In modern society, English, as an important language, is an indispensable tool for people to communicate and exchange. However, learning English is not limited to knowledge points, grammar and other aspects. With the development and progress of the times and the rapid improvement of the level of science and technology, the problem of how to cultivate students' interests has become increasingly prominent. Interest is the best teacher to learn, and it is also the most effective, direct and lasting way for students to learn English well and improve their level and ability. Cultivating good teaching habits can help us master knowledge and skills better. Starting from the importance of students' interest in learning, this paper discusses how to stimulate students' interest in learning and find the correct teaching methods in order to help students have a strong interest in English learning in class and help them acquire knowledge actively.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

This study explored how facilitation skills—defined as instructional techniques that accurately convey core messages in a trusting relationship and encourage self-directed learning participation among adult learners—affect the effectiveness of learning. The research focused on adult learners enrolled in lifelong education programs at seven universities, including general and vocational colleges in Busan. It aimed to examine the relationships between instructors’ facilitation skills, learner engagement, and learning outcomes, as well as the mediating effect of engagement on these relationships. A total of 213 valid survey responses were analyzed from an initial 215 responses, excluding 2 unsuitable entries. The findings are summarized as follows. First, facilitation skills were found to partially influence learner engagement. Second, learner engagement was shown to affect learning outcomes. Third, facilitation skills were found to have a partial effect on learning outcomes. Fourth, learner engagement partially mediated the relationship between facilitation skills and learning outcomes. Based on these results, this study is expected to contribute to a deeper understanding of the relationship between facilitation skills and learning outcomes in adult learners, providing practical guidelines for enhancing effectiveness in various educational contexts.

In the dynamic landscape of modern education, it is essential to understand and recognize the psychological habits that underpin students’ learning processes. These habits play a crucial role in shaping students’ learning outcomes, motivation, and overall educational experiences. This paper shifts the focus towards a more nuanced exploration of these psychological habits in learning, particularly among secondary school students. We propose an innovative assessment model that integrates multimodal data analysis with the quality function deployment theory and the subjective-objective assignment method. This model employs the G-1-entropy value method for an objective evaluation of students’ psychological learning habits. The G-1-entropy method stands out for its comprehensive, objective, and practical approach, offering valuable insights into students’ learning behaviors. By applying this method to assess the psychological aspects of learning, this study contributes to educational research and informs educational reforms. It provides a robust framework for understanding students’ learning habits, thereby aiding in the development of targeted educational strategies. The findings of this study offer strategic directions for educational management, teacher training, and curriculum development. This research not only advances theoretical knowledge in the field of educational psychology but also has practical implications for enhancing the quality of education. It serves as a scientific foundation for educators, administrators, and policymakers in shaping effective educational practices.

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.