Method and related device for teaching evaluation analysis oriented to achievement orientation

Abstract

The invention discloses an achievement-oriented teaching evaluation analysis method and a related device, and relates to the technical field of data processing, and the method comprises the steps: carrying out the standardization processing of the learning achievement data and learning video data of each teaching object; performing learning performance evaluation based on the standardized learning result data; performing concentration analysis and psychological state analysis based on the standardized learning video data; performing learning individual difference analysis based on the learning performance evaluation result and the psychological state data in combination with learning participation degree analysis; based on the learning performance evaluation result and the psychological state data of course learning in each stage, performing learning condition change analysis on each teaching object; and based on the learning individual difference analysis data and the learning condition change analysis data, teaching improvement analysis is carried out to improve teaching videos and assessment contents. According to the method, the initiative of a teaching object to course learning can be stimulated, and achievement-oriented teaching evaluation analysis achieves an ideal effect.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method and related device for teaching evaluation and analysis oriented to outcome-based education. Background Art

[0002] Outcome-based teaching evaluation is a teaching evaluation system that aims at the learning outcomes of teaching objects. It can effectively evaluate and analyze the abilities of teaching objects to clarify the improvement of teaching plans, thereby improving the learning enthusiasm of teaching objects. In this regard, schools have increasingly attached importance to outcome-based teaching evaluation and analysis. Currently, in outcome-based teaching evaluation and analysis, it is usually through relevant personnel to evaluate the learning performance of teaching objects in course learning, but this method is inefficient and will bring too heavy a workload to relevant personnel. The analysis of the psychological state of teaching objects in course learning is also an important part of teaching evaluation and analysis. Currently, it is also achieved through the subjective judgment of relevant personnel. At the same time, the existing psychological state analysis rarely combines the concentration of teaching objects in course learning, resulting in incomplete and inaccurate analysis of the psychological state of teaching objects, and then affecting the improvement of teaching plans. And in the current teaching evaluation and analysis, most lack the analysis of individual learning differences and the analysis of changes in learning situations, and cannot achieve personalized evaluation and analysis of each teaching object, resulting in the inability to comprehensively and accurately reflect the learning status of teaching objects, which is not conducive to accurately improving teaching plans, and making the outcome-based teaching evaluation and analysis fail to achieve the expected effect. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art. The present invention provides a method and related device for teaching evaluation and analysis oriented to outcome-based education, which can stimulate the initiative of teaching objects in course learning and make the outcome-based teaching evaluation and analysis achieve ideal results.

[0004] To solve the above technical problems, the present invention provides a method for teaching evaluation and analysis oriented to outcome-based education, and the method includes:

[0005] Performing standardization processing on the learning outcome data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning outcome data and standardized learning video data;

[0006] Based on the standardized learning outcome data, performing learning performance evaluation on each stage of course learning of each teaching object to obtain the learning performance evaluation results of each stage of course learning;

[0007] Based on the standardized learning video data, using information fusion to perform concentration analysis and psychological state analysis on each stage of course learning of each teaching object, and obtaining the psychological state data of each teaching object in each stage of course learning;

[0008] Conduct learning individual difference analysis based on the learning performance evaluation results and psychological state data of each teaching object to obtain learning individual difference analysis data;

[0009] Conduct learning situation change analysis based on the learning performance evaluation results and psychological state data of each teaching object to obtain learning situation change analysis data;

[0010] Conduct teaching improvement analysis based on the learning individual difference analysis data and learning situation change analysis data to obtain teaching improvement data, and adjust the original teaching video content and assessment content based on the teaching improvement data.

[0011] Optionally, the standardization processing of the learning achievement data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning achievement data and standardized learning video data includes:

[0012] Perform format unification processing and standardization processing on the learning achievement data and learning video data to obtain standardized learning achievement data and standardized learning video data.

[0013] Optionally, the learning performance evaluation of each stage of course learning of each teaching object based on the standardized learning achievement data to obtain the learning performance evaluation results of each stage of course learning includes:

[0014] Generate a learning evaluation result matrix for each teaching object based on the standardized learning achievement data;

[0015] Evaluate the knowledge mastery degree of each teaching object in each stage of course learning based on the target result matrix generated by singular value decomposition of the learning evaluation result matrix to obtain the corresponding first knowledge mastery degree data, and determine the target knowledge mastery degree data based on the first knowledge mastery degree data and the second knowledge mastery degree data generated by the knowledge tracking model;

[0016] Evaluate the learning performance of each teaching object in each stage of course learning using an evaluation item combination based on the standardized learning achievement data, target knowledge mastery degree data, and problem-solving ability evaluation data generated by the learning evaluation result matrix to obtain the corresponding learning performance evaluation results.

[0017] Optionally, the concentration analysis and psychological state analysis of each stage of course learning of each teaching object based on the standardized learning video data using information fusion to obtain the psychological state data of each teaching object in each stage of course learning includes:

[0018] The head pose estimation model based on multi-scale hourglass attention and multi-class multi-regression loss uses the standardized learning video data to estimate the head poses of each teaching object, obtaining head pose information;

[0019] Based on the pose Euler angles, use the head pose information to estimate the line of sight of each teaching object, obtaining line of sight estimation information;

[0020] Based on the D-S evidence fusion theory, fuse the head pose information, line of sight estimation information, and facial expression information identified from the standardized learning video data to obtain target fusion information;

[0021] Based on the target fusion information, perform concentration analysis on the course learning of each teaching object at each stage, obtaining concentration data for the course learning at each stage;

[0022] Based on the concentration data, pleasure information, and negative emotion information obtained from the facial expression information, perform psychological state analysis to obtain the psychological state data of each teaching object in the course learning at each stage.

[0023] Optionally, perform learning individual difference analysis based on the learning performance evaluation results and psychological state data of each teaching object, obtaining learning individual difference analysis data, including:

[0024] Analyze the interest data and degree of attention of each teaching object in the course learning at each stage based on the learning performance evaluation results and psychological state data;

[0025] Based on the standardized learning video data, perform interactive body posture feature analysis on the teaching objects, obtaining interactive body posture feature information, and based on the interactive body posture feature information, combine with the tension degree analysis to perform interactive type analysis, obtaining interactive type information;

[0026] Analyze the participation degree of each teaching object in the course learning at each stage based on the interactive type information, and perform learning individual difference analysis on the teaching objects based on the interest data, degree of attention, participation degree, learning performance evaluation results, and psychological state data, obtaining learning individual difference analysis data.

