CN116664011A - Teaching quality assessment system based on classroom student behavior analysis - Google Patents
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Abstract
The invention relates to the technical field of teaching quality assessment, in particular to a teaching quality assessment system based on classroom student behavior analysis, which comprises an assessment center, an interaction analysis unit, an active learning unit, an early warning display unit, a comprehensive quality analysis unit and a student management unit, wherein the assessment center is used for analyzing the behavior of students in a class; the invention evaluates and analyzes the teaching quality from two angles of teachers and students, improves the data basis for the teaching quality evaluation on one hand, and is beneficial to improving the accuracy and the comprehensiveness of analysis results on the other hand, and is beneficial to comprehensively judging the teaching quality through a combined feedback evaluation analysis mode, carrying out timely teaching adjustment according to the evaluation results so as to improve the teaching quality, and in addition, carrying out deep analysis on the behavior data of the students to judge the learning behavior condition of the students in the same discipline, so as to carry out management education on the students through a feedback mode and improve the learning enthusiasm of the students.
Description
Technical Field
The invention relates to the technical field of teaching quality assessment, in particular to a teaching quality assessment system based on classroom student behavior analysis.
Background
Along with the development of internet technology, various teaching management systems gradually appear, so that the teaching management work of schools is greatly facilitated, the classroom teaching is still the most basic and important teaching organization form of higher education, and meanwhile, the most important links of realizing talent training targets, guaranteeing and improving the education quality are also realized;
however, the system for evaluating the education and teaching quality in the prior art is less, so that the feedback effect of the later stage of education and teaching is poor, the later stage of education and teaching is unfavorable for improving the teaching quality, if the traditional questionnaire and other modes are adopted for teaching and evaluation, the data statistics and analysis are troublesome, the evaluation result error is large, meanwhile, some education and teaching evaluation systems in the prior art have single evaluation items, and the participation evaluation personnel are only students, so that the obtained evaluation result is unreal and the problem of high contingency is solved, and the actual value of evaluation is reduced;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a teaching quality assessment system based on classroom student behavior analysis, which is used for solving the technical defects, carrying out assessment analysis on teaching quality from two angles of teachers and students, improving data base for teaching quality assessment, improving accuracy and comprehensiveness of analysis results, comprehensively judging teaching quality by combining feedback assessment analysis modes, carrying out timely teaching adjustment according to assessment results, carrying out deep analysis on student behavior data, judging learning behavior conditions of students in the same discipline, and carrying out management education on students in a feedback mode to improve learning enthusiasm of the students.
The aim of the invention can be achieved by the following technical scheme: a teaching quality assessment system based on classroom student behavior analysis comprises an assessment center, an interaction analysis unit, an active learning unit, an early warning display unit, a comprehensive quality analysis unit and a student management unit;
after the assessment center generates a management instruction, the management instruction is sent to an interaction analysis unit and an active learning unit, the interaction analysis unit immediately collects interaction data of teachers in class after receiving the management instruction, the interaction data comprises knowledge point input values, questioning times and teaching sound decibel values, and the interaction data is subjected to classroom interaction atmosphere assessment analysis and sent to an early warning display unit;
the active learning unit immediately collects behavior data of students in class after receiving the management instruction, wherein the behavior data comprises the number of times of low head of the students and the number of the students trapped, carries out learning enthusiasm assessment analysis on the behavior data, and sends the obtained low-camouflage signal to the comprehensive quality analysis unit and the student management unit;
the comprehensive quality analysis unit immediately invokes the interactive atmosphere assessment coefficient H from the interactive analysis unit and the learning active risk assessment rate J from the active learning unit after receiving the low-camouflage signal, performs combined feedback assessment analysis on the interactive atmosphere assessment coefficient H and the learning active risk assessment rate J, and sends the obtained qualified signals and unqualified signals to the early warning display unit;
and the student management unit immediately invokes the behavior data from the active learning unit after receiving the low-quiz signal, performs deep feedback analysis on the operation data, and sends the obtained talking signal to the early warning display unit.
