Construction and application of a mathematics classroom dialogue behavior analysis framework for deep learning

Construction and application of a mathematics classroom dialogue behavior analysis framework for deep learning | 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 Construction and application of a mathematics classroom dialogue behavior analysis framework for deep learning Xundiao Ma, Wenhe Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8443142/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Developing classroom dialogue is a crucial manifestation of advancing mathematics teaching reform and serves as an effective pathway to promote deep learning. It holds significant importance in enhancing students' core competencies. This study, based on Bloom's taxonomy of cognitive objectives and informed by an analysis of the literature of mathematics classroom discourse, constructs a mathematics classroom dialogue behavior analysis framework comprising four primary dimensions—analytical dialogue, comprehensive dialogue, evaluative dialogue, and creative dialogue—along with 16 secondary indicators. To validate the effectiveness of this analytical framework, 10 mathematics classroom sessions were selected as research subjects for analysis across two dimensions: Frequency statistics and behavioral patterns. The findings demonstrate that this analytical framework can accurately identify and analyze the characteristics of dialogue behaviors in mathematics classrooms, revealing the intrinsic connections and evolutionary pathways among dialogues. This framework offers valuable support for future research on mathematics classroom dialogue behaviors, teaching practices, and policy formulation. Social science/Education Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Analytical framework Core competencies Deep learning Classroom dialogue Mathematics classroom Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction In the context of rapid socioeconomic development and the accelerated advancement of educational modernization, the goals of school education should not be limited to the acquisition of knowledge and skills alone. Instead, greater emphasis should be placed on enhancing students' comprehensive qualities, fostering the development of their thinking patterns, and cultivating their key competencies. To better achieve this goal, focusing on the classroom is crucial. It is essential to promote the transformation of teaching methods, shifting from traditional didactic instruction towards interactive and dialogic approaches (Song, 2020 ). The Full-Time Compulsory Education Mathematics Curriculum Standards issued by the Ministry of Education of the People's Republic of China in 2001 state: "Teachers should help students initially learn to observe and analyze real society using mathematical ways of thinking, learn to pose questions from a mathematical perspective, learn to cooperate with others, and be able to communicate the process and outcomes of their thinking with others" (Gao & Wang, 2003 ). The newly revised Full-Time Compulsory Education Mathematics Curriculum Standards issued in 2011 state: "Teachers should help students actively participate in learning activities, raise and analyze questions, cooperate and communicate with others, attempt to think from different perspectives, articulate their thought processes in an organized manner, and cultivate habits such as diligence, independent thinking, cooperative communication, reflection, and questioning" (Kong, 2006 ). In July 2019, the Central Committee of the Communist Party of China and the State Council issued the Opinions on Deepening Education and Teaching Reform to Comprehensively Improve the Quality of Compulsory Education , emphasizing the need to strengthen the primary role of the classroom. It advocates for heuristic, interactive, and inquiry-based teaching methods, guiding students to engage in active thinking, proactive questioning, and independent exploration. Discourse transformation lies at the heart of classroom reform. Classroom dialogue serves as the primary vehicle and implementation method for interactive teaching, playing a crucial role in promoting deep learning, stimulating innovative thinking, and enhancing problem-solving abilities among students (Song, 2020 ). Therefore, studying mathematics classroom dialogue holds significant importance for enhancing the quality of mathematics instruction and fostering students' deep learning. Through an analysis of the relevant literature on mathematics classroom dialogue behaviors, it has been observed that prior studies predominantly rely on the Flanders Interaction Analysis System. These studies typically focus on quantifying the frequency, duration, and percentage of various types of teacher-student dialogues in mathematics classrooms. For example, Cui et al.(2017), conducted an analysis of interactive behaviors in junior high school mathematics classrooms based on the Flanders Interaction Analysis System (FIAS). Zhang et al. ( 2016 ), building upon the FIAS, designed an instructional interaction analysis coding system tailored for smart classroom environments. They employed percentage-based statistical methods to analyze instructional interactions and conducted comparative analyses of student participation. Zhu et al. ( 2022 ), focused on teacher-student verbal interaction in classroom teaching, utilizing the FIAS to explore the instructional characteristics of high-quality mathematics classrooms Wu et al. ( 2018 ), based on the Flanders Interaction Analysis System, examined 10 exemplary junior high school mathematics teaching video lessons as observational subjects to investigate classroom instructional interaction behaviors. Previous research on classroom dialogue exhibits notable limitations. In terms of analytical methods, it often relies solely on counting the frequency, duration, and percentages of dialogue, which tends to be superficial. Regarding research content, there has been a failure to construct a dialogue behavior analysis framework oriented toward deep learning, and a neglect of in-depth analysis of dialogue behavior sequences. These studies fall short of uncovering the underlying patterns of high-quality classrooms and are unable to effectively delineate the developmental pathways of students' deep learning. To delve into the characteristics of dialogue behaviors in mathematics classrooms, this study constructs a dialogue behavior analysis framework oriented toward deep learning. It employs lag sequence analysis to code and analyze mathematics classroom teaching videos, aiming to address the following questions: What is the structure of the mathematics classroom dialogue behavior analysis framework oriented toward deep learning? How effective is the application of this framework? What are the characteristics of mathematics classroom dialogue behaviors oriented toward deep learning? 2 Literature review 2.1 Research on classroom dialogue behaviors Classroom dialogue refers to the constructive interaction and verbal exchange between teachers and students aimed at achieving educational goals (Song et al., 2021 ). Howe et al. (2013), define classroom dialogue as a communicative process in which an individual poses a question or initiates dialogue during classroom instruction, followed by a response from at least one other participant. Regarding the study of classroom dialogue models, Sinclair et al. (1979), proposed the "Initiation-Response-Feedback (IRF)" structure. Mehan ( 1979 ) further elaborated on the IRF structure, introducing the "Initiation-Response-Evaluation (IRE)" model, which emphasizes the evaluative role of teacher feedback. Cazden (1990) refined the IRE structure using ethnographic methods and proposed alternative models. In classroom dialogue, increasing the frequency of questioning and the responsiveness of participants can aid student learning. However, high-quality dialogue is the core of teacher-student interaction and the key to enhancing classroom effectiveness(Schwarz & Baker, 2016 ). Alexander (2013) proposed that high-quality classroom dialogue should possess characteristics such as collectivity, reciprocity, supportiveness, constructiveness, and purposefulness. Mercer (2017) pointed out that high-quality classroom dialogue should begin with open-ended questions, aim for knowledge construction, and manifest primarily through explanation, analysis, reasoning, summarization, and metacognition. Additionally, some researchers have examined the functions of classroom dialogue. For instance, classroom dialogue facilitates the exchange and sharing of information, promotes the collision of diverse perspectives, and fosters deep understanding (Shao et al., 2019 ; Huang et al., 2020 ). It also helps enhance critical reflection, cultivates higher-order thinking skills such as logical analysis, summarization, and abstract reasoning, and contributes to strengthening innovative awareness, thereby enabling collaborative and high-quality knowledge construction (Zuiker & Anderson, 2019 ). 2.2 Research on classroom dialogue behavior analysis frameworks Research on classroom dialogue behavior analysis frameworks can be divided into two main stages. The first stage primarily focused on the frequency and proportion of classroom dialogue, employing behaviorist theory and quantitative analysis to evaluate classroom interactions. A typical example is the FIAS. For instance, Flanders ( 1963 ) constructed a dialogue framework comprising three categories—teacher talk, student talk, and silence—along with ten subcategories Building on the FIAS, researchers have designed various classroom dialogue frameworks tailored to different instructional environments. For example, Authors (2016) integrated interactive whiteboards with the FIAS to develop an interaction analysis framework for interactive whiteboard-based teaching. Gu et al. (2004), constructed a Flanders-based interaction analysis framework with information technology support Li et al. ( 2024 ), also based on the FIAS, designed a dual-coding analysis framework for mathematics instruction. The second stage of classroom dialogue research is primarily grounded in constructivist learning theory. Studies in this phase place greater emphasis on the cognitive functions of classroom dialogue. For instance, the Classroom Dialogue Research Group at the University of Cambridge proposed a framework for analyzing high-quality classroom dialogue, encompassing elements such as analysis and interpretation, summarization, responsive construction, and transfer application (Sedova et al., 2014 ). Ma et al. ( 2023 ), constructed a mathematics classroom dialogue analysis framework aimed at fostering learners' higher-order thinking development, comprising eight primary indicators—foundational knowledge, personal expression, analytical reasoning, comparative induction, transfer and innovation, responsive construction, agreement and questioning, and instruction and guidance—along with 22 secondary indicators. Liu ( 2013 ) developed a content framework for analyzing mathematics classroom dialogue, structured around three dimensions—mathematical classroom language, mathematical meaning construction, and mathematical classroom culture—each containing ten core elements. Authors ( 2023 ) established a teacher-student dialogue framework for smart classrooms designed to promote higher-order thinking development, consisting of four primary indicators—problem identification, solution conception, solution verification, and artifact creation—and 16 secondary indicators . 2.3 Research on classroom dialogue analysis methods Classroom dialogue analysis methods primarily include the FIAS approach, S-T (Student-Teacher) analysis, and LSA (Lag Sequential Analysis). Using FIAS, a comparative analysis of classroom dialogue behaviors in multimedia classrooms versus smart classrooms concluded that the teaching model in smart classrooms reflects a learner-centered approach, with higher frequency of classroom-technology dialogue and greater student engagement (Shi et al., 2019 ). Using the FIAS to analyze 10 elementary school smart classroom teaching videos, it was found that the frequency of classroom dialogue in smart classrooms is relatively high, students' active participation is enhanced, and teachers use technological tools to demonstrate instructional content more frequently (Liu & Chen, 2021 ). By employing both FIAS and S-T analysis to examine 27 smart classroom teaching videos from a university over one semester, the study revealed that teacher-student dialogue in smart classrooms is more frequent and the teaching model has shifted from teacher-centered to student-centered (Jiang et al., 2018 ). Utilizing FIAS and LSA to analyze dialogue behaviors in smart classrooms, the research found that technology effectively supports classroom dialogue activities. Through various forms of effective interaction, classroom teaching facilitates students' design and creative activities, highlighting the learner-centered philosophy (Jiang et al., 2019 ) . 2.4 Research commentary Classroom dialogue has been extensively studied in terms of its connotation, patterns, frameworks, and methods, laying a solid foundation for this research. However, certain issues remain. Firstly, research has shifted from focusing on the frequency of classroom dialogue to emphasizing its quality. Early studies, represented by the FIAS, primarily concentrated on the quantitative statistical analysis of classroom dialogue behaviors. Subsequent research has emphasized exploring the characteristics of high-quality classroom dialogue, such as collectivity, reciprocity, and supportiveness. It underscores that guiding students in activities such as in-depth explanation, reasoning, and summarization is central and critical to enhancing the quality of classroom dialogue. This shift provides direction for constructing a classroom dialogue framework oriented toward deep learning. Secondly, in the construction of dialogue frameworks, researchers have delineated key elements of high-quality classroom dialogue across different dimensions, such as analytical argumentation, transfer and innovation, and responsive construction. These elements align with the emphases of deep learning—critical thinking, knowledge transfer, and problem-solving. However, researchers have not explicitly proposed a mathematics classroom dialogue behavior analysis framework oriented toward deep learning. Finally, in terms of dialogue analysis methods, approaches such as FIAS, S-T, and LSA are predominantly employed. Among these, FIAS and S-T analysis focus on quantifying the frequency and proportion of classroom dialogue through statistical measures. In contrast, the LSA method examines the linguistic content of classroom dialogue to uncover underlying semantic associations and behavioral patterns, thereby providing a basis for assessing the depth and quality of classroom dialogue. In summary, existing studies provide a research foundation for this paper, yet certain limitations remain. Currently, there is a lack of a classroom dialogue behavior analysis framework specifically designed for mathematics classrooms with a focus on deep learning. Moreover, in terms of methodology, FIAS and S-T analysis are predominantly employed. Therefore, this study aims to construct a mathematics classroom dialogue behavior analysis framework oriented toward deep learning and apply LSA for empirical analysis. This approach will effectively validate the proposed framework and further synthesize the characteristics of mathematics classroom dialogue behaviors. 