Deep analysis of classroom teaching behavior from the perspective of artificial intelligence: Centered on effective questioning models and exploration of "Four How" questions

Deep analysis of classroom teaching behavior from the perspective of artificial intelligence: Centered on effective questioning models and exploration of "Four How" questions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep analysis of classroom teaching behavior from the perspective of artificial intelligence: Centered on effective questioning models and exploration of "Four How" questions Qizhong Ou, Songqiao Wu, Xinglin Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5677350/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Classroom questioning is a critical element of teaching, facilitating interaction between teachers and students.Effective questioning plays a pivotal role in enhancing students’ cognitive abilities, sparking interest in learning, and supporting knowledge retention. This study analyzes recorded exemplary classroom videos using an intelligent classroom data analysis cloud platform, integrating an effective questioning model and the "Four How" theory. The analysis is based on five dimensions: teacher questioning type, student response method, student response type, teacher response attitude, and the "Four How" questions. The findings reveal that while teachers often respond positively to students, their questions tend to focus on "what" inquiries.Furthermore,students frequently provide mechanical or memory-based responses. In light of these findings,the study recommends optimizing question design to foster higher-order thinking, improving questioning techniques to enhance teacher-student interaction, strengthening student guidance for deeper engagement, and refining feedback to cultivate problem awareness. This research provides valuable data-driven insights into teaching behaviors and offers strategies for enhancing classroom teaching quality in schools. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Statistics Effective Questioning Classroom Questioning Four How Theory Artificial Intelligence Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Introduction In recent years, China has placed significant emphasis on education reform and development. In 2010, the Central Committee of the Communist Party of China and the State Council issued the Outline of the National Medium- and Long-Term Education Reform and Development Plan (2010–2020), which explicitly proposed focusing on improving education quality by establishing national education quality standards and creating a robust assurance system 1 . To further promote the modernization of education, the Ministry of Education released the Education Informatization 2.0 Action Plan in 2018, which highlighted the need to position information technology as an internal driver of systemic educational change, facilitating updates to educational concepts, transforming teaching models, and restructuring systems 2 . This marked China's entry into a new phase of driving educational innovation through digitalization. In 2022, the Ministry of Education published its "2022 Work Points", emphasizing the need to implement a digital education strategy by integrating intelligent technology with education and teaching to promote teacher professional development and improve teaching quality 3 . The Compulsory Education Curriculum Plan and Standards (2022 Edition), also released that year, further underscored the importance of advancing innovative teaching methods and enhancing the quality of education 4 . These policy documents emphasize the necessity for teachers to continually update their pedagogical concepts, improve their teaching methods, and utilize technology to analyze classroom behavior data. This approach aims to enhance teaching quality and support teachers' professional growth, particularly by leveraging artificial intelligence (AI) and data analytics to optimize classroom interactions and learning outcomes. Despite the strong emphasis on the application of information technology in education at the policy level, there are still numerous challenges in analyzing classroom teaching behaviors in practice. Traditional methods of analyzing classroom teaching behavior, such as classroom observation and interaction analysis, rely heavily on subjective judgment, limiting the reliability and generalizability of the analysis results. Moreover, these methods are complex, labor-intensive, and unsuitable for regular use due to the limited sample size. In recent years, with the development of AI and big data technologies, the automatic analysis of classroom teaching behavior and real-time feedback have become possible. However, existing research largely focuses on the development and application of technology, with limited attention to multidimensional analysis of classroom teaching behavior, particularly regarding teacher-student interaction, classroom atmosphere, and emotional feedback. Although some studies have explored the use of intelligent technology in classrooms, most focus primarily on the technical aspects, while insufficient attention is given to how these technologies integrate with actual classroom teaching behaviors. There is a lack of in-depth exploration into how these technologies can be used to optimize teaching practices. Therefore, a more comprehensive approach to analyzing classroom teaching behaviors using information technology, and using data-driven feedback to improve teaching, remains an urgent research issue. Classroom questioning is a key component of a teacher’s teaching behavior and the primary means of interaction between teachers and students. Effective classroom questioning plays a critical role in enhancing students' thinking abilities, stimulating interest in learning, and promoting knowledge mastery. Based on this, the present study selects exemplary classroom video recordings as the object of analysis, combined with data from a smart classroom data analysis cloud platform developed by the research team. From the perspective of AI technology, this study will examine the current state of classroom teaching behavior from five dimensions: teacher questioning types, student response methods, student response types, teacher feedback attitudes, and the analysis of "Four How" questions. This analysis will help teachers better understand the status of classroom teaching behavior, providing strong support for improving teaching quality and promoting professional development for teachers. Literature review Current Status of Research on Classroom Teaching Behavior Teaching behavior refers to the actions taken by teachers and students during the teaching process to achieve certain teaching objectives. Kratz's article "The Characteristics of Excellent Teachers" has opened up research on classroom teaching behavior abroad. In his article, he explores the characteristics that excellent teachers should possess, aiming to provide reference for schools to select and cultivate excellent teachers and improve teaching behavior(Kratz,H.E, 1896) 5 . This research result has made the teaching behavior of teachers the focus of researchers. Foreign research focuses on the effectiveness, influencing factors, and teacher-student interaction of teaching behavior. The initial research focused on the relationship between the external characteristics of teachers and teaching outcomes. Later studies gradually realized the importance of teaching behavior for effectiveness and began to study how to implement effective teaching behavior. Thomas L. Good's research shows that different teaching behaviors can bring different effects, and positive guidance, guidance, and interaction contribute to student success, while relying solely on guidance and evaluation of homework results is relatively limited(Naftulineetal.D.H., 1973) 6 . Researchers have established standards for effective teaching, and according to Loman's research, university teachers need to have clear communication skills, adopt structured teaching methods, and a fun teaching style, while respecting and caring for students and motivating them to actively learn༈J.Loman, 1996༉ 7 . YongSuzanne proposed 7 standards to guide effective teaching behavior among university teachers, including conveying ideas, designing teaching content, motivating students, promoting interaction, creating a good atmosphere, caring for students, and respecting students༈YongSuzanneetal, 1999༉ 8 . In addition, the study also analyzes the influencing factors of teaching behavior from the perspective of teachers' cognition, focusing on the influence of teachers' knowledge and thinking on teaching behavior, such as the important role of cognitive methods in teaching behavior. For example, Messick pointed out the important role of a teacher's cognitive style in teaching behavior༈Yuan Xufu, 2019༉ 9 . In the study of teaching behavior, the perspective of ecological psychology has been introduced, and researchers have begun to focus on the interaction between teachers and the environment, exploring the impact of external environment on teacher psychology and behavior. The interactive behavior between teachers and students plays an important role in the teaching process, and therefore receives much attention. According to the different subjects in teaching activities, teacher-student interaction behavior can be divided into three types: teacher centered, student centered, and content centered(Kang Xiaomei, 2001) 10 . The FIAS interaction analysis system proposed by Flanders provides an effective tool for educational researchers to quantitatively analyze the verbal interaction behavior between teachers and students in the classroom. Through this analytical method, researchers can objectively evaluate the interaction between teachers and students in the classroom, thereby better understanding the effectiveness and influencing factors of the teaching process༈Flanders, N. A, 1960༉ 11 . Compared with foreign countries, research on classroom teaching behavior analysis in China started relatively late. But it mainly focuses on the following aspects: research on the connotation of classroom teaching behavior, methods and models for analyzing classroom teaching behavior, empirical research on classroom teaching behavior, and research on evaluating classroom teaching behavior. Firstly, explore the connotation of classroom teaching behavior. Studying the connotation of classroom teaching behavior helps to understand it from the root and provides necessary boundaries for distinguishing other concepts in related fields. China has different definitions of classroom teaching behavior. Professor Yan Long from East China Normal University believes that classroom teaching behavior is the action taken by teachers in specific teaching environments based on their personal educational philosophy, teaching style, professional knowledge, and practical experience(Yan Long, 2007) 12 . Professor Fu Daochun believes that teaching behavior can be defined as the skillful use of different teaching elements and strategies by teachers in the classroom based on their knowledge level, experience, and personality traits, in order to actively promote student learning and growth༈Fu Daochun, 2001༉ 13 . Professor Duan Zuozhang from Jiangsu Normal University pointed out that teaching behavior is a comprehensive reflection of various behavioral styles adopted by teachers in the teaching process based on personal teaching concepts, skills, experiences, and psychological characteristics. These behavioral patterns constitute practical and actionable action patterns, reflecting the potential qualities of teachers such as teaching concepts, professional knowledge, emotions, and practical wisdom. The core subject of teaching behavior is the teacher. It has the characteristics of purposefulness, individuality, contextuality, and creativity ༈Duan Zuozhang, 2015༉ 14 . Different scholars have different views on teaching behavior, such as teaching or learning behavior, while others believe it is the behavior of teachers. This study refers to the classroom teaching behavior of teachers, which refers to the actions taken by teachers in specific teaching environments based on their personal educational philosophy, teaching style, professional knowledge, and practical experience. Secondly, research on methods and models for analyzing classroom teaching behavior. The research on classroom teaching behavior analysis in China has mainly gone through three stages: traditional classroom teaching behavior analysis, classroom teaching behavior analysis under information-based teaching environment, and classroom teaching behavior analysis under intelligent technology. But in our country, the analysis of classroom teaching behavior is based on the Flanders interaction system. Scholars such as Zhang Ludan conducted research on experienced information technology teachers by combining FIAS and classroom observation and interviews, and summarized the unique characteristics exhibited by expert level teachers during the teaching process(Zhang Ludan & Wang Ying & Pan Yuxia, 2011) 15 . Scholars such as Wu Xiaopeng used the Flanders interactive analysis method to analyze two lesson examples and found that the teaching characteristics of the two teachers were different, with regional differences༈Wu Xiaopeng & Zhang Yi, 2014༉ 16 . In the era of educational informatization, the classic Flanders interaction system has certain shortcomings in analyzing its classroom teaching behavior. Chinese scholars such as Gu Xiaoqing and Wang Wei have adjusted it based on the current situation of classroom teaching in the information-based teaching environment. The adjusted interaction analysis system is more suitable for information-based classroom teaching environment ༈Gu Xiaoqing & Wang Wei, 2004༉ 17 . Professor Fang Haiguang utilized the FIAS analysis method and the ITIAS system based on information technology to improve and optimize the coding system in the digital classroom teaching environment. He conceptualized a coding system suitable for digital classroom analysis and subsequently created an improved version of the Flanders Interactive Analysis System iFIAS༈Fang Haiguang et al,2012༉ 18 . Professors Mu Su and Zuo Pingping proposed a classroom teaching behavior system called TBAS system in an information-based teaching environment. They selected 14 classroom teaching videos for verification and analysis, and analyzed in detail the teaching behavior of teachers and students, the interaction between teachers and students in the classroom, and the application of media in classroom teaching. The analysis of data results found that the analysis system can objectively reflect the actual situation of classroom teaching activities (Mu Su & Zuo Pingping,2015) 19 . In the era of intelligent technology, Professor Wu Libao from Tianjin Normal University has established a practical path for classroom teaching evaluation under the background of artificial intelligence, covering three dimensions of classroom language analysis, classroom behavior analysis, and classroom emotion analysis(Wu Libao et al,2021) 20 . Professor Liu Qingtang and others conducted research and applied artificial intelligence technology to the field of education, and combined with the development of classroom teaching behavior analysis methods, proposed an intelligent analysis model. This model includes three functional modules: data collection and storage, behavior modeling and calculation, and intelligent services, which can help teachers intelligently analyze and evaluate classroom teaching behavior(Liu Qingtang et al,2019) 21 . In addition to the Flanders interaction system, there are also classroom observation and S-T analysis methods for analyzing classroom teaching behavior. Many scholars have also applied analytical methods to specific practical lesson examples for research. Again, empirical research on classroom teaching behavior. Empirical research on classroom teaching behavior can help reveal the relationship between teacher behavior and student behavior, thereby gaining a deeper understanding of the characteristics and influencing factors in the teaching process. Two scholars, Wu Huajun and He Juhou, used video coding tools to select 16 classroom teaching recorded videos of vocational school winning courses in the National Teaching Ability Competition as the research object. They found that vocational school teachers need to have a systematic and solid subject teaching method, and can combine information technology with teaching content to achieve deep integration in classroom teaching(Wu Huajun &He Juhou,2022) 22 . Professor Wang Jixin and others have utilized artificial intelligence technology, such as intelligent recommendation and data mining, to improve the teaching quality of rural schools and build an intelligent teaching and learning system(Wang Jixin et al,2020) 23 . Professor Jiang Libing from Central China Normal University used the self-designed CTBAS classroom teaching behavior analysis framework to analyze teacher-student activities in smart classrooms. The study found that teaching interaction in smart classrooms is more frequent; In terms of professional titles, the teaching of liberal arts courses is significantly higher than that of science courses(Jiang Libing et al,2018) 24 . Based on the CPUP model, scholar Yang Yong analyzed the characteristics of classroom teaching behavior between novice and experienced teachers. Taking the lesson "Important Compounds of Iron" as an example, the research results showed that experienced teachers had higher classroom teaching effectiveness than novice teachers(Yang Yong,2016) 25 . Zhang Junxia and other scholars conducted research using behavior analysis software to analyze 16 public classes. The results show that in the classroom, teachers have given students enough time for research and discussion. However, in terms of questioning and feedback strategies, teachers need to work harder and provide more opportunities for group discussions and presentations(Zhang Junxia & Ding Chaopeng,2014) 26 . Under the background of the implementation of the new curriculum, scholars such as Jiang Xiaogang used classroom observation and the classification theory of "teaching behavior pairs" to conduct a comparative study of the behavior of novice teachers and expert teachers in the process of chemistry