Preferably, the process of evaluating and analyzing the classroom interaction atmosphere of the interaction analysis unit is as follows:
acquiring the duration from the moment of starting to the moment of ending the lesson of a teacher, marking the duration as a time threshold, acquiring lesson-taking audio content of the teacher in the classroom in the time threshold, extracting features from the lesson-taking audio content, acquiring a knowledge point input value ZS of the teacher in the time threshold, wherein the knowledge point input value ZS refers to the sum of the textbook explanation duration, the knowledge point related question duration and the example explanation duration of the knowledge point, simultaneously acquiring the question number of the teacher in the classroom in the time threshold, marking the question number as i, i as a natural number larger than zero, acquiring the number of students corresponding to the question number in the time threshold, marking the number as an interaction value HDi, taking the question number as an X axis, establishing a rectangular coordinate system by taking the interaction value as a Y axis, drawing an interaction value curve in a point drawing manner, and acquiring an interaction trend value HQ from the interaction value curve;
dividing a time threshold into o sub-time nodes, wherein o is a natural number greater than zero, marking the number of rows in a teacher as k, and k is a natural number greater than one, acquiring a teaching sound decibel value JFok heard by each row in each sub-time node, acquiring a minimum value JFokmin of the teaching sound decibel value in each sub-time node, marking the minimum value JFokmin as a teaching decibel value SKo, comparing the teaching decibel value SKo with a preset teaching decibel value threshold, and if the teaching decibel value SKo is smaller than or equal to the preset teaching decibel value threshold, marking the ratio of the number of sub-time nodes corresponding to the teaching decibel value SKo smaller than or equal to the preset teaching decibel value threshold to the total sub-time node number as a teaching risk value SF, and carrying out formulated analysis on the interaction trend value HQ and the teaching risk value SF.
Preferably, the formulation analysis process of the interaction analysis unit is as follows:
according to the formulaObtaining an interactive atmosphere evaluation coefficient, wherein a1, a2 and a3 are respectively preset weight coefficients of a knowledge point input value, an interactive trend value and a teaching risk value, a1, a2 and a3 are positive numbers larger than zero, a4 is a preset compensation factor coefficient, the value is 1.548, H is an interactive atmosphere evaluation coefficient, and the interactive atmosphere evaluation coefficient H is compared with a preset interactive atmosphere evaluation coefficient threshold value recorded and stored in the interactive atmosphere evaluation coefficient H:
if the interaction atmosphere evaluation coefficient H is larger than a preset interaction atmosphere evaluation coefficient threshold value, no signal is generated;
and if the interactive atmosphere evaluation coefficient H is smaller than or equal to a preset interactive atmosphere evaluation coefficient threshold value, generating a risk signal.
Preferably, the learning aggressiveness evaluation analysis process of the active learning unit is as follows:
step one: extracting features from audio contents of lessons to obtain the number of times of low head of students in a time threshold, wherein the number of times of low head of students refers to the number of times that the number of degrees of included angles formed by the faces of the students and the desk surface of the lessons in a blackboard writing period and a blackboard explaining period of a teacher are smaller than a preset included angle number, comparing the number of times of low head of the students with a preset threshold of times of low head of the students, if the number of times of low head of the students is larger than the preset threshold of times of low head of the students, marking the part of the number of times of low head of the students larger than the preset threshold of times of low head of the students as the number of non-compliance risks, and obtaining the ratio of the number of students corresponding to the number of non-compliance risks to the total students in the time threshold as learning low-vaginoscope XD;
step two: obtaining the number of stranded students in a classroom within a time threshold, further obtaining the ratio of the number of stranded students to the total number of students, marking the ratio of the number of stranded students to the total number of students as a learning stranded value XF, wherein the value obtained by subtracting a preset stranded angle degree threshold from the angle degree formed by the face of the student and the desk surface of the classroom is marked as a stranded degree value, obtaining the number of stranded degree values within the time threshold, marking the number as an abnormal constant, comparing the abnormal constant with a preset abnormal constant threshold, and marking the student corresponding to the abnormal constant larger than the preset abnormal constant threshold as the stranded student if the abnormal constant is larger than the preset abnormal constant threshold;
step three: obtaining a learning active risk assessment rate J according to a formula, and comparing the learning active risk assessment rate J with a preset learning active risk assessment rate threshold value which is input and stored in the learning active risk assessment rate J:
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is smaller than one, no signal is generated;
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is greater than or equal to one, generating a low-fans signal.