[0027] Optionally, perform learning situation change analysis based on the learning performance evaluation results and psychological state data of each teaching object, obtaining learning situation change analysis data, including:

[0028] Construct a data matrix for evaluating the relationship between the performance and grades of teaching objects based on the learning performance evaluation results of the course learning at each stage, and analyze the learning stability state information of each teaching object based on the data matrix;

[0029] Analyze the learning psychological change information of each teaching object based on the learning psychological state data of the course learning at each stage.

[0030] Based on the learning psychological change information and learning stable state information, use the association rule algorithm to analyze the learning situation changes of each teaching object, and obtain the learning situation change analysis data.

[0031] Optionally, the teaching improvement analysis is performed based on the learning individual difference analysis data and the learning situation change analysis data to obtain the teaching improvement data, including:

[0032] Perform a cognitive load analysis of the teaching object based on the learning individual difference analysis data and the learning situation change analysis data to obtain the corresponding cognitive load data;

[0033] Based on the cognitive load data and combined with the learning individual difference analysis data, perform a learning blind spot analysis of the teaching object to obtain the learning blind spot analysis data;

[0034] Perform a teaching improvement analysis based on the cognitive load data and the learning blind spot analysis data to obtain the teaching improvement data.

[0035] In addition, the present invention also provides a device for outcome-based teaching evaluation analysis, and the device includes:

[0036] Data standardization module: used to perform standardization processing on the learning outcome data and learning video data generated by each teaching object in each stage of course learning to obtain the standardized learning outcome data and standardized learning video data;

[0037] Learning performance evaluation module: used to perform learning performance evaluation on each stage of course learning of each teaching object based on the standardized learning outcome data to obtain the learning performance evaluation results of each stage of course learning;

[0038] Mental state analysis module: used to perform concentration analysis and mental state analysis on each stage of course learning of each teaching object based on the standardized learning video data by using information fusion to obtain the mental state data of each teaching object in each stage of course learning;

[0039] Learning individual difference analysis module: used to perform learning individual difference analysis based on the learning performance evaluation results and mental state data of each teaching object to obtain the learning individual difference analysis data;

[0040] Learning situation change analysis module: used to perform learning situation change analysis based on the learning performance evaluation results and mental state data of each teaching object to obtain the learning situation change analysis data;

[0041] Teaching improvement analysis module: used to perform teaching improvement analysis based on the learning individual difference analysis data and the learning situation change analysis data to obtain the teaching improvement data, and adjust the original teaching video content and assessment content based on the teaching improvement data.

[0042] In addition, the present invention further provides an electronic device, which includes a processor and a memory. The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the above-mentioned outcome-based teaching evaluation analysis method.

[0043] In addition, the present invention further provides a computer-readable storage medium, which stores computer instructions. When the computer instructions run on an electronic device, the electronic device is enabled to execute the above-mentioned outcome-based teaching evaluation analysis method.

[0044] In the embodiments of the present invention, by using evaluation item combinations to evaluate the learning performance of each teaching object in each stage of course learning based on standardized learning outcome data, target knowledge point mastery degree data generated from the standardized learning outcome data, and problem-solving ability evaluation data, a more accurate learning performance evaluation result can be obtained, the efficiency of learning performance evaluation can be improved, and excessive human costs do not need to be invested. By using multi-scale hourglass attention to analyze the concentration of each teaching object in each stage of course learning based on standardized learning video data, the reliability of concentration analysis can be improved. Based on the concentration data, psychological state analysis is performed to obtain the psychological state data of each teaching object in each stage of course learning, making the obtained psychological state data more in line with the actual situation of the teaching object and being able to better reflect the psychological situation of the teaching object during the learning process. By combining the learning performance evaluation results and psychological state data of each teaching object in each stage of course learning with learning participation analysis to perform learning individual difference analysis of the teaching object, the individual differences of the teaching object can be taken into account, and personalized learning assessment analysis can be realized. By performing learning situation change analysis of each teaching object based on the learning performance evaluation results and psychological state data of each stage of course learning, the changes and development trends of the teaching object during the learning process can be reflected. By using cognitive load analysis for teaching improvement analysis based on learning individual difference analysis data and learning situation change analysis data to improve the original teaching plan, the reliability of teaching improvement analysis can be improved, the original teaching plan can be improved in a targeted manner, and outcome-based teaching evaluation analysis with the teaching object as the main body can be better realized. The improved teaching plan in this way can better stimulate the initiative of the teaching object in course learning, thereby effectively improving various abilities of the teaching object and making the outcome-based teaching evaluation analysis reach a more ideal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 is a schematic flowchart of a method for outcome-based teaching evaluation and analysis in an embodiment of the present invention;

[0047] Figure 2 is a schematic flowchart of a method for outcome-based teaching evaluation and analysis in another embodiment of the present invention;

[0048] Figure 3 is a schematic diagram of the structural composition of a device for outcome-based teaching evaluation and analysis in an embodiment of the present invention;

[0049] Figure 4 is a schematic diagram of the structural composition of an electronic device in an embodiment of the present invention. Specific Embodiments

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a method for outcome-based teaching evaluation and analysis in an embodiment of the present invention. The method includes:

[0053] S11: Standardize the learning outcome data and learning video data generated by each teaching object in the course learning at each stage to obtain standardized learning outcome data and standardized learning video data;

[0054] In the specific implementation process of the present invention, the step of standardizing the learning outcome data and learning video data generated by each teaching object in the course learning at each stage to obtain standardized learning outcome data and standardized learning video data includes: performing format unification processing and standardization processing on the learning outcome data and learning video data to obtain standardized learning outcome data and standardized learning video data.

