Remote Learning of COVID-19 Kinetic Analysis in a Physical Chemistry Laboratory Class
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ABSTRACT:
SUBMITTER: Hart K
PROVIDER: S-EPMC8547164 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The role and efficacy of the laboratory in chemical education have recently been a
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Project description:Student engagement in online learning enhance students performance and the outcomes of the learning process in online learning environment. The existed literature revealed various factors influencing student engagement in online leaning, however these studies were before the COVID-19 crisis. The purpose of the current paper is to explore the factors that influence student engagement in online learning during the COVID-19 crisis in middle school settings in developing countries where is a lack of studies about the factors influencing student's engagement in emergency remote learning during the crisis. A qualitative approach was used for data collection and analysis. Semi-structured interviews with 34 participants (14 students, 13 teachers, and 7 parents) were conducted for 20-30 min. Furthermore, online class observations were used for data collection; 13 online classes were observed. Each class was 40 min. A thematic analysis was used to categorize the findings into themes and subthemes. The findings of the study revealed that various factors influence student engagement in online learning during the crisis including infrastructure factors, cultural factors, digital inequality, and the threat to digital privacy. Cultural factors were the important factor that influences females because of parents' culture and their bias against females using online learning compared to male students. Teachers' presence and quality of content were the major factors that influence student engagement, where parental concerns, norms, and traditions emerged as the major factors in the crisis, influencing engagement. Most of the participants reported that teaching and learning online during the crisis has broadened the digital inequality and threatened their digital privacy which influenced negatively student engagement. The limitations of this research included the limited number of participants covering a large geographic area, and the research design using diverse and often limited educational software and delivery methods. Future studies could utilize a mixed-method approach and include more participants.Supplementary informationThe online version contains supplementary material available at 10.1007/s10639-021-10566-4.
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Project description:The global pandemic caused by the novel Coronavirus (COVID-19) in spring 2020
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Project description:Is remote learning associated with education inequalities? We use PISA 2018 data from five European countries-France, Germany, Italy, Spain and the United Kingdom-to investigate whether education outcomes are related to the possession of the resources needed for distance learning. After controlling for a wide set of covariates, fixed effects, different specifications and testing the stability of coefficients, we find that remote learning is positively associated with average education outcomes, but also with strong and significant education inequalities. Our results show that negative gaps are larger where online schooling is more widespread, across countries, locations, and school types. More generally, remote learning inequalities appear to be associated with technological network externalities: they increase as digital education spreads. Policy makers must guarantee to all students and schools the possession of the resources needed for remote learning, but to reach this goal efficiently they must adapt their actions to the characteristics of countries, areas and school systems.Supplementary informationThe online version contains supplementary material available at 10.1007/s10663-022-09556-7.
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Project description:This study utilized a nationally distributed survey to explore early childhood teachers' experience of providing remote learning to young children and their families during the early months of the U.S. response to the COVID-19 pandemic. A convergent parallel mixed-methods design was used to analyze 805 participants' responses to closed and open-ended survey questions. Results indicated that teachers provided various remote learning activities and spent more time planning instruction and communicating with families than providing instruction directly to children. Early childhood teachers reported several positive aspects of remote learning and various challenges during the initial months of the pandemic. Study findings are discussed in the context of policy and practical implications for supporting early childhood teachers to deliver high-quality and developmentally appropriate remote learning for all young children and their families.
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Project description:ObjectivesThe present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.MethodsWe developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters. Performance evaluation was conducted on the test data set and external validation data set from Brazil.ResultsThe accuracy values of all models ranged from 74% to 91%. The RF model trained from CC and CBC analytes showed the best performance on the present study's data set (accuracy, 85.3%; sensitivity, 79.6%; specificity, 91.2%). The RF model trained from only CBC parameters detected COVID-19 cases with 82.8% accuracy. The best performance on the external validation data set belonged to the SVM model trained from CC and CBC parameters (accuracy, 91.18%; sensitivity, 100%; specificity, 84.21%).ConclusionsML models presented in this study can be used as clinical decision support tools to contribute to physicians' clinical judgment for COVID-19 diagnoses.
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Project description:We examined how the shift in learning environment from in-person to online classes, due to the COVID-19 pandemic, impacted three constructs of student engagement: behavioral engagement, including students' frequency of participating in class discussions, meeting with instructors, and studying with peers outside of class; cognitive engagement, including students' sense of belonging and self-efficacy; and emotional engagement, including students' attitudes toward science, their perceived value of the course, and their stress. Seventy-three undergraduate STEM students from across the country completed five-point Likert-style surveys in these areas of student engagement, both prior to their science course transitioning online and at the end of the spring 2020 semester. We found that while overall behavioral engagement did not change, students participated less frequently in class discussions but met with professors more often outside of class. We saw no significant change in cognitive engagement, indicating that while students' sense of belonging and self-efficacy ideally increases over the course of the semester, in this case, it did not. Most alarmingly, we found a significant decrease in emotional engagement, with students reporting a drastic decline in positive attitudes toward science. Students' reported stress levels remained unchanged, and students reported a slight increase in their perceived value of the science course they were taking. These data shed light on how the transition to online learning had an overall negative impact on undergraduate student engagement in science courses.
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Project description:When a well-trained model learns a new class, the data distribution differences between the new and old classes inevitably cause catastrophic forgetting in order to perform better in the new class. This behavior differs from human learning. In this article, we propose a class incremental object detection method for remote sensing images to address the problem of catastrophic forgetting caused by distribution differences among different classes. First, we introduce a class similarity distillation (CSD) loss based on the similarity between new and old class prototypes, ensuring the model's plasticity to learn new classes and stability to detect old classes. Second, to better extract class similarity features, we propose a global similarity distillation (GSD) loss that maximizes the mutual information between the new class feature and old class features. Additionally, we present a region proposal network (RPN)-based method that assigns positive and negative labels to prevent mislearning issues. Experiments demonstrate that our method is more accurate for class incremental learning on public DOTA and DIOR datasets and significantly improves training efficiency compared to state-of-the-art class incremental object detection methods.
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