Assisted Instruction System Research Articles

Analysis of Effective Use of Interactive Video in International Chinese Education

With the passage of time, many colleges and universities around the world have set up online classes and integrated the course teaching assistance system into them. With the innovation of science and technology, the use of interactive video in offline teaching is becoming more and more common. By integrating it into the offline learning education system, students’ enthusiasm in class can be significantly improved, and it can also be widely used in international Chinese classes. Therefore, this paper takes international Chinese education as an example. By studying the overall situation of international Chinese classrooms, teachers discuss the operation mode of interactive video in offline teaching, and put forward relevant suggestions, hoping to help international Chinese classroom teachers improve teaching quality and consolidate students’ academic performance. By introducing new teaching concepts, teachers’ teaching methods can be greatly improved, and at the same time, students’ active participation can be promoted, making it easy and practical to conduct a robust assessment of students’ learning progress.

Development and implementation of digital pedagogical support systems in the context of educational equity

In response to the covid-19 pandemic, the digital strategy of education has become a key pedagogical approach for higher engineering disciplines. In this complex environment, the outcome-oriented higher engineering discipline faces the challenge of educational equity in terms of curriculum design and learning outcome assessment for its online engineering practical courses. In order to solve this problem, this study develops a digital teaching assistant system to facilitate the reform of educational equity in higher engineering disciplines. The teaching and learning functions of the system aim to support the interaction between teachers and students in the teaching process and the visual display of learning outcomes, and its intelligent evaluation and analysis function is based on the multi-criterion group decision-making model of Quality Function Deployment (QFD) combined with t-test to realize the evaluation of course learning outcomes. The results show that the academic performance of this system is significantly better than that of other similar systems. The statistical results of the teacher-student satisfaction survey of the two systems show that the teacher-student interaction and multi-dimensional learning evaluation analysis report function provided by the system are the most highly recognized, and the system also has better educational equity in resource recommendation, interaction and collaboration, learning achievement display and learning achievement evaluation, etc. In addition, the system is more in line with the teaching characteristics of higher engineering disciplines, which can provide effective assistance for teachers of higher engineering disciplines to improve teaching plan and cultivate students’ disciplinary practicality, consequently creating favorable conditions for the establishment of teaching equity.

Study on the Effectiveness of English Teaching in Universities Based on 5G Mobile Internet

Higher education is becoming increasingly competitive and all educational institutions are concentrating on improving quality and changing traditional higher education teaching methods. New-type classroom instruction has embraced a unique advancement opportunity with the arrival of the fifth generation (5G) era. It is critical to develop a teaching assistance system that makes use of high-speed network methodology and new-type display methodology. For the innovation and reform of higher education, this article combines soft computing techniques, artificial intelligence (AI), and 5G networks. This paper outlines the exact processes and measures for incorporating “5G” technology into higher education. Finally, conduct a comparative experiment to see how good the system is at learning AI knowledge when compared to standard learning methods. The outcomes of the experiments are examined to show that employing this approach to gain AI knowledge is successful and improves students' enthusiasm in learning as well as their hands-on abilities.

Research and Design of Auxiliary Teaching System for College Students’ Civics and Political Science Courses in the Context of Deep Learning

At present, the auxiliary teaching system for Civics and Politics courses has problems such as low accuracy of knowledge state prediction and insignificant effect of personalized learning, which affects the actual learning effect. In this paper, we analyze the demand for the teaching of college students’ Civics and Politics based on the background of deep learning, and discuss the overall design of the system. Based on the open-source online teaching system CAT-SOOP, a set of augmented learning algorithm-based Civics course assisted teaching system is designed and implemented, which is based on the student practice data, training student knowledge tracking model and augmented learning recommendation engine for assisting the personalized recommendation of student’s Civics exercises. The results show that compared with the random recommendation method, the relevance of the recommended exercises of this system is improved from 0.03 to 0.238, the reward value is more stable, and the maximum value is improved by 0.2. The Civics course assisted teaching system designed in this paper for college students achieves the expected goals and meets the diversified needs of the audience of intelligent Civics education.