[0055] Specifically, obtain the learning achievement data and learning video data generated by each teaching object during the learning of each stage of the course. The teaching object can be a student, and the course can be an ideological and political course. The learning of this course is divided into different stages. The learning achievement data includes the assessment data and learning process data of the student's learning of different stages of the course. The learning process data such as the learning duration, problem-solving duration, and learning frequency of each knowledge point in the learning of different course stages, etc. The learning video data is the classroom video data of the student during the learning of different stages of the course, including images and audio. Perform unified format processing on the learning achievement data and learning video data, that is, perform unified format conversion on the learning achievement data and learning video data for subsequent data analysis, and obtain the learning achievement data and learning video data after unified format processing. Perform standardization processing on the learning achievement data and learning video data after unified format processing, and perform data standardization processing through a preset graph structure combined with a preset standardization protocol to obtain standardized learning achievement data and standardized learning video data.

[0056] S12: Based on the standardized learning achievement data, conduct a learning performance evaluation on the learning of each stage of the course for each teaching object to obtain the learning performance evaluation results of each stage of the course learning;

[0057] In the specific implementation process of the present invention, the conducting a learning performance evaluation on the learning of each stage of the course for each teaching object based on the standardized learning achievement data to obtain the learning performance evaluation results of each stage of the course learning includes: generating a learning evaluation result matrix for each teaching object based on the standardized learning achievement data; evaluating the mastery degree of knowledge points in the learning of each stage of the course for each teaching object based on the target result matrix generated by the singular value decomposition of the learning evaluation result matrix, obtaining the corresponding first knowledge point mastery degree data, and determining the target knowledge point mastery degree data based on the first knowledge point mastery degree data and the second knowledge point mastery degree data generated by the knowledge tracking model; using the evaluation item combination to conduct a learning performance evaluation on the learning of each stage of the course for each teaching object based on the standardized learning achievement data, the target knowledge point mastery degree data, and the problem-solving ability evaluation data generated by the learning evaluation result matrix, and obtaining the corresponding learning performance evaluation results.

[0058] Specifically, a learning assessment result matrix for each teaching object is generated based on the standardized learning outcome data. The assessment data in the standardized learning outcome data is used as the elements of the result matrix to form an initial result matrix, and the learning process data in the standardized learning outcome data is used as the elements of the process matrix to form an initial process matrix. The initial result matrix and the initial process matrix constitute the learning assessment result matrix, and the singular value decomposition is performed on the learning assessment result matrix, that is, the singular value decomposition is performed on the initial result matrix in the learning assessment result matrix to obtain the first result matrix, the second result matrix, and the third result matrix. The first result matrix and the third result matrix are orthonormal unitary matrices, and the second result matrix is a diagonal matrix, that is, the target result matrix is obtained. Based on the target result matrix, the knowledge mastery degree of each teaching object in each stage of course learning is evaluated. It is judged whether the first row of the third result matrix in the target result matrix is greater than or equal to zero. If so, the value of the first column of the first result matrix is used as the knowledge mastery ability benchmark score. If not, the value of the first column of the first result matrix is taken as the opposite number as the knowledge mastery ability benchmark score. According to the knowledge mastery ability benchmark score and the standard deviation normalization method, the knowledge mastery degree is evaluated to obtain the corresponding first knowledge mastery degree data, and based on the first knowledge mastery degree data and the second knowledge mastery degree data generated by the knowledge tracking model, the target knowledge mastery degree data is determined. Based on the standardized learning outcome data, the knowledge tracking model is used to evaluate the knowledge mastery degree of each teaching object in each stage of course learning. The knowledge tracking model uses a long short-term memory network. The standardized learning outcome data is input into the knowledge tracking model for knowledge mastery degree evaluation to obtain the corresponding second knowledge mastery degree data. The two can be combined with their corresponding weights for calculation to obtain the final target knowledge mastery degree data, ensuring the objectivity and accuracy of the knowledge mastery degree evaluation and avoiding the limitations brought by single evaluation. Based on the learning assessment result matrix, the problem-solving ability of each teaching object in each stage of course learning is evaluated. The problem-solving ability benchmark score is generated according to the process matrix in the learning assessment result matrix. The benchmark score can include the benchmark learning frequency and the benchmark problem-solving duration, etc. The problem-solving ability is evaluated according to the problem-solving ability benchmark score to obtain the problem-solving ability evaluation data.Based on the standardized learning outcome data, the mastery degree data of target knowledge points, and the problem-solving ability evaluation data, use the evaluation item combinations to evaluate the learning performance of each teaching object in each stage of course learning. Take the assessment data and learning process data in the standardized learning outcome data, the mastery degree data of target knowledge points, and the problem-solving ability evaluation data as evaluation items, obtain their corresponding evaluation scores, arrange the assessment data and learning process data in the standardized learning outcome data, the mastery degree data of target knowledge points, and the problem-solving ability evaluation data in combination according to the preset combination rules to obtain several evaluation item combinations. Determine the corresponding learning performance evaluation grade scores according to the evaluation scores of each evaluation item in each evaluation item combination using the mapping relationship, and conduct a comprehensive learning performance evaluation based on each learning performance evaluation grade score to obtain the corresponding learning performance evaluation result.

[0059] S13: Based on the standardized learning video data, use information fusion to analyze the concentration and mental state of each teaching object in each stage of course learning, and obtain the mental state data of each teaching object in each stage of course learning;

[0060] In the specific implementation process of the present invention, the step of using information fusion based on the standardized learning video data to analyze the concentration and mental state of each teaching object in each stage of course learning, and obtaining the mental state data of each teaching object in each stage of course learning includes: using the standardized learning video data to estimate the head pose of each teaching object by a head pose estimation model based on multi-scale hourglass attention and multi-class multi-regression loss to obtain head pose information; using the head pose information to estimate the line of sight of each teaching object based on the pose Euler angle to obtain line of sight estimation information; fusing the head pose information, the line of sight estimation information, and the facial expression information identified from the standardized learning video data based on the D-S evidence fusion theory to obtain target fusion information; analyzing the concentration of each teaching object in each stage of course learning based on the target fusion information to obtain the concentration data of each stage of course learning; and analyzing the mental state based on the concentration data, the pleasure information and the negative emotion information obtained from the facial expression information to obtain the mental state data of each teaching object in each stage of course learning.

