Precise Teaching Research Articles

Empirical Research on AI Technology-Supported Precision Teaching in High School Science Subjects

The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers’ teaching burdens and students’ learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students’ academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students’ self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching.

The Transformation of Sports Teaching Development Path Based on the SWOT PEST Model

Traditional physical education teaching often adopts a “one size fits all” teaching model, ignoring individual differences and interest needs of students, resulting in poor teaching effectiveness. Lack of data collection and analysis on student learning behaviour, interest preferences, etc. makes it difficult to make precise teaching adjustments and optimizations. Based on the analysis of the SWOT PEST model, this paper discusses the transformation of the development path of physical education teaching. The development status of sports teaching is analyzed by the SWOT PEST model, and its advantages, disadvantages, opportunities and threats are found. According to the analysis results of the SWOT PEST model, the paper puts forward the strategies and suggestions for the transformation of the development path of sports teaching. Through the case study, the application effect of the SWOT PEST model in the development path transformation of sports teaching is verified. This study provides new ideas and methods for the development of sports teaching and is of great significance for promoting the reform and innovation of sports teaching. A talent big data recommendation system based on collaborative filtering is constructed. The development environment and advantages and disadvantages of modular talents are to facilitate the industry to adopt corresponding strategies.

Revolutionizing Higher Education Teaching Evaluation with Interactive Mobile and Mixed Reality: A Methodological and Applied Analysis

In higher education, it is imperative to maintain educational quality, which requires necessitates a thorough evaluation of teaching methods. The advent of mixed reality (MR) technology, combined with interactive mobile capabilities, introduces innovative possibilities for traditional educational assessment frameworks. This study is dedicated to investigating the application of MR and interactive mobile technologies in higher education teaching evaluations, with a focus on assessing their effectiveness and implementation outcomes. Research to date has explored MR’s educational applications. However, the integration of interactive mobile technologies alongside MR in developing specific methodologies and evaluative tools for teaching evaluations has not been fully realized. This underutilizes the combined potential of these technologies. This paper is anchored in two principal research endeavors. Initially, it delves into the construction of a meta-model facilitated by MR-aided and interactive mobile-enhanced teacher-student interactions, utilizing a hierarchical remote interaction pyramid model. This process involves developing extensible associative functions, determining index weights, and establishing evaluation levels, which collectively enhance the evaluation’s multidimensionality, interactivity, and scientific precision. Subsequently, the analytic hierarchy process (AHP) is employed to quantitatively assess the educational impact of MR and interactive mobile experiences on students. This approach provides support for customized and precise teaching evaluations. The findings reveal that integrating MR technology with interactive mobile capabilities significantly enhances the interactivity and systematic scientific approach of teaching evaluations. This fosters a more multidimensional and interactive framework for educational evaluation.

The Application of SPSS Correlation Analysis in the Study of Precision Teaching of English in Universities

Abstract Amidst the swift advancements in information technology, university-level English education encounters both challenges and opportunities for innovation. This study is dedicated to the development of a system tailored for precise teaching strategies in college English, employing students from M-school as the primary research subjects. Subsequently, the relationship between various teaching strategies and the outcomes of precision teaching was investigated through correlation analysis using SPSS. Furthermore, the factors influencing English performance were analyzed using Pearson’s correlation coefficient and grey correlation analysis within multiple regression analyses. The results show that the overall mean score for English precision teaching is 3.5162, which is in the middle of the teaching level. Furthermore, the significant (two-sided) p-values of the factors affecting the level of English precision teaching and English achievement are less than 0.05, which is significantly correlated with English achievement. College students’ English achievement = 0.534+0.142*Teaching objectives+0.195*Learning situation analysis+0.238*Teaching content+0.271* Teaching methods+0.187*Teaching organization+0.185*Teaching evaluation. The study shows that the precise teaching of English in colleges and universities can be optimized in terms of teaching objectives, learning situation analysis, teaching content, teaching methods, teaching organization form, and teaching evaluation.

