In this paper, the study aims to inquire into the impact of integrating creative thinking teaching through project-based learning in the engineering course. In the beginning, the quasi-experiment is implemented in the engineering course. The lecture-based teaching strategy is applied to the control group, while the experimental group applied the project-based learning strategy. All of the students are asked to propose their draft scheme of final project in the middle term, and then implement one system prototype as the final project in the end of semester. The draft scheme and prototype are reviewed by experts for measuring whether creativity has increased or not. Finally, a satisfaction questionnaire is delivered to evaluate the students’ opinions about the experimental classroom. The results show that the project-based learning approach is able to promote the creative thinking of students, especially with regard to fluency and flexibility. Moreover, the students in their interview thought suitable creativity thinking tools are an approach that can promote their creativity significantly. Hence, the various creative thinking tools must be provided for students in order to take advantage of developing creativity thinking habits.

The prevalence of COVID-19 has changed traditional teaching modes. For many teachers, online learning effectively compensated for the absence of traditional face-to-face instruction. Online learning can support students and schools and can create unique opportunities under emergency management. Educational institutions in various countries have launched large-scale online course modes in response to the pandemic. Additionally, online learning during a pandemic differs from traditional online learning modes. Through surveying students in higher education institutions, educational reform under emergency management can be explored. Therefore, university students were surveyed to investigate their continuance intention regarding online learning during the pandemic. Expectation confirmation theory was extended using the task-technology fit model to ascertain whether the technical support of promoting online learning helped student’s complete course learning tasks during the pandemic and spawned a continuance intention to use online learning in the future. Data were collected through online questionnaires. A total of 854 valid responses were collected, and partial least squares structural equation modeling was employed to verify the research hypotheses. The results revealed that the overall research framework largely explained continuance intention. Concrete suggestions are proposed for higher education institutions to promote online learning modes and methods after the COVID-19 pandemic.

In response to the Covid-19 pandemic, online learning has been carried out in many countries with different types of online learning models being promoted and implemented. In the global pandemic continues, the education environment is forced to change from traditional classroom or blended teaching mode to online learning teaching model. With the outbreak of COVID-19, China was the first to announce that online courses are to be implemented in February 2020. In China, whether online learning can replace traditional offline teaching has become a topic worth discussing. Therefore, this study investigates university students in China by questionnaires and discussions of this topic. The study is based on the Push–Pull Mooring model. Based on 854 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model. The results show that push effects (Perceived security risk, Learning convenience, and Service quality), pull effects (Usefulness, Ease of use, Teacher's Teaching Attitude, Task-technology Fit), and mooring effects (habit) all significantly influence users' switching intentions from offline to online learning platform. Finally, this study explores whether push–pull–mooring can be a reference for promoting and implementing online learning courses in Chinese colleges and universities in the future after the pandemic.

During the COVID-19 pandemic, teachers had to conduct online classes because of the breakdown of school learning. Teacher competence has a great impact on the students’ learning outcomes in online learning. Teacher resilience is also important to help teachers survive and achieve a high level of well-being in emergency situations. Previous studies have explored the protective and risk factors of teacher resilience, among which teacher competence in various aspects is included. In addition, teachers’ age differences in competence and resilience have been the focus of past studies. However, few studies have investigated the impact of teacher competence on students’ online learning outcomes, the mediating role of teacher resilience, and the moderating effect of age when teachers participate in emergent online teaching. To address the above gap, this study explored teachers’ perceptions of students’ online learning outcomes and how teacher competence in online teaching and resilience can predict these outcomes. The data of 159,203 participants were collected and subjected to correlation analyses and a moderated-mediation effect test. The results indicated that (1) teacher competence in online teaching was positively related to perceived online learning outcomes; (2) teacher resilience was positively related to the teachers’ perceived online learning outcomes; (3) teacher resilience played a partial mediating role between teacher competence in online teaching and perceived online learning outcomes; and (4) teachers’ age moderated the direct and indirect relation between teacher competence in online teaching and perceived online learning outcomes. The findings imply that teachers should strengthen their own teaching competence and their resilience before conducting online teaching. In addition, this study proposes intervention strategies to enhance teachers’ resilience and well-being through teacher competence cultivation and provides suggestions for different age levels of teachers to develop and train their online teaching competence and resilience in the future.

The heart is one of the most important organs of the human body. It circulates blood throughout the human body and delivers oxygen and nutrients to all the organs for metabolism. Cardiac muscle contraction results in blood circulation, which maintains the body temperature at approximately 37 °C. If the cardiac function is abnormal, then the body temperature will be affected. The cardiac function degenerates as the human body ages, and the degeneration can occasionally result in cardiovascular diseases. When the Internet of Medical Things is integrated into heart disease screening systems to detect heart diseases, people can perform self-examinations to evaluate whether their hearts exhibit irregularities for early heart disease detection. STM32 is used in this study as the main Internet-of-Medical Things controller and is combined with the Internet of Things devices—a sphygmomanometer cuff, temperature sensor, and pulse sensor—for instrument control and data acquisition. This assembly is used to develop a valvular heart disease screening system, whose structure incorporates deep learning for the development of fitting models and analysis. An experiment is performed where blood flow is blocked temporarily and released to observe changes in the surface temperature of the fingertip skin, and the blood supply capability of the heart is assessed indirectly based on the temperature change curve. Eighteen subjects were recruited in the experiment, where one subject exhibited cardiac valve insufficiency and arrhythmia. In the experiment, temperature curve variation data are successfully obtained from the healthy subjects, whereas the temperature curve irregularities of the patient with cardiac valve insufficiency are identified. This subject’s temperature range throughout three test steps is smaller by within 0.52 °C compared with those of most of the other subjects. In addition, during blood blocking and release, the overall temperature curve decreases, whereas some curves escalate first before plummeting slowly. The data analysis results show that the temperature curve variation and values of Subject 2 are similar to those of Subject 10, suggesting the incidence of valvular heart disease. This valvular heart disease screening system can successfully analyze and assess the characteristic signal values of patients with valvular heart disease.