A framework for optimizing extended belief rule base systems with improved Ball trees

Research on the state of blended learning among college students - A mixed-method approach

In the wake of the COVID-19 pandemic in 2019, China's education leaders began to focus on and promote blended learning. The process is still in its infancy in Chinese colleges and universities, and its development remains a problem to be solved. By combining technology acceptance and student participation, this article proposes an analysis model for assessing the factors influencing blended learning. A questionnaire was designed and distributed, and 796 valid responses were collected. The mean and variance were used to examine the status of students' technology acceptance and satisfaction with blended learning. The t-test method was employed to analyze the gender differences between students in regard to the topic. The results show that: (1) students majoring in computer science view the factors as having a high level of influence in blended learning. (2) There are major variances regarding the perception of service quality between male and female computer science major students. There is no significant difference between them in terms of perceived usefulness, perceived ease of use, or computer self-efficacy. (3) There are considerable disparities in the skill involvement and participation of computer science major college students. The results show that the technology acceptance and participation of students determine the effect of blended learning. Based on these findings, this article provides theoretical and practical suggestions for the implementation of blended learning to improve its effect.

The application of artificial intelligence assistant to deep learning in teachers' teaching and students' learning processes

With the emergence of big data, cloud computing, and other technologies, artificial intelligence (AI) technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of "artificial intelligence + education."