[0061] Specifically, the head pose estimation model based on multi-scale hourglass attention and multi-class multi-regression loss uses the standardized learning video data to perform head pose estimation for each teaching object. The face of each teaching object is located in the standardized learning video data to obtain the face image of each teaching object. The face image is input into the head pose estimation model for head pose estimation processing. The head pose estimation model uses a hybrid-scale attention network and ResNet50 as the backbone network, and multi-scale hourglass attention blocks are added to its four residual layers to aggregate more spatial and pose feature information. A multi-class multi-regression loss is added to the hybrid-scale attention network to make up for the error caused by the single-class single-regression loss in the hourglass attention block with a more fine-grained classification. Each fully connected layer in the head pose estimation model represents a different classification scale and outputs the corresponding classification result and the offset factor for expected fine-tuning. The cross-entropy and mean square error methods are used to calculate its classification loss and regression loss respectively. Multiple classification losses and regression losses are combined into a total loss. There is such a combined loss for each angle and they share the same feature map of the hybrid-scale attention network. The multi-scale hourglass attention block includes several convolutional blocks. First, a bottom-up network structure is constructed, and then a top-down network structure that is mirror-symmetric to it is connected to form an hourglass shape. The hourglass attention block inputs the output feature map into the convolutional block for the first downsampling process. In the convolutional block, after mirror padding, a 3×3 convolutional kernel is used for downsampling to obtain the first output feature map. The output feature map is processed through a convolutional block that does not change the size of the feature map to obtain the second output feature map. Then, the resolution of the feature map is increased through the nearest neighbor sampling method. The second output feature map after the resolution increase process and the second output feature map are added bit by bit and then upsampled. The method of fusing specific shallow features and abstract deep features is used to reduce the loss of pose feature information in the continuous convolutional downsampling operation, and more pose features of the original image are retained, thereby obtaining the head pose information. Based on the pose Euler angles, the head pose information is used to perform line-of-sight estimation for each teaching object. A three-dimensional coordinate system is constructed in the teaching space. In the three-dimensional coordinate system, the yaw angle and pitch angle of the face of each teaching object are obtained, and the line-of-sight landing position of the teaching object is calculated based on the yaw angle and pitch angle, that is, the line-of-sight estimation information is obtained. Facial expression recognition is performed based on the standardized learning video data. A deep residual network is used for facial expression recognition to obtain facial expression information, and the head pose information, line-of-sight estimation information, and facial expression information are fused based on the D-S evidence fusion theory. The evidence body and the identification framework are set according to the head pose information, line-of-sight estimation information, and facial expression information. The identification framework contains several events. The self-determination degree of the evidence body is calculated according to the basic probability assignment function and the identification framework. The calculation expression of the self-determination degree is:

[0062]

[0063] Among them, d is the self-determination degree, M is the number of events in the identification framework, j is the serial number of the event in the identification framework, A is the event in the identification framework, and m(A j ) is the basic probability assignment function. Calculate the Lance distance of the evidence body, construct a support degree matrix according to the Lance distance of the evidence body, construct a credibility matrix according to the support degree matrix and the self-determination degree, and perform fusion of head pose information, gaze estimation information, and facial expression information according to the credibility matrix to obtain target fusion information. Through the fusion of information, the accuracy of learning concentration analysis can be improved. Based on the target fusion information, use the concentration evaluation model to perform concentration analysis on the course learning of each teaching object at each stage to obtain the concentration data of the course learning at each stage. Based on the facial expression information, perform analysis of the pleasure degree and negative emotions of each teaching object to obtain the corresponding pleasure degree information and negative emotion information. Based on the concentration data, pleasure degree information, and negative emotion information, perform psychological state analysis to obtain the psychological state data of each teaching object in the course learning at each stage, that is, analyze the positive psychology and resistance psychology of each teaching object in the course learning at each stage according to the concentration data, pleasure degree learning, and negative emotion information, so as to know the preferences, interests, and mental concentration of each teaching object in the course learning at different stages.

[0064] S14: Perform learning individual difference analysis based on the learning performance evaluation results and psychological state data of each teaching object to obtain learning individual difference analysis data;

[0065] In the specific implementation process of the present invention, the performing learning individual difference analysis based on the learning performance evaluation results and psychological state data of each teaching object to obtain learning individual difference analysis data includes: analyzing the interest data and attention degree of each teaching object in the course learning at each stage based on the learning performance evaluation results and psychological state data; performing analysis of the interactive body gesture characteristics of the teaching object based on the standardized learning video data to obtain interactive body gesture characteristic information, and performing interactive type analysis based on the interactive body gesture characteristic information combined with the tension degree analysis to obtain interactive type information; analyzing the participation degree of each teaching object in the course learning at each stage based on the interactive type information, and performing learning individual difference analysis of the teaching object based on the interest data, attention degree, participation degree, learning performance evaluation results, and psychological state data to obtain learning individual difference analysis data.

[0066] Specifically, based on the learning performance evaluation results and psychological state data, analyze the interest data of each teaching object in the course learning at each stage. According to the learning performance evaluation and psychological state data of each teaching object in the course learning at different stages, analyze their interest degrees and interest tendencies in the course learning at each stage, that is, obtain the interest data. Based on the learning performance evaluation results, analyze the degree of attention of each teaching object to the course learning at each stage. According to the learning performance evaluation results, the course completion rate and learning duration of each teaching object can be known, and thus the degree of attention of each teaching object to the course learning at each stage can be known. Based on the standardized learning video data, analyze the interactive body gesture characteristics of the teaching object, that is, identify the action information of the teaching object during teacher-student interaction according to the standardized learning video data, such as raising hands to ask questions and standing up to answer, obtain the interactive body gesture characteristic information, and based on the interactive body gesture characteristic information, combine with the analysis of the tension degree to conduct interactive type analysis. Extract the audio data during teacher-student interaction according to the standardized learning video data, analyze the tension degree according to the speech pause frequency and pause duration in the audio data, and determine the interactive type of the teaching object according to the tension degree and interactive body gesture characteristics. The interactive types include active interaction, normal interaction, passive interaction, etc., and obtain the interactive type information. Based on the interactive type information, analyze the participation degree of each teaching object in the course learning at each stage, count the frequency of the interactive types of each teaching object in the course learning at each stage and the frequency of different psychological states, so as to determine the participation degree in the course learning at each stage, and based on the interest data, attention degree, participation degree, learning performance evaluation results and psychological state data, conduct learning individual difference analysis of the teaching object, that is, analyze the differences between different individuals according to the interest data, attention degree, participation degree, learning performance evaluation results and psychological state data of each teaching object, which can reflect the preferences, attention degrees and learning performance differences of each teaching object in course learning, so as to achieve a more comprehensive individual difference evaluation and obtain learning individual difference analysis data.