teaching. The research results indicate that there are significant differences in classroom teaching behavior between novice and expert teachers. Scholars have also proposed corresponding suggestions for improving the teaching behavior of novice teachers, such as classroom observation, post class communication (interviews), collective lesson preparation and discussion, as well as the habit of teaching reflection(Jiang Xiaogang et al,2013) 27 . Finally, research on classroom teaching behavior evaluation. The evaluation criteria are the foundation and prerequisite for conducting scientific and effective teaching evaluations, as the saying goes, "Without rules, there can be no circle.". Professor Lu Yuanyuan and others analyzed the application of intelligent technology in evaluating teacher classroom teaching behavior from the perspectives of evaluation data, methods, and results; Using voiceprint recognition technology for teacher identity recognition and speech tracking in classroom videos; Construct an application framework for evaluating teacher classroom teaching behavior from three dimensions: emotion, posture, and positional preferences (Lu Yuanyuan et al,2022) 28 . Professor Wen Juan and others have summarized the current situation of teacher classroom teaching evaluation, which mainly focuses on classroom teaching analysis, and proposed directions for continuous optimization and improvement of teacher classroom teaching evaluation, mainly including organic integration of multi-dimensional technical means, enriching the content of teacher classroom teaching evaluation, and reshaping the indicator system of teacher classroom teaching evaluation(Wen Juan et al,2021) 29 . Current Status of Classification Research on Teacher Classroom Questions Classroom questioning is one of the most common teaching behaviors among teachers, and it is the main method for communication and exchange between teachers and students during the teaching process. Classroom questioning is an important guarantee of teaching quality, but effective questioning in the classroom is not an easy task(Shao Huailing,2009) 30 .. In existing research on classroom questioning, the classification of teacher questioning is one of the focuses of many researchers. A study by scholars such as Yu Guowen on the questioning methods of middle school mathematics teachers in different countries in the classroom. Divide the questioning of teachers in mathematics classrooms into three dimensions: questioning object, questioning content, and questioning level. Further refine and divide the questioning objects into individual students, small groups, and the entire class; The questioning content includes knowledge points, topic information, and management related content; The level of questioning is divided into low-level recall, understanding, and application, as well as high-level analysis, synthesis, and evaluation(Yu Guowen & Cao Yiming,2019) 31 . Gu Lingyuan divided the types of questioning into five categories: conventional management questions, memory questions, reasoning questions, creative questions, and critical questions(Gu Lingyuan & Zhou Wei,1999) 32 . Tu Rongbao divides questioning types into four categories: recall questioning, comprehension questioning, analytical and comprehensive questioning, and evaluative questioning(Tu Rongbao,1999) 33 . Based on the role of teacher questioning and the corresponding cognitive level, researchers such as Ye Lijun divide questioning into seven types: management questioning, memorization questioning, repetition questioning, suggestion questioning, supplementary questioning, comprehension questioning, and evaluation questioning(Ye Lijun & Zheng Xin,2018) 34 . Professor Wang Lu and others divided classroom questions into four aspects for observation: the type of question asked, the way teachers choose to answer questions, the way students answer, and the type of student answer(Wang Lu & Zhang Minxia,2012) 35 . Therefore, this article will conduct data analysis on excellent lesson examples based on Professor Wang Lu's classification dimensions of classroom teaching questioning. Analysis and Research Status of Teacher Classroom Questions The commonly used method for analyzing classroom questioning is video analysis. From the perspective of classroom big data analysis by Professor Wang Lu and others, a stratified sampling method was used to select novice, competent, and mature teachers from the D, F, and M districts of B city. Video analysis, IRT modeling, induction, and deduction methods were used to analyze the tendency characteristics (openness, problem-solving, critical, and creative tendencies) and value orientation of classroom teaching questioning. It was found that in the dimension of open-ended questioning, novice middle school teachers were lower than competent and mature teachers, while mature primary school teachers had the lowest level of problem openness. In terms of critical and creative tendencies in asking questions, mature and competent primary school teachers in Zone F have outstanding advantages, while novice secondary school teachers in various regions are generally lower than competent and mature teachers; The problem-solving tendency of asking questions is the lowest among the three tendencies(Wang Lu & Cai Rongxiao,2016) 36 ; Yu Guowen et al. conducted a comparative analysis of classroom questioning in instructional videos of expert teachers from China, Australia, France, and Finland using NVivo, exploring the unique characteristics of classroom questioning among teachers from different countries(Yu Guowen & Cao Yiming,2019). Hu Qidi and other scholars analyzed the classroom teaching videos of three teachers and found that the main problems in middle school mathematics classroom teaching are the excessive quantity and frequency of questioning, uneven types of questioning, short thinking time left for students by questioning, and insufficient effectiveness of questioning feedback forms(Hu Qizhou & Sun Qingkuo,2015) 37 . Based on Anderson's taxonomy as the main theoretical basis, scholars such as Zhou Ying conducted a qualitative and quantitative comparative study on the classroom teaching questioning of outstanding mathematics teachers in China and the United States, using the same high school mathematics teaching topic as the research content, from four aspects: questioning types, teacher selection and answering methods, student answering types, and teacher reasoning and answering methods. Research has found that Chinese teachers tend to ask questions about factual knowledge, while American teachers ask questions about metacognitive knowledge; In terms of student response types, domestic teachers prefer students to answer together, while in the United States, they prefer students who have not raised their hands to answer; In terms of teacher response methods, American teachers place more emphasis on motivating students, while China places more emphasis on controlling classroom time progress; From the perspective of selecting the way teachers answer questions, American teachers pay more attention to the decomposition of knowledge and related questioning, while teachers pay more attention to the understanding and application of knowledge questioning(Zhou Ying & Wang Hua,2013) 38 . Zhang Wenyu et al. analyzed the classroom teaching videos of the first National Full time Education Master's Subject Teaching (Mathematics) Professional Teaching Skills Competition using NVivo10, and found that pre service teachers have shortcomings in asking questions in classroom teaching(Zhang Wenyu & Fan Huiyong,2019) 39 . Professors Zhang Chunli and others from the Education Department of Beijing Normal University conducted a comparative analysis between excellent and general lesson examples, and found that high-level questions accounted for a larger proportion and provided longer response times, while low-level questions had more diverse questioning methods; At the same time, feedback is directly provided for low-level problems, while "appreciative" feedback is given for high-level problems; In excellent lesson examples, teachers are better at breaking down problems and asking them in layers(Zhang Chunli & Ning Limen,2011) 40 . In summary, currently the research results on classroom teaching behavior in the education sector are relatively rich. Video analysis method is mainly used for classroom teaching questioning, but there are also significant shortcomings. The entire analysis process requires a lot of manpower and time, which makes it difficult to promote and apply this method on a large scale, and cannot accurately analyze the classroom questioning of more teachers. Based on this, this study will utilize artificial intelligence technologies such as machine learning and natural language processing for data mining and analysis, to better diagnose classroom teaching questions, thereby promoting teaching quality reform and improving classroom teaching effectiveness. Research Design and Methods Research Methods This study will use literature review, video analysis, and case analysis methods. Firstly, an in-depth analysis of relevant research literature on classroom teaching behavior is conducted, which provides solid theoretical support for the research in this article. This article collects relevant data on excellent lesson examples, and combines recorded video data to conduct research on the teaching behavior data of excellent lesson examples, providing data support for this article. Research Object This article combines literature research and data analysis methods, and with the support of effective questioning models and the "Four How" problem analysis theory. The recruitment period for this study started on June 15, 2023, and ended on July 31, 2023, carefully selects 8 teaching videos as research objects for excellent course cases. The course case videos include footage of the teacher and the voices of the students, with no appearance of students' faces in the videos. The study adhered to the principles outlined in the Helsinki Declaration and received approval from the Research Ethics Committee of the College of Computer and Information Engineering at Nanning Normal University (Protocol Code/Approval Number: NNUHR-2023-05). The study protocols were reviewed and approved by the ethics review board. Written informed consent was obtained from all participants, including teachers, students under the age of 16, and the parents or legal guardians of these students. Parents or legal guardians were informed about the study’s objectives and procedures and authorized their children’s participation by signing a written informed consent form. The selected courses cover multiple disciplines such as morality and the rule of law, art, history, mathematics, physics, English, and Chinese. The duration of recorded videos for each subject is controlled within 45 minutes (see Table 1 for details). The selected teachers have rich teaching experience and profound subject knowledge. Teachers are adept at flexibly applying various teaching methods and strategies, which can stimulate students' learning enthusiasm and help them achieve excellent grades. Teachers also pay great attention to the personality and needs of students, teach according to their aptitude, and actively cultivate the potential of each student. The teaching style of teachers is full of vitality and interactivity, which can attract students' attention and make learning more interesting and effective. Table 1 Basic information of video samples ID Subject Teaching Content Duration (min) A Moral and Rule of Law Dealing with natural disasters 43 min B art Physical rubbing 37 min 25 s C art A corner of the kitchen 42 min 58 s D history The Economic Development of the Song Dynasty 45 min E mathematics factorization 42 min 25 s F Physics The utilization of sound 39 min 23 s G English Section A3a-3b 37 min 3 s H Chinese Two short essays ("Inscription of the Humble House" and "Love Lotus Saying") 43min 20 s The data is sourced from classroom recorded videos of ministerial level premium courses in Guangxi Zhuang Autonomous Region Analysis methods This article is supported by two theories: effective questioning analysis and classroom teaching behavior analysis of the "Four How" questions. Effective Question Analysis Effective questioning analysis is a focused classroom observation method that records and analyzes the questions raised and questioning strategies adopted by teachers in the classroom. According to the research in this article, corresponding modifications will be made to record the types of questions, student response methods, student response types, and teacher response methods, as shown in Table 2 . Table 2 Effectiveness Question Observation Table Effective questioning by teachers percentage frequency Types of questions raised Inferential questions Memory issues Creative issues Critical question Student response methods Encourage students to ask or answer questions Call names or assign students to answer Free or collective answer Students actively ask or answer questions Student answer types Mechanical judgment answer Memory based answers Inferential answers Creative answers Teacher response methods sure negative Follow up questions "Four How" Question In the field of education, problem design is considered the key to cultivating students' higher-order thinking ability and comprehensive thinking. In 1972, McCarthy integrated research findings from fields such as education, psychology, neuroscience, and management, and creatively proposed a teaching model called 4MAT (Mode Application Techniques). (See Fig. 1 ). And depict the process of students learning knowledge as a closed loop composed of four quadrants, each quadrant is a combination of left and right brain functions. Based on the characteristic of alternating left and right brain functions, the learning process is refined into eight stages: connection, attention, imagination, disclosure, practice, expansion, extraction, and presentation. And the 4MAT mode proposes four types of problems based on the learning style of each quadrant, namely: what type of problem is it, why type of problem is it, how type of problem is it, and what if type of problem is it, abbreviated as "Four How" problems (see Table 3 ). Table 3 Observation Table of Four Questions type percentage frequency "What" questions "Why" questions "How" questions "What if" questions Research Tools This study utilizes a cloud-based intelligent classroom data analysis platform developed by the research team to perform a multidimensional analysis of classroom video recordings and teaching data. The tool was co-developed by the research team in collaboration with the Center for Intelligent Analysis of Educational Big Data at the School of Computer and Information Engineering,Nanning Normal University, and the Artificial Intelligence Education Center of the Guangxi Society for Educational Technology,demonstrates good reliability and validity and is easy to operate.The platform leverages AI technology to sample and analyze audio and video data from both teachers and students, integrating tools such as S-T analysis, Gagne’s Nine Events of Instruction timeline, the Rt-Ch chart, and Flanders’ Interaction Analysis Matrix to comprehensively evaluate classroom teaching.More importantly, it is capable of conducting an in-depth analysis of classroom teaching behaviors, particularly focusing on the analysis of teachers' questioning methods. The system generates detailed reports covering classroom overviews, key highlights, areas for improvement, and recommendations for optimization. By employing sound source detection, speech recognition, and semantic analysis, the platform accurately identifies and tracks teacher-student behaviors, producing a comprehensive analysis of teaching implementation capabilities. This study's primary focus is to identify and address existing classroom teaching behavior issues in exemplary course cases, providing strategies for improvement (see Fig. 2 ). Analysis of the Results of Classroom Teaching Behavior in Excellent Course Cases Analysis of teachers' questioning types After statistical analysis of the selected sample videos, we obtained the statistical results of the types of teachers' questions in different disciplines, as shown in Table 4 . Throughout the eight lessons, it is found that teachers of different subjects have certain differences in the types of questions. In general, teachers are more inclined to use "inference problems" and "memory problems", but pay less attention to "creative problems" and "critical problems". The specific lesson analysis found (see Figs. 3 to 6 for details) that in the discipline of morality and the rule of law, teachers use a large number of memory problems, indicating that teachers help students master the learning and application of basic knowledge in the classroom. However, excessive memory problems may only make students remember knowledge, which is not conducive to the cultivation of their thinking ability and innovative thinking. In contrast, in disciplines such as art and physics, the proportion of inferential questions is relatively high, indicating that teachers focus on stimulating students' thinking ability and innovative consciousness in the classroom. It is worth noting that teachers rarely use creative and critical questions, which further indicates that students' comprehensive thinking activities are relatively low, which is not conducive to deep learning and the cultivation of comprehensive thinking. Therefore, teachers should pay more attention to increasing the proportion of creative and critical questions when designing problems, in order to cultivate students' higher-order thinking abilities. At the same time, teachers should also flexibly use different types of questions when asking questions, and match them according to the characteristics of the subject and the students' situation to maximize the mobilization of students' thinking ability and learning enthusiasm. Table 4 Comparison table of teaching behaviors of "teacher question type" in different disciplines ID Subject Memory problem Inference problem Creative problem Critical problem A Moral and Rule of Law 57.5% 36.4% 0% 6.1% B art 33.7% 66.3% 0% 0% C art 44% 48% 8% 9.5% D history 44.4% 55.6% 0% 0% E mathematics 11.3% 86.4% 2.3% 0% F Physics 45.6% 50% 2.2% 2.2% G English 22.1% 73.1% 0% 5.8% H Chinese 44.4% 