Preferably, the combined feedback evaluation analysis process of the comprehensive quality analysis unit is as follows:
acquiring an interactive atmosphere evaluation coefficient H and a learning active risk evaluation rate J in a time threshold;
according to the formulaObtaining a teaching quality evaluation coefficient, wherein b1 and b2 are respectively an interactive atmosphere evaluation coefficient and a preset proportion coefficient for learning an active risk evaluation rate, b1 and b2 are both positive numbers larger than zero, b3 is a preset correction factor coefficient,the value is 2.532, Z is a teaching quality evaluation coefficient, and the teaching quality evaluation coefficient Z and a preset teaching quality evaluation coefficient threshold value recorded and stored in the teaching quality evaluation coefficient Z are subjected to difference analysis:
if the teaching quality evaluation coefficient Z minus the preset teaching quality evaluation coefficient threshold is a positive number, generating a qualified signal;
and if the teaching quality evaluation coefficient Z minus the preset teaching quality evaluation coefficient threshold is a negative number, generating a disqualified signal.
Preferably, the in-depth feedback analysis process of the student management unit is as follows:
acquiring the number of students corresponding to the number of non-compliance risks in a time threshold, constructing a set A of the number of students corresponding to the number of non-compliance risks, marking students corresponding to a subset in the set A as abnormal students m, m being a natural number larger than one, marking the duration of one week of history as analysis duration, acquiring the number of non-compliance risks of each abnormal student in the same subject in the analysis duration, marking the number as BCmv, v referring to the number of courses in the same subject, acquiring the average number of non-compliance risks PBm of each abnormal student in the analysis duration by abnormality, and comparing the average number of non-compliance risks PBm with a preset average non-compliance risk threshold value recorded and stored in the average number of non-compliance risks PBm in the analysis duration:
if the average non-compliance risk number PBm is smaller than or equal to a preset average non-compliance risk number threshold, no signal is generated;
if the average number of risk of non-compliance PBm is greater than the preset average number of risk of non-compliance threshold, generating a negotiation signal.
The beneficial effects of the invention are as follows:
according to the invention, interaction data of teachers are collected and classroom interaction atmosphere evaluation analysis is carried out so as to know teaching conditions of the teachers, so that the teaching modes of the teachers can be optimized in time, meanwhile, the teaching quality of the teachers can be improved, and the learning enthusiasm conditions of students can be judged by collecting behavior data of the students and carrying out learning enthusiasm evaluation analysis so as to carry out data support on the teaching quality;
the invention also evaluates and analyzes the teaching quality from two angles of teachers and students, improves the data basis for teaching quality evaluation on one hand, and is beneficial to improving the accuracy and comprehensiveness of analysis results on the other hand, and is beneficial to comprehensively judging the teaching quality through a combined feedback evaluation analysis mode, carrying out teaching adjustment timely according to the evaluation results, and in addition, carrying out deep analysis on the behavior data of the students to judge the learning behavior condition of the students in the same discipline, so that management education is carried out on the students through a feedback mode, and the learning enthusiasm of the students is improved.