[0067] S15: Based on the learning performance evaluation results and psychological state data of each teaching object, conduct analysis of the changes in learning situation, and obtain the analysis data of the changes in learning situation;

[0068] In the specific implementation process of the present invention, the analysis of the changes in learning situation based on the learning performance evaluation results and psychological state data of each teaching object to obtain the analysis data of the changes in learning situation includes: constructing a data matrix for evaluating the relationship between the performance and grades of the teaching object based on the learning performance evaluation results of the course learning at each stage, and analyzing the learning stability state information of each teaching object based on the data matrix; analyzing the learning psychological change information of each teaching object based on the learning psychological state data of the course learning at each stage; using the association rule algorithm to conduct analysis of the changes in learning situation of each teaching object based on the learning psychological change information and learning stability state information, and obtaining the analysis data of the changes in learning situation.

[0069] Specifically, a data matrix for evaluating the relationship between the performance and grade changes of the teaching object is constructed based on the learning performance evaluation results of each stage of course learning. The change rate of the learning performance evaluation grade score is analyzed according to the learning performance evaluation results of each stage of course learning. The change rates of the learning duration and learning frequency of the teaching object for each stage of course learning are analyzed. A data matrix for evaluating the relationship between the performance and grade changes of the teaching object is constructed based on the change rate of the learning performance evaluation grade score and the change rates of the learning duration and learning frequency. Based on the data matrix, the learning stability status information of each teaching object is analyzed. The difference value analysis is performed on the data of each row in the data matrix to obtain the corresponding difference value. The corresponding difference value is compared with the preset difference threshold. If the difference value is greater than or equal to the preset difference threshold, the learning status of the teaching object is an unstable learning status. If the difference value is less than the preset difference threshold, the learning status of the teaching object is a stable learning status or a self-regulating learning status. The learning psychological change information of each teaching object is analyzed based on the learning psychological state data of each stage of course learning, that is, the learning psychological change situation of each teaching object in the course learning is analyzed. Based on the learning psychological change information and the learning stability status information, the association rule algorithm is used to analyze the learning situation change of each teaching object. The learning psychological change information and the learning stability status information are encoded using the association rule mining technology to obtain the encoded learning psychological change information and the encoded learning stability status information. Based on the encoded learning psychological change information and the encoded learning stability status information, combined with the Apriori algorithm, the frequent item set is mined using item set generation and support calculation to obtain the target frequent item set. Based on the frequent pattern growth algorithm and using the target frequent item set, the strong association rule between the learning psychological change and the learning stability status is mined by constructing a frequent pattern tree and performing conditional pattern basis to obtain the target strong association rule. Based on the target strong association rule, using the principal component analysis method, the relationship is optimized through eigenvalue decomposition and dimension reduction to obtain the psychological-learning stability status correlation. It is possible to better understand the correlation between the psychological and learning stability status changes while retaining important change information. According to the psychological-learning stability status correlation, the learning situation and psychological changes of the teaching object are analyzed to obtain the learning situation change analysis data. Thus, it is possible to understand the psychological transformation and learning situation changes of each teaching object in the course learning, enhancing the pertinence and effectiveness of the teaching plan improvement.

[0070] S16: Based on the learning individual difference analysis data and the learning situation change analysis data, teaching improvement analysis is carried out to obtain teaching improvement data, and based on the teaching improvement data, the original teaching video content and assessment content are adjusted.

[0071] In the specific implementation process of the present invention, the teaching improvement analysis is performed based on the learning individual difference analysis data and the learning situation change analysis data to obtain teaching improvement data, including: performing cognitive load analysis on the teaching object based on the learning individual difference analysis data and the learning situation change analysis data to obtain corresponding cognitive load data; performing learning blind spot analysis on the teaching object based on the cognitive load data in combination with the learning individual difference analysis data to obtain learning blind spot analysis data; and performing teaching improvement analysis based on the cognitive load data and the learning blind spot analysis data to obtain teaching improvement data.

[0072] Specifically, based on the learning individual difference analysis data and the learning situation change analysis data, the cognitive load analysis of the teaching object is carried out. Based on the standardized learning video data, the eye movement analysis of the teaching object is carried out to obtain the corresponding eye movement analysis data. The eye movement analysis data includes the number of fixations, fixation bias and fixation density information. Based on the eye movement analysis data, the learning individual difference analysis data and the learning situation change analysis data, the cognitive load analysis of the teaching object is carried out by using data fusion combined with the cognitive load analysis model to obtain the corresponding cognitive load data. Too high or too low cognitive load will reduce the learning effect; too high cognitive load will make the brain in an over-tense or stressed state, thus affecting the learning and memory effects; while too low cognitive load will cause waste of resources such as time, and learners will feel bored and cause disgust, which also leads to a decline in learning effect. Therefore, introducing cognitive load analysis is conducive to the analysis of teaching problems. Based on the cognitive load data combined with the learning individual difference analysis data, the learning blind spot analysis of the teaching object is carried out. Based on the cognitive load data and the learning individual difference analysis data, the knowledge point requirement mastery analysis is carried out on the assessment data in the standardized learning achievement data to obtain the knowledge point requirement mastery analysis data, that is, to determine the knowledge points and knowledge levels that the teaching object needs to master according to the cognitive load data and the learning individual differences. The knowledge blind spot set is obtained by matching the knowledge point requirement mastery analysis data with the knowledge point set, that is, the learning blind spot analysis data is obtained. Based on the cognitive load data and the learning blind spot analysis data, the teaching improvement analysis is carried out. Based on the learning individual difference analysis data and the learning situation change analysis data, the cognitive transfer analysis of the teaching object is carried out, that is, to analyze the possible learning difficulties and knowledge point problems that are not firmly learned by the teaching object to obtain the corresponding cognitive transfer data. According to the cognitive load data, the cognitive transfer data and the learning blind spot analysis data, the teaching themes, difficulty levels and categories to be improved are determined, that is, the teaching improvement data is obtained. Based on the teaching improvement data, the original teaching plan is improved. According to the teaching improvement data and the interest data of the teaching object in the course learning at each stage, the original teaching video content and assessment content are improved. According to the teaching improvement data and the teaching object, the relevant knowledge points are matched. According to the relevant knowledge points, the theme and teaching content of the original teaching video content are adjusted, and the difficulty level and category of the assessment content are adjusted, so that the improved video content is more in line with the preferences of the teaching object, and the improved assessment content can better evaluate the learning situation of students. Thus, the teaching concept oriented to outcome-based can be better realized, and the improved teaching plan is more in line with the actual situation of each teaching object, so that each teaching object can participate more actively in the learning process.