55.6% 0% 0% The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region Students' response style - Analysis of teacher-student interaction This paper carries out statistical analysis on the selected sample videos, and the statistical results are shown in Table 5 . It reveals the differences in the way students answer in different subjects. Overall, students were more likely to name or assign students to answer, answer freely or answer collectively, followed by students' initiative to ask questions or answer, and the least likely to encourage students to ask questions or answer. This indicates that students receive knowledge more passively in the classroom, and teachers often play the role of knowledge transmission. At the same time, it also reflects that in teaching, teachers encourage students to participate actively, ask questions and improve the atmosphere of classroom interaction needs to be strengthened. Specifically (see Figs. 7 to 10 for details), students have different preferences in response methods. For example, in Chinese and art courses, the proportion of free or collective answers is relatively high, reflecting the emphasis on cultivating students' creative and expressive abilities in these two disciplines, encouraging them to express their opinions and ideas in a free and collective environment. In physics and English courses, the proportion of students who are called or assigned to answer is relatively high, which may be because these two subjects focus on learning basic knowledge and mastering skills, requiring teachers to check students' learning situation by calling or assigning students to answer. However, education and teaching should be a process of all-round development, which should not only pay attention to students' knowledge level and skills, but also pay attention to the cultivation of their thinking ability and creative ability. Therefore, in the future education and teaching, teachers should strengthen the classroom interactive atmosphere that encourages students to ask and answer questions, so as to make students more involved in the classroom, and at the same time provide more opportunities for students to choose and express themselves. Table 5 Comparison table of teaching behaviors of "students' response style" in different subjects ID Subject Encourage students to ask questions or answer Call or assign students to answer Free answer or group answer Students take the initiative to ask or answer questions A Moral and Rule of Law 0% 43.8% 56.2% 0% B art 0% 50% 50% 0% C art 0% 51.8% 24.1% 24.1% D history 0% 75% 25% 0% E mathematics 0% 36.8% 63.2% 0% F Physics 0% 57.1% 28.6% 14.3% G English 3.4% 45.4% 47.4% 3.4% H Chinese 0% 25% 75% 0% The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region. Analysis of students' answer types In the theory of effective questioning model, classroom teaching behavior is mainly analyzed from four dimensions, namely, the types of teachers' questioning, the types of students' answering, the ways of students' answering and the ways of teachers' responding. The research dimension of students' response types mainly includes four categories: "mechanical judgment response", "memory response", "inferential response" and "creative response", which reflects the performance of students' teaching behavior in class. According to Table 6 , it can be seen that there are significant differences in the way students answer teachers' questions in classes of different disciplines. On the whole, students are more inclined to use "mechanical judgment response" and "cognitive memory response" to answer questions. In contrast, "rational response" came in second, and "creative response" was the smallest. This result may reflect the students' emphasis on knowledge mastery under the current educational background, as well as the general lack of high-level thinking ability and creativity. Analysis of specific lesson examples (see Figs. 11 to 14 for details), for example: "Section A 3a-3b" In this English course, students' response methods are relatively balanced. In addition to mechanical judgment responses, the proportion of cognitive memory and reasoning responses is higher. Although the proportion of creative responses is lower, it is still 2.2%. This may be related to the fact that English subjects require students to have certain abilities in listening, speaking, reading, and writing, especially in terms of reading comprehension, which requires students to understand the implicit information in the article through reasoning and analysis. In the course 'Ethics and the Rule of Law - Responding to Natural Disasters', students have a relatively balanced way of answering questions, with little difference in the proportion of mechanical judgment answers, cognitive memory answers, and reasoning answers, while creative answers do not appear. This may be due to the fact that moral and rule of law courses require students to comprehensively apply various knowledge, including emotions, moral values, and other aspects, thus requiring students to have higher levels of thinking and expression abilities. In the course "Physics Sound Utilization", students mainly use mechanical judgment and cognitive memory responses, while inferential and creative responses are less common. This may be because the teaching content of physics mostly involves some formulas, theorems, and rules that need to be memorized and mastered proficiently, so students are more inclined to use mechanical judgment and cognitive memory responses. Inferential and creative responses require higher levels of thinking ability and creativity, as well as a deep understanding of physics knowledge, which may exceed students' abilities. On the whole, the types of answers students give in class may be influenced by a variety of factors. First of all, the way and difficulty of teachers' questions will directly affect the way students choose to answer. Some open questions or questions requiring reasoning and creative thinking may encourage students to adopt "inferential answer" or "creative answer". Secondly, subject characteristics will also affect students' answer methods. For example, students need to master a large number of memory knowledge points in mathematics and Chinese, which leads to students' more inclined to use "cognitive memory answer" or "mechanical judgment answer". To sum up, in classroom teaching, teachers should design appropriate questioning methods according to the characteristics of different disciplines, curriculum objectives, students' cognitive differences, teaching environment and other factors to encourage students to actively think, reason and innovate, so as to promote their all-round development. At the same time, students can also improve their thinking level and ability through a variety of learning ways and methods, so as to better cope with different types of problems and challenges. Table 6 A comparison table of teaching behaviors of "student response types" in different subjects ID Subject Mechanical judgment response Cognitive memory response Inferential response Creative answer A Moral and Rule of Law 47.1% 23.5% 29.4% 0% B art 57.7% 23.1% 19.2% 0% C art 21.9% 56.2% 18.8% 3.1% D history 37.5% 50% 12.5% 0% E mathematics 50% 25% 25% 0% F Physics 53.8% 46.2% 0% 0% G English 23.9% 34.8% 39.1% 2.2% H Chinese 50% 25% 25% 0% The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region Analysis of teachers' response attitude Teachers' response attitude to students can be divided into positive attitude, neutral attitude, negative attitude, questioning three kinds. According to the data in Table 7 , affirmative attitude accounts for the largest proportion, followed by questioning and less negative attitude. This reflects that in the teaching process, teachers adopt more positive attitudes and ways to communicate and interact with students, respect students' thinking and expression, and try to guide students to actively participate in classroom teaching activities. However, in the analysis of each specific lesson example (see Figs. 15 to 17 ), we can also observe some noteworthy phenomena. For example, in the English Section A 3a-3b lesson, although the proportion of affirmative attitude is the least, the proportion of questioning is 45.3%, which is higher than other lesson examples. This indicates that teachers pay attention to students' thinking and creativity when answering questions, and encourage students to exercise their freedom. And continuously guide and stimulate students' thinking and creativity from the perspective of their answers. Through this attitude, students can deepen their understanding of problems and stimulate their imagination and innovation ability. However, in classroom teaching, teachers still adopt a negative response, although it accounts for a relatively small proportion, it still has a certain impact in classroom teaching, which can make students feel frustrated and discouraged, reducing their enthusiasm and willingness to participate in the classroom. Therefore, teachers should avoid using negative responses as much as possible in future teaching processes, and instead use more positive responses and questioning to guide students to think and explore problems. In the four courses of "Responding to Natural Disasters", "Physical Rubbings", "Anti Japanese War in the Enemy Rear Battlefield" and "Factorization", teachers mainly adopt affirmative attitudes, which to some extent indicates that teachers are more encouraging or praising students, encouraging students to participate in classroom activities, so as to improve students' learning effectiveness and enthusiasm. Throughout the four classes, it was found that teachers rarely guide students' thinking and expression behavior, which leads to poor training of students' deep thinking and expression abilities. This indirectly indicates that teachers have not achieved the teaching objectives of the course well, and at the same time, they have not effectively promoted students' learning and development, thereby improving their learning effectiveness and enthusiasm. From the selection of lesson examples, it can be found that most teachers only use the affirmative way to stimulate students' learning interest and enthusiasm, but less guide students to think and express. Although this teaching method can stimulate students' learning enthusiasm and enthusiasm, it can't really promote students' learning and development. This problem deserves the attention of schools and teachers and needs continuous optimization and improvement. Table 7 A comparison table of "teacher responsive attitude" teaching behavior in different subjects ID Subject Affirmative response Negative response inquire A Moral and Rule of Law 100% 0% 0% B art 100% 0% 0% C art 91.7% 0% 8.3% D history 100% 0% 0% E mathematics 100% 0% 0% F Physics 71.4% 0% 28.6% G English 52.8% 1.9% 45.3% H Chinese 78.9% 0% 21.1% The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region Analysis of the "Four How" problem In the field of education and teaching, problem design is considered to be one of the key points in the cultivation of students' higher order thinking ability and comprehensive thinking. McCarthy pointed out in 4MAT model that problems are divided into four types, namely what kind of problems, why kind of problems, how kind of problems and what-if kind of problems, referred to as "Four How" problems. Among them, he why, how, and what if problems of the "Four How" problems belong to higher-order thinking problems(Wang Lu&Cai Rongxiao,2016). According to the data analysis in Table 8 , the "what" dimension accounts for a high proportion, followed by "how" questions, while "what if" and "why" questions account for a relatively low proportion. This indicates that teachers may pay more attention to imparts and understandings of knowledge and pay less attention to the cultivation of students' thinking ability and innovation ability when formulating questions. Analysis of specific lesson examples (see Figs. 18 to 21 for details). For example, in the course "The Use of Sound", although teachers considered four types of problems in problem design, in actual teaching, teachers mainly focused on "what" problems, reaching 81.9%, while "how" and "what if" problems accounted for a relatively low proportion. This indicates that in this course, teachers focus on imparting knowledge points to enable students to master the teaching content and basic knowledge of the course, while the training of students' thinking ability and innovation ability is not enough. In the course of "Two Short Essays (" Inscription of the Humble Chamber "and" Love Lotus Saying "), although the proportion of" what "questions is relatively high, compared to other courses, this course has a higher frequency of appearing in the" why "and" what if "questions. This indicates that teachers attach great importance to guiding students to think about the emotions, thoughts, and underlying reasons expressed in literary works in problem design, And pay attention to how students express their thoughts and feelings through literary language. Based on the above analysis, it is suggested that teachers should pay more attention to the diversity and multiple levels of question design in the future teaching process, and design more open, inspiring and challenging questions to stimulate students' comprehensive thinking quality and innovative ability. For example, in the moral and rule of law class, teachers can design a question about how to deal with emergencies, allowing students to freely use their imagination and creativity. In a physics class, a question about how to use sound for communication can be devised, allowing students to experiment and think to find the answer. In addition, teachers can also use classroom interaction and group cooperation to encourage students to actively participate in the cultivation of higher-order thinking skills. Activities such as group discussion, cooperative inquiry and problem solving can stimulate students' critical thinking, creative thinking and cooperative ability. Teachers can also set learning tasks and projects, and let students work together to solve complex problems in groups, so as to cultivate students' comprehensive thinking and cooperation ability. In a word, problem design plays a vital role in the teaching process, which directly affects the cultivation of students' thinking ability and innovation ability. Teachers should be aware of the importance of question design and strive to design diverse, challenging and inspiring questions to promote the development of students' higher order thinking skills. At the same time, teachers should also adopt interactive teaching methods and cooperative learning forms to encourage students' active participation, so as to improve their learning effect and enthusiasm. Table 8 Comparison of "Four Questions" teaching behaviors in different disciplines ID Subject What What if Why How A Moral and Rule of Law 73% 0% 0% 27% B art 91.7% 0% 0% 8.3% C art 80% 0% 6.7% 13.3% D history 50% 0% 0% 50% E mathematics 50% 0% 0% 50% F Physics 81.9% 2.5% 13.6% 4.5% G English 68.5% 2.9% 8.6% 20% H Chinese 53.4% 0% 33.3% 13.3% The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region Optimization strategies based on the effective questioning model and the Four How questions Optimize problem design and promote advanced thinking Optimize problem is the foothold of students' classroom learning, and different types of questions can promote the development of students' different abilities. From the analysis of classroom examples, it can be seen that "reasoning questions" and "memory questions" have less content on "creative questions" and "critical questions". Teachers pay more attention to "what" and "how", while neglecting "how" and "why", which is not conducive to the cultivation of students' thinking and learning abilities. Therefore, teachers should balance the knowledge value and thinking value of the problem in problem design, combine knowledge impartation with thinking guidance, and promote students' thinking advancement. On the one hand, teachers should prioritize core issues and highlight the essence of knowledge. Huang Aihua believes that the core issue is "to the essence, covering the key and difficult points of teaching, high-level, and exploratory issues". The core issues can mainly be teaching key and difficult points, knowledge clues, classroom theme issues, etc. The key and difficult teaching problems refer to the difficulties and confusion that students are prone to encounter in their thinking during the knowledge learning process, the knowledge clue problem refers to the problem of connecting a single thousand scattered knowledge points, and the classroom theme problem refers to the problem of commanding the classroom teaching content. Teachers should design teaching questions based on classroom content and teaching objectives, ensuring that the questions always serve the mastery of students' knowledge and skills and the cultivation of emotional attitudes. Teachers should distinguish between the value of questions, dividing them into core and auxiliary questions, ensuring clear questioning ideas and highlighting key points. For example, the key and difficult points and teaching theme of Factorization are to enable students to master the Factorization method. Teachers can use this as the main thread of classroom teaching and carry out modular teaching. On the other hand, teachers need to improve the problem structure under the leadership of core issues. The learning of students' knowledge has obvious progressive characteristics, always presenting a process from shallow to deep, from surface to inner, from phenomenon to essence, from preliminary understanding to deep understanding. Therefore, the teaching design of teachers should also respect this cognitive development law, emphasize the progressive order and logical relationship between problems, and enable students to find the connection between the old and new, so as to have a focus on the development of students' thinking. What types of problems belong to basic ability problems, and what types, how types, and how types of problems belong to higher-order thinking problems. The design of what type of problem should be minimal but precise, which can help students quickly recall knowledge and help teachers judge the students' mastery of basic knowledge. The other three types