Drawings
The invention is further described below with reference to the accompanying drawings;
FIG. 1 is a flow chart of the system of the present invention;
FIG. 2 is a partial analysis of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
Referring to fig. 1 to 2, the invention discloses a teaching quality assessment system based on classroom student behavior analysis, which comprises an assessment center, an interactive analysis unit, an active learning unit, an early warning display unit, a comprehensive quality analysis unit and a student management unit, wherein the assessment center is in unidirectional communication connection with the interactive analysis unit and the active learning unit, the interactive analysis unit and the active learning unit are in unidirectional communication connection with the comprehensive quality analysis unit, the interactive analysis unit is in unidirectional communication connection with the early warning display unit, the active learning unit is in unidirectional communication connection with the student management unit, and the student management unit and the comprehensive quality analysis unit are in unidirectional communication connection with the early warning display unit;
after the assessment center generates a management instruction, the management instruction is sent to the interaction analysis unit and the active learning unit, after the management instruction is received by the interaction analysis unit, interaction data of teachers in class are immediately collected, the interaction data comprise knowledge point input values, question times and teaching sound decibel values, and classroom interaction atmosphere assessment analysis is carried out on the interaction data so as to know teaching conditions of the teachers, and the specific classroom interaction atmosphere assessment analysis process is as follows:
acquiring the duration from the moment of starting to finish a lesson of a teacher, marking the duration as a time threshold, acquiring lesson-taking audio content of the teacher in the classroom in the time threshold, extracting features from the lesson-taking audio content, acquiring a knowledge point input value ZS of the teacher in the time threshold, wherein the knowledge point input value ZS refers to the sum of the textbook explanation duration, the knowledge point related question duration and the example explanation duration of the knowledge point, simultaneously acquiring the question number of the teacher in the classroom in the time threshold, marking the question number as a natural number which is greater than zero, acquiring the number of students corresponding to the question number in the time threshold as an interaction value HDi, taking the question number as an X-axis, establishing a rectangular coordinate system by taking the interaction value as a Y-axis, drawing an interaction value curve in a way of the interaction value, and acquiring an interaction trend value HQ from the interaction value curve, wherein the interaction trend value HQ is an influence parameter reflecting interaction on the way of the teacher, and the interaction between the students and the teacher is better when the number of the interaction trend HQ is larger;
dividing a time threshold into o sub-time nodes, wherein o is a natural number greater than zero, marking the number of rows in a teacher as k, wherein k is a natural number greater than one, acquiring a teaching sound decibel value JFok heard by each row in each sub-time node, acquiring a minimum value JFokmin of the teaching sound decibel value in each sub-time node, marking the minimum value JFokmin as a teaching decibel value SKo, comparing the teaching decibel value SKo with a preset teaching decibel value threshold, and if the teaching decibel value SKo is smaller than or equal to the preset teaching decibel value threshold, marking the ratio of the number of sub-time nodes corresponding to the teaching decibel value SKo smaller than or equal to the preset teaching decibel value threshold to the total sub-time node number as a teaching risk value, and marking the ratio as SF, wherein the teaching risk value is an influence parameter reflecting the receiving strength of a student knowledge point;
according to the formulaObtaining an interactive atmosphere assessment coefficient, wherein a1, a2 and a3 are respectively preset weight coefficients of a knowledge point input value, an interactive trend value and a teaching risk value, a1, a2 and a3 are positive numbers larger than zero, a4 is a preset compensation factor coefficient, the value is 1.548, H is an interactive atmosphere assessment coefficient, the coefficient is a specific numerical value obtained by quantifying each parameter, the subsequent comparison is convenient, as long as the proportional relation between the parameter and the quantified numerical value is not affected, and the interactive atmosphere assessment coefficient H is compared with a preset interactive atmosphere assessment coefficient threshold value which is recorded and stored in the interactive atmosphere assessment coefficient H:
if the interaction atmosphere evaluation coefficient H is larger than a preset interaction atmosphere evaluation coefficient threshold value, no signal is generated;
if the interactive atmosphere assessment coefficient H is smaller than or equal to a preset interactive atmosphere assessment coefficient threshold value, generating a risk signal, and sending the risk signal to an early warning display unit, wherein the early warning display unit immediately performs early warning display in a text teaching optimization mode after receiving the risk signal, so that the teaching mode of a teacher is optimized in time, and the teaching quality of the teacher is improved;
