on MOOC. However, its operation faces many challenges to educational administrators and teachers. The most important thing is that the completion rate of learners is usually low, and therefore it is not comprehensive and objective to directly use online data to assess and predict the student-learning effectiveness. In order to assess multi-stage learning effectiveness of students in MOOC comprehensively, we proposed MOLEAS, a multi-stage online learning effectiveness assessment scheme. First, MOLEAS uses matrix completion to predict missing learning data of students. We take two open courses offered in the icourse MOOC platform as examples to analyze online data, and then we study the student-learning effectiveness using the matrix completion. The prediction results prove the effectiveness and reliability of our model. Then, combined with the prediction, MOLEAS utilizes the influencing factors of student-learning effectiveness, and a series of measures to improve the entire learning effectiveness of students in MOOC. What is more, we design a simulation practice platform which presents strong support to practice online teaching.

Cardiovascular Innovations and Applications ; 5(3):165-172, 2021.
Article in English | Web of Science | ID: covidwho-1154911


Background: Since early December 2019, coronavirus disease 2019 (COVID-19) has emerged as a global pandemic and public health crisis. This study aims to explore the relationship between cardiac injury and inflammatory biomarkers in patients with severe COVID-19. Methods: We collected data on 91 patients with a confirmed diagnosis of severe COVID-19 from February 8 to March 31, 2020. Demographic characteristics, clinical data, and in-hospital outcomes were compared. The relationship between cardiac injury and inflammatory biomarkers was analyzed. Logistic regression was used to explore the independent risk factors for cardiac injury. Results: The mean age of all patients was 61 years +/- 14 years. About half of the patients were male. Hypertension and coronary heart disease were more common in the cardiac injury group. The levels of inflammatory biomarkers in patients who experienced cardiac injury were generally higher than the levels of those without cardiac injury, including interleukin-6, interleukin-2 receptor (IL-2R), procalcitonin, and high-sensitivity C-reactive protein. There were positive correlations between the levels of high-sensitivity troponin I and N-terminal prohormone of brain natriuretic peptide and the levels of inflammatory biomarkers. Logistic regression shows that IL-2R (odds ratio 1.001, 95% confidence interval 1.000-1.002, P = 0.045) and comorbidities (odds ratio 4.909, 95% confidence interval 1.231-19.579, P = 0.024) are independent risk factors for cardiac injury in patients with severe COVID-19. Conclusion: High levels of inflammatory biomarkers are associated with higher risk of cardiac injury in patients with severe COVID-19. IL-2R and comorbidities are predictors of cardiac injury.

International Eye Science ; 21(1):140-143, 2021.
Article in Chinese | EMBASE | ID: covidwho-1029207


AIM: To analyze the problems faced by teachers and undergraduates online teaching. METHODS: A self-designed questionnaire survey and result of examination comparison were used. The contents of the questionnaire include the time used before and after class, the confusion faced by online teaching and the self-evaluation of teaching effect. 63 students and all teachers were participants in the questionnaire survey. The survey is from May 2020 to June 2020. RESULTS: The average time spent by students before class of online teaching had no difference with that of offline teaching, while the average time spent by teachers for online teaching before class was significantly longer than that for offline teaching. 63% of the undergraduates considered that online teaching takes much more time to review after class. 95% of the students admitted that online teaching was easier to lose concentration because of lack of interaction with teachers, and 73% of teachers though that for online teaching they had less passionate compared to off line teaching. Regarding to the questionnaire survey, 73% of the instructors expected that the effect of online teaching would be worse than that of offline teaching. Surprisingly, 95% of the students thought that there had no significant difference in knowledge mastering between online and offline teaching after reviewing of courseware. For the future teaching model, 91% of the teachers and 79% of the students preferred the combination of watching pre-recorded video and live broadcasting. CONCLUSION: The lack of interaction is the primary issue of online teaching. Online teaching can achieve the same effect as offline teaching, whereas it needs more post-class time for students. The combination of watching pre-recorded video and live-broadcasting is the online teaching mode recommended by teachers and students.