of problems require selective design based on the core problem. For example, the discipline of morality and the rule of law emphasizes the cultivation of emotional attitudes, and questions of what kind and what kind can better trigger students' emotional thinking. Mathematics focuses on the development of students' logic and abstract thinking, and the questions of why and how can help students deeply analyze abstract knowledge. Optimize questioning methods and enhance teacher-student interaction Effective questioning methods are key to activating students' thinking and increasing their engagement in classroom participation. Active student participation is a fundamental prerequisite for deep learning and cognitive development. Currently, many students are passively receiving knowledge, with a predominance of one-on-one questioning methods and minimal teacher-student interaction. The classroom atmosphere is often passive, and students lack the awareness and ability to think critically. To address this, teachers should optimize their questioning methods to enhance interaction and stimulate both cognitive engagement and participation. One approach is to create a contextual framework for questioning. Effective contextualized questions can stimulate students' interest and aid in knowledge transfer. Curriculum standards across disciplines emphasize tailoring teaching content to students' lived experiences, and research shows that creating real-world problem situations is key to developing students' problem-solving abilities. Linking core classroom knowledge to real-life scenarios provides students with opportunities to observe, practice, and think critically, helping them discover problems, apply knowledge, and solve challenges. This process encourages students to understand "what," "why," and "how," deepening their learning and application. Another strategy is to design exploratory classroom questions. The advantage of exploratory questions is that they create an open intellectual space where students can freely think within a limited timeframe, and interaction can stimulate communication, generate ideas, and enhance students' critical thinking and judgment abilities. Exploratory questions should be challenging and interactive. For example, in a lesson on "Love of the Lotus," a teacher asked, "Who in your life could be described as 'coming out of the mud without being stained'?" This question, filled with openness and imagination, requires students to understand the metaphorical meaning of the lotus and compare it to real people. Such questions help deepen students' comprehension and critical reflection, fostering the development of critical and creative thinking. Lastly, fun question formats, such as role-playing or classroom competitions, can effectively stimulate student interest and increase engagement. Strengthen student guidance and promote deep participation From an overall classroom perspective, students tend to respond to questions with "mechanical judgment" or "cognitive memory" answers, showing a lack of initiative and critical thinking. To cultivate students' response skills and encourage the development of thinking and learning abilities, teachers can extend the waiting time after asking questions, promoting deeper classroom engagement. Psychological research suggests that students experience two high-speed cognitive phases after receiving a question. The first occurs immediately after the teacher's question, during which students quickly process the information and form a basic response. The second phase occurs while other students are answering, during which students refine their thoughts based on others' responses and generate new ideas. By extending response time, teachers can help students make full use of these cognitive phases and encourage deeper thinking. Optimize questioning feedback and cultivate problem awareness Teachers often respond positively to students, but this approach can lack the necessary guidance to challenge students' perspectives. This might lead students to believe they don't need to reflect on or question their own answers, thereby limiting their reasoning and innovative abilities. Teachers should be mindful of their tone and expressions, avoiding an overemphasis on "correct answers" and focusing instead on guiding students toward discovery and deeper understanding. In future teaching, teachers should aim to cultivate students' problem awareness, breaking the traditional constraints on student cognitive development. Teachers can achieve this by using more probing follow-up questions, allowing students more space to explore and think critically. Additionally, encouraging students to ask questions and share viewpoints can create cognitive conflict, enabling students to identify their own cognitive challenges during the questioning process and to develop their thinking through collaborative problem-solving. Summary and Outlook Artificial intelligence technology approaches classrooms and teaching in a completely new way, introducing new educational concepts and teaching methods. This paper utilizes an intelligent classroom data analysis cloud platform, developed by the research team, to analyze classroom teaching behavior in sample case videos by integrating an effective questioning model and the "Four How" theory. Through multiple dimensions, it reflects the actual state of classroom teaching and identifies several issues in exemplary course cases: teachers tend to adopt a positive attitude when responding to students, question design often focuses on "what" questions, and students primarily provide mechanical judgment responses or memory-based answers. In response, the study proposes optimization strategies in four areas: refining question design to promote higher-order thinking, enhancing questioning methods to improve teacher-student interaction, strengthening student guidance to encourage deeper engagement, and improving feedback on questioning to cultivate problem awareness. This research aims to provide theoretical support for scholars and assist schools in the Guangxi Zhuang Autonomous Region in continuously optimizing teaching behaviors and improving teaching quality. Based on current research progress, future work will focus primarily on two areas: first, employing multiple classroom teaching theories to more accurately assess teaching behavior, as analyzing solely from the perspective of effective questioning and the "Four How" theory is insufficiently precise; second, enhancing the intelligent classroom data analysis cloud platform, which currently relies on sound source detection, speech recognition, and semantic analysis for modeling. Future developments aim to achieve accurate real-time recognition of facial expressions, body language, and movements of both teachers and students. Declarations Competing interests The authors declare no competing interests. Ethical approval The study was conducted in accordance with the Helsinki Declaration and was approved by the Research Ethics Committee of the College of Computer and Information Engineering at Nanning Normal University (protocol code/approval number: NNUHR-2023-05). Informed consent informed consent was obtained from all participants involved in the study, including teachers, students under the age of 16, and the parents or legal guardians of these students. The parents or legal guardians were thoroughly informed about the objectives and procedures of the study and provided authorization for their children's participation by signing a written informed consent form, ensuring compliance with ethical research standards. Funding This work has been supported by the Ministry of Education's Humanities and Social Sciences Research Project under the title "Regional Integration Strategy and Collaborative Operation Research on Digital Education Resources in Primary and Secondary Schools in Border Ethnic Areas" (Project Approval Number: 22YJAZH084).Additionally, funding has been provided by the Guangxi Education Science "14th Five-Year Plan" for the year 2023, specifically for the special key project titled "Research on the Model of Enhancing the Quality of Application of 'Three Classrooms' in Border Areas through Digital Technology Resources" (Project Number: 2023ZJY524). Author Contribution Q.O. conceptualized the study and supervised the project. S.W. and X.C. developed the methodology, performed formal analysis, and wrote the original draft. X.C. curated the data. Q.O. and S.W. reviewed and edited the manuscript.All authors reviewed the manuscript. Acknowledgement We would like to sincerely thank the Center for Intelligent Analysis of Educational Big Data at the School of Computer and Information Engineering, Nanning Normal University, and the Artificial Intelligence Education Center of the Guangxi Society for Educational Technology for their invaluable technical support with the Intelligent Analysis Cloud Platform. Data Availability All data supporting the findings of the current study are available from the corresponding author upon reasonable request. References China, T. S. C. o. t. P. s. R. o. Report of the State Council on the Implementation of the National Medium- and Long-Term Education Reform and Development Plan Outline (2010–2020)Beijing,. (2010). China, M. o. E. o. t. P. s. R. o. Notice of the Ministry of Education of the People's Republic of China on Issuing the Action Plan for Education Informatization 2.0. (2018). China, M. o. E. o. t. P. s. R. o. 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13:53:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5677350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5677350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73311367,"identity":"cad73f3c-19cb-4223-8f63-32cb64623544","added_by":"auto","created_at":"2025-01-08 18:22:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136762,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of 4MAT teaching mode\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/1aa7b15be8dbb3da4dbc4f69.jpg"},{"id":73310312,"identity":"073c1dae-674f-44e8-9370-5789133a8b17","added_by":"auto","created_at":"2025-01-08 18:06:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24298,"visible":true,"origin":"","legend":"\u003cp\u003eTeaching Big Data 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4","display":"","copyAsset":false,"role":"figure","size":114737,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Inferential Questions\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/c6ae7450132543194e50ed95.jpg"},{"id":73311149,"identity":"41c529c1-801c-43c7-9acf-8b535fa687f7","added_by":"auto","created_at":"2025-01-08 18:14:52","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":79964,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Creative Questions\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/6564636b305611cccff671b7.jpg"},{"id":73310350,"identity":"37bc9b0d-59fc-4fe9-b7dd-7985dca9bfbc","added_by":"auto","created_at":"2025-01-08 18:06:54","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":85786,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Critical Questions\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/554967a5ad77a5dc77df843f.jpg"},{"id":73311369,"identity":"8f6da6ea-18bb-4a57-b46f-6ebb7e70712c","added_by":"auto","created_at":"2025-01-08 18:22:52","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":98859,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Encouraging Students to Ask or Answer\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/7333024223b2dc8da4320303.jpg"},{"id":73310314,"identity":"cd10f873-feac-4c11-b512-b36a3364d384","added_by":"auto","created_at":"2025-01-08 18:06:52","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":105768,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Roll Call or Designated Student Answers\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/cbe80a2cdb17d16f41eb7fd8.jpg"},{"id":73310316,"identity":"ce05cdd7-a84a-4cb3-81a3-5ef25d38c904","added_by":"auto","created_at":"2025-01-08 18:06:52","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":95617,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Free Answer or Collective Answer\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/773db42303b2c7e3cd35dbea.jpg"},{"id":73310330,"identity":"442ca89d-74ec-4b9c-b2f8-7bdb32e7301d","added_by":"auto","created_at":"2025-01-08 18:06:53","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":109940,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Students Proactively Asking or Answering\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/10ec31ed212990e65e091bf5.jpg"},{"id":73310319,"identity":"10f430a2-7359-48e2-8b9a-d1f83217ccfb","added_by":"auto","created_at":"2025-01-08 18:06:52","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":122805,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Mechanical Judgment Answers\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/d50d7f6cb8a8e57f2372da0f.jpg"},{"id":73311152,"identity":"de1aa717-4859-4917-9720-f1ce08999eed","added_by":"auto","created_at":"2025-01-08 18:14:52","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":110801,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Cognitive Memory Response\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/cf036da3526a5a8e3a9ce7d3.jpg"},{"id":73311151,"identity":"38dd74e9-5fe8-42d3-8192-0e0c28b796d3","added_by":"auto","created_at":"2025-01-08 18:14:52","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":96252,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors of \"Inferential Answers\" in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/60e27d8df1b185dcfa22b357.jpg"},{"id":73310389,"identity":"a2413232-4fe1-456c-a80e-c8512adead0f","added_by":"auto","created_at":"2025-01-08 18:06:55","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":99004,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"Creative Answer\" Teaching Behaviors in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/84504655e20369e9e628efe8.jpg"},{"id":73310369,"identity":"a4437d67-c40a-4e00-9d28-de2785c69cf8","added_by":"auto","created_at":"2025-01-08 18:06:54","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":154096,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"Teacher Response Attitude Positive Response\" Teaching Behaviors in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/64e503953932e5fa2ce8444f.jpg"},{"id":73310358,"identity":"4dd8f637-18b8-45a0-ae53-1b5804f9e4f7","added_by":"auto","created_at":"2025-01-08 18:06:54","extension":"jpg","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":92291,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"Teacher Response Attitude Negative Response\" Teaching Behaviors in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/dba3886d70b81d9a79e60b03.jpg"},{"id":73310357,"identity":"44e8f0b9-b924-437c-bac5-b68d4065c095","added_by":"auto","created_at":"2025-01-08 18:06:54","extension":"jpg","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":109369,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"Teacher Response Attitude Questioning Response\" Teaching Behaviors in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e","description":"","filename":"Picture17.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/eb17795a4f7265b50812d077.jpg"},{"id":73310322,"identity":"d2f5fc7d-ab7c-4f08-be1f-e58a737973f7","added_by":"auto","created_at":"2025-01-08 18:06:52","extension":"jpg","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":125319,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Teaching Behaviors in Different Disciplines\"What\"Questions. The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture18.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/25996f3521998372db3b67ac.jpg"},{"id":73311154,"identity":"4eeee081-e0a8-427b-9a56-3196e4b46691","added_by":"auto","created_at":"2025-01-08 18:14:52","extension":"jpg","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":87641,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Chart of \"What if\" Question Teaching Behaviors Across Different Subjects.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture19.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/dde4c068731f2662e4d22579.jpg"},{"id":73310408,"identity":"0142b55a-7c1d-49c7-8102-8c6e5435cfa8","added_by":"auto","created_at":"2025-01-08 18:06:56","extension":"jpg","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":109736,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"How\" Problem Teaching Behaviors in Different Disciplines The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture20.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/7e8afb1ae8d100ccb4c9127b.jpg"},{"id":73310386,"identity":"cba9d344-d5f3-42fb-a341-9c8dedc6d377","added_by":"auto","created_at":"2025-01-08 18:06:55","extension":"jpg","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":97265,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \"Why\" Problem Teaching Behaviors in Different Disciplines.The data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e","description":"","filename":"Picture21.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/732b7f98db273f320e80fd7f.jpg"},{"id":73312267,"identity":"6227c22e-88aa-446e-bdcc-b04005081d2b","added_by":"auto","created_at":"2025-01-08 18:31:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3280537,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5677350/v1/df975240-7e95-4775-92ea-e05afda38cf9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deep analysis of classroom teaching behavior from the perspective of artificial intelligence: Centered on effective questioning models and exploration of \"Four How\" questions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, China has placed significant emphasis on education reform and development. In 2010, the Central Committee of the Communist Party of China and the State Council issued the Outline of the National Medium- and Long-Term Education Reform and Development Plan (2010\u0026ndash;2020), which explicitly proposed focusing on improving education quality by establishing national education quality standards and creating a robust assurance system\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. To further promote the modernization of education, the Ministry of Education released the Education Informatization 2.0 Action Plan in 2018, which highlighted the need to position information technology as an internal driver of systemic educational change, facilitating updates to educational concepts, transforming teaching models, and restructuring systems\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This marked China's entry into a new phase of driving educational innovation through digitalization. In 2022, the Ministry of Education published its \"2022 Work Points\", emphasizing the need to implement a digital education strategy by integrating intelligent technology with education and teaching to promote teacher professional development and improve teaching quality\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The Compulsory Education Curriculum Plan and Standards (2022 Edition), also released that year, further underscored the importance of advancing innovative teaching methods and enhancing the quality of education\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These policy documents emphasize the necessity for teachers to continually update their pedagogical concepts, improve their teaching methods, and utilize technology to analyze classroom behavior data. This approach aims to enhance teaching quality and support teachers' professional growth, particularly by leveraging artificial intelligence (AI) and data analytics to optimize classroom interactions and learning outcomes.