the active learning unit immediately collects behavior data of students in class after receiving the management instruction, the behavior data comprises the number of times of low heads of the students and the number of the stranded students, and carries out learning enthusiasm assessment analysis on the behavior data so as to carry out data support on teaching quality, improve the accuracy of analysis results, and the specific learning enthusiasm assessment analysis process is as follows:
extracting features from audio contents of a lesson to obtain the number of times of low head of a student in a time threshold, wherein the number of times of low head of the student refers to the number of times that an included angle formed by a student face and a lesson desk surface in a blackboard writing period and a blackboard explaining period is smaller than a preset included angle number, the number of times of low head of the student is compared with a preset threshold value of the number of times of low head of the student, if the number of times of low head of the student is larger than the preset threshold value of the number of times of low head of the student, the part of the number of times of low head of the student larger than the preset threshold value of the number of times of low head of the student is marked as an irregular risk number, the ratio of the number of students corresponding to the irregular risk number of times of the student in the time threshold is obtained and marked as a learning low-class ratio, and the mark is marked as XD, and the learning low-class ratio XD is required to be explained is an influence parameter reflecting learning enthusiasm of the student in the classroom;
obtaining the number of stranded students in a classroom within a time threshold, further obtaining the ratio of the number of stranded students to the total number of students, marking the ratio of the number of stranded students to the total number of students as a learning stranded value XF, wherein the value obtained by subtracting a preset stranded angle degree threshold from the angle degree formed by the face of the student and the desk surface of the classroom is marked as a stranded degree value, obtaining the number of stranded degree values within the time threshold, marking the number as an abnormal constant, comparing the abnormal constant with a preset abnormal constant threshold, and marking the student corresponding to the abnormal constant larger than the preset abnormal constant threshold as the stranded student if the abnormal constant is larger than the preset abnormal constant threshold;
according to the formulaObtaining learning positive risk assessment rates, wherein alpha and beta are preset scale factor coefficients of learning low-vague ratio and learning trapping value respectively, the scale factor coefficients are used for correcting deviation of various parameters in a formula calculation process, so that calculation results are more accurate, epsilon is a preset correction coefficient, alpha, beta and epsilon are natural numbers larger than zero, J is the learning positive risk assessment rate, and the learning positive risk assessment rate J is compared with a preset learning positive risk assessment rate threshold value recorded and stored in the learning positive risk assessment rate J:
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is smaller than one, no signal is generated;
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is greater than or equal to one, generating a low-vage signal, and sending the low-vage signal to the comprehensive quality analysis unit and the student management unit;
the comprehensive quality analysis unit immediately invokes the interactive atmosphere assessment coefficient H from the interactive analysis unit and the learning active risk assessment rate J from the active learning unit after receiving the low-camouflage signal, and performs combined feedback assessment analysis on the interactive atmosphere assessment coefficient H and the learning active risk assessment rate J so as to comprehensively judge the teaching quality condition, namely, assess the teaching quality from two angles of a teacher and a student, wherein the specific combined feedback assessment analysis process is as follows:
acquiring an interactive atmosphere evaluation coefficient H and a learning active risk evaluation rate J in a time threshold;
according to the formulaObtaining a teaching quality evaluation coefficient, wherein b1 and b2 are respectively preset proportional coefficients of an interaction atmosphere evaluation coefficient and a learning positive risk evaluation rate, b1 and b2 are positive numbers larger than zero, b3 is a preset correction factor coefficient, the value is 2.532, Z is the teaching quality evaluation coefficient, and the teaching quality evaluation coefficient Z and a preset teaching quality evaluation coefficient threshold value recorded and stored in the teaching quality evaluation coefficient Z are subjected to difference analysis:
if the teaching quality evaluation coefficient Z minus the preset teaching quality evaluation coefficient threshold is a positive number, generating a qualified signal;
if the teaching quality evaluation coefficient Z is minus the preset teaching quality evaluation coefficient threshold value, generating an unqualified signal, sending the qualified signal and the unqualified signal to an early warning display unit, immediately making corresponding preset early warning operation of the qualified signal and the unqualified signal after receiving the qualified signal and the unqualified signal by the early warning display unit so as to make teaching adjustment timely, and evaluating the teaching quality from two angles of a teacher and a student, wherein on one hand, the teaching quality evaluation is improved by a data base, and on the other hand, the accuracy and the comprehensiveness of an analysis result are improved;
