Narrow Results

In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

With the advent of the era of big data, the teaching practice model of music major has also changed. The emergence and growth of social media allow users to act freely. Therefore, on the basis of social tagging, this paper makes full use of the personalized description information and project content information carried by tags in collaborative tagging. A personalized music recommendation algorithm based on social networks is proposed. In social networks, complex interactive relationships are formed between users and music content, as well as between users themselves. By analyzing users’ historical behavior data (such as receiving and listening to playlists, liking, and sharing), we can calculate the similarity between users. For example, if two users frequently listen to the same song or artist, then their similarity is higher. Restart type random walk is an algorithm that iteratively searches on a graph, adding a “restart” mechanism to the traditional random walk. Specifically, during each walk, the algorithm has a certain probability (usually a preset small value, such as 0.15) to return to the starting node, rather than continuing to randomly select the next adjacent node. This mechanism helps the algorithm to explore new nodes while maintaining attention to the starting node, thereby avoiding getting stuck in local optima. A personalized mobile music recommendation algorithm based on the music genome is proposed based on the similarity of the node structure of two graphs and the random walk of restart type. Based on the similarity of interests between different users, a personalized mobile music recommendation algorithm is proposed to realize the personalized service of mobile terminals.

An Intelligent Exam Management System (IEMS) improves education information and handles an important role in education administration and management. Test optimization is the practice of successfully reducing test execution time and expenses. However, this can come at the expense of accuracy, efficiency, and the quality of testing that existed prior to optimization. In addition to being time-consuming and labor-intensive for teachers, grading essay responses may lead to inequity since it is impossible to apply consistent requirements to all of the responses. Creating assessments is a challenging task that involves optimizing parameters while adhering to several limitations. The Genetic Algorithm (GA) has been enhanced to address the issues of speed and low paper quality in intelligent test paper development. Educational management at universities has been the suggested approach for IEMS-GA to facilitate smarter education, test and course management, promote the improvement of ideas related to school administration, and provide a more equal system of evaluation. Students rely on exams as a means of gauging their own academic strengths and weaknesses. Students’ test scores provide valuable information about their academic strengths and areas for improvement, allowing them to better plan for the future and attain their academic goals. Results are an important part of the educational process as they reveal a great deal about the way students are doing in class. Knowledge and skills necessary to succeed in the real world are evaluated by such assessments.

Introduction:A combination of internal characteristics, such as motivation, personality, beliefs, and dispositions that combine with external circumstances to affect student results is referred to as teaching efficacy. The English-speaking environment becomes a major problem as students learn by interaction. To communicate effectively, they do not acquire the speaking and listening abilities necessary in English.

Objective:This study examines the impact of computer programming courses and learning analytics on student’s computer programming skills.

Methods:In this study, we proposed a novel Dung Beetle Optimized Flexible Random Forest (DBO-FRF) to evaluate the teaching effectiveness. In this study, 175 students’ data were collected for teaching effectiveness. Using student data, prediction model was constructed based on the attributes of the students, their past academic records, their interactions with online resources, and their advancement in laboratory work related to programming. The proposed method is compared to other traditional algorithms.

Results and conclusion:The proposed method is implemented using Python software. The expected performance in the course and, in the instance that any submitted programs failed to meet the requirements, a programming recommendation from one of the class’s top students. The result shows the proposed method achieved better performance in terms of accuracy, precision, recall, and $F$1-score. This decreased the performance gap between students who performed lower and those who performed higher, enabling students who adjusted their programs to learn more.

