This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The study investigates the role of foreign language enjoyment (FLE) and engagement in the context of English language learning among Chinese students, emphasizing the significance of positive emotions in enhancing academic success. Utilizing a sample of 249 students majoring in international trade, the research employs the foreign language enjoyment scale to count their enjoyment level and foreign language engagement scale to assess various dimensions of student engagement, including cognitive, emotional, behavioral, and social engagement. By conducting regression analysis, the findings reveal that FLE positively influencing learners’ learning outcome while engagement doesn’t pose significant impact on their learning outcome. The study highlights the importance of fostering positive emotions in educational settings to improve language learning outcomes and suggests that understanding the interplay between FLE and other affective factors can lead to more effective teaching strategies in foreign language education. 

The COVID-19 pandemic has shifted education from traditional in-person classes to remote, online-dependent learning, often resulting in reduced learning effectiveness and satisfaction due to limited face-to-face interaction. To address these challenges, interactive teaching strategies, such as the flipped classroom approach, have gained attention. The flipped classroom model emphasizes individual preparation outside class and collaborative learning during class time, relying heavily on in-person interactions. To adapt this method to remote learning, the Remote Flipped Classroom (RFC) integrates the flipped classroom approach with online learning, allowing flexibility while maintaining interactive opportunities. RFC has incorporated short films as teaching tools, leveraging their ability to contextualize knowledge and cater to the preferences of visually-driven younger learners. However, research on the effectiveness of RFC with films remains limited, particularly in fields like nursing education, where practical engagement is crucial. This article shares the practical experience of applying RFC with films in a nursing education context. Positive feedback was observed, though many students still expressed a preference for in-person classes. These insights suggest that strategies like RFC with films could be valuable in maintaining engagement and learning efficiency in remote classrooms.