The Effectiveness and Issues of Using Feedback in Second Language Writing

Based on the description of feedback in the literature and the classification of feedback by different scholars, this paper discusses the efficiency and possible issues of the teachers’ use of different feedback in the teaching of second language writing. Through interviews with experienced English majors, this paper further demonstrates the importance of teachers' use of feedback in second language writing teaching. At the end of the paper, the author also shows that the combination of different feedback according to specific situations in teaching can better promote the writing proficiency of second language learners

Waterlogging Detection in Champaign County with Remote Sensing Imagery and Decision Tree Learning

Flooding has become the leading unresolved factor for maize yield loss. Extreme rainfall can cause large and separate waterlogging areas in crop fields, which makes loss prediction difficult. Current waterlogging detection projects usually apply traditional statistical models on public satellite imagery or drone imagery, which usually overlook the lack of resolution or scalability. In this research, we will solve these problems with high-resolution and wide-availability satellite imagery and decision tree learning models. 3-meter resolution PlanetScope CubeSat imagery is used in this research project. As no labels attached to this dataset, our team hand-labeled over two hundred satellite images and converted them to pixel labels. Then, decision tree models and random forest models are trained using these labels. We apply trained models to create pixel-by-pixel waterlogging maps in 2019 Champaign County, and finally, achieved above 94% accuracy.Ope

Conjunction of Factors Impacting the 2019-2020 Flu Season in the US

The 2019-2020 flu season is regarded as one of the most serious ones in decades. Previous researchers usually studied the effects of different factors on seasonal flu separately instead of their conjugate impact, so we wanted to find how multiple factors combine to affect the spread of influenza in the 2019-2020 flu season in America. We chose types of virus (A and B), environmental factors (temperature, precipitation, relative humidity), population density, and influenza vaccination status for different age groups which are statewide data containing monthly information from Sep. 2019 to May 2020. By principal component analysis, we could see the importance of different factors as well as the general relationship between them. Furthermore, using path analysis enabled us to investigate the causal relationship between factors more precisely than the previous method. We found that two virus types have different relationship patterns with other remaining variables: Type A virus is strongly negatively related to temperature (lower temperature tends to cause more cases), and is also somewhat related to some vaccination groups, while the significance of any vaccination group doesn’t show up in Type B virus. Moreover, there are relationships between factors, like the vaccination rates for different age groups are strongly correlated to each other. Our findings could provide general advice to you during flu season. For instance, if the temperature is relatively low one year, then you could be aware that it’s more likely for you to be infected. In addition, since the influenza situation is somewhat similar to COVID-19, our findings might also be helpful for you to protect yourself.https://digitalcommons.morris.umn.edu/urs_2021/1003/thumbnail.jp