the student management unit immediately retrieves the behavior data from the active learning unit after receiving the low-quiz signal, and performs deep feedback analysis on the operation data so as to perform management education on the students in a feedback mode, so that the learning enthusiasm of the students is improved, and the specific deep feedback analysis process is as follows:
acquiring the number of students corresponding to the number of non-compliance risks in a time threshold, constructing a set A of the number of students corresponding to the number of non-compliance risks, marking students corresponding to a subset in the set A as abnormal students m, m being a natural number larger than one, marking the duration of one week of history as analysis duration, acquiring the number of non-compliance risks of each abnormal student in the same subject in the analysis duration, marking the number as BCmv, v referring to the number of courses in the same subject, acquiring the average number of non-compliance risks PBm of each abnormal student in the analysis duration by abnormality, and comparing the average number of non-compliance risks PBm with a preset average non-compliance risk threshold value recorded and stored in the average number of non-compliance risks PBm in the analysis duration:
if the average non-compliance risk number PBm is smaller than or equal to a preset average non-compliance risk number threshold, no signal is generated;
if the average risk of non-compliance PBm is greater than the preset average risk of non-compliance threshold, generating an appointment signal, sending the appointment signal to an early warning display unit, and immediately displaying the name of the student corresponding to the appointment signal by the early warning display unit after receiving the appointment signal, so that management education is conducted on the student in a feedback mode, and the learning enthusiasm of the student is improved.
In summary, the interactive data of the teacher are collected, the assessment analysis of the classroom interaction atmosphere is performed so as to know the teaching condition of the teacher, the optimization processing of the teaching mode of the teacher is facilitated in time, meanwhile, the improvement of the teaching quality of the teacher is facilitated, the study enthusiasm condition of the student is judged by collecting the behavior data of the student and performing study enthusiasm assessment analysis, so that the data support is performed on the teaching quality, the assessment analysis is performed on the teaching quality from two angles of the teacher and the student, on the one hand, the data basis is improved for the teaching quality assessment, on the other hand, the accuracy and the comprehensiveness of the analysis result are facilitated, the comprehensive judgment of the teaching quality is facilitated, the teaching adjustment is performed in time according to the assessment result, in addition, the deep analysis of the behavior data of the student is also facilitated, the study behavior condition of the student in the same subject is judged, and the study enthusiasm of the student is managed through the feedback mode, so that the study enthusiasm of the student is improved.
The size of the threshold is set for ease of comparison, and regarding the size of the threshold, the number of cardinalities is set for each set of sample data depending on how many sample data are and the person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected. The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.
Claims (6)
1. The teaching quality assessment system based on classroom student behavior analysis is characterized by comprising an assessment center, an interaction analysis unit, an active learning unit, an early warning display unit, a comprehensive quality analysis unit and a student management unit;
after the assessment center generates a management instruction, the management instruction is sent to an interaction analysis unit and an active learning unit, the interaction analysis unit immediately collects interaction data of teachers in class after receiving the management instruction, the interaction data comprises knowledge point input values, questioning times and teaching sound decibel values, and the interaction data is subjected to classroom interaction atmosphere assessment analysis and sent to an early warning display unit;
the active learning unit immediately collects behavior data of students in class after receiving the management instruction, wherein the behavior data comprises the number of times of low head of the students and the number of the students trapped, carries out learning enthusiasm assessment analysis on the behavior data, and sends the obtained low-camouflage signal to the comprehensive quality analysis unit and the student management unit;
the comprehensive quality analysis unit immediately invokes the interactive atmosphere assessment coefficient H from the interactive analysis unit and the learning active risk assessment rate J from the active learning unit after receiving the low-camouflage signal, performs combined feedback assessment analysis on the interactive atmosphere assessment coefficient H and the learning active risk assessment rate J, and sends the obtained qualified signals and unqualified signals to the early warning display unit;
and the student management unit immediately invokes the behavior data from the active learning unit after receiving the low-quiz signal, performs deep feedback analysis on the operation data, and sends the obtained talking signal to the early warning display unit.
