Teaching scene simulation equipment

Abstract

The invention provides teaching scene simulation equipment. The device comprises a teacher operation management platform, a classroom audio acquisition device and a student interaction device. Classroom discussion audio, dialogue audio and learning expression characteristics of students are automatically collected and analyzed through the teaching scene simulation equipment, classroom discussion data, classroom concentration data and classroom interaction data are generated, the events are integrated and analyzed, a more comprehensive and objective classroom teaching evaluation result is provided for teachers, and the teaching efficiency is improved. The system helps teachers to better understand the learning behaviors of students, optimizes the teaching strategy, and improves the teaching effect.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular to a teaching scene simulation device. Background Art

[0002] In the modern education environment, accurate assessment of students’ learning behavior is crucial to improving teaching quality. Students’ learning behavior not only reflects their mastery of knowledge, but also reflects their learning attitude, participation and concentration, which directly affect teaching effectiveness.

[0003] At present, relevant technologies mainly rely on teachers' direct observation and subjective judgment. Teachers evaluate classroom teaching by observing students' classroom participation, discussion situations, facial expressions, etc.

[0004] However, the methods in related technologies are limited by teachers' attention and judgment ability, and it is difficult to fully cover the behavioral details of all students, especially in large classes, where it is more difficult for teachers to take into account the performance of each student. Summary of the invention

[0005] The present invention provides a teaching scene simulation device.

[0006] According to a first aspect of the present disclosure, a teaching scenario simulation device is provided, which includes: a teacher operation management platform, a classroom audio acquisition device and a student interaction device; the classroom audio acquisition device is used to receive classroom instructions issued by the teacher operation management platform, and according to the classroom instructions, collect classroom discussion audio, analyze to obtain classroom discussion data, so as to feed back the classroom discussion data to the teacher operation management platform; the student interaction device is used to receive classroom instructions issued by the teacher operation management platform, obtain students' classroom conversation audio and classroom learning expression features, and analyze the classroom conversation audio and classroom learning expression features to obtain classroom concentration data, so as to feed back the classroom concentration data to the teacher operation management platform; the teacher operation management platform is used to obtain classroom interaction data based on student interaction audio under student interaction events feedback by the student interaction device, so as to integrate and analyze classroom discussion data, classroom concentration data and classroom interaction data to evaluate classroom teaching situation.

[0007] In some embodiments of the present disclosure, a classroom audio acquisition device is used to obtain discussion audio of all students in a teaching classroom according to the discussion mode indicated by classroom instructions, and analyze the student discussion situation in the teaching classroom based on the audio decibel of the discussion audio and a preset decibel threshold, and determine the effective discussion time and invalid discussion time corresponding to the discussion audio, so as to send the effective discussion time and invalid discussion time as classroom discussion data to the teacher operation management platform.

[0008] In some embodiments of the present disclosure, the student interaction device includes: a student multimedia unit and an expression analysis unit; the student multimedia unit is used to determine the conversation duration corresponding to the classroom conversation audio of each student in the teaching class according to the concentration mode indicated by the classroom instructions, and based on the conversation duration, count the number of first students with abnormal conversation behavior in the teaching class, so as to send the number of first students as classroom concentration data to the teacher operation management platform; the expression analysis unit is used to analyze the facial expression changes corresponding to the facial expression features of each student in the teaching class according to the concentration mode indicated by the classroom instructions, and based on the facial expression changes, count the number of second students with inattentive facial expressions, so as to send the number of second students as classroom concentration data to the teacher operation management platform.

[0009] In some embodiments of the present disclosure, the teacher operation management platform includes: a teacher interaction unit; the student interaction device includes a student interaction unit; the student interaction unit is used to trigger student interaction events; and the teaching interaction unit is used to trigger teacher interaction events.

[0010] In some embodiments of the present disclosure, the teacher operation management platform is used to perform interaction matching operations based on the interaction event trigger signals sent by the teaching interaction unit and the student interaction unit, determine the student interaction device that matches the teaching interaction unit, and count the number of student interaction devices that have not successfully matched the teaching interaction unit, and use the number of student interactions as classroom interaction data; the multimedia unit in the student interaction device that matches the teaching interaction unit is used to collect student interaction audio, and feed the student interaction audio back to the teacher multimedia unit.

[0011] In some embodiments of the present disclosure, the teacher operation management platform includes a teacher multimedia unit; the teacher multimedia unit is used to collect teacher interaction audio, and based on a preset topic model or a preset knowledge point cluster, identify the teacher interaction content corresponding to the teacher interaction audio and the student interaction content corresponding to the student interaction audio, so as to use the interaction time and the number of failed interaction content recognitions corresponding to the student interaction audio obtained according to the recognition results as classroom interaction data.

[0012] In some embodiments of the present disclosure, the teacher multimedia unit is also used to perform hierarchical processing on textbook text knowledge points extracted from the textbook content to obtain basic knowledge point clusters; screen the first important knowledge point cluster based on the distribution of knowledge points in the basic knowledge point cluster; use a preset knowledge point screening model to screen qualified knowledge points from all knowledge points corresponding to the historical question bank to obtain a second important knowledge point cluster; use a hierarchical clustering algorithm and a correlation analysis algorithm to screen wrong question features under the target feature category from wrong question-related features, and obtain a third important knowledge point cluster based on all knowledge points corresponding to the wrong question features; based on the basic knowledge point cluster, the second important knowledge point cluster and the third important knowledge point cluster, combined with a cross-matching strategy, determine the matching verification results of the knowledge point clusters, so as to optimize the first important knowledge point cluster according to the matching verification results, and use the first important knowledge point cluster as the preset knowledge point cluster.

[0013] In some embodiments of the present disclosure, the teacher multimedia unit is used to perform a first matching verification on all the first important knowledge points at each level in the first important knowledge point cluster in turn based on the same-level matching rule, using the second important knowledge point cluster and the third important knowledge point cluster; if the first matching verification fails, cross-matching verification is performed on each second important knowledge point in the second important knowledge point cluster and each third important knowledge point in the third important knowledge point cluster; if the cross-matching verification is successful, the cross-matching knowledge points that are successfully cross-matched are determined, and based on the same-level matching rule, the cross-matching knowledge points are used to perform a second matching verification on all the basic knowledge points at each level in the basic knowledge point cluster in turn; if the second matching verification is successful, the matching knowledge points corresponding to the second successful matching are integrated into the first important knowledge point cluster to obtain the preset knowledge point cluster.

[0014] In some embodiments of the present disclosure, the teacher multimedia unit is also used to analyze the teaching situation based on classroom discussion data, classroom concentration data, and classroom interaction data, combined with a preset student performance evaluation algorithm, to determine classroom learning evaluation results.