Application of mathematical modeling in analyzing and optimizing English teaching methods in vocational education

Abstract In the era of big data, mathematical and scientific methods significantly contribute to educational reform, continuously revitalizing precision teaching. In this paper, we use the new Hawkes process to collect and vectorize data on how students learn. Then, we use the unique thermal coding method to combine the behavioral feature vector with the teaching feature vector. Finally, we classify the data and use the softmax function to predict the learning effect. Based on the prediction results, a mathematical model for precise teaching optimization of vocational education English has been established, and the decision function is used to solve the problem and optimize teaching methods. It was found that all the indicators of learning behavior characteristics of students in the experimental class applying the teaching method optimization model were significantly improved, and there was a significant difference at the level of 0.05 (P = 0.000<0.05). Furthermore, the students in the experimental class achieved higher academic performance in vocational education English than those in the control class under traditional teaching. This paper provides a scientific basis for teaching English to students in vocational education and provides methodological references and ideas for unfolding individualized and precise learning in classroom teaching.

Research on the Reform of English Precision Teaching in Colleges and Universities Facilitated by Artificial Intelligence Technology

Abstract The article analyzes the precise teaching reform by applying artificial intelligence technology in English teaching in colleges and universities to improve teaching efficiency and student learning effect. The study establishes an English teaching model based on learner profiling, using artificial intelligence technology for student data analysis to achieve personalized customization of teaching content. Through empirical analysis, this study found that the experimental class adopting this teaching model significantly improved classroom interaction, learner class profile, and English performance. The average score of English assessment of students in the practical class increased from 55.5465 to 78.7267, and the passing rate also increased significantly. In addition, the teaching mode can effectively reduce the degree of dispersion of students’ performance and reduce the performance differences among students. The application of artificial intelligence technology in English teaching in colleges and universities can effectively promote students’ learning interest and performance improvement, providing a new direction for reforming English teaching in colleges and universities.

Exploration of Digital Transformation Path of Ideological education in Colleges and Universities with the Help of Digital Technology

Abstract With the development of information technology and the advancement of education informatization, the use of mathematical modeling and extensive data analysis technology makes the realization of precision education possible. In this paper, we study the problem of precise teaching intervention from the perspective of multi-stage decision-making, introduce a reinforcement learning algorithm, portray the process attributes and temporal characteristics of teaching, use a Q-learning algorithm to construct a general framework for precise teaching intervention, and through the empirical study of Ideologicalcs teaching in colleges and universities, we show the specific application of this framework in teaching practice and analyze the digital transformation path of the Ideologicalcs education in colleges and universities. The results show that under precise teaching intervention, the students’ pre-post test scores of each Ideologicalcs behavioral characteristic index are significantly different at the 0.05 level. This study helps improve students’ learning effectiveness and provides support for teachers to conduct precise ideological teaching.

Research on Precision Teaching Reform of Japanese Language Education in Colleges and Universities Based on the CRS Model

Abstract The traditional Japanese language teaching method is challenging to mobilize students’ enthusiasm, and it is impossible to objectively understand students’ mastery of knowledge points. To address this teaching problem, this paper combines the CRS model with K-Means cluster analysis to construct a precise teaching model for teaching Japanese language in colleges and universities based on the CRS model. Through the CRS model to obtain the students’ mastery of knowledge and differentiated data, the K-Means algorithm is used to analyze the teaching model accurately, so Japanese teachers can make timely adjustments to the classroom teaching situation and improve the quality of teaching. After the model design was completed, a controlled teaching experiment was conducted in two colleges and universities in a province, and an independent square root t-test was performed. The ratio of teachers’ verbal behavior in the experimental class of Japanese language in colleges and universities was 71.58%, which was 18.86% lower than the ratio of teachers’ verbal behavior in the classroom of the control case, and the ratio of students’ verbal behavior was 21.58%, which was 18.36% higher. The average probability of students’ mastery of the Japanese language points studied in the semester was above 0.6. The independent square root t-test for the two classes was less than 0.05, showing a significant difference.

Research on the Reform Path of “Three Teachings” of Higher Vocational Mathematics in the Context of Digital Transformation of Education

Abstract According to the cultivation goal of vocational education, this paper analyzes the ontological connotation of digital transformation of vocational education from four aspects, namely, precise teaching, embodied learning experience, intelligent management mode, and visualization of evaluation path, and points out the direction of reform of “three-teaching” in higher vocational mathematics. The research subjects were selected and questionnaires were distributed to analyze the current situation of the “Three Teaching Reforms” of higher vocational mathematics under the perspective of digital education. The results summarize that 8 teachers believe that their ability to basically meet the needs of the “Three Teachings” reform, and the number of teachers who say that their ability is not quite able to meet the “Three Teachings” reform is the same, both accounting for 26.67%, while 53.33% of the teachers are still adopting traditional teaching methods and means. Teaching methods and methods. Based on the results of the current situation analysis, we propose a path for the reform of the three aspects of higher vocational mathematics under the perspective of digital education from the three dimensions of teacher cultivation, textbook innovation and teaching methods.