The social and ideology education, focus instruction, hands-on learning, and a variety of means, students are cultivated in their political quality, moral character, and sense of social responsibility while it is being guided in understanding the nation’s important policies and social current affairs. Ideological and political disciplines are implementing the problem of transformation within the framework of the major criteria of the new curriculum reform. In the conventional teaching process, students face several challenges in achieving the essential quality of ideological and political education. The artificial intelligence-assisted interactive modeling on the Internet of Things platform (AI-IM-IoT) is able to generate a smart system for ideological and political education, addressing various problems in the current situation. Performance in conventional ideological and political learning platforms, an investigation on the construction of intelligent media ideological and political learning platforms are based on AI technology. Hence AI-IM-IoT has been the Ideology of ideas and beliefs that influence social behavior and can possess an effect on educational achievements. Ideological viewpoints on education can influence educational institutions’ aims, values, and priorities, as well as how resources and opportunities are allocated. Students are encouraged to develop an understanding of social, economic, and political issues via ideological and political education programs.

Socially, politically, and morally, the world of sport is still changing. On the other hand, technology has been the most prevalent transition in the sport over the last century. Thanks to modern science, athletes can now go higher, run quicker, and, most importantly, remain healthy. Although academics, agencies, and policymakers had already urged physical education teachers to use technology in their classrooms, in many of these situations, technology is used for administrative purposes, including tracking enrolment and measuring, documenting, and reporting students’ work. Thus, this paper suggests an intelligent Student Actions Evaluation System using Deep Learning (iSAES-DL)for student monitoring in physical education. This model uses the deep convolution neural network for the classification of risky actions. This model further evaluates the learners’ degree of learning, retention, and achievements and suggests improvements and corrective measures. It highlights the benefits, uses, and limitations of applying deep learning techniques and IoT devices to develop learning analytics systems in the physical, educational domain. Eventually, output criteria such as comprehension, concentration, retention, and learner attainment are given a feature-by-feature analysis of the proposed methodology and traditional teaching-learning approaches. Finally, the classification algorithm is contrasted to other deep learning algorithms with an F1-score of 97.86%.

A social activity that uses certain ideas, concepts, political views, and moral values in a society or social group enriches students’ ideology and allows learners to form ideological and moral qualities that correspond to their social and political establishment. The continuous improvement of their complete quality and technical skills is at the heart of social and economic growth. In ideological and political education, risk factors are widely influenced, including the impact of educational purposes and education providers. In this paper, Deep Learning-Based Innovation Path Optimization Methodology (DL-IPOM) has been proposed to strengthen data awareness, improve the way of thinking in ideological and political education. The political instructional collaborative analysis is integrated with DL-IPOM to boost Ideological and political education excellence. The simulation analysis is conducted at (98.22%). The consistency of the proposed framework is demonstrated by efficiency, high accuracy (98.34%), overshoot index rate (94.2%), political thinking rate (93.6%), knowledge retention rate (80.2%), reliability rate (97.6%), performance (94.37%) when compared to other methods.

As increasingly more research efforts are geared towards creating robots that can teach and interact with children in educational contexts, it has been speculated that endowing robots with artificial empathy may facilitate learning. In this paper, we provide a background to the concept of empathy, and how it factors into learning. We then present our approach to equipping a robotic tutor with several empathic qualities, describing the technical architecture and its components, a map-reading learning scenario developed for an interactive multitouch table, as well as the pedagogical and empathic strategies devised for the robot. We also describe the results of a pilot study comparing the robotic tutor with these empathic qualities against a version of the tutor without them. The pilot study was performed with 26 school children aged 10–11 at their school. Results revealed that children in the test condition indeed rated the robot as more empathic than children in the control condition. Moreover, we explored several related measures, such as relational status and learning effect, yet no other significant differences were found. We further discuss these results and provide insights into future directions.

We describe the Cataway (Catapult Segway), a Segway RMP-based mobile robotic platform which can autonomously throw a ball at a given target, intended to be used as a companion for a human beach ball player. The platform’s throwing mechanism and software architecture were designed, implemented and tested as part of a students’ lab course in practical mobile robotics. The concept allows students to gain practical experience with an actual mobile robot through a motivating scenario and a gentle learning curve. We present results of a simple, yet effective machine learning approach which allows the robot to learn to hit its target from any position.