2. The teaching quality assessment system based on classroom student behavior analysis according to claim 1, wherein the classroom interaction atmosphere assessment analysis process of the interaction analysis unit is as follows:
acquiring the duration from the moment of starting to the moment of ending the lesson of a teacher, marking the duration as a time threshold, acquiring lesson-taking audio content of the teacher in the classroom in the time threshold, extracting features from the lesson-taking audio content, acquiring a knowledge point input value ZS of the teacher in the time threshold, wherein the knowledge point input value ZS refers to the sum of the textbook explanation duration, the knowledge point related question duration and the example explanation duration of the knowledge point, simultaneously acquiring the question number of the teacher in the classroom in the time threshold, marking the question number as i, i as a natural number larger than zero, acquiring the number of students corresponding to the question number in the time threshold, marking the number as an interaction value HDi, taking the question number as an X axis, establishing a rectangular coordinate system by taking the interaction value as a Y axis, drawing an interaction value curve in a point drawing manner, and acquiring an interaction trend value HQ from the interaction value curve;
dividing a time threshold into o sub-time nodes, wherein o is a natural number greater than zero, marking the number of rows in a teacher as k, and k is a natural number greater than one, acquiring a teaching sound decibel value JFok heard by each row in each sub-time node, acquiring a minimum value JFokmin of the teaching sound decibel value in each sub-time node, marking the minimum value JFokmin as a teaching decibel value SKo, comparing the teaching decibel value SKo with a preset teaching decibel value threshold, and if the teaching decibel value SKo is smaller than or equal to the preset teaching decibel value threshold, marking the ratio of the number of sub-time nodes corresponding to the teaching decibel value SKo smaller than or equal to the preset teaching decibel value threshold to the total sub-time node number as a teaching risk value SF, and carrying out formulated analysis on the interaction trend value HQ and the teaching risk value SF.
3. The teaching quality assessment system based on classroom student behavior analysis according to claim 2, wherein the formulation analysis process of the interactive analysis unit is as follows:
according to the formulaObtaining an interactive atmosphere evaluation coefficient, wherein a1, a2 and a3 are respectively preset weight coefficients of a knowledge point input value, an interactive trend value and a teaching risk value, a1, a2 and a3 are positive numbers larger than zero, a4 is a preset compensation factor coefficient, the value is 1.548, H is an interactive atmosphere evaluation coefficient, and the interactive atmosphere evaluation coefficient H is compared with a preset interactive atmosphere evaluation coefficient threshold value recorded and stored in the interactive atmosphere evaluation coefficient H:
if the interaction atmosphere evaluation coefficient H is larger than a preset interaction atmosphere evaluation coefficient threshold value, no signal is generated;
and if the interactive atmosphere evaluation coefficient H is smaller than or equal to a preset interactive atmosphere evaluation coefficient threshold value, generating a risk signal.
4. The teaching quality assessment system based on classroom student behavior analysis according to claim 1, wherein the learning aggressiveness assessment analysis process of the active learning unit is as follows:
step one: extracting features from audio contents of lessons to obtain the number of times of low head of students in a time threshold, wherein the number of times of low head of students refers to the number of times that the number of degrees of included angles formed by the faces of the students and the desk surface of the lessons in a blackboard writing period and a blackboard explaining period of a teacher are smaller than a preset included angle number, comparing the number of times of low head of the students with a preset threshold of times of low head of the students, if the number of times of low head of the students is larger than the preset threshold of times of low head of the students, marking the part of the number of times of low head of the students larger than the preset threshold of times of low head of the students as the number of non-compliance risks, and obtaining the ratio of the number of students corresponding to the number of non-compliance risks to the total students in the time threshold as learning low-vaginoscope XD;
step two: obtaining the number of stranded students in a classroom within a time threshold, further obtaining the ratio of the number of stranded students to the total number of students, marking the ratio of the number of stranded students to the total number of students as a learning stranded value XF, wherein the value obtained by subtracting a preset stranded angle degree threshold from the angle degree formed by the face of the student and the desk surface of the classroom is marked as a stranded degree value, obtaining the number of stranded degree values within the time threshold, marking the number as an abnormal constant, comparing the abnormal constant with a preset abnormal constant threshold, and marking the student corresponding to the abnormal constant larger than the preset abnormal constant threshold as the stranded student if the abnormal constant is larger than the preset abnormal constant threshold;
step three: obtaining a learning active risk assessment rate J according to a formula, and comparing the learning active risk assessment rate J with a preset learning active risk assessment rate threshold value which is input and stored in the learning active risk assessment rate J:
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is smaller than one, no signal is generated;
if the ratio of the learning active risk assessment rate J to the preset learning active risk assessment rate threshold is greater than or equal to one, generating a low-fans signal.