[0015] In some embodiments of the present disclosure, the teacher multimedia unit is used to determine the classroom learning evaluation index based on the classroom discussion data, the classroom concentration data, and the classroom interaction data, so as to analyze the classroom learning situation of the students under the classroom teaching according to the preset behavior performance evaluation algorithm of the following formula 1, combined with the classroom learning evaluation index, and obtain the classroom learning evaluation result; Formula 1 Among them, D is the classroom learning evaluation result, is the first classroom learning evaluation index, S is the number of student interactions in the classroom interaction data, is the preset interaction threshold. It is the evaluation index of the second classroom learning. is the interaction time in the classroom interaction data, is the total number of hours of teaching class. It is the third classroom learning evaluation indicator. is the effective discussion duration in the classroom discussion data, is the fourth classroom learning evaluation index, C is the number of failed interactive content recognition in classroom interactive data, The threshold for the number of failed interactive content recognitions is preset. is the fifth classroom learning evaluation indicator, A is the number of inattentive students in the classroom attention data, is the total number of students in the teaching class, It is the sixth classroom learning evaluation indicator. The ineffective discussion time in the classroom focus data, , , , , as well as They are the preset weights corresponding to the first classroom learning evaluation indicators, the second classroom learning evaluation indicators, the third classroom learning evaluation indicators, the fourth classroom learning evaluation indicators, the fifth classroom learning evaluation indicators and the sixth classroom learning evaluation indicators.

[0016] The teaching scene simulation device provided by the present invention includes a teacher operation management platform, a classroom audio acquisition device and a student interaction device; the classroom audio acquisition device is used to receive the classroom instructions issued by the teacher operation management platform, and according to the classroom instructions, collect the classroom discussion audio, analyze and obtain the classroom discussion data, so as to feed back the classroom discussion data to the teacher operation management platform; the student interaction device is used to receive the classroom instructions issued by the teacher operation management platform, obtain the students' classroom dialogue audio and classroom learning expression characteristics, and analyze the classroom dialogue audio and classroom learning expression characteristics to obtain classroom concentration data, so as to feed back the classroom concentration data to the teacher operation management platform; the teacher operation management platform is used to obtain the classroom interaction data based on the student interaction audio under the student interaction event feedback by the student interaction device, so as to integrate and analyze the classroom discussion data, classroom concentration data and classroom interaction data to evaluate the classroom teaching situation, so as to realize the automatic collection and analysis of the students' classroom discussion audio, dialogue audio and learning expression characteristics through the teaching scene simulation device, generate classroom discussion data, classroom concentration data and classroom interaction data, and integrate and analyze these data to provide teachers with comprehensive and objective classroom teaching evaluation results. Through accurate data feedback, teachers can more accurately understand students' learning status, participation and concentration, thereby optimizing teaching strategies, improving classroom teaching effectiveness and achieving significant improvement in teaching quality.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. Figure 1 A schematic diagram of a teaching scene simulation device provided by an embodiment of the present disclosure; Figure 2 A schematic diagram of a specific teaching scene simulation device provided by an embodiment of the present disclosure; Figure 3 A flow chart of a teaching management method applied to a teaching scene simulation device provided by an embodiment of the present disclosure; Figure 4 A structural schematic diagram of a teaching management device applied to a teaching scene simulation device provided by an embodiment of the present disclosure; Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0020] In the modern education environment, accurate assessment of students’ learning behavior is crucial to improving teaching quality. Students’ learning behavior not only reflects their mastery of knowledge, but also reflects their learning attitude, participation and concentration, which directly affect teaching effectiveness.

[0021] The relevant technology mainly relies on the teacher's direct observation and subjective judgment. Teachers evaluate students' learning behavior performance by observing their classroom participation, discussion situations, facial expressions, etc.

[0022] However, the main drawback of the relevant technology is that it relies on the subjective judgment of teachers, which can easily miss some of the students' behaviors, especially the details of classroom discussion, interaction and concentration. In addition, it is difficult for teachers to pay attention to the performance of multiple students at the same time, resulting in incomplete and objective evaluation results. This evaluation method also cannot provide quantitative data support, making it difficult to conduct accurate teaching effect analysis and improvement.

[0023] In order to solve the problem that related technologies rely on subjective observations by teachers and easily miss details of student behavior, the teaching scene simulation device proposed in this disclosure can automatically collect and analyze students' classroom discussion audio, conversation audio and learning expression characteristics by introducing teaching scene simulation equipment, including teacher operation management platform, classroom audio collection equipment and student interaction equipment, and generate classroom discussion data, classroom concentration data and classroom interaction data. After integration and analysis, these data can provide teachers with more comprehensive and objective classroom teaching evaluation results, help teachers better understand students' learning behavior performance, and then optimize teaching strategies and improve teaching effectiveness.

[0024] The following describes the teaching scene simulation device of the embodiment of the present disclosure, the teaching management method, device, electronic device and storage medium applied to the teaching scene simulation device with reference to the accompanying drawings.

[0025] Figure 1 Schematic diagram of a teaching scene simulation device provided by an embodiment of the present disclosure. Figure 1 As shown, the device includes: Teacher operation management platform 1, classroom audio acquisition equipment 2 and student interaction equipment 3; The classroom audio collection device 1 is used to receive classroom instructions issued by the teacher operation management platform, and collect classroom discussion audio according to the classroom instructions, analyze and obtain classroom discussion data, and feed the classroom discussion data back to the teacher operation management platform; The student interaction device 2 is used to receive classroom instructions issued by the teacher operation management platform, obtain students' classroom conversation audio and classroom learning expression features, and analyze the classroom conversation audio and classroom learning expression features to obtain classroom concentration data, so as to feed back the classroom concentration data to the teacher operation management platform; The teacher operation management platform 3 is used to obtain classroom interaction data based on the student interaction audio under the student interaction events fed back by the student interaction devices, so as to integrate and analyze the classroom discussion data, classroom concentration data and classroom interaction data to evaluate the classroom teaching situation.

[0026] The teacher operation management platform 1 can be located at the podium or the teacher operation area. The classroom audio collection equipment 2 is distributed in the classroom and is used to collect the discussion audio in the classroom. The student interaction equipment 3 is located at the student end and is used to monitor the student's classroom performance.

[0027] In the present disclosure, the teaching operation management platform may specifically be a podium console, the student interaction device may specifically be a student-end interaction device, and the classroom audio collection device may specifically be a sound collection device.

[0028] like Figure 2As shown, the present disclosure provides a schematic diagram of a specific teaching scene simulation device, which may include a teaching operation management platform and multiple student interaction devices and multiple classroom audio acquisition devices in a teaching classroom.

[0029] The podium console is used for course presentations, interaction with student-side interactive devices, data processing and other operations; the sound collection equipment is used to collect sounds in the classroom; the student-side interactive equipment is used to monitor student status, participate in classroom interactions, data collection and other operations.

[0030] Further, see Figure 2 The teaching scene simulation device disclosed in the present invention may also include: a smart blackboard, which is connected to the teacher operation management platform for synchronizing course content.