3 Research design 3.1 Research m ethods This study employs a combination of qualitative and quantitative methods, including literature analysis, lag sequential analysis, statistical analysis, and inductive approaches. The research process comprises four steps: Utilizing literature analysis to examine studies on classroom dialogue behavior analysis frameworks and Bloom's taxonomy of cognitive objectives, thereby constructing a deep learning-oriented mathematics classroom dialogue behavior analysis framework. Applying lag sequential analysis to code and analyze 10 mathematics classroom teaching videos based on the proposed framework, obtaining the transition frequencies of dialogue behavior sequences and adjusted residual values. Employing statistical analysis to calculate and analyze the frequencies of mathematics classroom dialogue behaviors based on sequence transition frequencies. Using inductive methods to summarize the patterns of mathematics classroom dialogue behaviors based on adjusted residual values.The specific flowchart of the research methodology is illustrated in Figure 1. 3.2 Research s ubjects The study selected lesson examples from the National Smart Education Resource Public Service Platform for Primary and Secondary Schools as the research subjects. This platform aggregates teaching content from primary and secondary school teachers across the country, with teachers voluntarily uploading their instructional videos. The rationale for choosing lesson examples from this platform primarily lies in two aspects: First, its cases extensively cover major provinces and regions in China, ensuring representativeness; Second, the open-source data provided by the platform facilitates comparative analysis and validation, supporting in-depth research. Additionally, the platform is officially endorsed and promoted by the Chinese education authorities, lending it high authority and advanced features, further enhancing the reliability and practicality of the study. During the research process, 10 lesson samples were selected as the data. Specific lesson examples are shown in Table 1. Table 1. Basic i nformation of m athematics c lassroom t eaching v ideos Lesson title Grade Duration Lesson title Grade Duration Isosceles triangle 7th grade 44 min 03 sec Mathematical inquiry 7th grade 45 min 11 sec Rational numbers 7th grade 45 min 08 sec Inverse proportional function 9th grade 40 min 28 sec Congruent triangles 8th grade 41 min 07 sec Tree planting problem 5th grade 44 min 26 sec Cylinders and cones 2nd grade 42 min 03 sec Tessellation 4th grade 40 min 12 sec Exploring properties of proportion 8th grade 46 min 09 sec Mathematics is fun 2nd grade 45 min 10 sec 3.3 Data c ollection and o rganization This study invited two researchers to independently code the mathematics classroom teaching videos. Sampling was conducted every 8 seconds, with the primary category of classroom dialogue within each 8-second interval recorded in chronological order. The consistency of the coding sequences between the two coders was examined, and the Cohen’s Kappa coefficient values for all video codings exceeded 0.75, indicating suitability for sequence analysis (Feng et al., 2022) . The results were imported into the lag sequential analysis software (GSEQ) to generate a transition frequency table for classroom dialogue, as detailed in Table 2. In the table, the first column represents the preceding behavior, the first row represents the subsequent behavior, and the values indicate the frequency of transitions from the preceding to the subsequent behavior, resulting in a total of 1,790 behavioral sequence relationships. The table content was then converted to standard scores to obtain adjusted residuals (Z-scores), as shown in Table 3. A Z-score greater than 1.96 indicates that these behavioral transition relationships are statistically significant (Tan et al., 2022) , with a total of 40 significant behavioral sequences identified. Table 2. Transition f requency t able of c lassroom d ialogue b ehaviors A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4 A1 40 0 0 0 0 0 0 5 0 0 0 0 40 20 7 12 124 A2 0 40 0 0 0 0 0 6 0 0 0 0 0 0 0 0 46 A3 0 0 48 0 0 0 0 0 0 0 0 0 30 15 7 9 109 A4 0 0 0 40 0 0 0 0 0 0 0 0 21 11 0 0 72 B1 0 0 0 0 10 0 0 0 0 0 0 0 8 4 0 4 26 B2 0 0 0 0 0 20 0 0 0 0 0 0 24 22 0 0 66 B3 0 0 0 0 0 0 42 0 0 0 0 0 12 3 11 5 73 B4 2 0 0 0 0 0 0 12 0 0 0 0 12 7 2 27 62 C1 0 0 0 0 0 0 0 0 20 0 0 0 8 4 1 4 37 C2 0 0 0 0 0 0 0 0 0 4 0 1 0 0 0 4 9 C3 0 0 0 0 0 0 0 0 0 0 10 1 8 34 30 34 117 C4 0 0 0 0 0 0 0 0 0 0 0 33 8 4 2 4 51 D1 47 12 30 0 8 0 12 16 8 0 8 10 62 62 63 0 338 D2 20 0 15 0 4 8 3 7 4 30 4 5 0 62 125 0 257 D3 9 0 7 0 20 0 11 24 11 5 30 7 0 0 76 93 263 D4 15 0 9 0 3 0 5 32 16 3 34 8 0 0 0 55 150 133 52 109 40 45 28 73 102 59 42 86 65 233 218 294 221 1790 Table 3. Adjusted r esidual v alues (Z-scores) of c lassroom d ialogue b ehaviors A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3 C4 D1 D2 D3 D4 A1 10.66 -2.03 -3.00 -1.78 -2.28 -1.48 -2.43 -0.91 -2.17 -0.97 -1.43 -2.28 6.36 1.23 -3.49 -1.07 A2 -1.98 33.80 -1.78 -1.06 -1.36 -0.88 -1.44 2.09 -1.29 -0.57 -0.85 -1.36 -2.71 -2.61 -3.11 -2.63 A3 -3.11 -1.90 16.75 -1.66 -2.13 -1.38 -2.26 -2.70 -2.03 -0.90 -1.33 -2.13 4.44 0.38 -3.04 -1.46 A4 -2.50 -1.53 -2.25 30.71 -1.71 -1.11 -1.82 -2.17 -1.63 -0.72 -1.07 -1.71 3.99 0.70 -3.92 -3.32 B1 -1.48 -0.90 -1.33 -0.79 9.38 -0.66 -1.08 -1.29 -0.97 -0.43 -0.63 -1.02 2.60 0.43 -2.32 0.40 B2 -2.39 -1.46 -2.15 -1.27 -1.64 18.83 -1.74 -2.07 -1.56 -0.69 -1.02 -1.64 5.56 5.17 -3.75 -3.17 B3 -2.52 -1.54 -2.26 -1.34 -1.72 -1.12 23.15 -2.19 -1.64 -0.73 -1.08 -1.72 0.76 -2.23 -0.45 -1.55 B4 -1.34 -1.41 -2.08 -1.23 -1.58 -1.03 -1.68 4.58 -1.51 -0.67 -0.99 -1.58 1.38 -0.32 -2.94 7.39 C1 -1.77 -1.08 -1.59 -0.95 -1.21 -0.79 -1.29 -1.54 17.16 -0.51 -0.76 -1.21 1.47 -0.33 -2.34 -0.36 C2 -0.87 -0.53 -0.78 -0.46 -0.59 -0.39 -0.63 -0.75 -0.57 15.86 -0.37 1.16 -1.19 -1.14 -1.36 2.85 C3 -1.51 -0.92 -1.36 -0.81 -1.03 -0.67 -1.10 -1.31 -0.98 -0.44 15.30 -0.01 2.48 2.35 2.37 2.32 C4 -2.09 -1.28 -1.88 -1.12 -1.43 -0.93 -1.52 -1.81 -1.36 -0.61 -0.90 23.23 0.47 -1.04 -2.52 -1.07 D1 4.78 0.65 2.17 -3.15 -1.50 -2.63 -0.68 -1.01 -1.18 -1.71 1.46 -0.86 2.93 2.98 3.55 -7.84 D2 0.06 -3.06 -0.33 -2.67 -2.01 2.06 -2.64 -2.34 -1.77 -1.45 0.08 -1.66 -6.85 6.03 14.64 -6.65 D3 -2.82 -3.10 -2.64 -2.71 3.56 -2.26 -0.03 2.41 0.75 2.56 -2.18 -1.01 -6.95 -6.69 5.58 11.92 D4 0.59 -2.42 -0.57 -2.11 1.06 -1.76 -0.87 7.54 4.54 1.77 0.96 0.68 -5.42 -5.21 -6.21 8.05 4 Results 4.1 Construction of the c lassroom d ialogue b ehavior a nalysis f ramework 4.1.1 Primary i ndicators From the perspective of deep learning, the goal of mathematics classroom dialogue is oriented toward higher-order thinking. The indicators at each level of classroom dialogue should reflect the characteristics of higher-order thinking. The primary indicators are guided by theories of higher-order thinking. Regarding higher-order thinking, the American educational psychologist Bloom categorized it into six levels from low to high: knowledge, comprehension, application, analysis, synthesis, and evaluation (Krathwohl et al., 1956). The first three levels correspond to lower-order thinking, while the latter three correspond to higher-order thinking. To align with the evolution of contemporary educational concepts, Anderson et al.(2001), revised this taxonomy. The revised objectives, from low to high, are: remembering, understanding, applying, analyzing, evaluating, and creating. Remembering, understanding, and applying pertain to lower-order thinking, while analyzing, evaluating, and creating pertain to higher-order thinking. Based on higher-order thinking theory, this study categorizes the primary indicators of the classroom dialogue behavior analysis framework into analytical dialogue, comprehensive dialogue, evaluative dialogue, and creative dialogue. 4.1.2 Secondary i ndicators This study employed literature analysis to refine the secondary indicators. Centered on the core question—“What elements constitute a deep learning-oriented mathematics classroom dialogue behavior analysis framework?”—30 core papers were initially selected. Through further screening, 7 core papers were ultimately identified. Analysis of these 7 papers led to the establishment of 16 secondary indicators. The correspondence between the papers and the screened indicators is presented in Table 4. Table 4. Correspondence b etween c ore p apers and s creened i ndicators Source of literature Secondary indicators Research on the construction of an artificial intelligence evaluation system for mathematics classroom dialogue oriented toward education 2030 (Cao et al., 2022) Experiential learning, Transfer application, Logical connection, Model building, Problem analysis and solving, Illustrative explanation, Interpretive argumentation, Personal expression. A comparative study on classroom dialogue between expert and novice mathematics teachers based on the cultivation of higher-order thinking (Ma et al., 2023) Analyzing and solving problems, Illustrative explanation, Interpretive argumentation, Personal expression, Inferring and hypothesizing,Response construction. The psychology of problem-solving courts (Miller et al., 2025) Critical questioning and skepticism, Evaluation and discrimination, Examination and judgment, Evaluation analysis, Imaginative construction Coding classroom dialogue: Methodological considerations for researchers (Hennessy et al., 2020) Experiential learning, Transfer application, Logical connection, Model building, Analyzing and solving problems, Illustrative explanation, Interpretive argumentation, Problem generation, Inferring and hypothesizing, Response construction, Method innovation Fostering thinking: Intelligent classroom teaching analysis and application based on precise annotation technology(Song et al., 2023) Evaluation and discrimination, Examination and judgment, Evaluation analysis Mathematics interdisciplinary problem solving oriented toward core competencies: An assessment framework and application effectiveness(Guo al., 2025) Transfer application, Logical connection, Model building, Analyzing and solving problems, Analyzing and solving problems, Interpretive argumentation Research on adaptive assessment of mathematics disciplinary abilities for accuracy and efficiency (Guo & Rong, 2025) Inferring and hypothesizing, Evaluation analysis, Method innovation, Inferring and hypothesizing, Interpretive argumentation Based on the secondary indicators screened in Table 4 and aligned with the connotations of the primary indicators, a comprehensive integration of the secondary indicators was conducted, ultimately yielding the teacher-student dialogue framework presented in Table 5. Table 5. Mathematics c lassroom d ialogue b ehavior a nalysis f ramework for d eep l earning Primary indicators Secondary indicators Specific description. A Analytical dialogue A1 Illustrative explanation By providing examples (both positive and counterexamples) to help students comprehend complex, abstract knowledge, thereby clarifying key points and addressing challenging concepts. A2 Analyzing and solving problems. Deconstructing problems, extracting key information from the question, or identifying entry points for problem-solving. A3 Logical connection Identifying the logical connections between mathematical knowledge, and holistically grasping the structure and interrelatedness of the content. A4 Model building Expressing real-world applied problems using mathematical symbols and language, and establishing mathematical models to solve them. B Comprehensive dialogue B1 Transfer application The transfer and application of mathematical knowledge, skills, and thinking methods. B2 Experiential learning Sharing personal experiences, direct activity experiences, etc. B3 Interpretive argumentation Logically reasoning through problems, providing clear and structured explanations and arguments to resolve them. B4 Method integration When facing complex problems, one can integrate multiple problem-solving strategies or thinking methods, explaining why such a combination is chosen and how they function synergistically. C Evaluative dialogue C1 Critical questioning Critically questioning mathematical concepts, solutions, or viewpoints, and identifying potential errors, inconsistencies, or limitations based on mathematical logic or evidence.。 C2 Evaluation and discrimination Assessing whether conclusions have limitations or determining whether the established mathematical model accurately reflects the real-world situation. C3 Examination and judgment Examining whether a mathematical proof or reasoning process is logically rigorous, or comparing the efficiency, conciseness, and generalizability of different solutions to make an optimal choice. C4 Evaluation analysis Evaluating others' viewpoints based on mathematical reasoning. D Creative dialogue D1 Problem generation Instead of being content with solving given problems, actively and creatively proposing new, valuable mathematical questions. D2 Inferring and hypothesizing Using existing knowledge and information to infer or hypothesize about conclusions. D3 Imaginative construction Expressing one's own ideas or viewpoints on a particular subject or event, without necessarily relying on mathematical reasoning, or proposing imaginative and unconventional thoughts by raising questions through imagination. D4 Method innovation Proposing original and insightful problem-solving approaches, methods, or theoretical perspectives. 4.2 Application of the c lassroom d ialogue b ehavior a nalysis f ramework 4.2.1 Frequency s tatistics of c lassroom d ialogue b ehaviors The frequency statistics of classroom dialogue behaviors reveal a clear tendency in the distribution of behavior types. Analytical and creative dialogues occur at higher frequencies, while comprehensive and evaluative dialogues are relatively lower. This reflects an imbalance in dialogues across different cognitive levels in teaching. The specific data are presented in Table 2. In terms of analytical dialogue, all categories maintained relatively high frequencies. Among them, "A3 Logical connection" occurred 48 times, while "A1 Illustrative explanation," "A2 Analyzing and solving problems," and "A4 Model building" each occurred 40 times. This indicates that teachers place significant emphasis on guiding students to identify logical connections between mathematical concepts and to grasp the overall structure and interrelatedness of knowledge. The frequency of comprehensive dialogue is relatively low. Apart from "B3 Interpretive argumentation," which reached 42 instances, "B2 Experiential learning" occurred 20 times, while "B1 Transfer application" and "B4 Method integration" were recorded only 10 and 12 times, respectively. This indicates that in mathematics classrooms, teachers frequently guide students in logical reasoning, structured explanation, and argumentation to solve problems. Conversely, there is less emphasis on guiding students in the transfer and application of mathematical knowledge, skills, and thinking methods. In evaluative dialogue, the frequency of "C4 Evaluation analysis" was 33 instances, indicating that students possess a certain level of evaluation ability based on mathematical reasoning. However, "C1 Critical questioning" and "C3 Examination and judgment" occurred only 20 and 10 times, respectively, with "C2 Evaluation and discrimination" being particularly low at just 4 instances. This reflects a significant gap in the guidance of higher-order evaluative thinking, such as critical questioning and logical rigor scrutiny, in teaching. There is an urgent need to enhance the cultivation of students' independent judgment and systematic evaluation abilities. In creative dialogue, the performance is particularly notable. "D3 Imaginative construction" recorded the highest frequency (76 instances), while "D1 Problem generation" and "D2 Inferring and hypothesizing" both occurred 62 times, and "D4 Method innovation" was observed 55 times. This indicates that teachers are adept at creating a heuristic classroom environment, encouraging students to propose new questions, make reasonable inferences, and expand their thinking through responses, demonstrating a strong awareness of fostering innovative thinking. 