A Study on the Precision Effect of Blended Learning in English Teaching and Learning

Abstract Realizing personalized and precise teaching using traditional English methods is challenging. This paper proposes a blended precision teaching model that relies on student portraits, using technical methods to provide tailored learning resources for every student. A deep neural network is used to extract student features, and the k-means algorithm is used to construct a clustered portrait of students. Based on the student portraits, the similarity between student objects is calculated, and the collaborative filtering method is combined to achieve personalized recommendations for English learning resources. And the learning warning model is established by considering the ranking order relationship when predicting students’ English scores. Setting up the experimental class and the control class to analyze the effect of blended teaching precision, in terms of English scores, the average score of the experimental class is 6.06 points higher than that of the control class, with a significant P-value of less than 0.05, which shows a considerable difference. Its classroom teaching observation dimension score totaled 91.6, students’ classroom performance and teaching effect performed well, English literacy was improved, and the mean values of each satisfaction of emotional experience showed significant differences (P<0.05). The mean values of several dimensions of learning motivation were higher than those of the control class, with highly significant differences in the dimensions of extrinsic goal orientation, learning beliefs, and intrinsic goal orientation (P<0.01).

Innovative Development of Civic and Political Education in Higher Vocational Colleges and Universities in the Context of Multiculturalism

Abstract Multicultural integration brings opportunities and challenges to the field of education, and the innovation of Civics education in higher vocational colleges and universities becomes increasingly essential. In this paper, the reinforcement learning algorithm is used to construct a Civics teaching mode with precise teaching intervention, which quantifies students’ learning ability and learning status by combining the Civics learning ability level parameter. The difficulty coefficient of Civics exercises determines the students’ mastery of Civics content. Based on the students’ Civics learning ability and learning status, a precise teaching intervention strategy was formulated, and the teaching intervention was used in practice to explore the effectiveness of the accurate intervention Civics teaching model in terms of the change of students’ ability and performance. The results show that under the Precision Intervention Civics Teaching Model, students with low collaborative learning and high problem-solving ability changed from unidirectional PR→RS to bidirectional PA↔PR, indicating that the Precision Intervention Teaching Model can improve students’ learning ability. The passing rate of students under this model were maintained at 70%, the average score was above 70, and the students’ performance in Civics was significantly improved compared with the traditional teaching model.

Construction and Practical Application of Precision Evaluation System for Labor Education Teaching in Higher Vocational Colleges and Universities

Abstract This paper builds a precise evaluation system for labor education teaching, and uses the Delphi method to test and eliminate the rationality of the indicators of the evaluation system to ensure the rationality of the evaluation system. The hierarchical analysis method is used to establish the hierarchical structure matrix of each index in the evaluation system and to calculate the weight value of each index in the evaluation system. Then, verify the fuzzy comprehensive evaluation set according to the weight value, calculate the value of each index system in the evaluation set, and calculate the comprehensive evaluation results using the weighted average type-bounded operator. The analysis of the precision evaluation of labor education teaching in Higher Vocational College A using this evaluation system reveals that the score of precision evaluation for labor education teaching in this school is low. Teachers lack precise guidance in teaching, and the average evaluation score of the indicator “quality of labor education for A32 students” is only 2.52 points. After implementing this evaluation system in labor education teaching, students’ performance in labor education has significantly changed. The average score of the experimental class is 27.13 points higher than that of the control class, with a significant difference (P=0.003<0.05). The evaluation system in this paper provides a reference for the evaluation of precise teaching of labor education in higher vocational colleges. It lays a foundation for improving the teaching quality of labor education.