[0031] In some embodiments of the present disclosure, a classroom audio acquisition device is used to obtain discussion audio of all students in a teaching classroom according to the discussion mode indicated by classroom instructions, and analyze the student discussion situation in the teaching classroom based on the audio decibel of the discussion audio and a preset decibel threshold, and determine the effective discussion time and invalid discussion time corresponding to the discussion audio, so as to send the effective discussion time and invalid discussion time as classroom discussion data to the teacher operation management platform.

[0032] Specifically, the teacher's operation management platform will issue classroom instructions, instructing the classroom to enter the discussion mode (such as group discussion, whole class discussion, etc.). After receiving the instructions, the classroom audio acquisition device starts to collect the discussion audio of all students in the teaching classroom. The classroom audio acquisition device collects students' discussion audio in real time through microphones or sound sensors distributed in the classroom. The collected discussion audio includes the content of students' speeches, the activity of the discussion, and background noise.

[0033] After collecting the discussion audio, the classroom audio collection device analyzes the collected discussion audio, mainly based on the decibel value (volume) of the audio and the preset decibel threshold. Among them, the preset decibel threshold can be set to a reasonable decibel threshold according to the classroom environment and discussion requirements, which is used to distinguish between effective discussions and invalid discussions, and is not limited in the present embodiment.

[0034] When the audio decibel value is greater than or equal to the preset threshold, it is considered that the students are having an effective discussion (such as active speaking and interactive communication). When the audio decibel value is less than the preset threshold, it is considered that the discussion is in an invalid state (such as silence, inefficient communication or background noise).

[0035] The classroom audio acquisition device counts the effective duration and invalid duration of the discussion based on the comparison result of the audio decibel value and the preset threshold. That is, the effective discussion duration is the sum of the time periods when the audio decibel value is greater than or equal to the preset threshold, indicating the time when the students actively participate in the discussion. The invalid discussion duration is the sum of the time periods when the audio decibel value is lower than the preset decibel threshold, indicating the time when the students do not actively participate in the discussion. The disclosure fully reflects the students' participation and discussion efficiency in the discussion through the effective discussion duration and the invalid discussion duration.

[0036] The classroom audio acquisition equipment will analyze the effective and invalid discussion time as classroom discussion data and feed it back to the teacher operation management platform so that the teacher operation management platform can further integrate these data, generate classroom discussion reports, and help teachers understand students' discussion performance.

[0037] In some embodiments of the present disclosure, the student interaction device includes: a student multimedia unit and an expression analysis unit; the student multimedia unit is used to determine the conversation duration corresponding to the classroom conversation audio of each student in the teaching class according to the concentration mode indicated by the classroom instructions, and based on the conversation duration, count the number of first students with abnormal conversation behavior in the teaching class, so as to send the number of first students as classroom concentration data to the teacher operation management platform; the expression analysis unit is used to analyze the facial expression changes corresponding to the facial expression features of each student in the teaching class according to the concentration mode indicated by the classroom instructions, and based on the facial expression changes, count the number of second students with inattentive facial expressions, so as to send the number of second students as classroom concentration data to the teacher operation management platform.

[0038] Specifically, the student multimedia unit may be a student audio and video acquisition unit, which may enter a focus mode according to classroom instructions issued by the teacher operation management platform, collect and analyze students' classroom conversation audio, and evaluate students' conversation behavior.

[0039] The teacher operates the management platform to issue instructions, instructing the device to enter the focus mode. The student multimedia unit begins to collect the classroom conversation audio of each student, analyzes the collected conversation audio, and counts the conversation duration of each student in the classroom (that is, the time the student speaks privately or participates in the conversation).

[0040] The student multimedia unit determines whether the student's conversation behavior is abnormal according to a preset conversation duration threshold. Abnormal conversation behavior includes a conversation duration greater than the preset conversation duration threshold, a conversation number less than or equal to the preset conversation duration threshold greater than a preset conversation number threshold.

[0041] The student multimedia unit counts the number of students with abnormal conversation behaviors and records it as the number of first students. The student multimedia unit feeds back the counted number of first students to the teacher operation management platform as part of the classroom concentration data.

[0042] The expression analysis unit can enter the focus mode according to the classroom instructions issued by the teacher operation management platform, collect and analyze students' facial expression characteristics, and evaluate students' classroom concentration.

[0043] The teacher operation management platform issues a command to instruct the device to enter the focus mode, and the expression analysis unit begins to collect the facial expression features of each student and analyze the changes in facial expressions corresponding to each student's facial expression features. That is, the expression analysis unit uses a camera or image sensor to capture students' facial expressions (such as eyes, mouth corners, eyebrows, etc.) in real time, and analyzes the changes in students' facial expressions based on artificial intelligence algorithms to determine whether they are in a focused state. The expression analysis unit identifies inattentive expressions (such as wandering, dozing, distraction, etc.) based on the preset expression concentration model, and counts the number of students with inattentive facial expressions, which is recorded as the number of second students. The expression analysis unit uses the number of second students obtained as part of the classroom concentration data and feeds it back to the teacher operation management platform.

[0044] In addition, the teacher operation management platform can receive classroom concentration data (number of first students and number of second students) from the student multimedia unit and expression analysis unit, integrate these data, and generate a comprehensive classroom concentration report to help teachers understand students' classroom performance. Among them, the number of first students with abnormal dialogue behavior reflects the students' participation in classroom discussions or interactions; the number of second students with inattentive expressions reflects the students' concentration in class. Teachers can adjust teaching strategies based on these data, such as adding interactive links, optimizing teaching content, or paying attention to the performance of specific students.

[0045] In some embodiments of the present disclosure, the teacher operation management platform includes: a teacher interaction unit; the student interaction device includes a student interaction unit; the student interaction unit is used to trigger student interaction events; and the teaching interaction unit is used to trigger teacher interaction events.

[0046] The teacher operation management platform in the present invention is used to perform interaction matching operations based on the interaction event trigger signals sent by the teaching interaction unit and the student interaction unit, determine the student interaction devices that match the teaching interaction unit, and count the number of student interaction devices that have not successfully matched the teaching interaction unit, and use the number of student interactions as classroom interaction data; the multimedia unit in the student interaction device that matches the teaching interaction unit is used to collect student interaction audio and feed the student interaction audio back to the teacher multimedia unit.

[0047] Specifically, the teacher operation management platform can realize the teacher-student interaction in the classroom through the interactive unit with the student interactive device, collect interactive data and evaluate the classroom interactive effect. The teacher interactive unit can be a teacher interactive device, and the teacher can initiate classroom interactive tasks, such as asking questions, voting or group discussions, through the teacher interactive unit. The student interactive unit can be a student interactive device, and students can respond to the teacher's interactive tasks, such as answering questions or participating in discussions, through the student interactive unit.

[0048] The teacher interaction unit triggers teacher interaction events (such as initiating a question or task), and the student interaction unit triggers student interaction events (such as students answering questions or participating in tasks).