4.2.2 Classroom d ialogue b ehavioral p atterns Comprehensive-Evaluative-Creative b ehavioral p attern. This dialogue behavior pattern centers on creative dialogue. Under the guidance and organization of the teacher, students progress through four cognitive developmental stages: "D1 Problem generation→D2 Inferring and hypothesizing→D3 Imaginative construction→D4 Method innovation". Specifically, under the teacher's guidance, students proactively pose innovative mathematical questions. To solve these problems, students engage in reasonable inference and scientific hypothesis based on their own knowledge and experience. During the problem-solving process, they conduct in-depth analysis and construction of the problems through multi-path exploration, interdisciplinary knowledge integration, or persistent questioning. After obtaining preliminary solutions, students further refine their learning methods and propose problem-solving approaches that are insightful and original (D1→D2→D3→D4). This process reflects the complete cognitive journey from problem formulation to method innovation, highlighting the importance of cultivating students' mathematical creative thinking. The development of creative dialogue relies on the support of analytical and evaluative dialogues. The specific support mechanisms are as follows: During the problem generation phase (D1), when students pose multiple creative questions, teachers should promptly guide them to compare and evaluate the questions and their potential solutions, selecting the most valuable one for further inquiry (D1 → C3). In the inferring and hypothesizing phase (D2), as students use existing knowledge and information to infer or hypothesize conclusions, they need to carefully examine whether the hypothesizing process is logically rigorous (D2→C3). Additionally, they can share personal experiences or direct activity experiences to further consolidate their hypotheses (B2→D2). This process can be repeated to deepen the reinforcement of inferences or hypotheses, ensuring the validity of the conclusions (D2→B2). During the imaginative construction phase (D3), when students generate questions through imagination, teachers should guide them to develop potential solutions for the proposed problems. By comparing different solutions, students can make optimal choices (C3→D3). In the method innovation phase (D4), when students propose original insights or theories, they should be guided to refine their solutions through evaluative dialogue (C3→D4). Furthermore, by integrating analytical dialogue methods (B4), students can be encouraged to employ diverse thinking approaches to develop innovative problem-solving strategies (B4→D4). Analytical-Comprehensive-Creative classroom d ialogue b ehavior s equence. This dialogue behavior pattern begins with analytical dialogue, uses comprehensive dialogue as an intermediary, and ultimately leads to the realization of creative dialogue, reflecting a progressive relationship in cognitive levels from shallow to deep, from fragmented to integrated, and ultimately to innovative. Specifically, teachers and students first engage in analytical dialogue centered on mathematical problems, such as deconstructing, organizing, and structuring the problem through "A1 Illustrative explanation," "A3 Logical connection," or "A4 Model building". Subsequently, during the comprehensive dialogue phase, students integrate and transfer knowledge, methods, or experiences based on the analytical results (e.g., "B1 Transfer application" or "B4 Method integration"), forming a more systematic problem-solving approach. Finally, in the creative dialogue phase, students propose new questions (D1), make reasonable inferences (D2), or suggest innovative solutions (D4), completing the cognitive leap from understanding to creation. This sequence demonstrates that analytical dialogue provides the cognitive foundation for deep learning, comprehensive dialogue facilitates the structuring and transfer of knowledge, and creative dialogue signifies the genuine occurrence of higher-order thinking. Analytical-Comprehensive-Evaluative-Creative classroom d ialogue b ehavior. This behavioral pattern builds upon the "Analytical-Comprehensive-Creative" framework by incorporating an evaluative dialogue component, forming a more complete and deliberate deep learning pathway. In this model, teachers and students first clarify the essence and structure of the problem through analytical dialogue (e.g., A1, A3, A4). They then proceed to comprehensive dialogue (e.g., B1, B4), integrating and transferring the analytical results. Next, evaluative dialogue (e.g., C3 critical examination and judgment, C4 evaluative analysis) is introduced to conduct logical scrutiny, efficiency comparisons, or value assessments of existing solutions, thereby optimizing decision-making. Finally, the process leads to creative dialogue (e.g., D3 imaginative construction, D4 method innovation), where more insightful and original problems or solutions are proposed based on evaluation. This sequence highlights the critical role of evaluation in deep learning—it not only involves scrutinizing and refining comprehensive outcomes but also serves as a key catalyst for stimulating creative thinking. It reflects an iterative cycle of "analysis→integration→evaluation→ creation" and a deepening cognitive process. 5 Conclusions and discussion The deep learning-oriented mathematics classroom dialogue behavior analysis framework constructed in this study can not only be used to analyze the effectiveness and quality of mathematics classroom teaching but also provides a profound interpretation of how deep learning is manifested in mathematics classrooms. The four dimensions of this framework—analytical, comprehensive, evaluative, and creative dialogues—reveal the dialogic patterns of deep learning in mathematics classrooms. Based on this framework, 10 mathematics classroom teaching videos were selected as research subjects, and lag sequential analysis was employed to analyze and discuss the frequency and patterns of classroom dialogue behaviors. The following conclusions were drawn. (1) Deep learning is translated into observable and analyzable classroom dialogue behaviors. Deep learning is an in-depth learning approach aimed at cultivating learners' higher-order thinking. As a high-level cognitive objective, higher-order thinking corresponds to analysis, synthesis, evaluation, and creation in the Bloom-Anderson taxonomy of educational objectives (Anderson & Krathwohl, 2001). This behavioral analysis framework translates abstract higher-order cognitive processes into observable and analyzable dialogue behaviors within mathematics classrooms. For example, the frequencies of analytical and creative dialogue behaviors are relatively high, while those of comprehensive and evaluative dialogue behaviors are relatively low. This reflects a greater emphasis on analytical and creative learning approaches in mathematics classrooms, with insufficient attention given to comprehensive and evaluative learning methods. Furthermore, within "analytical dialogue," behaviors such as "model building" and "logical connection" illustrate how students deconstruct complex real-world problems and transform them into mathematical models. They further explain the logical relationships among various elements or knowledge points within these models, which serves as the foundation for deep learning. In "comprehensive dialogue," the behavior of "method integration" reflects students' application of multiple problem-solving approaches to address complex mathematical problems, representing a process of knowledge integration and transfer. (2) It reveals the progressive and interdependent relationships among the dimensions of classroom dialogue. The four dimensions do not exist in isolation; rather, they collectively form a developmental pathway for classroom dialogue centered on students' deep learning. Analytical dialogue focuses on "deconstructing" complex mathematical problems, aiming to clarify their internal structure, causal relationships. Comprehensive dialogue centers on "integration." Evaluative dialogue revolves around "judgment." Creative dialogue is oriented toward "generation," aiming to produce original solutions that are unprecedented. Analytical dialogue serves as the foundation for deep learning, comprehensive dialogue integrates the fragmented knowledge generated from analytical dialogue, evaluative dialogue assesses and refines the integrated solutions, and creative dialogue generates novel problems or solutions. The relationships among these four dialogue dimensions also manifest as higher-level dialogues expanding the needs of lower-level dialogues, while lower-level dialogues support the development of higher-level dialogues. This relationship further reflects a layer-by-layer support and progressive expansion dynamic. (3) It reveals the dynamic and non-linear relationships among classroom dialogues. Based on the classroom dialogue behavior analysis framework, this study employs lag sequential analysis to code and analyze classroom teaching videos, revealing the dynamic and non-linear relationships among classroom dialogue behaviors. For example, the identified behavioral sequence patterns—such as "Analysis-Evaluation-Creation," "Analysis-Comprehension-Creation," and "Analysis-Comprehension-Evaluation-Creation"—provide evidence for how deep learning manifests within classroom dialogue behaviors. These sequences indicate that deep learning is not an isolated display of a single cognitive behavior but rather a complex iterative process. This process involves analytical dialogue for problem deconstruction, comprehensive dialogue for constructing knowledge networks, evaluative dialogue for critical monitoring, and ultimately leads to creative dialogue for achieving cognitive breakthroughs. These findings translate the abstract theory of deep learning into clear dialogic pathways, representing a significant deepening of existing theories through this framework. (4) It provides a reference standard for mathematics classroom practice and offers direction for deep learning. The research on the mathematics classroom dialogue behavior analysis framework primarily builds upon the FIAS by revising certain dimensions and refining specific indicators. The FIAS, proposed in the last century, reflects a period when the primary focus of teaching was on students' acquisition of knowledge and skill enhancement, with less emphasis on fostering deep learning or addressing the unique characteristics of mathematics as a discipline. If a mathematics classroom dialogue behavior analysis framework is constructed merely by revising some dimensions and indicators of FIAS and adding dialogue-related metrics, it would only be capable of assessing students' mastery of knowledge and skills, failing to adequately measure the development of their thinking abilities or the state of deep learning. In this study, an analytical framework is developed based on the distinctive features of mathematics, incorporating dimensions such as analytical, comprehensive, evaluative, and creative dialogue, directly targeting deep learning. This framework enables teachers and researchers to accurately identify mathematics classroom dialogue behaviors that contribute to deep learning. For example, in a classroom dominated by rote dialogue but lacking evaluative or creative dialogue, the goal of deep learning is difficult to achieve. Therefore, this framework not only serves as an analytical tool but also informs teaching practice, guiding teachers to consciously design and elicit dialogues at higher cognitive levels, thereby effectively implementing deep learning. 6 Limitations and future research directions This study has several limitations that inform future directions. The sample, drawn from publicly available exemplary lessons, may not fully represent regular classroom dynamics, limiting the generalizability of findings. While Lag Sequential Analysis effectively mapped dialogue patterns, it provided limited insight into contextual factors, non-verbal interactions, and individual cognitive differences. Furthermore, the framework's indicators require further validation across diverse grade levels and mathematical topics. Future research should address these gaps through large-scale studies in varied instructional contexts to test the framework’s diagnostic utility and explore its adaptation to different pedagogical models. A mixed-methods approach, integrating qualitative tools like interviews, could uncover the psychological and social mechanisms driving productive dialogue sequences. Finally, translating the framework into practical tools—such as AI-supported classroom analytics or teacher professional development systems—would bridge theory and practice, enabling real-time feedback and fostering deeper learning in everyday mathematics instruction. Declarations Ethical Approval This study involving human participants was reviewed and approved by the Research Ethics Committee of Qujing Normal University (Approval ID: [201601], Approval Date: [2026.1.15]). All research procedures were conducted in accordance with the ethical standards of the institutional and national research committees, and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants. Informed Consent All classroom teaching videos used in this study were obtained from the "National Smart Education Resource Public Service Platform for Primary and Secondary Schools." The resources on this platform have been publicly authorized by the uploading teachers or schools, permitting their use for non-commercial educational and research purposes. By utilizing these publicly accessible resources, researchers are considered to have obtained the corresponding usage authorization by default. Author Contribution X.D. Ma: conceptualization, methodology, writing – original draft. W.H. Chen: investigation, writing – review & editing. Both authors approved the final manuscript. Acknowledgement This study was funded by the “National Natural Science Foundation of China under Grant 62306128” Data Availability The classroom videos analyzed in this study were sourced from the publicly accessible "National Smart Education Resource Public Service Platform for Primary and Secondary Schools" (https://www.zxx.edu.cn/). These video resources are openly available to the public and can be used for educational research. References Campbell. (1990). Classroom Discourse: The Language of Teaching and Learning. Australian Reading Association. Cao, Y., Song, Y., Zhao, W., et al. (2022). Research on the construction of an artificial intelligence evaluation system for mathematics classroom dialogue oriented toward Education 2030. Journal of Mathematics Education, 31 (1), 7–12. Cui, L., & Dong, L. (2017). An empirical study on classroom interaction in middle schools: Taking junior high mathematics classrooms as an example. E-Education Research , 38(5), 123–128. https://doi.org/10.13811/j.cnki.eer.2017.05.020 Feng, Y., Cao, F., & Zhang, H. (2022). A comparative study of instructional decision-making behaviors based on lag sequential analysis. Modern Educational Technology, 32 (2), 63–71. Flanders, N. A. (1963). Intent, action and feedback: A preparation for teaching. Journal of Teacher Education, 14 (3), 250–259. Gao, X., & Wang, S. (2003). Interpretation of the Full-Time Compulsory Education Mathematics Curriculum Standards (Experimental Version). Educational Science Research , (6), 4. https://doi.org/CNKI:SUN:JYKY.0.2003-06-011 Gu, X., & Wang, W. (2004). New explorations in classroom analysis technology for supporting teacher professional development. China