Design of Intelligent Diagnosis and Intervention Model of College Students’ Civic Education for New Media

Abstract With the continuous promotion of education informatization and the rapid development of “Internet+” classroom teaching, online teaching of Civics has become an important means of ideological and political education in colleges and universities. In this study, we propose a multi-feature-enhanced deep knowledge tracking model, which utilizes recurrent neural networks to process students’ online learning data to track the changes in students’ knowledge status and further integrates the attention mechanism to enhance the predictive performance and interpretability of the model. In the experiments of predicting students’ performance, the AUC value of the DKT model with multiple features is much higher than that of the standard DKT model, and compared with the MF-DKT model, the MFA-DKT model has the most significant improvement in ACC (5.23%), AUC (3.87%) and F1 (5.99%) on the D4 dataset, which proves that the feature-rich and attentional mechanisms can effectively improve the prediction performance of the model. Improve the model’s prediction performance. The heat map of a student’s knowledge tracking results indicates that there are smooth changes in knowledge levels, which indicates that the MFA-DKT model can be easily understood. The student had the highest level of mastery of U1 and the lowest level of mastery of U4. After the precise teaching intervention, his mastery level of U4 knowledge points increased from 0.39 to 0.62, and his mastery level of U3 knowledge points increased from 0.54 to 0.83, while his weak points were strengthened.

Research on the Mechanism of Dynamic Monitoring of Undergraduate Students’ Learning Situation and Adaptive Adjustment of Teaching Mode--Based on the Perspective of Educational Evaluation Reform

Abstract Dynamic monitoring of undergraduate learning is a tool for the accurate pulse of education and teaching, and an in-depth study of the quality monitoring data of students’ learning can discover the hidden problems and shortboards in education and teaching. The article utilizes information technology to build a dynamic monitoring platform for undergraduates. Then, it establishes an accurate teaching model for undergraduates with the assistance of this platform. The SHAP interpretable model is used to obtain the online learning behavior characteristics of undergraduates, and the LSTM autocoder is used to construct the time-varying feature sequence of undergraduates’ learning behavior, which is inputted into the LSTM model to establish the undergraduate learning situation early warning model. Taking the data of students’ online behavioral characteristics as an example, the validation of the importance of undergraduate learning behavioral characteristics and early warning is carried out, and the effect of the precise teaching model is also analyzed. The SHAP value of undergraduate students taking online tests is 0.969, which significantly impacts their online learning behavior. The accuracy of undergraduate students’ learning alert was 0.822, which was about 3.53% higher than the FWTS-CNN model with sub-optimal performance, and the results of the learning adaptability retest were 3.24 points higher than the initial test results. From the perspective of educational evaluation reform, combining undergraduate students’ learning dynamic monitoring data can enable adaptive adjustment of teaching content and mode.

Innovation of Teaching Methods of Civic and Political Education in Colleges and Universities with the Assistance of Artificial Intelligence

Abstract Artificial intelligence empowers education and promotes its integration and innovation, enabling the use of learning analytics to accurately diagnose the learning situation and implement precise teaching. In this paper, we have leveraged artificial intelligence to innovate traditional teaching methods, developed a wisdom teaching model specifically for college civic education, and concentrated on the precise application of wisdom teaching in this context. First, it creates student profiles based on academic data and then provides personalized recommendations for learning resources by combining a collaborative filtering algorithm and an improved K-means algorithm. Finally, we analyze the actual impact of the teaching intervention by comparing the student portraits before and after the intervention. When the number of neighbors K takes the same value, the improved cosine similarity calculation method outperforms other calculation methods, and the RMSE, MAE, accuracy, recall, and F1 values are 0.14, 0.24, 0.99, 0.74, and 0.85 when K = 60, respectively, which indicates that the algorithm improvement in this paper is practical. This paper categorizes the sample students into four groups based on their learning behavior and outcomes: potential, excellent, marginal, and hardworking. All these student types have demonstrated some progress following the teaching intervention, confirming the efficacy of this paper’s artificial intelligence-based teaching approach for college civic education. Simultaneously, the created profiles of these student types establish the groundwork for future personalized and accurate instruction.

Research on Physical Education Management System in Higher Education Institutions in the Context of Deep Learning

Abstract This paper focuses on the management of physical movement sports disciplines and combines relevant algorithms to design an artificial intelligence-based physical education management system framework. In terms of related algorithms, the first is for the research of character recognition algorithms. Considering the intra-class differences between individual movements, the LR-based feature extraction algorithm is proposed. Secondly, for the research of the action evaluation algorithm, the statistical value, DTW parameter action time series difference and correlation coefficient based on the statistical value and DTW parameter are used as the features, combined with SVM-based feature extraction algorithm for perceiving the action differences. Finally, with the help of deep neural networks for movement analysis, we can improve students’ movement techniques during training. In the course schedule supported by the system, the proportion of moderate-intensity exercise time is high, basically, more than 40%, which meets the WHO requirements for high-intensity exercise time in physical education classes. The post-course RPE feedback from students was spread out between 12 and 15, and the highest post-course RPE value was 14.82<unk>1.38, which was evaluated within a reasonable range of intervals. The teaching management system in this paper can meet the requirements of physical training and teaching, and provide quantifiable basis and management tools for the informatization and precise teaching of physical movement subjects.