[0049] The teacher interaction unit and the student interaction unit send the interaction event trigger signal to the teacher operation management platform, and the teacher operation management platform performs the interaction matching operation based on the received interaction event trigger signal. That is, the teacher interaction event is associated with the student interaction event to determine which student interaction devices have successfully responded to the teacher interaction event. The teacher operation management platform counts the number of student interaction devices that have not been successfully matched with the teacher interaction unit, and the number of students corresponding to these devices who have failed to respond in time. The teacher operation management platform uses the number of student interaction devices that have not been successfully matched as classroom interaction data. The number of student interaction devices that have not been successfully matched can represent the number of student interactions to reflect the enthusiasm of student interactions.

[0050] The student interaction device that successfully matches the teacher interaction unit can collect the student's interactive audio (such as the voice of answering questions) in the interactive event through the multimedia unit, and feed the collected student interaction audio back to the teacher multimedia unit of the teacher operation management platform.

[0051] The teacher's multimedia unit receives the students' interactive audio and further analyzes the students' interactive performance.

[0052] In some embodiments of the present disclosure, the teacher operation management platform includes a teacher multimedia unit; the teacher multimedia unit is used to collect teacher interaction audio, and based on a preset topic model or a preset knowledge point cluster, identify the teacher interaction content corresponding to the teacher interaction audio and the student interaction content corresponding to the student interaction audio, so as to use the interaction time and the number of failed interaction content recognitions corresponding to the student interaction audio obtained according to the recognition results as classroom interaction data.

[0053] The teacher multimedia unit may specifically be a teacher audio and video acquisition unit, which uses a microphone or audio acquisition device to collect the teacher's interactive audio (such as questions, explanations, feedback, etc.) in real time in the classroom. For example, the teacher asks questions or initiates discussions in the classroom, and the teacher comments on the students' answers or provides additional explanations.

[0054] Teachers' multimedia units can pre-set a series of subject models according to the course outline or teaching content (such as "geometry" in mathematics, "ancient poetry" in Chinese, etc.), or pre-set knowledge point clusters according to the subject knowledge system (such as "mechanics" in physics, "chemical reactions" in chemistry, etc.).

[0055] The teacher multimedia unit performs speech recognition and semantic analysis on the collected teacher interaction audio, and matches the topic or knowledge point to which the teacher interaction content belongs based on the preset topic model or knowledge point cluster. The teacher multimedia unit can also perform speech recognition and semantic analysis on the received student interaction audio, and match the topic or knowledge point to which the student interaction content belongs based on the preset topic model or knowledge point cluster.

[0056] The teacher's multimedia unit can match the identified topics or knowledge points, and based on the matching results, count the effective interaction time of students in the interactive task (i.e., the time students speak or participate in discussions after the interactive content is successfully matched). The interaction time reflects the students' participation and enthusiasm in classroom interaction. The purpose of matching topics or knowledge points in this disclosure is to verify the accuracy of students' answers.

[0057] The teacher multimedia unit can also count the number of times the interactive content identified by the preset topic model or knowledge point cluster fails to match.

[0058] The teacher multimedia unit disclosed in the present invention uses the interaction time and the number of failed interaction content recognition as classroom interaction data and feeds it back to the teacher operation management platform. The interaction time reflects the students' participation in the interactive task, and the number of failed interaction content recognition reflects the relevance of the students' answers to the classroom topic. The teacher operation management platform generates a classroom interaction report based on these data to help teachers evaluate the effectiveness of classroom interaction, such as identifying inefficient links in the interaction (such as students' answers deviating from the topic) to optimize the interaction design and improve the quality of classroom interaction.

[0059] In some embodiments of the present disclosure, since the matching of interactive content requires preset knowledge point clusters, the teacher multimedia unit can also perform hierarchical processing on the textbook text knowledge points extracted from the textbook content to obtain basic knowledge point clusters; screen the first important knowledge point cluster based on the distribution of knowledge points in the basic knowledge point cluster; use the preset knowledge point screening model to screen qualified knowledge points from all knowledge points corresponding to the historical question bank to obtain the second important knowledge point cluster; use the hierarchical clustering algorithm and the correlation analysis algorithm to screen the wrong question features under the target feature category from the wrong question related features, and obtain the third important knowledge point cluster based on all knowledge points corresponding to the wrong question features; based on the basic knowledge point cluster, the second important knowledge point cluster and the third important knowledge point cluster, combined with the cross-matching strategy, determine the matching verification results of the knowledge point clusters, so as to optimize the first important knowledge point cluster according to the matching verification results, and use the first important knowledge point cluster as the preset knowledge point cluster.

[0060] Specifically, the process of obtaining the basic knowledge point cluster is as follows: The teacher multimedia unit can use text analysis technologies such as text mining, natural language, and semantic analysis to extract textbook knowledge points from the textbook text of the textbook content, and grade the textbook knowledge points (for example, grade them according to chapters, difficulty, learning stages, etc.), encode the identification of each textbook knowledge point, and obtain the identification coding value corresponding to each textbook knowledge point, so as to construct a set of identification vectors corresponding to the textbook knowledge point identification based on the identification coding value, generate a textbook knowledge point network U, and group all textbook knowledge point networks into the basic knowledge point cluster Uori=[An, Bm, Ck] in this disclosure.

[0061] In the present disclosure, the hierarchical identification can be divided into n levels. For example, when n=3, the textbook knowledge point network U=[An, Bm, Ck], the general expression: U=(k, ), k is the set of knowledge points, is the edge set.

[0062] When n=3, the textbook knowledge point network U in the present disclosure can be a three-level classification system: the first-level classification An, the second-level classification Bm and the third-level classification Ck. Each level of classification represents a level in the textbook knowledge point network, where the first-level classification is the main node, the second-level classification is the slave node of the first-level classification, and the third-level classification is the slave node of the second-level classification. That is, the first-level classification An is the main node, and it is classified by chapter, and An=[A1, A2...An] can be obtained; the second-level classification Bm is the slave node of An, and it is classified by section, and Bm=[A1b1, A1b2, A2b1..Anbm] can be obtained; the third-level classification Ck is the slave node of Bm, and it is classified by unit results, and Ck=[A1b1c1, A1b2c2, A1b2c3..Anbmck] can be obtained.

[0063] Because textbook knowledge points can appear in the form of a single unit or a combination of multiple units depending on the difficulty and learning depth, when constructing the textbook knowledge point network, it is determined that the first-level classification exists in the form of a single-unit identification vector, and the second and third levels exist in the form of multiple units, with identification vectors superimposed in sequence.

[0064] The present disclosure forms a final basic knowledge point cluster by combining multiple textbook knowledge point networks corresponding to the identification code values ​​of textbook knowledge points. The basic knowledge point cluster of the present disclosure can cover all possible combinations of textbook knowledge points, providing a rich material library for the push of questions. In practical applications, the teacher's multimedia unit can also further intelligently select textbook knowledge points of different difficulties according to the students' learning situation and needs, and display the textbook knowledge points on the teacher's operation management platform. For example, for beginners or students who need to consolidate the basics, the system can display the knowledge points of the first level classification; for students who want to study in depth, the knowledge points of the second or third level classification can be displayed. This personalized push method not only improves learning efficiency, but also enhances students' interest and motivation in learning.