Educational Technology , (7), 18–21. https://doi.org/10.3969/j.issn.1006-9860.2004.07.004 Guo, J., & Rong, Q. (2025). Research on adaptive assessment of mathematics competency toward accuracy and efficiency. China Educational Technology , (3), 103–110. https://doi.org/10.3969/j.issn.1006-9860.2025.03.014 Guo, K., Du, B., & Ji, Z. (2025). Interdisciplinary problem-solving in mathematics oriented toward core competencies: Assessment framework and application effects. Renmin University of China Education Journal , 1–18. https://link.cnki.net/urlid/11.5978.g4.20250327.1442.002 Hennessy, S., Howe, C., Mercer, N., et al. (2020). Coding classroom dialogue: Methodological considerations for researchers. Learning, Culture and Social Interaction, 25 , 100404. https://doi.org/10.1016/j.lcsi.2020.100404 Howe, C., & Abedin, M. (2013). Classroom dialogue: A systematic review across four decades of research. Cambridge Journal of Education, 43 (3), 325–356. https://doi.org/10.1080/0305764X.2013.786024 Huang, Z., Zhou, R., & Wan, L. (2020). Design and empirical study of an interactive classroom ecosystem in blended learning environments. E-Education Research , 41(4), 78–85. https://doi.org/10.13811/j.cnki.eer.2020.04.011 Jiang, L., Mao, Q., Wan, Z., et al. (2018). A performance study on smart classrooms promoting teaching reform in higher education: Based on the analysis of classroom teaching behaviors. China Educational Technology , (6), 52–58. https://doi.org/10.3969/j.issn.1006-9860.2018.06.008 Jiang, Y., Wang, W., Li, C., et al. (2019). A study on teacher–student interaction behaviors in smart classroom environments. Modern Distance Education , (3), 13–21. https://doi.org/10.3969/j.issn.1001-8700.2019.03.002 Kong, Q. (2006). An appraisal of the Full-Time Compulsory Education Mathematics Curriculum Standards. Global Education Review , 35(9), 4. https://doi.org/CNKI:SUN:WGJN.0.2006-09-007 Li, H., Bian, P., & Xu, M. (2024). Development of a double coding analysis system and behavior pattern mining for interactive mathematics teaching in smart classrooms. Modern Educational Technology , 34(3), 105–115. https://doi.org/10.3969/j.issn.1009-8097.2024.03.011 Liu, L. (2013). Construction of a content framework for analyzing teacher–student dialogue in mathematics classrooms. Global Education Review , 42(5), 68–76. https://doi.org/CNKI:SUN:WGJN.0.2013-05-012 Liu, Z., & Chen, N. (2021). Analysis of classroom teaching interaction behaviors in smart classroom environments. Modern Educational Technology, 31 (9), 28–36. https://doi.org/10.3969/j.issn.1009-8097.2021.09.004 Ma, J., Zhao, W., & Cao, Y. (2023). A comparative study of classroom dialogue between expert and novice mathematics teachers based on higher-order thinking cultivation. Journal of Mathematics Education, 32 (3), 68–72, 102. Authors (2023). Studies in Educational Evaluation . Authors (2023). Modern Educational Technology . Mehan, H. (1979). "What time is it, Denise?": Asking known information questions in classroom discourse. T heory Into Practice, 18 (4), 285–294. https://doi.org/10.1080/00405847909542846 Mercer, N., Hennessy, S., & Warwick, P. (2017). Dialogue, thinking together and digital technology in the classroom: Some educational implications of a continuing line of inquiry. Pergamon . https://doi.org/10.17863/CAM.13777 Miller, M. K., Yelderman, L. A., Holbrook, M. A., et al. (2025). The psychology of problem-solving courts. In Society, science, and problem-solving courts . Oxford University Press . https://doi.org/10.1093/oso/9780190059804.003.0004 Schwarz, B., & Baker, M. (2016). Dialogue, argumentation and education: History, theory and practice . https://doi.org/10.1017/9781316493960 Sedova, K., Salamounova, Z., & Svaricek, R. (2014). Troubles with dialogic teaching. Learning, Culture and Social Interaction, 3 (4), 274–285. https://doi.org/10.1016/j.lcsi.2014.04.001 Shao, F., Hu, W., Zhang, H., et al. (2019). Sequential analysis of argumentative discourse in the classroom: Elementary school students’ scientific reasoning. Journal of East China Normal University (Educational Sciences), 37 (6), 48–60. https://doi.org/10.16382/j.cnki.1000-5560.2019.06.005 Shi, Y., Peng, C., Zhang, J., et al. (2019). Analysis of teacher–student interaction behaviors in smart classroom environments in higher education. Modern Educational Technology, 29 (1), 45–51. https://doi.org/10.3969/j.issn.1009-8097.2019.01.007 Song, Y., Wu, B., & Hao, T. (2021). Exploring the patterns of classroom dialogue for knowledge construction. E-Education Research , 42(3), 111–119. https://doi.org/10.13811/j.cnki.eer.2021.03.016 Song, Y., Xu, C., Zhu, J., et al. (2023). Oriented toward thinking cultivation: Intelligent classroom teaching analysis and application based on precise annotation technology. Journal of East China Normal University (Educational Sciences), 41 (8), 79–89. https://doi.org/10.16382/j.cnki.1000-5560.2023.08.008 Song, Y. (2020). Research hotspots and frontier trends in the field of classroom dialogue. Global Education Review, 49 (12), 27–40. https://doi.org/10.3969/j.issn.1009-9670.2020.12.003 Tan, J., Wu, S., Wu, L., et al. (2022). Analysis of discourse interaction patterns in expert–novice pair programming based on behavior sequences. E-Education Research , 43(7), 106–113. https://doi.org/10.13811/j.cnki.eer.2022.07.014 Wu, X., Kong, Q., & Liu, Y. (2018). A comparative study of teaching language in flipped classrooms and traditional classrooms based on double coding. Modern Educational Technology, 28 (10), 8. https://doi.org/CNKI:SUN:XJJS.0.2018-10-009 Zhang, Y., Zhu, Y., Bai, Q., et al. (2016). A study on the characteristics of classroom interaction behaviors in primary school mathematics teaching within smart classrooms. China Educational Technology , (6), 43–48, 64. https://doi.org/10.3969/j.issn.1006-9860.2016.06.009 Zhu, H., Wang, T., Deng, M., et al. (2022). A study on teacher–student verbal interaction in mathematics classroom teaching in special education schools: Based on the improved Flanders Interaction Analysis System (iFIAS). Chinese Journal of Special Education , (1), 39–46. https://doi.org/10.3969/j.issn.1007-3728.2022.01.006 Zuiker, S. J., & Anderson, K. T. (2019). Fostering peer dialogic engagement in science classrooms with an educational videogame. Research in Science Education, 49 (2), 407–429. https://doi.org/10.1007/s11165-019-9842-z Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Editor invited by journal 05 Feb, 2026 Submission checks completed at journal 02 Feb, 2026 First submitted to journal 02 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8443142","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627431598,"identity":"481712a6-2ec5-4fb5-b415-b92004c8fed6","order_by":0,"name":"Xundiao Ma","email":"","orcid":"","institution":"Qujing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xundiao","middleName":"","lastName":"Ma","suffix":""},{"id":627431600,"identity":"bbe503a2-92ed-4a9a-83a5-e9a2282f7f50","order_by":1,"name":"Wenhe Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYDACCSBmbLBhhvDYiNeSBtTCTJqWwwzEa+Gf3XzsMe+O8+zyM/IPMHwoOwwUaSBgyZ1j6ca8Z24zG9xIZmCcce4wUOQAfi0GEjlm0rltQC0SyQzMvG2HgSIJhLTkfwNqOccsPwOo5S9xWnLYgFoOMDMAHcbMSIwWiRtpZtJ/25KZDc48NjjYcy6dR+IGAS38M5KfSc5ss0uWb098+OBHmbUc/wwCWmAgGUQcAGIe4tQDgR3RKkfBKBgFo2DkAQCfiz1qzs9sFwAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Wenhe","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-12-24 13:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8443142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8443142/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107746492,"identity":"e05edbb1-40c0-4729-95e0-9e81fecbc076","added_by":"auto","created_at":"2026-04-24 16:08:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":90248,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of research methodology\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/163d6dbb260c7b02c73e8035.png"},{"id":107746493,"identity":"e1b5ab82-a092-4681-9b10-cc265f8ea43e","added_by":"auto","created_at":"2026-04-24 16:08:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29779,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency statistics of classroom dialogue behaviors\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/adf5e9fddba7ea8aa6e0636a.png"},{"id":107746494,"identity":"074b0cbc-3acf-4ed0-ae2c-7326bed0708d","added_by":"auto","created_at":"2026-04-24 16:08:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":34334,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive-Evaluative-Creative behavioral pattern\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/1ef1912fdcb0a15ecdf636e4.png"},{"id":107746495,"identity":"b8275e86-0622-4efa-b278-39226ae808cf","added_by":"auto","created_at":"2026-04-24 16:08:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25409,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical-Comprehensive-Creative behavioral pattern\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/3cd2acc1492f59cfb62591c6.png"},{"id":107746496,"identity":"ebac920a-ee96-4b1b-9359-3281a0496b6e","added_by":"auto","created_at":"2026-04-24 16:08:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41491,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical-Comprehensive-Evaluative-Creative behavioral pattern\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/9d3328c535a6029d0cde5d79.png"},{"id":108491121,"identity":"155b62f1-d2ba-482a-9812-3e5b37034d02","added_by":"auto","created_at":"2026-05-05 09:52:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":845556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8443142/v1/967e84ca-d4a1-4b78-9080-acfec6c1a0ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and application of a mathematics classroom dialogue behavior analysis framework for deep learning","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn the context of rapid socioeconomic development and the accelerated advancement of educational modernization, the goals of school education should not be limited to the acquisition of knowledge and skills alone. Instead, greater emphasis should be placed on enhancing students' comprehensive qualities, fostering the development of their thinking patterns, and cultivating their key competencies. To better achieve this goal, focusing on the classroom is crucial. It is essential to promote the transformation of teaching methods, shifting from traditional didactic instruction towards interactive and dialogic approaches (Song, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u003cem\u003eFull-Time Compulsory Education Mathematics Curriculum Standards\u003c/em\u003e issued by the Ministry of Education of the People's Republic of China in 2001 state: \"Teachers should help students initially learn to observe and analyze real society using mathematical ways of thinking, learn to pose questions from a mathematical perspective, learn to cooperate with others, and be able to communicate the process and outcomes of their thinking with others\" (Gao \u0026amp; Wang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The newly revised Full-Time Compulsory Education Mathematics Curriculum Standards issued in 2011 state: \"Teachers should help students actively participate in learning activities, raise and analyze questions, cooperate and communicate with others, attempt to think from different perspectives, articulate their thought processes in an organized manner, and cultivate habits such as diligence, independent thinking, cooperative communication, reflection, and questioning\" (Kong, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In July 2019, the Central Committee of the Communist Party of China and the State Council issued the \u003cem\u003eOpinions on Deepening Education and Teaching Reform to Comprehensively Improve the Quality of Compulsory Education\u003c/em\u003e, emphasizing the need to strengthen the primary role of the classroom. It advocates for heuristic, interactive, and inquiry-based teaching methods, guiding students to engage in active thinking, proactive questioning, and independent exploration. Discourse transformation lies at the heart of classroom reform. Classroom dialogue serves as the primary vehicle and implementation method for interactive teaching, playing a crucial role in promoting deep learning, stimulating innovative thinking, and enhancing problem-solving abilities among students (Song, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, studying mathematics classroom dialogue holds significant importance for enhancing the quality of mathematics instruction and fostering students' deep learning.\u003c/p\u003e \u003cp\u003eThrough an analysis of the relevant literature on mathematics classroom dialogue behaviors, it has been observed that prior studies predominantly rely on the Flanders Interaction Analysis System. These studies typically focus on quantifying the frequency, duration, and percentage of various types of teacher-student dialogues in mathematics classrooms. For example, Cui et al.(2017), conducted an analysis of interactive behaviors in junior high school mathematics classrooms based on the Flanders Interaction Analysis System (FIAS). Zhang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), building upon the FIAS, designed an instructional interaction analysis coding system tailored for smart classroom environments. They employed percentage-based statistical methods to analyze instructional interactions and conducted comparative analyses of student participation. Zhu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), focused on teacher-student verbal interaction in classroom teaching, utilizing the FIAS to explore the instructional characteristics of high-quality mathematics classrooms Wu et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), based on the Flanders Interaction Analysis System, examined 10 exemplary junior high school mathematics teaching video lessons as observational subjects to investigate classroom instructional interaction behaviors. Previous research on classroom dialogue exhibits notable limitations. In terms of analytical methods, it often relies solely on counting the frequency, duration, and percentages of dialogue, which tends to be superficial. Regarding research content, there has been a failure to construct a dialogue behavior analysis framework oriented toward deep learning, and a neglect of in-depth analysis of dialogue behavior sequences. These studies fall short of uncovering the underlying patterns of high-quality classrooms and are unable to effectively delineate the developmental pathways of students' deep learning.\u003c/p\u003e \u003cp\u003eTo delve into the characteristics of dialogue behaviors in mathematics classrooms, this study constructs a dialogue behavior analysis framework oriented toward deep learning. It employs lag sequence analysis to code and analyze mathematics classroom teaching videos, aiming to address the following questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the structure of the mathematics classroom dialogue behavior analysis framework oriented toward deep learning?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow effective is the application of this framework?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat are the characteristics of mathematics classroom dialogue behaviors oriented toward deep learning?