\u003c/p\u003e \u003cp\u003eDespite the strong emphasis on the application of information technology in education at the policy level, there are still numerous challenges in analyzing classroom teaching behaviors in practice. Traditional methods of analyzing classroom teaching behavior, such as classroom observation and interaction analysis, rely heavily on subjective judgment, limiting the reliability and generalizability of the analysis results. Moreover, these methods are complex, labor-intensive, and unsuitable for regular use due to the limited sample size. In recent years, with the development of AI and big data technologies, the automatic analysis of classroom teaching behavior and real-time feedback have become possible. However, existing research largely focuses on the development and application of technology, with limited attention to multidimensional analysis of classroom teaching behavior, particularly regarding teacher-student interaction, classroom atmosphere, and emotional feedback. Although some studies have explored the use of intelligent technology in classrooms, most focus primarily on the technical aspects, while insufficient attention is given to how these technologies integrate with actual classroom teaching behaviors. There is a lack of in-depth exploration into how these technologies can be used to optimize teaching practices. Therefore, a more comprehensive approach to analyzing classroom teaching behaviors using information technology, and using data-driven feedback to improve teaching, remains an urgent research issue.\u003c/p\u003e \u003cp\u003eClassroom questioning is a key component of a teacher\u0026rsquo;s teaching behavior and the primary means of interaction between teachers and students. Effective classroom questioning plays a critical role in enhancing students' thinking abilities, stimulating interest in learning, and promoting knowledge mastery. Based on this, the present study selects exemplary classroom video recordings as the object of analysis, combined with data from a smart classroom data analysis cloud platform developed by the research team. From the perspective of AI technology, this study will examine the current state of classroom teaching behavior from five dimensions: teacher questioning types, student response methods, student response types, teacher feedback attitudes, and the analysis of \"Four How\" questions. This analysis will help teachers better understand the status of classroom teaching behavior, providing strong support for improving teaching quality and promoting professional development for teachers.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCurrent Status of Research on Classroom Teaching Behavior\u003c/h2\u003e \u003cp\u003eTeaching behavior refers to the actions taken by teachers and students during the teaching process to achieve certain teaching objectives. Kratz's article \"The Characteristics of Excellent Teachers\" has opened up research on classroom teaching behavior abroad. In his article, he explores the characteristics that excellent teachers should possess, aiming to provide reference for schools to select and cultivate excellent teachers and improve teaching behavior(Kratz,H.E, 1896)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This research result has made the teaching behavior of teachers the focus of researchers.\u003c/p\u003e \u003cp\u003eForeign research focuses on the effectiveness, influencing factors, and teacher-student interaction of teaching behavior. The initial research focused on the relationship between the external characteristics of teachers and teaching outcomes. Later studies gradually realized the importance of teaching behavior for effectiveness and began to study how to implement effective teaching behavior. Thomas L. Good's research shows that different teaching behaviors can bring different effects, and positive guidance, guidance, and interaction contribute to student success, while relying solely on guidance and evaluation of homework results is relatively limited(Naftulineetal.D.H., 1973)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Researchers have established standards for effective teaching, and according to Loman's research, university teachers need to have clear communication skills, adopt structured teaching methods, and a fun teaching style, while respecting and caring for students and motivating them to actively learn༈J.Loman, 1996༉\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. YongSuzanne proposed 7 standards to guide effective teaching behavior among university teachers, including conveying ideas, designing teaching content, motivating students, promoting interaction, creating a good atmosphere, caring for students, and respecting students༈YongSuzanneetal, 1999༉\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In addition, the study also analyzes the influencing factors of teaching behavior from the perspective of teachers' cognition, focusing on the influence of teachers' knowledge and thinking on teaching behavior, such as the important role of cognitive methods in teaching behavior. For example, Messick pointed out the important role of a teacher's cognitive style in teaching behavior༈Yuan Xufu, 2019༉\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the study of teaching behavior, the perspective of ecological psychology has been introduced, and researchers have begun to focus on the interaction between teachers and the environment, exploring the impact of external environment on teacher psychology and behavior. The interactive behavior between teachers and students plays an important role in the teaching process, and therefore receives much attention. According to the different subjects in teaching activities, teacher-student interaction behavior can be divided into three types: teacher centered, student centered, and content centered(Kang Xiaomei, 2001)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The FIAS interaction analysis system proposed by Flanders provides an effective tool for educational researchers to quantitatively analyze the verbal interaction behavior between teachers and students in the classroom. Through this analytical method, researchers can objectively evaluate the interaction between teachers and students in the classroom, thereby better understanding the effectiveness and influencing factors of the teaching process༈Flanders, N. A, 1960༉\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCompared with foreign countries, research on classroom teaching behavior analysis in China started relatively late. But it mainly focuses on the following aspects: research on the connotation of classroom teaching behavior, methods and models for analyzing classroom teaching behavior, empirical research on classroom teaching behavior, and research on evaluating classroom teaching behavior.\u003c/p\u003e \u003cp\u003eFirstly, explore the connotation of classroom teaching behavior. Studying the connotation of classroom teaching behavior helps to understand it from the root and provides necessary boundaries for distinguishing other concepts in related fields. China has different definitions of classroom teaching behavior. Professor Yan Long from East China Normal University believes that classroom teaching behavior is the action taken by teachers in specific teaching environments based on their personal educational philosophy, teaching style, professional knowledge, and practical experience(Yan Long, 2007)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Professor Fu Daochun believes that teaching behavior can be defined as the skillful use of different teaching elements and strategies by teachers in the classroom based on their knowledge level, experience, and personality traits, in order to actively promote student learning and growth༈Fu Daochun, 2001༉\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Professor Duan Zuozhang from Jiangsu Normal University pointed out that teaching behavior is a comprehensive reflection of various behavioral styles adopted by teachers in the teaching process based on personal teaching concepts, skills, experiences, and psychological characteristics. These behavioral patterns constitute practical and actionable action patterns, reflecting the potential qualities of teachers such as teaching concepts, professional knowledge, emotions, and practical wisdom. The core subject of teaching behavior is the teacher. It has the characteristics of purposefulness, individuality, contextuality, and creativity ༈Duan Zuozhang, 2015༉\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Different scholars have different views on teaching behavior, such as teaching or learning behavior, while others believe it is the behavior of teachers. This study refers to the classroom teaching behavior of teachers, which refers to the actions taken by teachers in specific teaching environments based on their personal educational philosophy, teaching style, professional knowledge, and practical experience.\u003c/p\u003e \u003cp\u003eSecondly, research on methods and models for analyzing classroom teaching behavior. The research on classroom teaching behavior analysis in China has mainly gone through three stages: traditional classroom teaching behavior analysis, classroom teaching behavior analysis under information-based teaching environment, and classroom teaching behavior analysis under intelligent technology. But in our country, the analysis of classroom teaching behavior is based on the Flanders interaction system. Scholars such as Zhang Ludan conducted research on experienced information technology teachers by combining FIAS and classroom observation and interviews, and summarized the unique characteristics exhibited by expert level teachers during the teaching process(Zhang Ludan \u0026amp; Wang Ying \u0026amp; Pan Yuxia, 2011)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Scholars such as Wu Xiaopeng used the Flanders interactive analysis method to analyze two lesson examples and found that the teaching characteristics of the two teachers were different, with regional differences༈Wu Xiaopeng \u0026amp; Zhang Yi, 2014༉\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the era of educational informatization, the classic Flanders interaction system has certain shortcomings in analyzing its classroom teaching behavior. Chinese scholars such as Gu Xiaoqing and Wang Wei have adjusted it based on the current situation of classroom teaching in the information-based teaching environment. The adjusted interaction analysis system is more suitable for information-based classroom teaching environment ༈Gu Xiaoqing \u0026amp; Wang Wei, 2004༉\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Professor Fang Haiguang utilized the FIAS analysis method and the ITIAS system based on information technology to improve and optimize the coding system in the digital classroom teaching environment. He conceptualized a coding system suitable for digital classroom analysis and subsequently created an improved version of the Flanders Interactive Analysis System iFIAS༈Fang Haiguang et al,2012༉\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Professors Mu Su and Zuo Pingping proposed a classroom teaching behavior system called TBAS system in an information-based teaching environment. They selected 14 classroom teaching videos for verification and analysis, and analyzed in detail the teaching behavior of teachers and students, the interaction between teachers and students in the classroom, and the application of media in classroom teaching. The analysis of data results found that the analysis system can objectively reflect the actual situation of classroom teaching activities (Mu Su \u0026amp; Zuo Pingping,2015)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In the era of intelligent technology, Professor Wu Libao from Tianjin Normal University has established a practical path for classroom teaching evaluation under the background of artificial intelligence, covering three dimensions of classroom language analysis, classroom behavior analysis, and classroom emotion analysis(Wu Libao et al,2021)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Professor Liu Qingtang and others conducted research and applied artificial intelligence technology to the field of education, and combined with the development of classroom teaching behavior analysis methods, proposed an intelligent analysis model. This model includes three functional modules: data collection and storage, behavior modeling and calculation, and intelligent services, which can help teachers intelligently analyze and evaluate classroom teaching behavior(Liu Qingtang et al,2019)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In addition to the Flanders interaction system, there are also classroom observation and S-T analysis methods for analyzing classroom teaching behavior. Many scholars have also applied analytical methods to specific practical lesson examples for research.\u003c/p\u003e \u003cp\u003eAgain, empirical research on classroom teaching behavior. Empirical research on classroom teaching behavior can help reveal the relationship between teacher behavior and student behavior, thereby gaining a deeper understanding of the characteristics and influencing factors in the teaching process. Two scholars, Wu Huajun and He Juhou, used video coding tools to select 16 classroom teaching recorded videos of vocational school winning courses in the National Teaching Ability Competition as the research object. They found that vocational school teachers need to have a systematic and solid subject teaching method, and can combine information technology with teaching content to achieve deep integration in classroom teaching(Wu Huajun \u0026amp;He Juhou,2022)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Professor Wang Jixin and others have utilized artificial intelligence technology, such as intelligent recommendation and data mining, to improve the teaching quality of rural schools and build an intelligent teaching and learning system(Wang Jixin et al,2020)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Professor Jiang Libing from Central China Normal University used the self-designed CTBAS classroom teaching behavior analysis framework to analyze teacher-student activities in smart classrooms. The study found that teaching interaction in smart classrooms is more frequent; In terms of professional titles, the teaching of liberal arts courses is significantly higher than that of science courses(Jiang Libing et al,2018)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Based on the CPUP model, scholar Yang Yong analyzed the characteristics of classroom teaching behavior between novice and experienced teachers. Taking the lesson \"Important Compounds of Iron\" as an example, the research results showed that experienced teachers had higher classroom teaching effectiveness than novice teachers(Yang Yong,2016)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Zhang Junxia and other scholars conducted research using behavior analysis software to analyze 16 public classes. The results show that in the classroom, teachers have given students enough time for research and discussion. However, in terms of questioning and feedback strategies, teachers need to work harder and provide more opportunities for group discussions and presentations(Zhang Junxia \u0026amp; Ding Chaopeng,2014)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Under the background of the implementation of the new curriculum, scholars such as Jiang Xiaogang used classroom observation and the classification theory of \"teaching behavior pairs\" to conduct a comparative study of the behavior of novice teachers and expert teachers in the process of chemistry teaching. The research results indicate that there are significant differences in classroom teaching behavior between novice and expert teachers. Scholars have also proposed corresponding suggestions for improving the teaching behavior of novice teachers, such as classroom observation, post class communication (interviews), collective lesson preparation and discussion, as well as the habit of teaching reflection(Jiang Xiaogang et al,2013)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFinally, research on classroom teaching behavior evaluation. The evaluation criteria are the foundation and prerequisite for conducting scientific and effective teaching evaluations, as the saying goes, \"Without rules, there can be no circle.