[0073] In the embodiments of the present invention, by using a combination of evaluation items to evaluate the learning performance of each teaching object in each stage of course learning based on standardized learning outcome data, target knowledge point mastery degree data generated from the standardized learning outcome data, and problem-solving ability evaluation data, a more accurate learning performance evaluation result can be obtained, the efficiency of learning performance evaluation can be improved, and excessive human costs do not need to be invested. By using multi-scale hourglass attention based on standardized learning video data to analyze the concentration of each teaching object in each stage of course learning, the reliability of concentration analysis can be improved. Based on the concentration data, psychological state analysis is carried out to obtain the psychological state data of each teaching object in each stage of course learning, making the obtained psychological state data more in line with the actual situation of the teaching object and being able to better reflect the psychological situation of the teaching object during the learning process. By combining the learning performance evaluation results and psychological state data of each teaching object in each stage of course learning with learning participation analysis to conduct learning individual difference analysis of the teaching object, the individual differences of the teaching object can be taken into account, and personalized learning evaluation analysis can be realized. By conducting learning situation change analysis of each teaching object based on the learning performance evaluation results and psychological state data of each stage of course learning, the changes and development trends of the teaching object during the learning process can be reflected. By using cognitive load analysis based on learning individual difference analysis data and learning situation change analysis data to conduct teaching improvement analysis to improve the original teaching plan, the reliability of teaching improvement analysis can be improved, the original teaching plan can be improved in a targeted manner, and teaching evaluation analysis mainly based on the teaching object can be better realized. The improved teaching plan can better stimulate the initiative of the teaching object in course learning, thereby effectively improving various abilities of the teaching object and making the teaching evaluation analysis oriented towards outcome-based achieve a more ideal effect.

[0074] Embodiment 2

[0075] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of a method for outcome-based teaching evaluation analysis in another embodiment of the present invention. The method includes:

[0076] S201: Standardize the learning outcome data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning outcome data and standardized learning video data;

[0077] S202: Based on the standardized learning outcome data, evaluate the learning performance of each teaching object in each stage of course learning to obtain the learning performance evaluation results of each stage of course learning;

[0078] S203: Based on the standardized learning video data, use information fusion to analyze the concentration and psychological state of each teaching object in each stage of course learning, and obtain the psychological state data of each teaching object in each stage of course learning;

[0079] S204: Analyze the learning individual differences based on the learning performance evaluation results and psychological state data of each teaching object to obtain the learning individual difference analysis data;

[0080] S205: Construct a data matrix for evaluating the relationship between the performance and achievement changes of teaching objects based on the learning performance evaluation results of each stage of course learning, and analyze the learning stability state information of each teaching object based on the data matrix;

[0081] S206: Analyze the learning psychological change information of each teaching object based on the learning psychological state data of each stage of course learning;

[0082] S207: Analyze the learning situation change of each teaching object using the association rule algorithm based on the learning psychological change information and learning stability state information to obtain the learning situation change analysis data;

[0083] S208: Conduct teaching improvement analysis based on the learning individual difference analysis data and learning situation change analysis data to obtain teaching improvement data, and adjust the original teaching video content and assessment content based on the teaching improvement data.

[0084] In the embodiments of the present invention, by using the evaluation item combination to evaluate the learning performance of each teaching object in each stage of course learning based on the standardized learning achievement data, the target knowledge point mastery degree data generated from the standardized learning achievement data, and the problem-solving ability evaluation data, a more accurate learning performance evaluation result can be obtained, the efficiency of learning performance evaluation can be improved, and excessive human costs do not need to be invested. By using multi-scale hourglass attention based on the standardized learning video data to analyze the concentration of each teaching object in each stage of course learning, the reliability of concentration analysis can be improved. Based on the concentration data, psychological state analysis is carried out to obtain the psychological state data of each teaching object in each stage of course learning, making the obtained psychological state data more in line with the actual situation of the teaching object and being able to better reflect the psychological situation of the teaching object in the learning process. By combining the learning participation analysis with the learning performance evaluation results and psychological state data of each teaching object in each stage of course learning to conduct learning individual difference analysis of the teaching object, the individual differences of the teaching object can be taken into account, and personalized learning evaluation analysis can be realized. By conducting learning situation change analysis of each teaching object based on the learning performance evaluation results and psychological state data of each stage of course learning, the changes and development trends of the teaching object in the learning process can be reflected. By using cognitive load analysis based on the learning individual difference analysis data and learning situation change analysis data to conduct teaching improvement analysis to improve the original teaching plan, the reliability of teaching improvement analysis can be improved, the original teaching plan can be improved pertinently, and teaching evaluation analysis mainly based on the teaching object can be better realized. The improved teaching plan can better stimulate the initiative of the teaching object in course learning, thereby effectively improving various abilities of the teaching object and making the teaching evaluation analysis oriented to outcome-based achieve a more ideal effect.