[0065] The process of obtaining the first important knowledge point cluster is as follows: After obtaining the basic knowledge point cluster, the teacher multimedia unit in the present disclosure can count the number of basic knowledge points at each level in the basic knowledge point cluster, and determine the basic knowledge point cluster score based on the number of basic knowledge points at each level according to the following formula, so as to screen and obtain the first important knowledge point cluster according to the basic knowledge point cluster score result;

[0066] in, is the score of the basic knowledge point cluster; Z is the number of basic knowledge points at the first level in the basic knowledge point cluster; X is the number of basic knowledge points at the second level in the basic knowledge point cluster; V is the number of basic knowledge points at the third level in the basic knowledge point cluster; M is the number of basic knowledge points at the nth level in the basic knowledge point cluster; is the weight of the number of basic knowledge points at the first level, is the weight of the number of basic knowledge points at the second level, is the weight of the number of basic knowledge points at the third level; is the weight of the number of basic knowledge points at the nth level. < < , because as the knowledge points become more refined, their importance accounts for a larger proportion.

[0067] The teacher multimedia unit of the present disclosure can compare the basic knowledge point cluster scoring result Q with the preset screening threshold T1. When Q is greater than or equal to T1, the basic knowledge point identification vector in the screened basic knowledge point cluster is used as the first important knowledge point cluster. The preset screening threshold can be set by self-defined, average method, median, etc., which is not limited in the embodiments of the present disclosure.

[0068] The process of obtaining the second most important knowledge point cluster is as follows: The teacher multimedia unit performs graded processing on all knowledge points extracted from the history question bank text to obtain a history question bank knowledge point cluster, and determines the knowledge point score of each knowledge point in the history question bank knowledge point cluster according to the preset knowledge point screening model of the following formula, so as to screen the qualified knowledge points from the knowledge point score of each knowledge point to obtain the second most important knowledge point cluster;

[0069] in, Score the knowledge point P in the knowledge point cluster of the history question bank, is the number of occurrences of knowledge point P, is the number of errors in knowledge point P, is the number of knowledge points at the level corresponding to knowledge point P, is the weight corresponding to the number of occurrences, The weight corresponding to the number of errors, is the weight corresponding to the number of knowledge points.

[0070] After obtaining the knowledge point score of each knowledge point, the present disclosure can compare the knowledge point score of each knowledge point. and the preset scoring threshold T2, when When it is greater than or equal to T2, the important knowledge point combinations are screened out to form the second important knowledge point cluster Znew.

[0071] The process of obtaining the third important knowledge point cluster is as follows: The teacher multimedia unit disclosed in the present invention can obtain the wrong question correlation characteristics of the subjective and objective factors that affect students' wrong answers collected by the teaching management system, and use cluster analysis and correlation evaluation models to screen out the wrong question characteristics caused by objective factors, and then determine the feature group or features with the greatest impact, and accordingly construct the third important knowledge point cluster Onew that focuses on students' mastery of knowledge points.

[0072] The present disclosure collects factors that affect wrong answers through a teaching management system, including emotions, concentration, answering style, knowledge point mastery, answering efficiency, question difficulty level, etc., and collects students' answering video images through a teaching management system simulating an exam scene, and can obtain a wrong answering correlation feature data set of subjective factors through Single Shot Multibox Detector (SSD) facial expression recognition technology and eye tracking technology; collects students' answering conditions through computers in the teaching management system to obtain a wrong answering correlation feature data set of objective factors. After obtaining these wrong answering correlation features, the teaching management system feeds these wrong answering correlation features back to the teacher's multimedia unit.

[0073] Among them, subjective factors include: emotional characteristics (such as positive, indifferent, negative, etc.), concentration characteristics (such as concentration, distraction, susceptibility to external factors, etc.), test-taking style characteristics (such as carefulness, carelessness, lack of patience, etc.). Objective factors include: knowledge point mastery characteristics (such as proficiency, proficiency, unproficiency, etc.), test-taking efficiency characteristics (such as fast, medium, slow, etc.), and question difficulty level characteristics (such as simple, ordinary, difficult, etc.).

[0074] The teacher multimedia unit of the present disclosure can use clustered wrong question related feature groups to analyze the influence of different groups of wrong question related features on wrong questions. That is, the present disclosure can use random combination of all wrong question related features to find out the objective factors that really affect wrong questions. This is because there is data interference from subjective factors, and it is impossible to accurately judge whether students have mastered the corresponding knowledge points. Therefore, the present disclosure needs to filter out the wrong question feature data set caused by objective factors from the wrong question related feature data set, thereby improving the accuracy of the third important knowledge point cluster.

[0075] Among them, when performing cluster analysis, the present disclosure needs to first perform data preprocessing on the acquired wrong question related features, such as processing missing values ​​and outliers for the wrong question related features to ensure data quality, and perform Z-score standardization on the wrong question related features.

[0076] Afterwards, due to the difficulty of the questions, the efficiency of answering questions is slow, which is a normal phenomenon. Therefore, the teacher multimedia unit disclosed in the present invention needs to first use a hierarchical clustering algorithm to group the pre-processed wrong question related features, and use the Pearson correlation coefficient distance algorithm of the following formula to calculate the distance matrix between each wrong question related feature to obtain the first correlation analysis result.

[0077]

[0078] in, is the result of the first correlation analysis, , is the average value of wrong question related features a and wrong question related features b, where a and b are wrong question related features. The value range is [0, 1]. The closer the value is to 1, the looser the linear correlation between the wrong question association feature and the wrong question feature.

[0079] Based on the above first correlation analysis results, hierarchical clustering is selected to gradually merge or split clusters to generate a dendrogram to obtain the correlation distribution between the associated features of wrong questions.

[0080] For example, the present disclosure may select the shortest distance linking method: d(Ccluster,Dcluster)=min{d(c,d):c∈C,d∈D}, where d(Ccluster,Dcluster) represents the distance between cluster C and cluster D, and d(Ccluster,Dcluster) represents the distance between a point c in cluster C and a point d in cluster D. Determine the number of groups k of wrong question-related features, and assign the wrong question-related features to the corresponding groups to obtain the correlation distribution between the wrong question-related features; Analyze each set of wrong question-related features, evaluate their relationship with the wrong question features again, and obtain the second correlation analysis result (i.e., generate the correlation distribution between the wrong question-related feature sets based on the second correlation analysis result, and obtain whether there is correlation between the wrong question-related feature sets) through the pre-trained correlation evaluation model: The relevance evaluation model is as follows:

[0081] Among them, y is the result of the second correlation analysis, is the intercept, is the partial regression coefficient, and x1, x2...xn are the associated features of wrong questions.