\". Professor Lu Yuanyuan and others analyzed the application of intelligent technology in evaluating teacher classroom teaching behavior from the perspectives of evaluation data, methods, and results; Using voiceprint recognition technology for teacher identity recognition and speech tracking in classroom videos; Construct an application framework for evaluating teacher classroom teaching behavior from three dimensions: emotion, posture, and positional preferences (Lu Yuanyuan et al,2022)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Professor Wen Juan and others have summarized the current situation of teacher classroom teaching evaluation, which mainly focuses on classroom teaching analysis, and proposed directions for continuous optimization and improvement of teacher classroom teaching evaluation, mainly including organic integration of multi-dimensional technical means, enriching the content of teacher classroom teaching evaluation, and reshaping the indicator system of teacher classroom teaching evaluation(Wen Juan et al,2021)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCurrent Status of Classification Research on Teacher Classroom Questions\u003c/h3\u003e\n\u003cp\u003eClassroom questioning is one of the most common teaching behaviors among teachers, and it is the main method for communication and exchange between teachers and students during the teaching process. Classroom questioning is an important guarantee of teaching quality, but effective questioning in the classroom is not an easy task(Shao Huailing,2009)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e..\u003c/p\u003e \u003cp\u003eIn existing research on classroom questioning, the classification of teacher questioning is one of the focuses of many researchers. A study by scholars such as Yu Guowen on the questioning methods of middle school mathematics teachers in different countries in the classroom. Divide the questioning of teachers in mathematics classrooms into three dimensions: questioning object, questioning content, and questioning level. Further refine and divide the questioning objects into individual students, small groups, and the entire class; The questioning content includes knowledge points, topic information, and management related content; The level of questioning is divided into low-level recall, understanding, and application, as well as high-level analysis, synthesis, and evaluation(Yu Guowen \u0026amp; Cao Yiming,2019)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Gu Lingyuan divided the types of questioning into five categories: conventional management questions, memory questions, reasoning questions, creative questions, and critical questions(Gu Lingyuan \u0026amp; Zhou Wei,1999)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Tu Rongbao divides questioning types into four categories: recall questioning, comprehension questioning, analytical and comprehensive questioning, and evaluative questioning(Tu Rongbao,1999)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Based on the role of teacher questioning and the corresponding cognitive level, researchers such as Ye Lijun divide questioning into seven types: management questioning, memorization questioning, repetition questioning, suggestion questioning, supplementary questioning, comprehension questioning, and evaluation questioning(Ye Lijun \u0026amp; Zheng Xin,2018)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Professor Wang Lu and others divided classroom questions into four aspects for observation: the type of question asked, the way teachers choose to answer questions, the way students answer, and the type of student answer(Wang Lu \u0026amp; Zhang Minxia,2012)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, this article will conduct data analysis on excellent lesson examples based on Professor Wang Lu's classification dimensions of classroom teaching questioning.\u003c/p\u003e\n\u003ch3\u003eAnalysis and Research Status of Teacher Classroom Questions\u003c/h3\u003e\n\u003cp\u003eThe commonly used method for analyzing classroom questioning is video analysis. From the perspective of classroom big data analysis by Professor Wang Lu and others, a stratified sampling method was used to select novice, competent, and mature teachers from the D, F, and M districts of B city. Video analysis, IRT modeling, induction, and deduction methods were used to analyze the tendency characteristics (openness, problem-solving, critical, and creative tendencies) and value orientation of classroom teaching questioning. It was found that in the dimension of open-ended questioning, novice middle school teachers were lower than competent and mature teachers, while mature primary school teachers had the lowest level of problem openness. In terms of critical and creative tendencies in asking questions, mature and competent primary school teachers in Zone F have outstanding advantages, while novice secondary school teachers in various regions are generally lower than competent and mature teachers; The problem-solving tendency of asking questions is the lowest among the three tendencies(Wang Lu \u0026amp; Cai Rongxiao,2016)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; Yu Guowen et al. conducted a comparative analysis of classroom questioning in instructional videos of expert teachers from China, Australia, France, and Finland using NVivo, exploring the unique characteristics of classroom questioning among teachers from different countries(Yu Guowen \u0026amp; Cao Yiming,2019). Hu Qidi and other scholars analyzed the classroom teaching videos of three teachers and found that the main problems in middle school mathematics classroom teaching are the excessive quantity and frequency of questioning, uneven types of questioning, short thinking time left for students by questioning, and insufficient effectiveness of questioning feedback forms(Hu Qizhou \u0026amp; Sun Qingkuo,2015)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Based on Anderson's taxonomy as the main theoretical basis, scholars such as Zhou Ying conducted a qualitative and quantitative comparative study on the classroom teaching questioning of outstanding mathematics teachers in China and the United States, using the same high school mathematics teaching topic as the research content, from four aspects: questioning types, teacher selection and answering methods, student answering types, and teacher reasoning and answering methods. Research has found that Chinese teachers tend to ask questions about factual knowledge, while American teachers ask questions about metacognitive knowledge; In terms of student response types, domestic teachers prefer students to answer together, while in the United States, they prefer students who have not raised their hands to answer; In terms of teacher response methods, American teachers place more emphasis on motivating students, while China places more emphasis on controlling classroom time progress; From the perspective of selecting the way teachers answer questions, American teachers pay more attention to the decomposition of knowledge and related questioning, while teachers pay more attention to the understanding and application of knowledge questioning(Zhou Ying \u0026amp; Wang Hua,2013)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Zhang Wenyu et al. analyzed the classroom teaching videos of the first National Full time Education Master's Subject Teaching (Mathematics) Professional Teaching Skills Competition using NVivo10, and found that pre service teachers have shortcomings in asking questions in classroom teaching(Zhang Wenyu \u0026amp; Fan Huiyong,2019)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Professors Zhang Chunli and others from the Education Department of Beijing Normal University conducted a comparative analysis between excellent and general lesson examples, and found that high-level questions accounted for a larger proportion and provided longer response times, while low-level questions had more diverse questioning methods; At the same time, feedback is directly provided for low-level problems, while \"appreciative\" feedback is given for high-level problems; In excellent lesson examples, teachers are better at breaking down problems and asking them in layers(Zhang Chunli \u0026amp; Ning Limen,2011)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, currently the research results on classroom teaching behavior in the education sector are relatively rich. Video analysis method is mainly used for classroom teaching questioning, but there are also significant shortcomings. The entire analysis process requires a lot of manpower and time, which makes it difficult to promote and apply this method on a large scale, and cannot accurately analyze the classroom questioning of more teachers. Based on this, this study will utilize artificial intelligence technologies such as machine learning and natural language processing for data mining and analysis, to better diagnose classroom teaching questions, thereby promoting teaching quality reform and improving classroom teaching effectiveness.\u003c/p\u003e"},{"header":"Research Design and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eResearch Methods\u003c/h2\u003e \u003cp\u003eThis study will use literature review, video analysis, and case analysis methods. Firstly, an in-depth analysis of relevant research literature on classroom teaching behavior is conducted, which provides solid theoretical support for the research in this article. This article collects relevant data on excellent lesson examples, and combines recorded video data to conduct research on the teaching behavior data of excellent lesson examples, providing data support for this article.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eResearch Object\u003c/h2\u003e \u003cp\u003eThis article combines literature research and data analysis methods, and with the support of effective questioning models and the \"Four How\" problem analysis theory. The recruitment period for this study started on June 15, 2023, and ended on July 31, 2023, carefully selects 8 teaching videos as research objects for excellent course cases. The course case videos include footage of the teacher and the voices of the students, with no appearance of students' faces in the videos. The study adhered to the principles outlined in the Helsinki Declaration and received approval from the Research Ethics Committee of the College of Computer and Information Engineering at Nanning Normal University (Protocol Code/Approval Number: NNUHR-2023-05). The study protocols were reviewed and approved by the ethics review board. Written informed consent was obtained from all participants, including teachers, students under the age of 16, and the parents or legal guardians of these students. Parents or legal guardians were informed about the study\u0026rsquo;s objectives and procedures and authorized their children\u0026rsquo;s participation by signing a written informed consent form.\u003c/p\u003e \u003cp\u003eThe selected courses cover multiple disciplines such as morality and the rule of law, art, history, mathematics, physics, English, and Chinese. The duration of recorded videos for each subject is controlled within 45 minutes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details). The selected teachers have rich teaching experience and profound subject knowledge. Teachers are adept at flexibly applying various teaching methods and strategies, which can stimulate students' learning enthusiasm and help them achieve excellent grades. Teachers also pay great attention to the personality and needs of students, teach according to their aptitude, and actively cultivate the potential of each student. The teaching style of teachers is full of vitality and interactivity, which can attract students' attention and make learning more interesting and effective.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic information of video samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeaching Content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDuration (min)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDealing with natural disasters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical rubbing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 min 25 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA corner of the kitchen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 min 58 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe Economic Development of the Song Dynasty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 min\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efactorization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 min 25 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe utilization of sound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 min 23 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSection A3a-3b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 min 3 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo short essays (\"Inscription of the Humble House\" and \"Love Lotus Saying\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43min 20 s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from classroom recorded videos of ministerial level premium courses in Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis methods\u003c/h3\u003e\n\u003cp\u003eThis article is supported by two theories: effective questioning analysis and classroom teaching behavior analysis of the \"Four How\" questions.\u003c/p\u003e\n\u003ch3\u003eEffective Question Analysis\u003c/h3\u003e\n\u003cp\u003eEffective questioning analysis is a focused classroom observation method that records and analyzes the questions raised and questioning strategies adopted by teachers in the classroom. According to the research in this article, corresponding modifications will be made to record the types of questions, student response methods, student response types, and teacher response methods, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffectiveness Question Observation Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective questioning by teachers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTypes of questions raised\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInferential questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMemory issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreative issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical question\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStudent response methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncourage students to ask or answer questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCall names or assign students to answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFree or collective answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudents actively ask or answer questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eStudent answer types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical judgment answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMemory based answers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInferential answers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreative answers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTeacher response methods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFollow up questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e\"Four How\" Question\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the field of education, problem design is considered the key to cultivating students' higher-order thinking ability and comprehensive thinking. In 1972, McCarthy integrated research findings from fields such as education, psychology, neuroscience, and management, and creatively proposed a teaching model called 4MAT (Mode Application Techniques). (See Fig.\u0026nbsp;\u003cspan refid=\"Fig22\" class=\"InternalRef\"\u003e1\u003c/span\u003e). And depict the process of students learning knowledge as a closed loop composed of four quadrants, each quadrant is a combination of left and right brain functions. Based on the characteristic of alternating left and right brain functions, the learning process is refined into eight stages: connection, attention, imagination, disclosure, practice, expansion, extraction, and presentation. And the 4MAT mode proposes four types of problems based on the learning style of each quadrant, namely: what type of problem is it, why type of problem is it, how type of problem is it, and what if type of problem is it, abbreviated as \"Four How\" problems (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eObservation Table of Four Questions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003efrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"What\" questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"Why\" questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"How\" questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\"What if\" questions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResearch Tools\u003c/h2\u003e \u003cp\u003eThis study utilizes a cloud-based intelligent classroom data analysis platform developed by the research team to perform a multidimensional analysis of classroom video recordings and teaching data. The tool was co-developed by the research team in collaboration with the Center for Intelligent Analysis of Educational Big Data at the School of Computer and Information Engineering,Nanning Normal University, and the Artificial Intelligence Education Center of the Guangxi Society for Educational Technology,demonstrates good reliability and validity and is easy to operate.The platform leverages AI technology to sample and analyze audio and video data from both teachers and students, integrating tools such as S-T analysis, Gagne\u0026rsquo;s Nine Events of Instruction timeline, the Rt-Ch chart, and Flanders\u0026rsquo; Interaction Analysis Matrix to comprehensively evaluate classroom teaching.More importantly, it is capable of conducting an in-depth analysis of classroom teaching behaviors, particularly focusing on the analysis of teachers' questioning methods. The system generates detailed reports covering classroom overviews, key highlights, areas for improvement, and recommendations for optimization. By employing sound source detection, speech recognition, and semantic analysis, the platform accurately identifies and tracks teacher-student behaviors, producing a comprehensive analysis of teaching implementation capabilities. This study's primary focus is to identify and address existing classroom teaching behavior issues in exemplary course cases, providing strategies for improvement (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the Results of Classroom Teaching Behavior in Excellent Course Cases\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eAnalysis of teachers' questioning types\u003c/h2\u003e \u003cp\u003eAfter statistical analysis of the selected sample videos, we obtained the statistical results of the types of teachers' questions in different disciplines, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Throughout the eight lessons, it is found that teachers of different subjects have certain differences in the types of questions. In general, teachers are more inclined to use \"inference problems\" and \"memory problems\", but pay less attention to \"creative problems\" and \"critical problems\".