[0085] Embodiment 3

[0086] Please refer to Figure 3 , Figure 3 which is a schematic structural composition diagram of the device for outcome-based teaching evaluation analysis in the embodiments of the present invention. The device includes:

[0087] Data standardization module 31: used to perform standardization processing on the learning achievement data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning achievement data and standardized learning video data;

[0088] Learning performance evaluation module 32: used to evaluate the learning performance of each teaching object in each stage of course learning based on the standardized learning achievement data to obtain the learning performance evaluation results of each stage of course learning;

[0089] Psychological state analysis module 33: used to perform concentration analysis and psychological state analysis on the course learning of each teaching object in each stage by using information fusion based on the standardized learning video data, and obtain the psychological state data of each teaching object in the course learning of each stage;

[0090] Learning individual difference analysis module 34: used to perform learning individual difference analysis based on the learning performance evaluation results and psychological state data of each teaching object, and obtain learning individual difference analysis data;

[0091] Learning situation change analysis module 35: used to perform learning situation change analysis based on the learning performance evaluation results and psychological state data of each teaching object, and obtain learning situation change analysis data;

[0092] Teaching improvement analysis module 36: used to perform teaching improvement analysis based on the learning individual difference analysis data and learning situation change analysis data, obtain teaching improvement data, and adjust the original teaching video content and assessment content based on the teaching improvement data.

[0093] In the specific implementation process of the present invention, the specific implementation manner of the device item can refer to the implementation manner of the above method item, and will not be elaborated here.

[0094] In the embodiments of the present invention, by using an evaluation item combination to evaluate the learning performance of each teaching object in each stage of course learning based on standardized learning outcome data, target knowledge point mastery degree data generated from the standardized learning outcome data, and problem-solving ability evaluation data, a more accurate learning performance evaluation result can be obtained, the efficiency of learning performance evaluation can be improved, and excessive human costs do not need to be invested. By using multi-scale hourglass attention based on standardized learning video data to analyze the concentration of each teaching object in each stage of course learning, the reliability of concentration analysis can be improved. Based on the concentration data, psychological state analysis is carried out to obtain the psychological state data of each teaching object in each stage of course learning, so that the obtained psychological state data is more in line with the actual situation of the teaching object and can better reflect the psychological situation of the teaching object in the learning process. By combining the learning performance evaluation result and psychological state data of each teaching object in each stage of course learning with learning participation analysis to conduct learning individual difference analysis of the teaching object, the individual differences of the teaching object can be taken into account, and personalized learning evaluation analysis can be realized. By conducting learning situation change analysis of each teaching object based on the learning performance evaluation result and psychological state data of each stage of course learning, the changes and development trends of the teaching object in the learning process can be reflected. By using cognitive load analysis based on learning individual difference analysis data and learning situation change analysis data to conduct teaching improvement analysis to improve the original teaching plan, the reliability of teaching improvement analysis can be improved, the original teaching plan can be improved pertinently, and teaching evaluation analysis mainly based on teaching objects can be better realized. The improved teaching plan can better stimulate the initiative of the teaching object in course learning, thereby effectively improving various abilities of the teaching object and making the teaching evaluation analysis oriented to outcome-based achieve a more ideal effect.

[0095] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored, and when the program is executed by a processor, it implements the method for outcome-based teaching evaluation and analysis in any one of the above embodiments. Among them, the computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disk, hard disk, optical disk, CD-ROM, and magneto-optical disk), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic card or optical card. That is, the storage device includes any medium that can store or transmit information in a readable form by a device (such as a computer, mobile phone), and can be a read-only memory, a magnetic disk or an optical disk, etc.

[0096] Embodiment 4

[0097] Please refer to Figure 4 , Figure 4 which is a schematic diagram of the structural composition of the electronic device in the embodiment of the present invention.

[0098] The embodiment of the present invention also provides an electronic device, as Figure 4 shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. Those skilled in the art can understand, Figure 3The illustrated electronic device does not constitute a limitation on all devices and may include more or fewer components than shown, or combine certain components. The memory 41 can be used to store the computer program 42 and each functional module. The processor 43 runs the computer program 42 stored in the memory 41 to execute various functional applications and data processing of the device. The memory can be an internal memory or an external memory, or include both an internal memory and an external memory. The internal memory can include a read-only memory (ROM), a programmable ROM (PROM), an electrically programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, or a random access memory. The external memory can include a hard disk, a floppy disk, a ZIP disk, a USB flash drive, a magnetic tape, etc. The processor 43 can be a central processing unit (CPU), or can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, a single-chip microcomputer, or the processor 43 can also be any conventional processor, etc. The processors and memories disclosed in the present invention include, but are not limited to, these types of processors and memories. The processors and memories disclosed in the present invention are only examples and not limitations.

[0099] As an embodiment, the electronic device includes: one or more processors 43, a memory 41, one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the method for outcome-oriented teaching evaluation analysis in any of the above embodiments. For the specific implementation process, please refer to the above embodiments and will not be elaborated here.

[0100] In the embodiments of the present invention, by using the evaluation item combination to evaluate the learning performance of each teaching object in each stage of course learning based on the standardized learning achievement data, the target knowledge point mastery degree data generated from the standardized learning achievement data, and the problem-solving ability evaluation data, a more accurate learning performance evaluation result can be obtained, the efficiency of learning performance evaluation can be improved, and excessive human costs do not need to be invested. By using multi-scale hourglass attention based on the standardized learning video data to analyze the concentration of each teaching object in each stage of course learning, the reliability of concentration analysis can be improved. Based on the concentration data, psychological state analysis is carried out to obtain the psychological state data of each teaching object in each stage of course learning, making the obtained psychological state data more in line with the actual situation of the teaching object and being able to better reflect the psychological situation of the teaching object in the learning process. By combining the learning participation analysis with the learning performance evaluation results and psychological state data of each teaching object in each stage of course learning to conduct learning individual difference analysis of the teaching object, the individual differences of the teaching object can be taken into account, and personalized learning assessment analysis can be realized. By conducting learning situation change analysis of each teaching object based on the learning performance evaluation results and psychological state data of each stage of course learning, the changes and development trends of the teaching object in the learning process can be reflected. By using cognitive load analysis based on the learning individual difference analysis data and learning situation change analysis data to conduct teaching improvement analysis to improve the original teaching plan, the reliability of teaching improvement analysis can be improved, the original teaching plan can be improved pertinently, and teaching evaluation analysis mainly based on the teaching object can be better realized. The improved teaching plan can better stimulate the initiative of the teaching object in course learning, thereby effectively improving various abilities of the teaching object and making the teaching evaluation analysis oriented to outcome-based achieve a more ideal effect.