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"2 Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research on classroom dialogue behaviors\u003c/h2\u003e \u003cp\u003eClassroom dialogue refers to the constructive interaction and verbal exchange between teachers and students aimed at achieving educational goals (Song et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Howe et al. (2013), define classroom dialogue as a communicative process in which an individual poses a question or initiates dialogue during classroom instruction, followed by a response from at least one other participant. Regarding the study of classroom dialogue models, Sinclair et al. (1979), proposed the \"Initiation-Response-Feedback (IRF)\" structure. Mehan (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) further elaborated on the IRF structure, introducing the \"Initiation-Response-Evaluation (IRE)\" model, which emphasizes the evaluative role of teacher feedback. Cazden (1990) refined the IRE structure using ethnographic methods and proposed alternative models. In classroom dialogue, increasing the frequency of questioning and the responsiveness of participants can aid student learning. However, high-quality dialogue is the core of teacher-student interaction and the key to enhancing classroom effectiveness(Schwarz \u0026amp; Baker, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Alexander (2013) proposed that high-quality classroom dialogue should possess characteristics such as collectivity, reciprocity, supportiveness, constructiveness, and purposefulness. Mercer (2017) pointed out that high-quality classroom dialogue should begin with open-ended questions, aim for knowledge construction, and manifest primarily through explanation, analysis, reasoning, summarization, and metacognition. Additionally, some researchers have examined the functions of classroom dialogue. For instance, classroom dialogue facilitates the exchange and sharing of information, promotes the collision of diverse perspectives, and fosters deep understanding (Shao et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It also helps enhance critical reflection, cultivates higher-order thinking skills such as logical analysis, summarization, and abstract reasoning, and contributes to strengthening innovative awareness, thereby enabling collaborative and high-quality knowledge construction (Zuiker \u0026amp; Anderson, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research on classroom dialogue behavior analysis frameworks\u003c/h2\u003e \u003cp\u003eResearch on classroom dialogue behavior analysis frameworks can be divided into two main stages. The first stage primarily focused on the frequency and proportion of classroom dialogue, employing behaviorist theory and quantitative analysis to evaluate classroom interactions. A typical example is the FIAS. For instance, Flanders (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) constructed a dialogue framework comprising three categories\u0026mdash;teacher talk, student talk, and silence\u0026mdash;along with ten subcategories Building on the FIAS, researchers have designed various classroom dialogue frameworks tailored to different instructional environments. For example, Authors (2016) integrated interactive whiteboards with the FIAS to develop an interaction analysis framework for interactive whiteboard-based teaching. Gu et al. (2004), constructed a Flanders-based interaction analysis framework with information technology support Li et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), also based on the FIAS, designed a dual-coding analysis framework for mathematics instruction.\u003c/p\u003e \u003cp\u003eThe second stage of classroom dialogue research is primarily grounded in constructivist learning theory. Studies in this phase place greater emphasis on the cognitive functions of classroom dialogue. For instance, the Classroom Dialogue Research Group at the University of Cambridge proposed a framework for analyzing high-quality classroom dialogue, encompassing elements such as analysis and interpretation, summarization, responsive construction, and transfer application (Sedova et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Ma et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), constructed a mathematics classroom dialogue analysis framework aimed at fostering learners' higher-order thinking development, comprising eight primary indicators\u0026mdash;foundational knowledge, personal expression, analytical reasoning, comparative induction, transfer and innovation, responsive construction, agreement and questioning, and instruction and guidance\u0026mdash;along with 22 secondary indicators. Liu (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) developed a content framework for analyzing mathematics classroom dialogue, structured around three dimensions\u0026mdash;mathematical classroom language, mathematical meaning construction, and mathematical classroom culture\u0026mdash;each containing ten core elements. Authors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) established a teacher-student dialogue framework for smart classrooms designed to promote higher-order thinking development, consisting of four primary indicators\u0026mdash;problem identification, solution conception, solution verification, and artifact creation\u0026mdash;and 16 secondary indicators .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research on classroom dialogue analysis methods\u003c/h2\u003e \u003cp\u003eClassroom dialogue analysis methods primarily include the FIAS approach, S-T (Student-Teacher) analysis, and LSA (Lag Sequential Analysis). Using FIAS, a comparative analysis of classroom dialogue behaviors in multimedia classrooms versus smart classrooms concluded that the teaching model in smart classrooms reflects a learner-centered approach, with higher frequency of classroom-technology dialogue and greater student engagement (Shi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Using the FIAS to analyze 10 elementary school smart classroom teaching videos, it was found that the frequency of classroom dialogue in smart classrooms is relatively high, students' active participation is enhanced, and teachers use technological tools to demonstrate instructional content more frequently (Liu \u0026amp; Chen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By employing both FIAS and S-T analysis to examine 27 smart classroom teaching videos from a university over one semester, the study revealed that teacher-student dialogue in smart classrooms is more frequent and the teaching model has shifted from teacher-centered to student-centered (Jiang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Utilizing FIAS and LSA to analyze dialogue behaviors in smart classrooms, the research found that technology effectively supports classroom dialogue activities. Through various forms of effective interaction, classroom teaching facilitates students' design and creative activities, highlighting the learner-centered philosophy (Jiang et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Research commentary\u003c/h2\u003e \u003cp\u003eClassroom dialogue has been extensively studied in terms of its connotation, patterns, frameworks, and methods, laying a solid foundation for this research. However, certain issues remain.\u003c/p\u003e \u003cp\u003eFirstly, research has shifted from focusing on the frequency of classroom dialogue to emphasizing its quality. Early studies, represented by the FIAS, primarily concentrated on the quantitative statistical analysis of classroom dialogue behaviors. Subsequent research has emphasized exploring the characteristics of high-quality classroom dialogue, such as collectivity, reciprocity, and supportiveness. It underscores that guiding students in activities such as in-depth explanation, reasoning, and summarization is central and critical to enhancing the quality of classroom dialogue. This shift provides direction for constructing a classroom dialogue framework oriented toward deep learning.\u003c/p\u003e \u003cp\u003eSecondly, in the construction of dialogue frameworks, researchers have delineated key elements of high-quality classroom dialogue across different dimensions, such as analytical argumentation, transfer and innovation, and responsive construction. These elements align with the emphases of deep learning\u0026mdash;critical thinking, knowledge transfer, and problem-solving. However, researchers have not explicitly proposed a mathematics classroom dialogue behavior analysis framework oriented toward deep learning.\u003c/p\u003e \u003cp\u003eFinally, in terms of dialogue analysis methods, approaches such as FIAS, S-T, and LSA are predominantly employed. Among these, FIAS and S-T analysis focus on quantifying the frequency and proportion of classroom dialogue through statistical measures. In contrast, the LSA method examines the linguistic content of classroom dialogue to uncover underlying semantic associations and behavioral patterns, thereby providing a basis for assessing the depth and quality of classroom dialogue.\u003c/p\u003e \u003cp\u003eIn summary, existing studies provide a research foundation for this paper, yet certain limitations remain. Currently, there is a lack of a classroom dialogue behavior analysis framework specifically designed for mathematics classrooms with a focus on deep learning. Moreover, in terms of methodology, FIAS and S-T analysis are predominantly employed. Therefore, this study aims to construct a mathematics classroom dialogue behavior analysis framework oriented toward deep learning and apply LSA for empirical analysis. This approach will effectively validate the proposed framework and further synthesize the characteristics of mathematics classroom dialogue behaviors.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Research design","content":"\u003cp\u003e\u003cstrong\u003e3.1 Research\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employs a combination of qualitative and quantitative methods, including literature analysis, lag sequential analysis, statistical analysis, and inductive approaches. The research process comprises four steps:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eUtilizing literature analysis to examine studies on classroom dialogue behavior analysis frameworks and Bloom\u0026apos;s taxonomy of cognitive objectives, thereby constructing a deep learning-oriented mathematics classroom dialogue behavior analysis framework.\u003c/li\u003e\n \u003cli\u003eApplying lag sequential analysis to code and analyze 10 mathematics classroom teaching videos based on the proposed framework, obtaining the transition frequencies of dialogue behavior sequences and adjusted residual values.\u003c/li\u003e\n \u003cli\u003eEmploying statistical analysis to calculate and analyze the frequencies of mathematics classroom dialogue behaviors based on sequence transition frequencies.\u003c/li\u003e\n \u003cli\u003eUsing inductive methods to summarize the patterns of mathematics classroom dialogue behaviors based on adjusted residual values.The specific flowchart of the research methodology is illustrated in Figure 1.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Research\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study selected lesson examples from the National Smart Education Resource Public Service Platform for Primary and Secondary Schools as the research subjects. This platform aggregates teaching content from primary and secondary school teachers across the country, with teachers voluntarily uploading their instructional videos. The rationale for choosing lesson examples from this platform primarily lies in two aspects: First, its cases extensively cover major provinces and regions in China, ensuring representativeness; Second, the open-source data provided by the platform facilitates comparative analysis and validation, supporting in-depth research. Additionally, the platform is officially endorsed and promoted by the Chinese education authorities, lending it high authority and advanced features, further enhancing the reliability and practicality of the study. During the research process, 10 lesson samples were selected as the data. Specific lesson examples are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Basic\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003enformation of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eathematics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eeaching\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ev\u003c/strong\u003e\u003cstrong\u003eideos\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eLesson title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eLesson title\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eIsosceles triangle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003e7th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003e44 min 03 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eMathematical inquiry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003e7th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003e45 min 11 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eRational numbers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003e7th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003e45 min 08 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eInverse proportional function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003e9th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003e40 min 28 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eCongruent triangles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003e8th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003e41 min 07 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eTree planting problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003e5th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003e44 min 26 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eCylinders and cones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003e2nd grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003e42 min 03 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eTessellation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003e4th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003e40 min 12 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22.8155%;\"\u003e\n \u003cp\u003eExploring properties of proportion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.1068%;\"\u003e\n \u003cp\u003e8th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.6958%;\"\u003e\n \u003cp\u003e46 min 09 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.521%;\"\u003e\n \u003cp\u003eMathematics is fun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.9741%;\"\u003e\n \u003cp\u003e2nd grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8867%;\"\u003e\n \u003cp\u003e45 min 10 sec\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Data\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eollection and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eo\u003c/strong\u003e\u003cstrong\u003erganization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study invited two researchers to independently code the mathematics classroom teaching videos. Sampling was conducted every 8 seconds, with the primary category of classroom dialogue within each 8-second interval recorded in chronological order. The consistency of the coding sequences between the two coders was examined, and the Cohen\u0026rsquo;s Kappa coefficient values for all video codings exceeded 0.75, indicating suitability for sequence analysis (Feng et al., 2022)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e. The results were imported into the lag sequential analysis software (GSEQ) to generate a transition frequency table for classroom dialogue, as detailed in Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the table, the first column represents the preceding behavior, the first row represents the subsequent behavior, and the values indicate the frequency of transitions from the preceding to the subsequent behavior, resulting in a total of 1,790 behavioral sequence relationships. The table content was then converted to standard scores to obtain adjusted residuals (Z-scores), as shown in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA Z-score greater than 1.96 indicates that these behavioral transition relationships are statistically significant (Tan et al., 2022)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e, with a total of 40 significant behavioral sequences identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Transition\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003cstrong\u003erequency\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eable of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 6.69935%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.39216%;\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.02614%;\"\u003e\n \u003cp\u003e1790\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Adjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003er\u003c/strong\u003e\u003cstrong\u003eesidual\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ev\u003c/strong\u003e\u003cstrong\u003ealues (Z-scores) of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"723\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e10.