Enhanced Design of a Tai Chi Teaching Assistance System Integrating DTW Algorithm and SVM

Physical education using technology has enabled traditional practices like Tai Chi, a martial art known for its multiple health benefits and meditative aspects, to set coordinated goals. This research presents an intelligent Tai Chi Teaching Assistance System supported by the integration of the Dynamic Time Warping algorithm and Support Vector Machine, in which can practitioners providing real-time feedback to improve Tai Chi learning and quality. In the system, the DTWA Dynamic Time Warping Algorithm was used to accurately compare a practitioner’s complex body movements with the Tai Chi standard movements dataset, taking into account execution speed deviations and others. Meanwhile, the SVM was employed to classify the movement as to quality and correctness, thereby being able to provide precise, individual feedback. This hybrid approach ensures a high-motion recognition accuracy rate while also adhering to nuanced Tai Chi requirements. The system was evaluated through detailed testing with various levels of Tai Chi experience. Evaluation showed that the students’ performance and understanding of most Taijiquan movements and related physical exercises improved significantly. It indicates the system has a practical application value for also beginners and intermediate and last expert, respectively. It also shows the effectiveness of combining DTW and SVM to support learners ‘body movement trajectory in a physical learning environment, opening them up to additional technology-assisted physical training applications. This provides implications for a more promising generation of future physical education involving the incorporation of complex AI technology.

Video Analysis of Physical Education Teaching and Design of Intelligent Assistant Teaching System: Based on Motion Recognition and Optimization Algorithm

Nowadays, science and technology has rapidly increasing due to the specific requirements of education. The main aim of this technique is to enhance the physical education activities and helps sports man to finest teaching facilities of these activities. Moreover, the body changes with sports activities based on Deep Learning (DL) frameworks and classifier technology are examined to promote the practical application. Therefore, this paper Deep Reinforcement Learning based Chimp optimization (DRLbCO) algorithm was developed to improve the teaching efficiency of physical education system through the intelligent computer aided assessment models. Also, this model improving the performance, enhancing coaching effectiveness, and gaining competitive advantages in sports. Consequently, the proposed system is based on DL based image detection model DRL, which can analyse and provides proper actions in teaching students and enhance the ability of motion analysis in sports video training quality. Moreover, the DL model is integrated with optimization algorithm to optimize the performance. This could involve reinforcement learning, and other classifiers such as Support Vector Machine (SVM). Based on this provides feedback to students and teachers during physical education classes. The developed model is significantly enhances the physical education teaching student action in virtual sports instruction. Overall intention of the video assessment system of the sports training performance in analyzed.

Research on the Innovative Application and Effectiveness of AI Intelligence in Intelligent Assisted Teaching System for University Yoga Classroom

With the rapid development of artificial intelligence (AI) technology, there is growing interest in its application in university settings, particularly in enhancing teaching methodologies. This study investigates the innovative application and effectiveness of AI in intelligent assisted teaching systems for university yoga classrooms. Using a comparative analysis approach, various AI-based teaching systems are assessed for their impact on personalized learning support, real-time feedback mechanisms, and optimization of educational resources. The findings highlight significant improvements in student engagement, learning outcomes, and overall yoga instruction quality. Ultimately, this research underscores the transformative potential of AI in revolutionizing educational practices within university yoga classrooms.

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

A Study of Data-Driven Optimization of Personalized Instructional Strategies for Physical Education Training

Abstract As a new type of sports training, the application of sports information technology can effectively improve the athletes’ sports level and sports performance. In this paper, based on the way and effect of sports information technology application, a system for sports teaching and training assistance is designed. Through sports training intensity monitoring, the training intensity of the athletes is obtained, and a high load alarm is triggered. At the same time, the improved hybrid Kalman filter is used to solve the posture of training movements, and the DTW algorithm is used to recognize the training movements and compare them with the standard movements to obtain the training scores. Coaches can be assisted in formulating personalized teaching strategies by using the system’s obtained sports training data. This paper’s sports teaching and training assistance system has a significantly shorter response time than the two comparative systems, which suggests it has better response performance and practicality. The RMSE values of the improved hybrid Kalman filter are 0.92, 0.84 and 1.47 for the roll, pitch and yaw angles, respectively, which are better than that of the extended Kalman filter, indicating that it is closer to the original data and has better data fusion performance. Compared with the control class using traditional teaching strategies, the students in the experimental class using the personalized teaching strategies of this paper have higher skill improvement, indicating that the strategies of this paper have an important guiding role for personalized physical education teaching.