[0082] The present disclosure selects the wrong question-associated feature group or feature that has the greatest impact on wrong questions through the first correlation analysis result and the second correlation analysis result (i.e., selects the wrong question-associated feature with the maximum mean value of the first correlation analysis result and the second correlation analysis result of each wrong question-associated feature, or selects the wrong question-associated feature with the maximum value of the weighted fusion of the first correlation analysis and the second correlation analysis result of each wrong question-associated feature), and determines the feature category of each screened wrong question-associated feature (feature categories include subjective categories and objective categories). The present disclosure uses the wrong question-associated feature whose feature category is the target feature category (i.e., objective category) as the wrong question feature, thereby constructing the third important knowledge point cluster Onew.

[0083] Among them, the subjective category reflects the students' own psychology, not the reason for making mistakes due to unfamiliarity with knowledge points. What needs to be improved is their own psychological quality; the objective category reflects that students make mistakes due to insufficient knowledge points. Because the subjective category requires students to adjust themselves or a third party to assist in guidance, and only the objective category is the essential reason for making mistakes due to insufficient knowledge points, so the objective category is used as the target feature category.

[0084] After obtaining the basic knowledge point cluster, the first important knowledge point cluster, the second important knowledge point cluster and the third important knowledge point cluster through the above content, the present disclosure can perform matching verification on the first important knowledge point cluster based on the basic knowledge point cluster, the second important knowledge point cluster and the third important knowledge point cluster, and optimize the first important knowledge point cluster based on the matching verification result to obtain the optimized first important knowledge point cluster, that is, the preset knowledge point cluster.

[0085] Specifically, the teacher multimedia unit performs a first matching verification on all the first important knowledge points at each level in the first important knowledge point cluster in turn using the second important knowledge point cluster and the third important knowledge point cluster based on the same-level matching rule; if the first matching verification fails, a cross-matching verification is performed on each second important knowledge point in the second important knowledge point cluster and each third important knowledge point in the third important knowledge point cluster; if the cross-matching verification is successful, the cross-matching knowledge points that are successfully cross-matched are determined, and based on the same-level matching rule, the cross-matching knowledge points are used to perform a second matching verification on all the basic knowledge points at each level in the basic knowledge point cluster in turn; if the second matching verification is successful, the matching knowledge points corresponding to the second successful matching are integrated into the first important knowledge point cluster to obtain the preset knowledge point cluster.

[0086] It should be noted that, for the matching verification process in the present disclosure, if the matching verification of the current level fails, the matching verification of the next level will be automatically performed, until all current levels have been matched and verified, and different operations will be performed according to the matching verification results. The cross-matching in the present disclosure means that after the second knowledge point cluster and the third knowledge point cluster fail to perform the first matching verification on the first important knowledge point cluster, the second knowledge point cluster and the third knowledge point cluster are separately matched for the same level to determine whether there are matching knowledge points in the second knowledge point cluster and the third knowledge point cluster, so as to avoid the situation of knowledge point recognition errors caused by system errors.

[0087] If the first matching verification is successful, the successfully matched knowledge point is bound to the question bank and imported into the first key knowledge point cluster. If the cross matching verification fails or the second matching verification fails, the knowledge point cluster currently undergoing matching verification is stored in the candidate library.

[0088] Among them, the teacher multimedia unit can obtain the first matching result and the second matching result according to the following formula to comprehensively obtain the matching verification result of each matching verification;

[0089] in, is the first matching result between the knowledge point cluster G and the knowledge point cluster H, where the knowledge point cluster G and the knowledge point cluster H are any two knowledge point clusters that need to be matched and verified among the basic knowledge cluster, the first important knowledge point cluster, the second important knowledge point cluster, and the third important knowledge point cluster. represents the number of knowledge points in the intersection of knowledge point cluster G and knowledge point cluster H, Represents the number of knowledge points in the union of knowledge point cluster G and knowledge point cluster H;

[0090] in, is the second matching result between knowledge point cluster G and knowledge point cluster H, n is the nth knowledge point, N is the total number of knowledge points after knowledge point cluster G and knowledge point cluster H are aligned, is the nth knowledge point vector in the knowledge point cluster G, is the mean of the nth knowledge point vector after the knowledge point cluster G and the knowledge point cluster H are aligned, is the nth knowledge point vector in the knowledge point cluster H.

[0091] In the present disclosure, when the first match verification result is greater than or equal to the first match verification threshold, and the second match verification result is greater than the second match verification threshold, the current match verification result is determined to be a successful match.

[0092] It is understandable that, since the verification matching in the present disclosure adopts the principle of same-level matching, if only relying on the matching verification algorithm of the first formula above (for example, the Jaccard similarity algorithm), you may encounter a situation: even if the matching degree of the two objects seems similar overall (such as the output result of q (1, 0, 1)), there is a mismatch in the more detailed secondary dimension (such as the secondary dimension value is 0), which does not meet the consistency standard. Therefore, in order to make up for this deficiency, the present disclosure further combines the matching verification algorithm of the second formula above (for example, the improved cosine similarity algorithm) for verification. The improved cosine similarity algorithm is improved compared with other algorithms in measuring the difference in the values ​​of each dimension. If the improved cosine similarity algorithm is used alone, its accuracy still has certain limitations. Therefore, by combining the above two algorithms, the present disclosure can achieve complementary advantages, thereby improving the accuracy and reliability of matching verification.

[0093] In some embodiments of the present disclosure, the teacher multimedia unit is also used to analyze the teaching situation based on classroom discussion data, classroom concentration data, and classroom interaction data, combined with a preset student performance evaluation algorithm, to determine classroom learning evaluation results.

[0094] In some embodiments of the present disclosure, the teacher multimedia unit may determine the classroom learning evaluation index based on the classroom discussion data, the classroom concentration data, and the classroom interaction data, so as to analyze the classroom learning situation of the students under classroom teaching in combination with the classroom learning evaluation index according to the preset behavior performance evaluation algorithm of the following formula 1, and obtain the classroom learning evaluation result; Formula 1 Among them, D is the classroom learning evaluation result, is the first classroom learning evaluation index, S is the number of student interactions in the classroom interaction data, is the preset interaction threshold. It is the evaluation index of the second classroom learning. is the interaction time in the classroom interaction data, is the total number of hours of teaching class. It is the third classroom learning evaluation indicator. is the effective discussion duration in the classroom discussion data, is the fourth classroom learning evaluation index, C is the number of failed interactive content recognition in classroom interactive data, The threshold for the number of failed interactive content recognitions is preset. is the fifth classroom learning evaluation indicator, A is the number of inattentive students in the classroom attention data, is the total number of students in the teaching class, It is the sixth classroom learning evaluation indicator. The ineffective discussion time in the classroom focus data, , , , , as well as They are the preset weights corresponding to the first classroom learning evaluation indicators, the second classroom learning evaluation indicators, the third classroom learning evaluation indicators, the fourth classroom learning evaluation indicators, the fifth classroom learning evaluation indicators and the sixth classroom learning evaluation indicators.