The Construction of a Precision Education Model of English in Colleges and Universities Based on Regression Modelling

Abstract This paper first clarifies the goal of English precision teaching, constructs a big data precision teaching model by combining it with Internet technology, and designs a precision teaching flow chart. Then the regression model is used to construct the assessment model of the precise teaching program, portraying the relationship between the teaching effect and students’ characteristics and behavioral performance under the teaching program. Then, the parameters of the assessment model were evaluated using a similar least squares method to optimize the model. Finally, the assessment model was used to analyze the level and impact of English precision teaching. The results show that there is a significant improvement in students’ performance after precision teaching p=0.00<00.05. In terms of the level of teaching, there is 78.76% of classroom teaching conformity, 69.68% of precision optimization teaching conformity, and 66.67% of precision diagnosis teaching conformity, which is close to the level of comparative conformity. This study has important reference value for English teaching in colleges and universities.

Exploring the Reform and Innovation of College English Teaching Based on the Knowledge Diffusion Model

Abstract This paper analyzes the technical and educational aspects of English teaching informatization, explores the impact of university English teaching reform on traditional English teaching in the informatization era, and designs an information technology-assisted precise English teaching model in three stages: before, during and after class. To realize accurate teaching, the FCE_DFM teaching resource recommendation model is constructed by the fuzzy C-mean clustering method in combination with the recommendation model based on self-encoder and FM. The RMSE and MAE values of this paper were reduced by about 0.12 and 0.2, respectively, compared with traditional recommended teaching, the attendance rate was increased by 22%, and about 2.0 points improved the emotional attitude toward learning through English teaching reform practice.

Research on the mechanism of cultivating the English listening ability of college students based on the background of big data

Abstract Teaching intervention decision is the key to realizing accurate teaching intervention based on big data. This paper portrays students’ English listening learning status and characteristics based on the analysis of students’ behavioral performance data and accordingly provides English listening teaching interventions for different students at the appropriate time. The paper also addresses the problem that the existing big data-based precise teaching interventions are not dynamic enough, introduces reinforcement learning into the teaching intervention decision, and builds a reinforcement learning-based teaching intervention decision model. The mean score of students in the experimental class was 36.31 with a standard deviation of 11.03 on the pre-test and 39.79 with a standard deviation of 6.14 on the post-test, and the mean score of students in the control class was 36.5 with a standard deviation of 10.15 on the pre-test and 36.13 with a standard deviation of 6.14 on the post-test. The difference between the experimental and control classes’ mean scores was only 0.18 on the pre-test and 3.66 on the post-test. Therefore, the precision teaching intervention framework based on big data constructed in this paper has achieved better results in English listening teaching and has strong applicability.

Research Status and Prospect of Teaching Evaluation in the Context of Big Data

In order to investigate the research status and future development trend of teaching evaluation in the context of big data, this paper takes 325 papers in the Chinese database of China Knowledge Network from 2013 to 2022 as the research samples, and analyzes the co-citation network and keyword co-occurrence network of the collected literature with the help of CiteSpace software to explore the development trend, research overview and current research hotspots in this field. The study found that (1) in terms of the annual number of papers issued, the research on teaching evaluation in the context of big data is in an upward trend from 2013 to 2020, and reaches the climax of papers issued in 2020. (2) From the analysis results of core authors as well as author institution networks, there are connections among relevant authors that can promote the development of the field, but in general, communication and cooperation among scholars need to be further strengthened to lead to the emergence of more cohesive research teams. And there are few institutions studying teaching evaluation in the context of big data, but there is no cooperative relationship among various institutions. (3) From the analysis results of research hotspots, the research hotspots of teaching evaluation in the context of big data are mainly focused on big data, teaching evaluation, teaching mode, artificial intelligence, smart classroom, precise teaching and so on. (4) The frontier research on teaching evaluation in the context of big data mainly focuses on big data, information technology, smart classroom, mooc, and evaluation system.