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e33.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e16.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e30.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e18.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e23.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e7.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e17.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e15.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e23.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e4.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e6.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e14.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e-6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e11.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-5.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.87413%;\"\u003e\n \u003cp\u003e-6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.01399%;\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003e\u003cstrong\u003e4.1 Construction of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavior\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003cstrong\u003eramework\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e4.1.1 Primary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the perspective of deep learning, the goal of mathematics classroom dialogue is oriented toward higher-order thinking. The indicators at each level of classroom dialogue should reflect the characteristics of higher-order thinking. The primary indicators are guided by theories of higher-order thinking. Regarding higher-order thinking, the American educational psychologist Bloom categorized it into six levels from low to high: knowledge, comprehension, application, analysis, synthesis, and evaluation (Krathwohl et al., 1956). The first three levels correspond to lower-order thinking, while the latter three correspond to higher-order thinking. To align with the evolution of contemporary educational concepts, Anderson et al.(2001), revised this taxonomy. The revised objectives, from low to high, are: remembering, understanding, applying, analyzing, evaluating, and creating. Remembering, understanding, and applying pertain to lower-order thinking, while analyzing, evaluating, and creating pertain to higher-order thinking. Based on higher-order thinking theory, this study categorizes the primary indicators of the classroom dialogue behavior analysis framework into analytical dialogue, comprehensive dialogue, evaluative dialogue, and creative dialogue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1.2 Secondary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed literature analysis to refine the secondary indicators. Centered on the core question\u0026mdash;\u0026ldquo;What elements constitute a deep learning-oriented mathematics classroom dialogue behavior analysis framework?\u0026rdquo;\u0026mdash;30 core papers were initially selected. Through further screening, 7 core papers were ultimately identified. Analysis of these 7 papers led to the establishment of 16 secondary indicators. The correspondence between the papers and the screened indicators is presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Correspondence\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eetween\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eore\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eapers and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003ecreened\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endicators\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eSource of literature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eSecondary indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eResearch on the construction of an artificial intelligence evaluation system for mathematics classroom dialogue oriented toward education 2030 (Cao et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eExperiential learning, Transfer application, Logical connection, Model building, Problem analysis and solving, Illustrative explanation, Interpretive argumentation, Personal expression.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eA comparative study on classroom dialogue between expert and novice mathematics teachers based on the cultivation of higher-order thinking (Ma et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eAnalyzing and solving problems, Illustrative explanation, Interpretive argumentation, Personal expression, Inferring and hypothesizing,Response construction.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eThe psychology of problem-solving courts (Miller et al., 2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eCritical questioning and skepticism, Evaluation and discrimination, Examination and judgment, Evaluation analysis, Imaginative construction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eCoding classroom dialogue: Methodological considerations for researchers (Hennessy\u0026nbsp;et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eExperiential learning, Transfer application, Logical connection, Model building, Analyzing and solving problems, Illustrative explanation, Interpretive argumentation, Problem generation, Inferring and hypothesizing, Response construction, Method innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eFostering thinking: Intelligent classroom teaching analysis and application based on precise annotation technology(Song\u0026nbsp;et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eEvaluation and discrimination, Examination and judgment, Evaluation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eMathematics interdisciplinary problem solving oriented toward core competencies: An assessment framework and application effectiveness(Guo\u0026nbsp;al., 2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eTransfer application, Logical connection, Model building, Analyzing and solving problems, Analyzing and solving problems, Interpretive argumentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.2171%;\"\u003e\n \u003cp\u003eResearch on adaptive assessment of mathematics disciplinary abilities for accuracy and efficiency (Guo \u0026amp; Rong, 2025)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53.7829%;\"\u003e\n \u003cp\u003eInferring and hypothesizing, Evaluation analysis, Method innovation, Inferring and hypothesizing, Interpretive argumentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBased on the secondary indicators screened in Table 4 and aligned with the connotations of the primary indicators, a comprehensive integration of the secondary indicators was conducted, ultimately yielding the teacher-student dialogue framework presented in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Mathematics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavior\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003cstrong\u003eramework for\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eeep\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003c/strong\u003e\u003cstrong\u003eearning\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePrimary indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eSecondary indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eSpecific description.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 72px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003cp\u003eAnalytical dialogue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003cp\u003eIllustrative explanation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eBy providing examples (both positive and counterexamples) to help students comprehend complex, abstract knowledge, thereby clarifying key points and addressing challenging concepts.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003cp\u003eAnalyzing and solving problems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eDeconstructing problems, extracting key information from the question, or identifying entry points for problem-solving.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003cp\u003eLogical connection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eIdentifying the logical connections between mathematical knowledge, and holistically grasping the structure and interrelatedness of the content.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003cp\u003eModel building\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eExpressing real-world applied problems using mathematical symbols and language, and establishing mathematical models to solve them.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 72px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003cp\u003eComprehensive dialogue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eB1\u003c/p\u003e\n \u003cp\u003eTransfer application\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eThe transfer and application of mathematical knowledge, skills, and thinking methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eB2\u003c/p\u003e\n \u003cp\u003eExperiential learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eSharing personal experiences, direct activity experiences, etc.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eB3\u003c/p\u003e\n \u003cp\u003eInterpretive argumentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eLogically reasoning through problems, providing clear and structured explanations and arguments to resolve them.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eB4\u003c/p\u003e\n \u003cp\u003eMethod integration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003e\u0026nbsp;When facing complex problems, one can integrate multiple problem-solving strategies or thinking methods, explaining why such a combination is chosen and how they function synergistically.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 72px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eEvaluative dialogue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003cp\u003eCritical questioning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eCritically questioning mathematical concepts, solutions, or viewpoints, and identifying potential errors, inconsistencies, or limitations based on mathematical logic or evidence.。\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003cp\u003eEvaluation and discrimination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eAssessing whether conclusions have limitations or determining whether the established mathematical model accurately reflects the real-world situation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003cp\u003eExamination and judgment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eExamining whether a mathematical proof or reasoning process is logically rigorous, or comparing the efficiency, conciseness, and generalizability of different solutions to make an optimal choice.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003cp\u003eEvaluation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eEvaluating others\u0026apos; viewpoints based on mathematical reasoning.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 72px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003cp\u003eCreative dialogue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003cp\u003eProblem generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eInstead of being content with solving given problems, actively and creatively proposing new, valuable mathematical questions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003cp\u003eInferring and hypothesizing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eUsing existing knowledge and information to infer or hypothesize about conclusions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003cp\u003eImaginative construction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eExpressing one\u0026apos;s own ideas or viewpoints on a particular subject or event, without necessarily relying on mathematical reasoning, or proposing imaginative and unconventional thoughts by raising questions through imagination.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003cp\u003eMethod innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 406px;\"\u003e\n \u003cp\u003eProposing original and insightful problem-solving approaches, methods, or theoretical perspectives.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Application of the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavior\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003enalysis\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003cstrong\u003eramework\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003e4.2.1 Frequency\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etatistics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003elassroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehaviors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe frequency statistics of classroom dialogue behaviors reveal a clear tendency in the distribution of behavior types. Analytical and creative dialogues occur at higher frequencies, while comprehensive and evaluative dialogues are relatively lower. This reflects an imbalance in dialogues across different cognitive levels in teaching. The specific data are presented in Table 2.\u003c/p\u003e\n\u003cp\u003eIn terms of analytical dialogue, all categories maintained relatively high frequencies. Among them, \u0026quot;A3 Logical connection\u0026quot; occurred 48 times, while \u0026quot;A1 Illustrative explanation,\u0026quot; \u0026quot;A2 Analyzing and solving problems,\u0026quot; and \u0026quot;A4 Model building\u0026quot; each occurred 40 times. This indicates that teachers place significant emphasis on guiding students to identify logical connections between mathematical concepts and to grasp the overall structure and interrelatedness of knowledge.\u003c/p\u003e\n\u003cp\u003eThe frequency of comprehensive dialogue is relatively low. Apart from \u0026quot;B3 Interpretive argumentation,\u0026quot; which reached 42 instances, \u0026quot;B2 Experiential learning\u0026quot; occurred 20 times, while \u0026quot;B1 Transfer application\u0026quot; and \u0026quot;B4 Method integration\u0026quot; were recorded only 10 and 12 times, respectively. This indicates that in mathematics classrooms, teachers frequently guide students in logical reasoning, structured explanation, and argumentation to solve problems. Conversely, there is less emphasis on guiding students in the transfer and application of mathematical knowledge, skills, and thinking methods.