\u003c/p\u003e \u003cp\u003eThe specific lesson analysis found (see Figs.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Fig26\" class=\"InternalRef\"\u003e6\u003c/span\u003e for details) that in the discipline of morality and the rule of law, teachers use a large number of memory problems, indicating that teachers help students master the learning and application of basic knowledge in the classroom. However, excessive memory problems may only make students remember knowledge, which is not conducive to the cultivation of their thinking ability and innovative thinking. In contrast, in disciplines such as art and physics, the proportion of inferential questions is relatively high, indicating that teachers focus on stimulating students' thinking ability and innovative consciousness in the classroom. It is worth noting that teachers rarely use creative and critical questions, which further indicates that students' comprehensive thinking activities are relatively low, which is not conducive to deep learning and the cultivation of comprehensive thinking. Therefore, teachers should pay more attention to increasing the proportion of creative and critical questions when designing problems, in order to cultivate students' higher-order thinking abilities. At the same time, teachers should also flexibly use different types of questions when asking questions, and match them according to the characteristics of the subject and the students' situation to maximize the mobilization of students' thinking ability and learning enthusiasm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison table of teaching behaviors of \"teacher question type\" in different disciplines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMemory problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInference problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCreative problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCritical problem\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStudents' response style - Analysis of teacher-student interaction\u003c/h2\u003e \u003cp\u003eThis paper carries out statistical analysis on the selected sample videos, and the statistical results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. It reveals the differences in the way students answer in different subjects. Overall, students were more likely to name or assign students to answer, answer freely or answer collectively, followed by students' initiative to ask questions or answer, and the least likely to encourage students to ask questions or answer. This indicates that students receive knowledge more passively in the classroom, and teachers often play the role of knowledge transmission. At the same time, it also reflects that in teaching, teachers encourage students to participate actively, ask questions and improve the atmosphere of classroom interaction needs to be strengthened.\u003c/p\u003e \u003cp\u003eSpecifically (see Figs.\u0026nbsp;\u003cspan refid=\"Fig27\" class=\"InternalRef\"\u003e7\u003c/span\u003e to \u003cspan refid=\"Fig30\" class=\"InternalRef\"\u003e10\u003c/span\u003e for details), students have different preferences in response methods. For example, in Chinese and art courses, the proportion of free or collective answers is relatively high, reflecting the emphasis on cultivating students' creative and expressive abilities in these two disciplines, encouraging them to express their opinions and ideas in a free and collective environment. In physics and English courses, the proportion of students who are called or assigned to answer is relatively high, which may be because these two subjects focus on learning basic knowledge and mastering skills, requiring teachers to check students' learning situation by calling or assigning students to answer.\u003c/p\u003e \u003cp\u003eHowever, education and teaching should be a process of all-round development, which should not only pay attention to students' knowledge level and skills, but also pay attention to the cultivation of their thinking ability and creative ability. Therefore, in the future education and teaching, teachers should strengthen the classroom interactive atmosphere that encourages students to ask and answer questions, so as to make students more involved in the classroom, and at the same time provide more opportunities for students to choose and express themselves.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison table of teaching behaviors of \"students' response style\" in different subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEncourage students to ask questions or answer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCall or assign students to answer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree answer or group answer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStudents take the initiative to ask or answer questions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of students' answer types\u003c/h2\u003e \u003cp\u003eIn the theory of effective questioning model, classroom teaching behavior is mainly analyzed from four dimensions, namely, the types of teachers' questioning, the types of students' answering, the ways of students' answering and the ways of teachers' responding. The research dimension of students' response types mainly includes four categories: \"mechanical judgment response\", \"memory response\", \"inferential response\" and \"creative response\", which reflects the performance of students' teaching behavior in class.\u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, it can be seen that there are significant differences in the way students answer teachers' questions in classes of different disciplines. On the whole, students are more inclined to use \"mechanical judgment response\" and \"cognitive memory response\" to answer questions. In contrast, \"rational response\" came in second, and \"creative response\" was the smallest. This result may reflect the students' emphasis on knowledge mastery under the current educational background, as well as the general lack of high-level thinking ability and creativity.\u003c/p\u003e \u003cp\u003eAnalysis of specific lesson examples (see Figs.\u0026nbsp;\u003cspan refid=\"Fig31\" class=\"InternalRef\"\u003e11\u003c/span\u003e to \u003cspan refid=\"Fig34\" class=\"InternalRef\"\u003e14\u003c/span\u003e for details), for example: \"Section A 3a-3b\" In this English course, students' response methods are relatively balanced. In addition to mechanical judgment responses, the proportion of cognitive memory and reasoning responses is higher. Although the proportion of creative responses is lower, it is still 2.2%. This may be related to the fact that English subjects require students to have certain abilities in listening, speaking, reading, and writing, especially in terms of reading comprehension, which requires students to understand the implicit information in the article through reasoning and analysis. In the course 'Ethics and the Rule of Law - Responding to Natural Disasters', students have a relatively balanced way of answering questions, with little difference in the proportion of mechanical judgment answers, cognitive memory answers, and reasoning answers, while creative answers do not appear. This may be due to the fact that moral and rule of law courses require students to comprehensively apply various knowledge, including emotions, moral values, and other aspects, thus requiring students to have higher levels of thinking and expression abilities. In the course \"Physics Sound Utilization\", students mainly use mechanical judgment and cognitive memory responses, while inferential and creative responses are less common. This may be because the teaching content of physics mostly involves some formulas, theorems, and rules that need to be memorized and mastered proficiently, so students are more inclined to use mechanical judgment and cognitive memory responses. Inferential and creative responses require higher levels of thinking ability and creativity, as well as a deep understanding of physics knowledge, which may exceed students' abilities.\u003c/p\u003e \u003cp\u003eOn the whole, the types of answers students give in class may be influenced by a variety of factors. First of all, the way and difficulty of teachers' questions will directly affect the way students choose to answer. Some open questions or questions requiring reasoning and creative thinking may encourage students to adopt \"inferential answer\" or \"creative answer\". Secondly, subject characteristics will also affect students' answer methods. For example, students need to master a large number of memory knowledge points in mathematics and Chinese, which leads to students' more inclined to use \"cognitive memory answer\" or \"mechanical judgment answer\". To sum up, in classroom teaching, teachers should design appropriate questioning methods according to the characteristics of different disciplines, curriculum objectives, students' cognitive differences, teaching environment and other factors to encourage students to actively think, reason and innovate, so as to promote their all-round development. At the same time, students can also improve their thinking level and ability through a variety of learning ways and methods, so as to better cope with different types of problems and challenges.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison table of teaching behaviors of \"student response types\" in different subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMechanical judgment response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCognitive memory response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInferential response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCreative answer\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of teachers' response attitude\u003c/h2\u003e \u003cp\u003eTeachers' response attitude to students can be divided into positive attitude, neutral attitude, negative attitude, questioning three kinds. According to the data in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, affirmative attitude accounts for the largest proportion, followed by questioning and less negative attitude. This reflects that in the teaching process, teachers adopt more positive attitudes and ways to communicate and interact with students, respect students' thinking and expression, and try to guide students to actively participate in classroom teaching activities.\u003c/p\u003e \u003cp\u003eHowever, in the analysis of each specific lesson example (see Figs.\u0026nbsp;\u003cspan refid=\"Fig35\" class=\"InternalRef\"\u003e15\u003c/span\u003e to \u003cspan refid=\"Fig37\" class=\"InternalRef\"\u003e17\u003c/span\u003e), we can also observe some noteworthy phenomena. For example, in the English Section A 3a-3b lesson, although the proportion of affirmative attitude is the least, the proportion of questioning is 45.3%, which is higher than other lesson examples. This indicates that teachers pay attention to students' thinking and creativity when answering questions, and encourage students to exercise their freedom. And continuously guide and stimulate students' thinking and creativity from the perspective of their answers. Through this attitude, students can deepen their understanding of problems and stimulate their imagination and innovation ability. However, in classroom teaching, teachers still adopt a negative response, although it accounts for a relatively small proportion, it still has a certain impact in classroom teaching, which can make students feel frustrated and discouraged, reducing their enthusiasm and willingness to participate in the classroom. Therefore, teachers should avoid using negative responses as much as possible in future teaching processes, and instead use more positive responses and questioning to guide students to think and explore problems. In the four courses of \"Responding to Natural Disasters\", \"Physical Rubbings\", \"Anti Japanese War in the Enemy Rear Battlefield\" and \"Factorization\", teachers mainly adopt affirmative attitudes, which to some extent indicates that teachers are more encouraging or praising students, encouraging students to participate in classroom activities, so as to improve students' learning effectiveness and enthusiasm. Throughout the four classes, it was found that teachers rarely guide students' thinking and expression behavior, which leads to poor training of students' deep thinking and expression abilities. This indirectly indicates that teachers have not achieved the teaching objectives of the course well, and at the same time, they have not effectively promoted students' learning and development, thereby improving their learning effectiveness and enthusiasm.\u003c/p\u003e \u003cp\u003eFrom the selection of lesson examples, it can be found that most teachers only use the affirmative way to stimulate students' learning interest and enthusiasm, but less guide students to think and express. Although this teaching method can stimulate students' learning enthusiasm and enthusiasm, it can't really promote students' learning and development. This problem deserves the attention of schools and teachers and needs continuous optimization and improvement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA comparison table of \"teacher responsive attitude\" teaching behavior in different subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAffirmative response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNegative response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003einquire\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the \"Four How\" problem\u003c/h2\u003e \u003cp\u003eIn the field of education and teaching, problem design is considered to be one of the key points in the cultivation of students' higher order thinking ability and comprehensive thinking. McCarthy pointed out in 4MAT model that problems are divided into four types, namely what kind of problems, why kind of problems, how kind of problems and what-if kind of problems, referred to as \"Four How\" problems. Among them, he why, how, and what if problems of the \"Four How\" problems belong to higher-order thinking problems(Wang Lu\u0026amp;Cai Rongxiao,2016). According to the data analysis in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the \"what\" dimension accounts for a high proportion, followed by \"how\" questions, while \"what if\" and \"why\" questions account for a relatively low proportion. This indicates that teachers may pay more attention to imparts and understandings of knowledge and pay less attention to the cultivation of students' thinking ability and innovation ability when formulating questions.\u003c/p\u003e \u003cp\u003eAnalysis of specific lesson examples (see Figs.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e to \u003cspan refid=\"Fig39\" class=\"InternalRef\"\u003e21\u003c/span\u003e for details). For example, in the course \"The Use of Sound\", although teachers considered four types of problems in problem design, in actual teaching, teachers mainly focused on \"what\" problems, reaching 81.9%, while \"how\" and \"what if\" problems accounted for a relatively low proportion. This indicates that in this course, teachers focus on imparting knowledge points to enable students to master the teaching content and basic knowledge of the course, while the training of students' thinking ability and innovation ability is not enough. In the course of \"Two Short Essays (\" Inscription of the Humble Chamber \"and\" Love Lotus Saying \"), although the proportion of\" what \"questions is relatively high, compared to other courses, this course has a higher frequency of appearing in the\" why \"and\" what if \"questions. This indicates that teachers attach great importance to guiding students to think about the emotions, thoughts, and underlying reasons expressed in literary works in problem design, And pay attention to how students express their thoughts and feelings through literary language.\u003c/p\u003e \u003cp\u003eBased on the above analysis, it is suggested that teachers should pay more attention to the diversity and multiple levels of question design in the future teaching process, and design more open, inspiring and challenging questions to stimulate students' comprehensive thinking quality and innovative ability. For example, in the moral and rule of law class, teachers can design a question about how to deal with emergencies, allowing students to freely use their imagination and creativity. In a physics class, a question about how to use sound for communication can be devised, allowing students to experiment and think to find the answer. In addition, teachers can also use classroom interaction and group cooperation to encourage students to actively participate in the cultivation of higher-order thinking skills. Activities such as group discussion, cooperative inquiry and problem solving can stimulate students' critical thinking, creative thinking and cooperative ability. Teachers can also set learning tasks and projects, and let students work together to solve complex problems in groups, so as to cultivate students' comprehensive thinking and cooperation ability.