[0101] In addition, the above has introduced in detail a method and related device for outcome-based teaching evaluation analysis provided by the embodiments of the present invention. Specific examples should have been used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for outcome-oriented teaching evaluation and analysis, characterized in that: The method comprises: Standardizing the learning achievement data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning achievement data and standardized learning video data; Based on the standardized learning achievement data, the learning performance of each teaching object at each stage of the course learning is evaluated to obtain the learning performance evaluation results of the course learning at each stage; Based on the standardized learning video data, using information fusion to analyze the concentration and psychological state of each teaching object at each stage of course learning, to obtain the psychological state data of each teaching object at each stage of course learning; Conduct individual learning difference analysis based on the learning performance evaluation results and psychological state data of each teaching object to obtain individual learning difference analysis data; Based on the learning performance evaluation results and psychological state data of each teaching object, the learning situation change analysis is carried out to obtain the learning situation change analysis data; Based on the learning individual difference analysis data and the learning situation change analysis data, teaching improvement analysis is performed to obtain teaching improvement data, and the original teaching video content and assessment content are adjusted based on the teaching improvement data.

2. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The standardized learning achievement data and learning video data generated by each teaching object in each stage of course learning are processed to obtain standardized learning achievement data and standardized learning video data, including: The learning achievement data and learning video data are subjected to format unification processing and standardization processing to obtain standardized learning achievement data and standardized learning video data.

3. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The learning performance evaluation of each stage of course learning of each teaching object based on the standardized learning achievement data is performed to obtain the learning performance evaluation results of each stage of course learning, including: Generate a learning assessment achievement matrix for each teaching object based on the standardized learning achievement data; Based on the target result matrix generated by performing singular value decomposition of the learning assessment outcome matrix, the mastery degree of each teaching object in each stage of course learning is evaluated to obtain the corresponding first knowledge point mastery degree data, and the target knowledge point mastery degree data is determined based on the first knowledge point mastery degree data and the second knowledge point mastery degree data generated by the knowledge tracking model; Based on the standardized learning outcome data, target knowledge point mastery data and problem-solving ability evaluation data generated by the learning assessment outcome matrix, the learning performance of each teaching object at each stage of course learning is evaluated using a combination of evaluation items to obtain corresponding learning performance evaluation results.

4. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The method of using information fusion to analyze the concentration and psychological state of each teaching object at each stage of course learning based on the standardized learning video data to obtain the psychological state data of each teaching object at each stage of course learning includes: A head posture estimation model based on multi-scale hourglass attention and multi-classification multi-regression loss uses the standardized learning video data to estimate the head posture of each teaching object to obtain head posture information; Based on the posture Euler angle, the head posture information is used to estimate the sight line of each teaching object to obtain the sight line estimation information; Based on DS evidence fusion theory, the head posture information, the line of sight estimation information and the facial expression information identified by the standardized learning video data are fused to obtain target fusion information; Based on the target fusion information, the concentration of each teaching object at each stage of course learning is analyzed to obtain the concentration data of each stage of course learning; Based on the concentration data, the pleasure information obtained from the facial expression information and the negative emotion information, the psychological state analysis is performed to obtain the psychological state data of each teaching object at each stage of course learning.

5. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The individual learning difference analysis is performed based on the learning performance evaluation results and psychological state data of each teaching object to obtain the individual learning difference analysis data, including: Analyze the interest data and importance of each teaching subject in learning courses at each stage based on the learning performance evaluation results and psychological state data; Performing interactive posture feature analysis of the teaching object based on the standardized learning video data to obtain interactive posture feature information, and performing interactive type analysis based on the interactive posture feature information combined with tension analysis to obtain interactive type information; Based on the interaction type information, the participation of each teaching object in the course learning at each stage is analyzed, and based on the interest data, degree of attention, participation, learning performance evaluation results and psychological state data, the individual learning differences of the teaching objects are analyzed to obtain the individual learning differences analysis data.

6. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The learning situation change analysis is performed based on the learning performance evaluation results and psychological state data of each teaching object to obtain the learning situation change analysis data, including: Based on the learning performance evaluation results of each stage of course learning, a data matrix for evaluating the relationship between the performance and grade changes of the teaching object is constructed, and based on the data matrix, the learning stable state information of each teaching object is analyzed; Analyze the learning psychology change information of each teaching object based on the learning psychology state data of each stage of course learning; Based on the learning psychology change information and the learning stable state information, an association rule algorithm is used to analyze the learning situation changes of each teaching object to obtain learning situation change analysis data.

7. The method for result-oriented teaching evaluation and analysis according to claim 1, characterized in that: The teaching improvement analysis is performed based on the learning individual difference analysis data and the learning situation change analysis data to obtain the teaching improvement data, including: Based on the learning individual difference analysis data and the learning situation change analysis data, a cognitive load analysis of the teaching object is performed to obtain corresponding cognitive load data; Based on the cognitive load data and the learning individual difference analysis data, a learning blind spot analysis of the teaching object is performed to obtain learning blind spot analysis data; Based on the cognitive load data and the learning blind spot analysis data, teaching improvement analysis is performed to obtain teaching improvement data.

8. A device for result-oriented teaching evaluation and analysis, characterized in that: The device comprises: Data standardization module: used to standardize the learning achievement data and learning video data generated by each teaching object in each stage of course learning to obtain standardized learning achievement data and standardized learning video data; Learning performance evaluation module: used to evaluate the learning performance of each teaching object at each stage of course learning based on the standardized learning achievement data to obtain the learning performance evaluation results of each stage of course learning; Psychological state analysis module: used to analyze the concentration and psychological state of each teaching object at each stage of course learning by using information fusion based on the standardized learning video data, and obtain the psychological state data of each teaching object at each stage of course learning; Learning individual difference analysis module: used to analyze learning individual differences based on the learning performance evaluation results and psychological state data of each teaching object, and obtain learning individual difference analysis data; Learning situation change analysis module: used to analyze learning situation changes based on the learning performance evaluation results and psychological state data of each teaching object, and obtain learning situation change analysis data; Teaching improvement analysis module: used to perform teaching improvement analysis based on the learning individual difference analysis data and the learning situation change analysis data, obtain teaching improvement data, and adjust the original teaching video content and assessment content based on the teaching improvement data.

9. An electronic device, comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the result-oriented teaching evaluation and analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the result-oriented teaching evaluation analysis method as described in any one of claims 1 to 7.

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