Ideological and Political Classroom Assistant Teaching System Based on Big Data Mining and Machine Learning

Ideological and Political Classroom Assistant Teaching System Based on Big Data Mining and Machine Learning

Research on Evaluation of Online Education Effectiveness Based on AI Teaching Aid System

Abstract In the age of technology, the application of intelligent assistive systems in teaching is getting more and more attention. After systematically describing the framework of the AI teaching assistance system and the process of online education effect evaluation, the study selects the course of an AI teaching assistance platform as a research case, collects the students’ learning behavior data, and applies the K-means-based clustering algorithm to divide them into clusters to obtain the learner portrait. The association rule algorithm based on Apriori is used to initially mine the connection between online learning behavior and educational effects. Furthermore, the multivariate linear regression method is applied to determine specific associations between online learning behaviors and educational effects to complete the assessment of online educational effects. Through the analysis, four types of learners were summarized: general (37.89%), negative (6.84%), interactive (9.48%), and positive (45.79%). Significant correlations between students’ online learning behaviors and educational outcomes were obtained, with regression equations between them. The effectiveness of online education is greatly influenced by students’ time commitment and learning motivation. Subsequent teaching can start from these two aspects to promote the improvement of the quality of teaching in online education.

Research on Deep Learning-based English Vocabulary Teaching Aid System in Colleges and Universities

Abstract Currently, English teaching in colleges and universities faces the challenge of low efficiency in vocabulary teaching. Deep learning technology, an advanced artificial intelligence technology, provides new ideas for solving this problem. Constructing an efficient teaching aid system can achieve personalized vocabulary learning and significantly improve learning efficiency and effectiveness. Aiming to address the efficiency and effectiveness problems in English vocabulary teaching in colleges and universities, this study develops an English vocabulary teaching assistance system based on deep learning. Deep learning algorithms allow the system to analyze students’ learning habits and abilities and provide personalized vocabulary learning suggestions. The research constructs neural network models, uses big data to explore students’ learning behaviors, and develops an intelligent pushing mechanism. The system in this paper achieved face recognition accuracy of 81.08% and 98.84% on the FER and C.K. datasets, which verify the model’s effectiveness. Through the teaching experiments conducted on two classes in Y school in W city, the average score of the English vocabulary test of the class taught with this system increased from 75 to 93, significantly higher than that of the traditional teaching methods. This auxiliary system can improve the effectiveness and efficiency of English vocabulary learning, which also offers new technical support for English teaching in colleges and universities.

Retracted: A Deep Learning-Based Assisted Teaching System for Oral English

Retracted: A Deep Learning-Based Assisted Teaching System for Oral English

Construction and Application of a Complex Network-Based Case Knowledge Base in an Assisted Instruction System

With the development of the business administration major, case teaching has become an effective teaching method. However, a lot of case resources exist in the business administration field, and the correlation between these cases is complex, which poses challenges to teachers and students in terms of information overload and knowledge organization. Existing case knowledge bases in assisted instruction systems often have shortcomings in terms of knowledge relationship strength calculation, text semantic similarity calculation, hierarchical knowledge clustering, and propagation evolution. To solve these problems, this study proposed a method of constructing and applying a case knowledge base in an assisted instruction system based on a complex network. This method mainly includes three aspects: calculation and reasoning of ontology relationship strength, calculation of ontology text semantic similarity, and hierarchical knowledge clustering and propagation evolution in complex networks. Through a comprehensive study of these three aspects, a more efficient and intelligent case knowledge base in an assisted instruction system was constructed, which not only improved the teaching efficiency and quality of the business administration major but also had the potential to promote teaching in other disciplines and fields. In addition, this study also provided new perspectives and methods in related fields, which is of great significance for efficiently organizing and utilizing knowledge.