\u003c/p\u003e\n\u003cp\u003eIn evaluative dialogue, the frequency of \u0026quot;C4 Evaluation analysis\u0026quot; was 33 instances, indicating that students possess a certain level of evaluation ability based on mathematical reasoning. However, \u0026quot;C1 Critical questioning\u0026quot; and \u0026quot;C3 Examination and judgment\u0026quot; occurred only 20 and 10 times, respectively, with \u0026quot;C2 Evaluation and discrimination\u0026quot; being particularly low at just 4 instances. This reflects a significant gap in the guidance of higher-order evaluative thinking, such as critical questioning and logical rigor scrutiny, in teaching. There is an urgent need to enhance the cultivation of students\u0026apos; independent judgment and systematic evaluation abilities.\u003c/p\u003e\n\u003cp\u003eIn creative dialogue, the performance is particularly notable. \u0026quot;D3 Imaginative construction\u0026quot; recorded the highest frequency (76 instances), while \u0026quot;D1 Problem generation\u0026quot; and \u0026quot;D2 Inferring and hypothesizing\u0026quot; both occurred 62 times, and \u0026quot;D4 Method innovation\u0026quot; was observed 55 times. This indicates that teachers are adept at creating a heuristic classroom environment, encouraging students to propose new questions, make reasonable inferences, and expand their thinking through responses, demonstrating a strong awareness of fostering innovative thinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.2 Classroom\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavioral\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eatterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComprehensive-Evaluative-Creative\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavioral\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eattern.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis dialogue behavior pattern centers on creative dialogue. Under the guidance and organization of the teacher, students progress through four cognitive developmental stages: \u0026quot;D1 Problem generation\u0026rarr;D2 Inferring and hypothesizing\u0026rarr;D3 Imaginative construction\u0026rarr;D4 Method innovation\u0026quot;. Specifically, under the teacher\u0026apos;s guidance, students proactively pose innovative mathematical questions. To solve these problems, students engage in reasonable inference and scientific hypothesis based on their own knowledge and experience. During the problem-solving process, they conduct in-depth analysis and construction of the problems through multi-path exploration, interdisciplinary knowledge integration, or persistent questioning. After obtaining preliminary solutions, students further refine their learning methods and propose problem-solving approaches that are insightful and original (D1\u0026rarr;D2\u0026rarr;D3\u0026rarr;D4). This process reflects the complete cognitive journey from problem formulation to method innovation, highlighting the importance of cultivating students\u0026apos; mathematical creative thinking.\u003c/p\u003e\n\u003cp\u003eThe development of creative dialogue relies on the support of analytical and evaluative dialogues. The specific support mechanisms are as follows:\u003c/p\u003e\n\u003cp\u003eDuring the problem generation phase (D1), when students pose multiple creative questions, teachers should promptly guide them to compare and evaluate the questions and their potential solutions, selecting the most valuable one for further inquiry (D1 \u0026rarr; C3).\u003c/p\u003e\n\u003cp\u003eIn the inferring and hypothesizing phase (D2), as students use existing knowledge and information to infer or hypothesize conclusions, they need to carefully examine whether the hypothesizing process is logically rigorous (D2\u0026rarr;C3). Additionally, they can share personal experiences or direct activity experiences to further consolidate their hypotheses (B2\u0026rarr;D2). This process can be repeated to deepen the reinforcement of inferences or hypotheses, ensuring the validity of the conclusions (D2\u0026rarr;B2).\u003c/p\u003e\n\u003cp\u003eDuring the imaginative construction phase (D3), when students generate questions through imagination, teachers should guide them to develop potential solutions for the proposed problems. By comparing different solutions, students can make optimal choices (C3\u0026rarr;D3).\u003c/p\u003e\n\u003cp\u003eIn the method innovation phase (D4), when students propose original insights or theories, they should be guided to refine their solutions through evaluative dialogue (C3\u0026rarr;D4). Furthermore, by integrating analytical dialogue methods (B4), students can be encouraged to employ diverse thinking approaches to develop innovative problem-solving strategies (B4\u0026rarr;D4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical-Comprehensive-Creative\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eclassroom\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavior\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003eequence.\u003c/strong\u003e This dialogue behavior pattern begins with analytical dialogue, uses comprehensive dialogue as an intermediary, and ultimately leads to the realization of creative dialogue, reflecting a progressive relationship in cognitive levels from shallow to deep, from fragmented to integrated, and ultimately to innovative. Specifically, teachers and students first engage in analytical dialogue centered on mathematical problems, such as deconstructing, organizing, and structuring the problem through \u0026quot;A1 Illustrative explanation,\u0026quot; \u0026quot;A3 Logical connection,\u0026quot; or \u0026quot;A4 Model building\u0026quot;. Subsequently, during the comprehensive dialogue phase, students integrate and transfer knowledge, methods, or experiences based on the analytical results (e.g., \u0026quot;B1 Transfer application\u0026quot; or \u0026quot;B4 Method integration\u0026quot;), forming a more systematic problem-solving approach. Finally, in the creative dialogue phase, students propose new questions (D1), make reasonable inferences (D2), or suggest innovative solutions (D4), completing the cognitive leap from understanding to creation. This sequence demonstrates that analytical dialogue provides the cognitive foundation for deep learning, comprehensive dialogue facilitates the structuring and transfer of knowledge, and creative dialogue signifies the genuine occurrence of higher-order thinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical-Comprehensive-Evaluative-Creative\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eclassroom\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eialogue\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eehavior.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis behavioral pattern builds upon the \u0026quot;Analytical-Comprehensive-Creative\u0026quot; framework by incorporating an evaluative dialogue component, forming a more complete and deliberate deep learning pathway. In this model, teachers and students first clarify the essence and structure of the problem through analytical dialogue (e.g., A1, A3, A4). They then proceed to comprehensive dialogue (e.g., B1, B4), integrating and transferring the analytical results. Next, evaluative dialogue (e.g., C3 critical examination and judgment, C4 evaluative analysis) is introduced to conduct logical scrutiny, efficiency comparisons, or value assessments of existing solutions, thereby optimizing decision-making. Finally, the process leads to creative dialogue (e.g., D3 imaginative construction, D4 method innovation), where more insightful and original problems or solutions are proposed based on evaluation. This sequence highlights the critical role of evaluation in deep learning\u0026mdash;it not only involves scrutinizing and refining comprehensive outcomes but also serves as a key catalyst for stimulating creative thinking. It reflects an iterative cycle of \u0026quot;analysis\u0026rarr;integration\u0026rarr;evaluation\u0026rarr; creation\u0026quot; and a deepening cognitive process.\u003c/p\u003e"},{"header":"5 Conclusions and discussion","content":"\u003cp\u003eThe deep learning-oriented mathematics classroom dialogue behavior analysis framework constructed in this study can not only be used to analyze the effectiveness and quality of mathematics classroom teaching but also provides a profound interpretation of how deep learning is manifested in mathematics classrooms. The four dimensions of this framework\u0026mdash;analytical, comprehensive, evaluative, and creative dialogues\u0026mdash;reveal the dialogic patterns of deep learning in mathematics classrooms. Based on this framework, 10 mathematics classroom teaching videos were selected as research subjects, and lag sequential analysis was employed to analyze and discuss the frequency and patterns of classroom dialogue behaviors. The following conclusions were drawn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Deep learning is translated into observable and analyzable classroom dialogue behaviors.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDeep learning is an in-depth learning approach aimed at cultivating learners\u0026apos; higher-order thinking. As a high-level cognitive objective, higher-order thinking corresponds to analysis, synthesis, evaluation, and creation in the Bloom-Anderson taxonomy of educational objectives (Anderson \u0026amp; Krathwohl, 2001). This behavioral analysis framework translates abstract higher-order cognitive processes into observable and analyzable dialogue behaviors within mathematics classrooms. For example, the frequencies of analytical and creative dialogue behaviors are relatively high, while those of comprehensive and evaluative dialogue behaviors are relatively low. This reflects a greater emphasis on analytical and creative learning approaches in mathematics classrooms, with insufficient attention given to comprehensive and evaluative learning methods. Furthermore, within \u0026quot;analytical dialogue,\u0026quot; behaviors such as \u0026quot;model building\u0026quot; and \u0026quot;logical connection\u0026quot; illustrate how students deconstruct complex real-world problems and transform them into mathematical models. They further explain the logical relationships among various elements or knowledge points within these models, which serves as the foundation for deep learning. In \u0026quot;comprehensive dialogue,\u0026quot; the behavior of \u0026quot;method integration\u0026quot; reflects students\u0026apos; application of multiple problem-solving approaches to address complex mathematical problems, representing a process of knowledge integration and transfer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) It reveals the progressive and interdependent relationships among the dimensions of classroom dialogue.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe four dimensions do not exist in isolation; rather, they collectively form a developmental pathway for classroom dialogue centered on students\u0026apos; deep learning. Analytical dialogue focuses on \u0026quot;deconstructing\u0026quot; complex mathematical problems, aiming to clarify their internal structure, causal relationships. Comprehensive dialogue centers on \u0026quot;integration.\u0026quot; Evaluative dialogue revolves around \u0026quot;judgment.\u0026quot; Creative dialogue is oriented toward \u0026quot;generation,\u0026quot; aiming to produce original solutions that are unprecedented. Analytical dialogue serves as the foundation for deep learning, comprehensive dialogue integrates the fragmented knowledge generated from analytical dialogue, evaluative dialogue assesses and refines the integrated solutions, and creative dialogue generates novel problems or solutions. The relationships among these four dialogue dimensions also manifest as higher-level dialogues expanding the needs of lower-level dialogues, while lower-level dialogues support the development of higher-level dialogues. This relationship further reflects a layer-by-layer support and progressive expansion dynamic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) It reveals the dynamic and non-linear relationships among classroom dialogues.\u003c/strong\u003e Based on the classroom dialogue behavior analysis framework, this study employs lag sequential analysis to code and analyze classroom teaching videos, revealing the dynamic and non-linear relationships among classroom dialogue behaviors. For example, the identified behavioral sequence patterns\u0026mdash;such as \u0026quot;Analysis-Evaluation-Creation,\u0026quot; \u0026quot;Analysis-Comprehension-Creation,\u0026quot; and \u0026quot;Analysis-Comprehension-Evaluation-Creation\u0026quot;\u0026mdash;provide evidence for how deep learning manifests within classroom dialogue behaviors. These sequences indicate that deep learning is not an isolated display of a single cognitive behavior but rather a complex iterative process. This process involves analytical dialogue for problem deconstruction, comprehensive dialogue for constructing knowledge networks, evaluative dialogue for critical monitoring, and ultimately leads to creative dialogue for achieving cognitive breakthroughs. These findings translate the abstract theory of deep learning into clear dialogic pathways, representing a significant deepening of existing theories through this framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(4) It provides a reference standard for mathematics classroom practice and offers direction for deep learning.\u0026nbsp;\u003c/strong\u003eThe research on the mathematics classroom dialogue behavior analysis framework primarily builds upon the FIAS by revising certain dimensions and refining specific indicators. The FIAS, proposed in the last century, reflects a period when the primary focus of teaching was on students\u0026apos; acquisition of knowledge and skill enhancement, with less emphasis on fostering deep learning or addressing the unique characteristics of mathematics as a discipline. If a mathematics classroom dialogue behavior analysis framework is constructed merely by revising some dimensions and indicators of FIAS and adding dialogue-related metrics, it would only be capable of assessing students\u0026apos; mastery of knowledge and skills, failing to adequately measure the development of their thinking abilities or the state of deep learning. In this study, an analytical framework is developed based on the distinctive features of mathematics, incorporating dimensions such as analytical, comprehensive, evaluative, and creative dialogue, directly targeting deep learning. This framework enables teachers and researchers to accurately identify mathematics classroom dialogue behaviors that contribute to deep learning. For example, in a classroom dominated by rote dialogue but lacking evaluative or creative dialogue, the goal of deep learning is difficult to achieve. Therefore, this framework not only serves as an analytical tool but also informs teaching practice, guiding teachers to consciously design and elicit dialogues at higher cognitive levels, thereby effectively implementing deep learning.\u003c/p\u003e"},{"header":"6 Limitations and future research directions","content":"\u003cp\u003eThis study has several limitations that inform future directions. The sample, drawn from publicly available exemplary lessons, may not fully represent regular classroom dynamics, limiting the generalizability of findings. While Lag Sequential Analysis effectively mapped dialogue patterns, it provided limited insight into contextual factors, non-verbal interactions, and individual cognitive differences. Furthermore, the framework's indicators require further validation across diverse grade levels and mathematical topics. Future research should address these gaps through large-scale studies in varied instructional contexts to test the framework\u0026rsquo;s diagnostic utility and explore its adaptation to different pedagogical models. A mixed-methods approach, integrating qualitative tools like interviews, could uncover the psychological and social mechanisms driving productive dialogue sequences. Finally, translating the framework into practical tools\u0026mdash;such as AI-supported classroom analytics or teacher professional development systems\u0026mdash;would bridge theory and practice, enabling real-time feedback and fostering deeper learning in everyday mathematics instruction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study involving human participants was reviewed and approved by the Research Ethics Committee of Qujing Normal University (Approval ID: [201601], Approval Date: [2026.1.15]). All research procedures were conducted in accordance with the ethical standards of the institutional and national research committees, and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAll classroom teaching videos used in this study were obtained from the \u0026quot;National Smart Education Resource Public Service Platform for Primary and Secondary Schools.