\u003c/p\u003e \u003cp\u003eIn a word, problem design plays a vital role in the teaching process, which directly affects the cultivation of students' thinking ability and innovation ability. Teachers should be aware of the importance of question design and strive to design diverse, challenging and inspiring questions to promote the development of students' higher order thinking skills. At the same time, teachers should also adopt interactive teaching methods and cooperative learning forms to encourage students' active participation, so as to improve their learning effect and enthusiasm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of \"Four Questions\" teaching behaviors in different disciplines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhat if\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWhy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoral and Rule of Law\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehistory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe data is sourced from the Intelligent Classroom Data Analysis Cloud Platform of Guangxi Zhuang Autonomous Region\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOptimization strategies based on the effective questioning model and the Four How questions\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eOptimize problem design and promote advanced thinking\u003c/h2\u003e \u003cp\u003eOptimize problem is the foothold of students' classroom learning, and different types of questions can promote the development of students' different abilities. From the analysis of classroom examples, it can be seen that \"reasoning questions\" and \"memory questions\" have less content on \"creative questions\" and \"critical questions\". Teachers pay more attention to \"what\" and \"how\", while neglecting \"how\" and \"why\", which is not conducive to the cultivation of students' thinking and learning abilities. Therefore, teachers should balance the knowledge value and thinking value of the problem in problem design, combine knowledge impartation with thinking guidance, and promote students' thinking advancement.\u003c/p\u003e \u003cp\u003eOn the one hand, teachers should prioritize core issues and highlight the essence of knowledge. Huang Aihua believes that the core issue is \"to the essence, covering the key and difficult points of teaching, high-level, and exploratory issues\". The core issues can mainly be teaching key and difficult points, knowledge clues, classroom theme issues, etc. The key and difficult teaching problems refer to the difficulties and confusion that students are prone to encounter in their thinking during the knowledge learning process, the knowledge clue problem refers to the problem of connecting a single thousand scattered knowledge points, and the classroom theme problem refers to the problem of commanding the classroom teaching content. Teachers should design teaching questions based on classroom content and teaching objectives, ensuring that the questions always serve the mastery of students' knowledge and skills and the cultivation of emotional attitudes. Teachers should distinguish between the value of questions, dividing them into core and auxiliary questions, ensuring clear questioning ideas and highlighting key points. For example, the key and difficult points and teaching theme of Factorization are to enable students to master the Factorization method. Teachers can use this as the main thread of classroom teaching and carry out modular teaching.\u003c/p\u003e \u003cp\u003eOn the other hand, teachers need to improve the problem structure under the leadership of core issues. The learning of students' knowledge has obvious progressive characteristics, always presenting a process from shallow to deep, from surface to inner, from phenomenon to essence, from preliminary understanding to deep understanding. Therefore, the teaching design of teachers should also respect this cognitive development law, emphasize the progressive order and logical relationship between problems, and enable students to find the connection between the old and new, so as to have a focus on the development of students' thinking. What types of problems belong to basic ability problems, and what types, how types, and how types of problems belong to higher-order thinking problems. The design of what type of problem should be minimal but precise, which can help students quickly recall knowledge and help teachers judge the students' mastery of basic knowledge. The other three types of problems require selective design based on the core problem. For example, the discipline of morality and the rule of law emphasizes the cultivation of emotional attitudes, and questions of what kind and what kind can better trigger students' emotional thinking. Mathematics focuses on the development of students' logic and abstract thinking, and the questions of why and how can help students deeply analyze abstract knowledge.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eOptimize questioning methods and enhance teacher-student interaction\u003c/h2\u003e \u003cp\u003eEffective questioning methods are key to activating students' thinking and increasing their engagement in classroom participation. Active student participation is a fundamental prerequisite for deep learning and cognitive development. Currently, many students are passively receiving knowledge, with a predominance of one-on-one questioning methods and minimal teacher-student interaction. The classroom atmosphere is often passive, and students lack the awareness and ability to think critically. To address this, teachers should optimize their questioning methods to enhance interaction and stimulate both cognitive engagement and participation.\u003c/p\u003e \u003cp\u003eOne approach is to create a contextual framework for questioning. Effective contextualized questions can stimulate students' interest and aid in knowledge transfer. Curriculum standards across disciplines emphasize tailoring teaching content to students' lived experiences, and research shows that creating real-world problem situations is key to developing students' problem-solving abilities. Linking core classroom knowledge to real-life scenarios provides students with opportunities to observe, practice, and think critically, helping them discover problems, apply knowledge, and solve challenges. This process encourages students to understand \"what,\" \"why,\" and \"how,\" deepening their learning and application.\u003c/p\u003e \u003cp\u003eAnother strategy is to design exploratory classroom questions. The advantage of exploratory questions is that they create an open intellectual space where students can freely think within a limited timeframe, and interaction can stimulate communication, generate ideas, and enhance students' critical thinking and judgment abilities. Exploratory questions should be challenging and interactive. For example, in a lesson on \"Love of the Lotus,\" a teacher asked, \"Who in your life could be described as 'coming out of the mud without being stained'?\" This question, filled with openness and imagination, requires students to understand the metaphorical meaning of the lotus and compare it to real people. Such questions help deepen students' comprehension and critical reflection, fostering the development of critical and creative thinking.\u003c/p\u003e \u003cp\u003eLastly, fun question formats, such as role-playing or classroom competitions, can effectively stimulate student interest and increase engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengthen student guidance and promote deep participation\u003c/h2\u003e \u003cp\u003eFrom an overall classroom perspective, students tend to respond to questions with \"mechanical judgment\" or \"cognitive memory\" answers, showing a lack of initiative and critical thinking. To cultivate students' response skills and encourage the development of thinking and learning abilities, teachers can extend the waiting time after asking questions, promoting deeper classroom engagement. Psychological research suggests that students experience two high-speed cognitive phases after receiving a question. The first occurs immediately after the teacher's question, during which students quickly process the information and form a basic response. The second phase occurs while other students are answering, during which students refine their thoughts based on others' responses and generate new ideas. By extending response time, teachers can help students make full use of these cognitive phases and encourage deeper thinking.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eOptimize questioning feedback and cultivate problem awareness\u003c/h2\u003e \u003cp\u003eTeachers often respond positively to students, but this approach can lack the necessary guidance to challenge students' perspectives. This might lead students to believe they don't need to reflect on or question their own answers, thereby limiting their reasoning and innovative abilities. Teachers should be mindful of their tone and expressions, avoiding an overemphasis on \"correct answers\" and focusing instead on guiding students toward discovery and deeper understanding.\u003c/p\u003e \u003cp\u003eIn future teaching, teachers should aim to cultivate students' problem awareness, breaking the traditional constraints on student cognitive development. Teachers can achieve this by using more probing follow-up questions, allowing students more space to explore and think critically. Additionally, encouraging students to ask questions and share viewpoints can create cognitive conflict, enabling students to identify their own cognitive challenges during the questioning process and to develop their thinking through collaborative problem-solving.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSummary and Outlook\u003c/h2\u003e \u003cp\u003eArtificial intelligence technology approaches classrooms and teaching in a completely new way, introducing new educational concepts and teaching methods. This paper utilizes an intelligent classroom data analysis cloud platform, developed by the research team, to analyze classroom teaching behavior in sample case videos by integrating an effective questioning model and the \"Four How\" theory. Through multiple dimensions, it reflects the actual state of classroom teaching and identifies several issues in exemplary course cases: teachers tend to adopt a positive attitude when responding to students, question design often focuses on \"what\" questions, and students primarily provide mechanical judgment responses or memory-based answers. In response, the study proposes optimization strategies in four areas: refining question design to promote higher-order thinking, enhancing questioning methods to improve teacher-student interaction, strengthening student guidance to encourage deeper engagement, and improving feedback on questioning to cultivate problem awareness. This research aims to provide theoretical support for scholars and assist schools in the Guangxi Zhuang Autonomous Region in continuously optimizing teaching behaviors and improving teaching quality.\u003c/p\u003e \u003cp\u003eBased on current research progress, future work will focus primarily on two areas: first, employing multiple classroom teaching theories to more accurately assess teaching behavior, as analyzing solely from the perspective of effective questioning and the \"Four How\" theory is insufficiently precise; second, enhancing the intelligent classroom data analysis cloud platform, which currently relies on sound source detection, speech recognition, and semantic analysis for modeling. Future developments aim to achieve accurate real-time recognition of facial expressions, body language, and movements of both teachers and students.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003eThe study was conducted in accordance with the Helsinki Declaration and was approved by the Research Ethics Committee of the College of Computer and Information Engineering at Nanning Normal University (protocol code/approval number: NNUHR-2023-05).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003einformed consent was obtained from all participants involved in the study, including teachers, students under the age of 16, and the parents or legal guardians of these students. The parents or legal guardians were thoroughly informed about the objectives and procedures of the study and provided authorization for their children's participation by signing a written informed consent form, ensuring compliance with ethical research standards.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work has been supported by the Ministry of Education's Humanities and Social Sciences Research Project under the title \"Regional Integration Strategy and Collaborative Operation Research on Digital Education Resources in Primary and Secondary Schools in Border Ethnic Areas\" (Project Approval Number: 22YJAZH084).Additionally, funding has been provided by the Guangxi Education Science \"14th Five-Year Plan\" for the year 2023, specifically for the special key project titled \"Research on the Model of Enhancing the Quality of Application of 'Three Classrooms' in Border Areas through Digital Technology Resources\" (Project Number: 2023ZJY524).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.O. conceptualized the study and supervised the project. S.W. and X.C. developed the methodology, performed formal analysis, and wrote the original draft. X.C. curated the data. Q.O. and S.W. reviewed and edited the manuscript.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to sincerely thank the Center for Intelligent Analysis of Educational Big Data at the School of Computer and Information Engineering, Nanning Normal University, and the Artificial Intelligence Education Center of the Guangxi Society for Educational Technology for their invaluable technical support with the Intelligent Analysis Cloud Platform.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data supporting the findings of the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChina, T. S. C. o. t. P. s. R. o. Report of the State Council on the Implementation of the National Medium- and Long-Term Education Reform and Development Plan Outline (2010\u0026ndash;2020)Beijing,. (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina, M. o. E. o. t. P. s. R. o. Notice of the Ministry of Education of the People's Republic of China on Issuing the Action Plan for Education Informatization 2.0. (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina, M. o. E. o. t. P. s. R. o. Notice of the Ministry of Education on Issuing the Work Points of the Basic Education Department of the Ministry of Education for 2022. (Beijing, 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina, M. o. E. o. t. P. s. R. o. Notice of the Ministry of Education on Issuing the Curriculum Plan and Curriculum Standards for Compulsory Education (Beijing, 2022). (2022) Edition.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKratz, H. 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Methods\u003c/em\u003e. \u003cb\u003e31\u003c/b\u003e, 35\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.19877/j.cnki.kcjcjf.2011.10.006\u003c/span\u003e\u003cspan address=\"10.19877/j.cnki.kcjcjf.2011.10.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Effective Questioning, Classroom Questioning, Four How Theory, Artificial Intelligence Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5677350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5677350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClassroom questioning is a critical element of teaching, facilitating interaction between teachers and students.Effective questioning plays a pivotal role in enhancing students\u0026rsquo; cognitive abilities, sparking interest in learning, and supporting knowledge retention. This study analyzes recorded exemplary classroom videos using an intelligent classroom data analysis cloud platform, integrating an effective questioning model and the \"Four How\" theory. The analysis is based on five dimensions: teacher questioning type, student response method, student response type, teacher response attitude, and the \"Four How\" questions. The findings reveal that while teachers often respond positively to students, their questions tend to focus on \"what\" inquiries.Furthermore,students frequently provide mechanical or memory-based responses. In light of these findings,the study recommends optimizing question design to foster higher-order thinking, improving questioning techniques to enhance teacher-student interaction, strengthening student guidance for deeper engagement, and refining feedback to cultivate problem awareness. This research provides valuable data-driven insights into teaching behaviors and offers strategies for enhancing classroom teaching quality in schools.\u003c/p\u003e","manuscriptTitle":"Deep analysis of classroom teaching behavior from the perspective of artificial intelligence: Centered on effective questioning models and exploration of \"Four How\" questions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 18:06:46","doi":"10.21203/rs.3.rs-5677350/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-12T06:33:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T06:40:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T12:40:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T03:53:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-11T01:27:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56625351120720537947487874483973266973","date":"2025-07-10T15:14:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317568955422992700632018543150401308324","date":"2025-07-10T13:12:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"219620910392916336048278165088571222190","date":"2025-07-10T13:03:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331729774972479414530149162470759586062","date":"2025-07-10T12:28:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201398268104512595561120440212378999413","date":"2025-07-10T11:36:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T11:32:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T16:15:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-08T04:43:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-06T14:09:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-19T13:48:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5140a1e2-511a-4a82-b252-6479479b22d8","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-12T06:33:02+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":42434170,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":42434171,"name":"Physical sciences/Mathematics and computing/Information technology"},{"id":42434172,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":42434173,"name":"Physical sciences/Mathematics and computing/Statistics"}],"tags":[],"updatedAt":"2026-05-12T06:44:07+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 18:06:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5677350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5677350","identity":"rs-5677350","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}