Anomaly Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones

This article presents a dataset containing messages from the Digital Teaching Assistant (DTA) system, which records the results from the automatic verification of students’ solutions to unique programming exercises of 11 various types. These results are automatically generated by the system, which automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). The DTA system is trained to distinguish between approaches to solve programming exercises, as well as to identify correct and incorrect solutions, using intelligent algorithms responsible for analyzing the source code in the DTA system using vector representations of programs based on Markov chains, calculating pairwise Jensen–Shannon distances for programs and using a hierarchical clustering algorithm to detect high-level approaches used by students in solving unique programming exercises. In the process of learning, each student must correctly solve 11 unique exercises in order to receive admission to the intermediate certification in the form of a test. In addition, a motivated student may try to find additional approaches to solve exercises they have already solved. At the same time, not all students are able or willing to solve the 11 unique exercises proposed to them; some will resort to outside help in solving all or part of the exercises. Since all information about the interactions of the students with the DTA system is recorded, it is possible to identify different types of students. First of all, the students can be classified into 2 classes: those who failed to solve 11 exercises and those who received admission to the intermediate certification in the form of a test, having solved the 11 unique exercises correctly. However, it is possible to identify classes of typical, motivated and suspicious students among the latter group based on the proposed dataset. The proposed dataset can be used to develop regression models that will predict outbursts of student activity when interacting with the DTA system, to solve clustering problems, to identify groups of students with a similar behavior model in the learning process and to develop intelligent data classifiers that predict the students’ behavior model and draw appropriate conclusions, not only at the end of the learning process but also during the course of it in order to motivate all students, even those who are classified as suspicious, to visualize the results of the learning process using various tools.

Electronic Commerce Assistant Teaching System Based on Artificial Intelligence Technology

Electronic Commerce Assistant Teaching System Based on Artificial Intelligence Technology

Retracted: Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN

Retracted: Research on English Hybrid Assisted Teaching System Using Contextual Support of R-CNN

Platform-independent and curriculum-oriented intelligent assistant for higher education

Miscommunication between instructors and students is a significant obstacle to post-secondary learning. Students may skip office hours due to insecurities or scheduling conflicts, which can lead to missed opportunities for questions. To support self-paced learning and encourage creative thinking skills, academic institutions must redefine their approach to education by offering flexible educational pathways that recognize continuous learning. To this end, we developed an AI-augmented intelligent educational assistance framework based on a powerful language model (i.e., GPT-3) that automatically generates course-specific intelligent assistants regardless of discipline or academic level. The virtual intelligent teaching assistant (TA) system, which is at the core of our framework, serves as a voice-enabled helper capable of answering a wide range of course-specific questions, from curriculum to logistics and course policies. By providing students with easy access to this information, the virtual TA can help to improve engagement and reduce barriers to learning. At the same time, it can also help to reduce the logistical workload for instructors and TAs, freeing up their time to focus on other aspects of teaching and supporting students. Its GPT-3-based knowledge discovery component and the generalized system architecture are presented accompanied by a methodical evaluation of the system’s accuracy and performance.

Analysis of music teaching technology based on a data mining model

Abstract To solve the problem of low teaching efficiency in the traditional music course online assistant teaching system, the author proposes a music teaching technology analysis method based on a data mining model. The author proposes a music education model based on the RBF algorithm, which will help improve students’ exploration skills and play an important role for teachers. Through learning and teaching, students can become the center of learning and promote their interest and knowledge in music education. Firstly, the research status of intelligent interactive music learning is analyzed, and then the creation of a neuron model and RBF algorithm using intelligent technology, the definition of music learning methods, and the creation of an independent learning platform are discussed. Select an existing manual for comparative testing. The experimental results show that the training performance of the proposed method exceeds 90%. Although the training performance of both systems is improving, the demonstration of the planning process is very high, and there are some opportunities. Conclusion: Intelligent machines play a great role in students’ music learning, providing better learning opportunities, and providing useful aspects for educational research.

Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

This paper presents a dataset containing automatically collected source codes solving unique programming exercises of different types. The programming exercises were automatically generated by the Digital Teaching Assistant (DTA) system that automates a massive Python programming course at MIREA—Russian Technological University (RTU MIREA). Source codes of the small programs grouped by the type of the solved task can be used for benchmarking source code classification and clustering algorithms. Moreover, the data can be used for training intelligent program synthesizers or benchmarking mutation testing frameworks, and more applications are yet to be discovered. We describe the architecture of the DTA system, aiming to provide detailed insight regarding how and why the dataset was collected. In addition, we describe the algorithms responsible for source code analysis in the DTA system. These algorithms use vector representations of programs based on Markov chains, compute pairwise Jensen–Shannon divergences of programs, and apply hierarchical clustering algorithms in order to automatically discover high-level concepts used by students while solving unique tasks. The proposed approach can be incorporated into massive programming courses when there is a need to identify approaches implemented by students.