\u0026quot; The resources on this platform have been publicly authorized by the uploading teachers or schools, permitting their use for non-commercial educational and research purposes. By utilizing these publicly accessible resources, researchers are considered to have obtained the corresponding usage authorization by default.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eX.D. Ma: conceptualization, methodology, writing \u0026ndash; original draft. W.H. Chen: investigation, writing \u0026ndash; review \u0026amp; editing. Both authors approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis study was funded by the \u0026ldquo;National Natural Science Foundation of China under Grant 62306128\u0026rdquo;\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe classroom videos analyzed in this study were sourced from the publicly accessible \u0026quot;National Smart Education Resource Public Service Platform for Primary and Secondary Schools\u0026quot; (https://www.zxx.edu.cn/). These video resources are openly available to the public and can be used for educational research.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eCampbell. (1990). Classroom Discourse: The Language of Teaching and Learning. \u003cem\u003eAustralian Reading Association.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCao, Y., Song, Y., Zhao, W., et al. (2022). Research on the construction of an artificial intelligence evaluation system for mathematics classroom dialogue oriented toward Education 2030. \u003cem\u003eJournal of Mathematics Education, 31\u003c/em\u003e(1), 7\u0026ndash;12.\u003c/p\u003e\n\u003cp\u003eCui, L., \u0026amp; Dong, L. (2017). An empirical study on classroom interaction in middle schools: Taking junior high mathematics classrooms as an example. \u003cem\u003eE-Education Research\u003c/em\u003e, 38(5), 123\u0026ndash;128. https://doi.org/10.13811/j.cnki.eer.2017.05.020\u003c/p\u003e\n\u003cp\u003eFeng, Y., Cao, F., \u0026amp; Zhang, H. (2022). A comparative study of instructional decision-making behaviors based on lag sequential analysis. \u003cem\u003eModern Educational Technology, 32\u003c/em\u003e(2), 63\u0026ndash;71.\u003c/p\u003e\n\u003cp\u003eFlanders, N. A. (1963). Intent, action and feedback: A preparation for teaching. \u003cem\u003eJournal of Teacher Education, 14\u003c/em\u003e(3), 250\u0026ndash;259.\u003c/p\u003e\n\u003cp\u003eGao, X., \u0026amp; Wang, S. (2003). Interpretation of the \u003cem\u003eFull-Time Compulsory Education Mathematics Curriculum Standards (Experimental Version).\u0026nbsp;\u003c/em\u003e\u003cem\u003eEducational Science Research\u003c/em\u003e, (6), 4. https://doi.org/CNKI:SUN:JYKY.0.2003-06-011\u003c/p\u003e\n\u003cp\u003eGu, X., \u0026amp; Wang, W. (2004). New explorations in classroom analysis technology for supporting teacher professional development. \u003cem\u003eChina Educational Technology\u003c/em\u003e, (7), 18\u0026ndash;21. https://doi.org/10.3969/j.issn.1006-9860.2004.07.004\u003c/p\u003e\n\u003cp\u003eGuo, J., \u0026amp; Rong, Q. (2025). Research on adaptive assessment of mathematics competency toward accuracy and efficiency. \u003cem\u003eChina Educational Technology\u003c/em\u003e, (3), 103\u0026ndash;110. https://doi.org/10.3969/j.issn.1006-9860.2025.03.014\u003c/p\u003e\n\u003cp\u003eGuo, K., Du, B., \u0026amp; Ji, Z. (2025). Interdisciplinary problem-solving in mathematics oriented toward core competencies: Assessment framework and application effects. \u003cem\u003eRenmin University of China Education Journal\u003c/em\u003e, 1\u0026ndash;18.\u003c/p\u003e\n\u003cp\u003ehttps://link.cnki.net/urlid/11.5978.g4.20250327.1442.002\u003c/p\u003e\n\u003cp\u003eHennessy, S., Howe, C., Mercer, N., et al. (2020). Coding classroom dialogue: Methodological considerations for researchers. \u003cem\u003eLearning, Culture and Social Interaction, 25\u003c/em\u003e, 100404. https://doi.org/10.1016/j.lcsi.2020.100404\u003c/p\u003e\n\u003cp\u003eHowe, C., \u0026amp; Abedin, M. (2013). Classroom dialogue: A systematic review across four decades of research. \u003cem\u003eCambridge Journal of Education, 43\u003c/em\u003e(3), 325\u0026ndash;356.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.1080/0305764X.2013.786024\u003c/p\u003e\n\u003cp\u003eHuang, Z., Zhou, R., \u0026amp; Wan, L. (2020). Design and empirical study of an interactive classroom ecosystem in blended learning environments. \u003cem\u003eE-Education Research\u003c/em\u003e, 41(4), 78\u0026ndash;85.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.13811/j.cnki.eer.2020.04.011\u003c/p\u003e\n\u003cp\u003eJiang, L., Mao, Q., Wan, Z., et al. (2018). A performance study on smart classrooms promoting teaching reform in higher education: Based on the analysis of classroom teaching behaviors. \u003cem\u003eChina Educational Technology\u003c/em\u003e, (6), 52\u0026ndash;58.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.3969/j.issn.1006-9860.2018.06.008\u003c/p\u003e\n\u003cp\u003eJiang, Y., Wang, W., Li, C., et al. (2019). A study on teacher\u0026ndash;student interaction behaviors in smart classroom environments. \u003cem\u003eModern Distance Education\u003c/em\u003e, (3), 13\u0026ndash;21.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.3969/j.issn.1001-8700.2019.03.002\u003c/p\u003e\n\u003cp\u003eKong, Q. (2006). An appraisal of the Full-Time Compulsory Education Mathematics Curriculum Standards. \u003cem\u003eGlobal Education Review\u003c/em\u003e, 35(9), 4.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/CNKI:SUN:WGJN.0.2006-09-007\u003c/p\u003e\n\u003cp\u003eLi, H., Bian, P., \u0026amp; Xu, M. (2024). Development of a double coding analysis system and behavior pattern mining for interactive mathematics teaching in smart classrooms. \u003cem\u003eModern Educational Technology\u003c/em\u003e, 34(3), 105\u0026ndash;115.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.3969/j.issn.1009-8097.2024.03.011\u003c/p\u003e\n\u003cp\u003eLiu, L. (2013). Construction of a content framework for analyzing teacher\u0026ndash;student dialogue in mathematics classrooms. \u003cem\u003eGlobal Education Review\u003c/em\u003e, 42(5), 68\u0026ndash;76.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/CNKI:SUN:WGJN.0.2013-05-012\u003c/p\u003e\n\u003cp\u003eLiu, Z., \u0026amp; Chen, N. (2021). Analysis of classroom teaching interaction behaviors in smart classroom environments. \u003cem\u003eModern Educational Technology, 31\u003c/em\u003e(9), 28\u0026ndash;36.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.3969/j.issn.1009-8097.2021.09.004\u003c/p\u003e\n\u003cp\u003eMa, J., Zhao, W., \u0026amp; Cao, Y. (2023). A comparative study of classroom dialogue between expert and novice mathematics teachers based on higher-order thinking cultivation. \u003cem\u003eJournal of Mathematics Education, 32\u003c/em\u003e(3), 68\u0026ndash;72, 102.\u003c/p\u003e\n\u003cp\u003eAuthors (2023). \u003cem\u003eStudies in Educational Evaluation\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAuthors (2023). \u003cem\u003eModern Educational Technology\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMehan, H. (1979). \u0026quot;What time is it, Denise?\u0026quot;: Asking known information questions in classroom discourse. \u003cem\u003eT\u003c/em\u003e\u003cem\u003eheory Into Practice, 18\u003c/em\u003e(4), 285\u0026ndash;294.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.1080/00405847909542846\u003c/p\u003e\n\u003cp\u003eMercer, N., Hennessy, S., \u0026amp; Warwick, P. (2017). Dialogue, thinking together and digital technology in the classroom: Some educational implications of a continuing line of inquiry. \u003cem\u003ePergamon\u003c/em\u003e. https://doi.org/10.17863/CAM.13777\u003c/p\u003e\n\u003cp\u003eMiller, M. K., Yelderman, L. A., Holbrook, M. A., et al. (2025). The psychology of problem-solving courts. In \u003cem\u003eSociety, science, and problem-solving courts\u003c/em\u003e. \u003cem\u003eOxford University Press\u003c/em\u003e. https://doi.org/10.1093/oso/9780190059804.003.0004\u003c/p\u003e\n\u003cp\u003eSchwarz, B., \u0026amp; Baker, M. (2016). \u003cem\u003eDialogue, argumentation and education:\u0026nbsp;\u003c/em\u003e\u003cem\u003eHistory, theory and practice\u003c/em\u003e. https://doi.org/10.1017/9781316493960\u003c/p\u003e\n\u003cp\u003eSedova, K., Salamounova, Z., \u0026amp; Svaricek, R. (2014). Troubles with dialogic teaching. \u003cem\u003eLearning, Culture and Social Interaction, 3\u003c/em\u003e(4), 274\u0026ndash;285. https://doi.org/10.1016/j.lcsi.2014.04.001\u003c/p\u003e\n\u003cp\u003eShao, F., Hu, W., Zhang, H., et al. (2019). Sequential analysis of argumentative discourse in the classroom: Elementary school students\u0026rsquo; scientific reasoning. \u003cem\u003eJournal of East China Normal University (Educational Sciences), 37\u003c/em\u003e(6), 48\u0026ndash;60.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.16382/j.cnki.1000-5560.2019.06.005\u003c/p\u003e\n\u003cp\u003eShi, Y., Peng, C., Zhang, J., et al. (2019). Analysis of teacher\u0026ndash;student interaction behaviors in smart classroom environments in higher education. \u003cem\u003eModern Educational Technology, 29\u003c/em\u003e(1), 45\u0026ndash;51. https://doi.org/10.3969/j.issn.1009-8097.2019.01.007\u003c/p\u003e\n\u003cp\u003eSong, Y., Wu, B., \u0026amp; Hao, T. (2021). Exploring the patterns of classroom dialogue for knowledge construction. \u003cem\u003eE-Education Research\u003c/em\u003e, 42(3), 111\u0026ndash;119.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.13811/j.cnki.eer.2021.03.016\u003c/p\u003e\n\u003cp\u003eSong, Y., Xu, C., Zhu, J., et al. (2023). Oriented toward thinking cultivation: Intelligent classroom teaching analysis and application based on precise annotation technology. \u003cem\u003eJournal of East China Normal University (Educational Sciences), 41\u003c/em\u003e(8), 79\u0026ndash;89.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.16382/j.cnki.1000-5560.2023.08.008\u003c/p\u003e\n\u003cp\u003eSong, Y. (2020). Research hotspots and frontier trends in the field of classroom dialogue. \u003cem\u003eGlobal\u0026nbsp;\u003c/em\u003e\u003cem\u003eEducation Review, 49\u003c/em\u003e(12), 27\u0026ndash;40. https://doi.org/10.3969/j.issn.1009-9670.2020.12.003\u003c/p\u003e\n\u003cp\u003eTan, J., Wu, S., Wu, L., et al. (2022). Analysis of discourse interaction patterns in expert\u0026ndash;novice pair programming based on behavior sequences. \u003cem\u003eE-Education Research\u003c/em\u003e, 43(7), 106\u0026ndash;113. https://doi.org/10.13811/j.cnki.eer.2022.07.014\u003c/p\u003e\n\u003cp\u003eWu, X., Kong, Q., \u0026amp; Liu, Y. (2018). A comparative study of teaching language in flipped classrooms and traditional classrooms based on double coding. \u003cem\u003eModern Educational Technology, 28\u003c/em\u003e(10), 8. https://doi.org/CNKI:SUN:XJJS.0.2018-10-009\u003c/p\u003e\n\u003cp\u003eZhang, Y., Zhu, Y., Bai, Q., et al. (2016). A study on the characteristics of classroom interaction behaviors in primary school mathematics teaching within smart classrooms. \u003cem\u003eChina Educational Technology\u003c/em\u003e, (6), 43\u0026ndash;48, 64.\u003c/p\u003e\n\u003cp\u003ehttps://doi.org/10.3969/j.issn.1006-9860.2016.06.009\u003c/p\u003e\n\u003cp\u003eZhu, H., Wang, T., Deng, M., et al. (2022). A study on teacher\u0026ndash;student verbal interaction in mathematics classroom teaching in special education schools: Based on the improved Flanders Interaction Analysis System (iFIAS). \u003cem\u003eChinese Journal of Special Education\u003c/em\u003e, (1), 39\u0026ndash;46. https://doi.org/10.3969/j.issn.1007-3728.2022.01.006\u003c/p\u003e\n\u003cp\u003eZuiker, S. J., \u0026amp; Anderson, K. T. (2019). Fostering peer dialogic engagement in science classrooms with an educational videogame. \u003cem\u003eResearch in Science Education, 49\u003c/em\u003e(2), 407\u0026ndash;429. https://doi.org/10.1007/s11165-019-9842-z\u003c/p\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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Analytical framework, Core competencies, Deep learning, Classroom dialogue, Mathematics classroom","lastPublishedDoi":"10.21203/rs.3.rs-8443142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8443142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeveloping classroom dialogue is a crucial manifestation of advancing mathematics teaching reform and serves as an effective pathway to promote deep learning. It holds significant importance in enhancing students' core competencies. This study, based on Bloom's taxonomy of cognitive objectives and informed by an analysis of the literature of mathematics classroom discourse, constructs a mathematics classroom dialogue behavior analysis framework comprising four primary dimensions\u0026mdash;analytical dialogue, comprehensive dialogue, evaluative dialogue, and creative dialogue\u0026mdash;along with 16 secondary indicators. To validate the effectiveness of this analytical framework, 10 mathematics classroom sessions were selected as research subjects for analysis across two dimensions: Frequency statistics and behavioral patterns. The findings demonstrate that this analytical framework can accurately identify and analyze the characteristics of dialogue behaviors in mathematics classrooms, revealing the intrinsic connections and evolutionary pathways among dialogues. This framework offers valuable support for future research on mathematics classroom dialogue behaviors, teaching practices, and policy formulation.\u003c/p\u003e","manuscriptTitle":"Construction and application of a mathematics classroom dialogue behavior analysis framework for deep learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 16:07:51","doi":"10.21203/rs.3.rs-8443142/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-22T04:15:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269759184555793201094074966180007495521","date":"2026-04-16T14:47:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25117284483912647265705984195075042611","date":"2026-04-16T12:01:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T11:50:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T01:09:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-05T05:18:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-02T12:49:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-02T12:28:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b94e875b-e173-4988-95b0-531180c05679","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66778832,"name":"Social science/Education"},{"id":66778833,"name":"Physical sciences/Mathematics and computing"},{"id":66778834,"name":"Biological sciences/Psychology"},{"id":66778835,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-04-24T16:07:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 16:07:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8443142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8443142","identity":"rs-8443142","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}