5. The teaching quality assessment system based on classroom student behavior analysis according to claim 1, wherein the combined feedback assessment analysis process of the comprehensive quality analysis unit is as follows:
acquiring an interactive atmosphere evaluation coefficient H and a learning active risk evaluation rate J in a time threshold;
according to the formulaObtain the teaching quality evaluationEstimating coefficients, wherein b1 and b2 are preset proportional coefficients of an interactive atmosphere estimating coefficient and a learning positive risk estimating rate respectively, b1 and b2 are positive numbers larger than zero, b3 is a preset correction factor coefficient, the value is 2.532, Z is a teaching quality estimating coefficient, and the teaching quality estimating coefficient Z and a preset teaching quality estimating coefficient threshold value recorded and stored in the teaching quality estimating coefficient Z are subjected to difference analysis:
if the teaching quality evaluation coefficient Z minus the preset teaching quality evaluation coefficient threshold is a positive number, generating a qualified signal;
and if the teaching quality evaluation coefficient Z minus the preset teaching quality evaluation coefficient threshold is a negative number, generating a disqualified signal.
6. The teaching quality assessment system based on classroom student behavior analysis according to claim 1, wherein the in-depth feedback analysis process of the student management unit is as follows: acquiring the number of students corresponding to the number of non-compliance risks in a time threshold, constructing a set A of the number of students corresponding to the number of non-compliance risks, marking students corresponding to a subset in the set A as abnormal students m, m being a natural number larger than one, marking the duration of one week of history as analysis duration, acquiring the number of non-compliance risks of each abnormal student in the same subject in the analysis duration, marking the number as BCmv, v referring to the number of courses in the same subject, acquiring the average number of non-compliance risks PBm of each abnormal student in the analysis duration by abnormality, and comparing the average number of non-compliance risks PBm with a preset average non-compliance risk threshold value recorded and stored in the average number of non-compliance risks PBm in the analysis duration:
if the average non-compliance risk number PBm is smaller than or equal to a preset average non-compliance risk number threshold, no signal is generated;
if the average number of risk of non-compliance PBm is greater than the preset average number of risk of non-compliance threshold, generating a negotiation signal.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117201742A (en) * | 2023-09-11 | 2023-12-08 | 江苏财经职业技术学院 | Video monitoring system for intelligent classroom |
| CN117437100A (en) * | 2023-12-21 | 2024-01-23 | 西安优学电子信息技术有限公司 | Micro-class practical training management system based on digital teaching |
| CN117670612A (en) * | 2023-11-29 | 2024-03-08 | 广东省教育研究院 | Learner state evaluation method and system based on virtual reality equipment |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117201742A (en) * | 2023-09-11 | 2023-12-08 | 江苏财经职业技术学院 | Video monitoring system for intelligent classroom |
| CN117670612A (en) * | 2023-11-29 | 2024-03-08 | 广东省教育研究院 | Learner state evaluation method and system based on virtual reality equipment |
| CN117437100A (en) * | 2023-12-21 | 2024-01-23 | 西安优学电子信息技术有限公司 | Micro-class practical training management system based on digital teaching |
| CN117437100B (en) * | 2023-12-21 | 2024-04-19 | 西安优学电子信息技术有限公司 | Micro-class practical training management system based on digital teaching |
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