[0095] In summary, the teaching scene simulation device provided by the present invention includes a teacher operation management platform, a classroom audio acquisition device and a student interaction device; the classroom audio acquisition device is used to receive classroom instructions issued by the teacher operation management platform, and according to the classroom instructions, collect classroom discussion audio, analyze to obtain classroom discussion data, and feed back the classroom discussion data to the teacher operation management platform; the student interaction device is used to receive classroom instructions issued by the teacher operation management platform, obtain students' classroom conversation audio and classroom learning expression characteristics, and analyze the classroom conversation audio and classroom learning expression characteristics to obtain classroom concentration data, and feed back the classroom concentration data to the teacher operation management platform; the teacher operation management platform is used to obtain classroom interaction data based on the student interaction audio under the student interaction event feedback by the student interaction device, so as to integrate and analyze the classroom discussion data, classroom concentration data and classroom interaction data to evaluate the classroom teaching situation, and realize the automatic collection and analysis of students' classroom discussion audio, conversation audio and learning expression characteristics through the teaching scene simulation device, generate classroom discussion data, classroom concentration data and classroom interaction data, and integrate and analyze these data to provide teachers with comprehensive and objective classroom teaching evaluation results. Through accurate data feedback, teachers can more accurately understand students' learning status, participation and concentration, thereby optimizing teaching strategies, improving classroom teaching effectiveness and achieving significant improvement in teaching quality.

[0096] based on Figure 1 , Figure 2 The teaching scene simulation equipment shown, Figure 3 The following is a flow chart of a teaching management method for a teaching scene simulation device provided by an embodiment of the present disclosure. Figure 3 As shown, the method is mainly applied to teaching scene simulation equipment, and the method includes: Step 101: According to classroom instructions, obtain students' classroom conversation audio, classroom learning expression characteristics and classroom discussion audio, and analyze to obtain classroom discussion data and classroom concentration data.

[0097] In the present disclosure, obtaining classroom discussion data is achieved through an audio acquisition unit, and obtaining classroom concentration data is achieved through a student interaction device.

[0098] Step 102: Obtain classroom interaction data based on the student interaction audio under the student interaction event.

[0099] In the present disclosure, obtaining classroom interaction data is achieved through the teacher operation management platform.

[0100] Step 103: Integrate and analyze classroom discussion data, classroom focus data, and classroom interaction data to evaluate classroom teaching conditions.

[0101] In the present disclosure, the evaluation of classroom teaching situation is achieved through the teacher operation management platform.

[0102] In some embodiments, the principles of the embodiments of step 101 and step 102 are the same as those described above. Figure 1 The principles of the related embodiments shown are the same, and can be referred to Figure 1 The relevant description of the illustrated embodiment will not be repeated here.

[0103] In summary, the technical solution provided by the present disclosure obtains students' classroom conversation audio, classroom learning expression characteristics and classroom discussion audio according to classroom instructions, analyzes to obtain classroom discussion data and classroom concentration data, obtains classroom interaction data based on student interaction audio under student interaction events, and integrates and analyzes classroom discussion data, classroom concentration data and classroom interaction data to evaluate classroom teaching. Classroom discussion data reflects students' participation and effectiveness in discussions, classroom concentration data reveals students' concentration, and classroom interaction data evaluates students' performance in interactive tasks. By integrating and analyzing these data, the present disclosure enables teachers to comprehensively and objectively evaluate classroom teaching situations, accurately identify efficient and inefficient links in the classroom, thereby optimizing teaching strategies, improving students' classroom participation and learning outcomes, and ultimately achieving a significant improvement in teaching quality.

[0104] Corresponding to the teaching management method applied to the teaching scene simulation device, the present invention also proposes a teaching management device applied to the teaching scene simulation device. Since the device embodiment of the present invention corresponds to the method embodiment described above, the details not disclosed in the device embodiment can be referred to the method embodiment described above, and will not be repeated in the present invention.

[0105] Figure 4 A structural diagram of a teaching management device applied to a teaching scene simulation device provided by an embodiment of the present disclosure, such as Figure 4 As shown, the device comprises: The first acquisition unit 310 is used to acquire students' classroom conversation audio, classroom learning expression features and classroom discussion audio according to classroom instructions, and analyze them to obtain classroom discussion data and classroom concentration data.

[0106] The second acquisition unit 320 is used to obtain classroom interaction data based on the student interaction audio under the student interaction event.

[0107] The analysis and evaluation unit 330 is used to integrate and analyze classroom discussion data, classroom concentration data, and classroom interaction data to evaluate classroom teaching conditions.

[0108] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment, and the principle is the same, which is not limited in this embodiment.

[0109] Based on the above Figure 3 The method shown in the figure, accordingly, this embodiment also provides a computer program product, including a computer program, which implements the above-mentioned Figure 3 The method shown.

[0110] Based on the above Figure 3 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 3 The method shown.

[0111] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0112] like Figure 5 The figure shows a hardware structure diagram of an electronic device of the present invention, including: at least one processor 401; and, A memory 402 is communicatively connected to at least one of the processors 401; wherein, The memory 402 stores instructions that can be executed by at least one of the processors, and the instructions are executed by at least one of the processors so that at least one of the processors can execute the teaching management method applied to the teaching scene simulation device as described above.

[0113] Figure 5 A processor 401 is taken as an example.

[0114] The electronic device may further include: an input device 403 and a display device 404 .

[0115] The processor 401, the memory 402, the input device 403 and the display device 404 may be connected via a bus or other means, and the figure takes the connection via a bus as an example.

[0116] The memory 402 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the review content generation method in the embodiment of the present application, for example, Figure 3 The processor 401 executes various functional applications and data processing by running the non-volatile software programs, instructions and modules stored in the memory 402, that is, the teaching management method applied to the teaching scene simulation device in the above embodiment is realized.

[0117] The memory 402 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required by at least one function; the data storage area may store data created according to the use of the review content generation method, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some embodiments, the memory 402 may optionally include a memory remotely arranged relative to the processor 401, and these remote memories may be connected to the device for executing the review content generation method via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0118] The input device 403 can receive user clicks and generate signal inputs related to user settings and function controls of the review content generation method. The display device 404 can include display devices such as a display screen.

[0119] The one or more modules are stored in the memory 402, and when executed by the one or more processors 401, the teaching management method applied to the teaching scene simulation device in any of the above method embodiments is executed.

[0120] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0121] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0122] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0123] Through the description of the above implementation methods, the technicians in this field can clearly understand that the present application can be implemented by means of software plus the necessary general hardware platform, or by hardware. By applying the scheme of this embodiment, compared with the current prior art, this embodiment obtains the students' classroom conversation audio, classroom learning expression characteristics and classroom discussion audio according to the classroom instructions, analyzes and obtains classroom discussion data and classroom focus data, and obtains classroom interaction data based on the student interaction audio under the student interaction event, and integrates and analyzes the classroom discussion data, classroom focus data and classroom interaction data to evaluate the classroom teaching situation. The classroom discussion data reflects the students' participation and effectiveness in the discussion, the classroom focus data reveals the students' concentration, and the classroom interaction data evaluates the students' performance in the interactive tasks. By integrating and analyzing these data, the present disclosure enables teachers to comprehensively and objectively evaluate the classroom teaching situation, accurately identify the efficient and inefficient links in the classroom, thereby optimizing the teaching strategy, improving the students' classroom participation and learning effect, and ultimately achieving a significant improvement in the teaching quality.

[0124] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0125] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A teaching scene simulation device, characterized in that: The device comprises: Teacher operation management platform, classroom audio acquisition equipment and student interaction equipment; The classroom audio collection device is used to receive classroom instructions issued by the teacher operation management platform, and collect classroom discussion audio according to the classroom instructions, analyze and obtain classroom discussion data, and feed the classroom discussion data back to the teacher operation management platform; The student interaction device is used to receive classroom instructions issued by the teacher operation management platform, obtain students' classroom conversation audio and classroom learning expression features, and analyze the classroom conversation audio and classroom learning expression features to obtain classroom concentration data, so as to feed back the classroom concentration data to the teacher operation management platform; The teacher operation management platform is used to obtain classroom interaction data based on the student interaction audio under the student interaction events fed back by the student interaction device, so as to integrate and analyze the classroom discussion data, the classroom concentration data and the classroom interaction data to evaluate the classroom teaching situation.

2. The device according to claim 1, characterized in that The classroom audio acquisition device is used to obtain the discussion audio of all students in the teaching classroom according to the discussion mode indicated by the classroom instructions, and analyze the student discussion situation in the teaching classroom based on the audio decibel of the discussion audio and a preset decibel threshold, and determine the discussion effective time and discussion invalid time corresponding to the discussion audio, so as to send the discussion effective time and discussion invalid time as the classroom discussion data to the teacher operation management platform.

3. The device according to claim 1, characterized in that The student interaction device comprises: a student multimedia unit and an expression analysis unit; The student multimedia unit is used to determine the conversation duration corresponding to the classroom conversation audio of each student in the teaching class according to the concentration mode indicated by the classroom instruction, and based on the conversation duration, count the number of first students with abnormal conversation behavior in the teaching class, so as to send the first number of students as the classroom concentration data to the teacher operation management platform; The expression analysis unit is used to analyze the facial expression changes corresponding to the facial expression features of each student in the teaching classroom according to the concentration mode indicated by the classroom instructions, and based on the facial expression changes, count the number of second students whose facial expressions are inattention, so as to send the second number of students as the classroom concentration data to the teacher operation management platform.

4. The device according to claim 1, characterized in that The teacher operation management platform includes: a teacher interaction unit; The student interaction device includes a student interaction unit; The student interaction unit is used to trigger a student interaction event; The teaching interaction unit is used to trigger teacher interaction events.

5. The device according to claim 4, characterized in that The teacher operation management platform is used to perform an interactive matching operation based on the interactive event trigger signals sent by the teaching interactive unit and the student interactive unit, determine the student interactive device that matches the teaching interactive unit, and count the number of student interactive devices that have not successfully matched the teaching interactive unit, and use the number of student interactions as the classroom interaction data; The multimedia unit in the student interaction device matched with the teaching interaction unit is used to collect student interaction audio and feed the student interaction audio back to the teacher multimedia unit.

6. The device according to claim 5, characterized in that The teacher operation management platform includes a teacher multimedia unit; The teacher multimedia unit is used to collect teacher interaction audio, and based on a preset topic model or a preset knowledge point cluster, identify the teacher interaction content corresponding to the teacher interaction audio and the student interaction content corresponding to the student interaction audio, so as to use the interaction time and the number of failed interaction content recognitions corresponding to the student interaction audio obtained according to the recognition results as the classroom interaction data.

7. The device according to claim 6, characterized in that The teacher multimedia unit is also used to perform classification processing on the textbook text knowledge points extracted from the textbook content to obtain basic knowledge point clusters; Filtering to obtain a first important knowledge point cluster based on the knowledge point distribution in the basic knowledge point cluster; Using the preset knowledge point screening model, the qualified knowledge points are screened from all the knowledge points corresponding to the historical question bank to obtain the second most important knowledge point cluster; Using a hierarchical clustering algorithm and a correlation analysis algorithm, the wrong question features under the target feature category are screened from the wrong question related features, and the third important knowledge point cluster is obtained based on all the knowledge points corresponding to the wrong question features; Based on the basic knowledge point cluster, the second important knowledge point cluster and the third important knowledge point cluster, combined with the cross-matching strategy, the matching verification result of the knowledge point cluster is determined to optimize the first important knowledge point cluster according to the matching verification result, and use the first important knowledge point cluster as the preset knowledge point cluster.

8. The device according to claim 7, characterized in that The teacher multimedia unit is used to perform a first matching verification on all the first important knowledge points at each level in the first important knowledge point cluster in turn by using the second important knowledge point cluster and the third important knowledge point cluster based on the same level matching rule; If the first matching verification fails, cross-matching verification is performed on each second important knowledge point in the second important knowledge point cluster and each third important knowledge point in the third important knowledge point cluster; If the cross-matching verification is successful, determining the cross-matching knowledge points that are successfully cross-matched, and based on the same-level matching rule, using the cross-matching knowledge points to sequentially perform a second matching verification on all basic knowledge points of each level in the basic knowledge point cluster; If the second matching verification is successful, the matching knowledge points corresponding to the second successful matching are integrated into the first important knowledge point cluster to obtain the preset knowledge point cluster.

9. The device according to claim 1, characterized in that The teacher multimedia unit is also used to analyze the teaching situation based on the classroom discussion data, the classroom concentration data and the classroom interaction data, combined with a preset student performance evaluation algorithm, to determine the classroom learning evaluation results.

10. The device according to claim 9, characterized in that The teacher multimedia unit is used to determine the classroom learning evaluation index based on the classroom discussion data, the classroom concentration data and the classroom interaction data, so as to analyze the classroom learning situation of the students under the classroom teaching in combination with the classroom learning evaluation index according to the preset behavior performance evaluation algorithm of the following formula 1, and obtain the classroom learning evaluation result; Formula 1 Wherein, D is the classroom learning evaluation result, is the first classroom learning evaluation index, S is the number of student interactions in the classroom interaction data, is the preset interaction threshold. It is the evaluation index of the second classroom learning. is the interaction duration in the classroom interaction data, is the total class hours of the teaching class, It is the third classroom learning evaluation indicator. is the effective duration of the discussion in the classroom discussion data, is the fourth classroom learning evaluation indicator, C is the number of failed interactive content recognition in the classroom interactive data, The threshold for the number of failed interactive content recognitions is preset. is the fifth classroom learning evaluation indicator, A is the number of inattentive students in the classroom attention data, is the total number of students in the teaching class, It is the sixth classroom learning evaluation indicator. The ineffective discussion time in the class focus data. , , , , as well as They are respectively the preset weights corresponding to the first classroom learning evaluation indicator, the second classroom learning evaluation indicator, the third classroom learning evaluation indicator, the fourth classroom learning evaluation indicator, the fifth classroom learning evaluation indicator and the sixth classroom learning evaluation indicator.

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