Dynamic analysis result generation method and device, equipment, medium and program product

Abstract

The invention relates to the technical field of teaching analysis, and discloses a dynamic analysis result generation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring a total teaching activity event at a preset time interval; performing classification processing on the total teaching activity events to obtain teaching activity events of various categories; and generating a dynamic analysis result based on a preset time interval, the teaching activity event of the first target category, the teaching activity event of the second target category and the teaching activity event of the second category. Through the preset time interval, different types of teaching activity events and correlation analysis among the teaching activity events, the timeliness and the newest of the total teaching activity event are ensured, so that the first target subject activity event and the second target subject activity event are ensured to receive equal attention, and the change rule in the teaching process is presented more accurately.

Description

Technical Field

[0001] The present invention relates to the field of teaching analysis technology, and in particular to a method, device, equipment, medium and program product for generating dynamic analysis results. Background Art

[0002] In today's education field, with the rapid development of information technology and the strengthening of the trend of education dataization, refined management and in-depth analysis of teaching activities have become important means to improve teaching quality and optimize the allocation of educational resources.

[0003] Current teaching analysis techniques often rely on students' regular test scores or teachers' subjective evaluations to determine whether students are in a highly efficient learning state.

[0004] However, the method of using regular test scores or teachers' subjective evaluation not only lags behind the real-time nature of the teaching process, but the teachers' subjective evaluation is also subjective to a certain extent, making it impossible to accurately determine the students' learning status. Summary of the invention

[0005] In view of this, the present invention provides a method, device, equipment, medium and program product for generating dynamic analysis results to solve the problem that regular test scores or teachers' subjective evaluations not only lag behind the real-time nature of the teaching process, but also the teachers' subjective evaluations have a certain degree of subjectivity, thus failing to accurately determine students' learning status.

[0006] In the first aspect, the present invention provides a method for generating dynamic analysis results, the method comprising: obtaining the total teaching activity events within a preset time interval; classifying the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and second categories; the first category includes multiple second categories, the teaching activity events of the second category include first target subject activity events and / or second target subject activity events, and the first category includes: a first target category and a second target category; based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category, generate dynamic analysis results.

[0007] The method for generating dynamic analysis results provided in this embodiment can ensure the timeliness and latestness of the total teaching activity events through the total teaching activity events at preset time intervals, thereby ensuring that the first target subject activity events and the second target subject activity events receive equal attention, and more accurately present the changing rules in the teaching process. In addition, based on the preset time intervals, different categories of teaching activity events and the correlation analysis between them, the generated dynamic analysis results have a high degree of decision-making support, thereby accurately showing the current status and trends of teaching activities.

[0008] In an optional embodiment, a dynamic analysis result is generated based on a preset time interval, teaching activity events of the first target category, teaching activity events of the second target category, and teaching activity events of the second category, including: based on the teaching activity events of the second category, determining that the teaching activity events of each second category respectively account for a first percentage of the total teaching activity events; based on the teaching activity events of the first target category and the teaching activity events of the second target category, determining that the teaching activity events of the first target category account for the second percentage of the teaching activity events of the second target category; based on the preset time interval, the first percentage and the second percentage, generating a teaching behavior analysis result.

[0009] The method for generating dynamic analysis results provided by the present embodiment quantifies the proportion of each second-category teaching activity event to the total teaching activity event and the proportion of the first target category teaching activity event to the second target category teaching activity event, so that the analysis results of teaching behavior are more intuitive and easy to understand, thereby facilitating the discovery of key issues and trends in the teaching process, and providing specific digital support for subsequent decision-making. In addition, by considering the preset time interval, the teaching behavior analysis results are time-sensitive. By regularly analyzing the teaching behavior data in different time periods, the changing trend of teaching behavior can be tracked, and potential teaching problems can be discovered and responded to in a timely manner.

[0010] In an optional embodiment, the total teaching activity events are classified and processed to obtain teaching activity events of various categories, including: classifying the total teaching activity events through a learning pyramid to determine teaching activity events of a first target category and teaching activity events of a second target category; wherein the first target category is used to represent active learning categories, and the second target category is used to represent passive learning categories; based on the first target category and a preset first association relationship, determining the teaching activity events of the second category corresponding to the first target category; based on the second target category and a preset second association relationship, determining the teaching activity events of the second category corresponding to the second target category.

[0011] The method for generating dynamic analysis results provided in this embodiment clearly divides teaching activity events into two categories: active learning and passive learning through the learning pyramid, and can accurately determine the teaching activity events of each category through the classification of the learning pyramid, the first target category and the preset first association relationship, and the second target category and the preset second association relationship.

[0012] In an optional embodiment, a teaching behavior analysis result is generated based on a preset time interval, a first percentage, and a second percentage, including: determining a first valid behavior value of a first target category based on the product of the preset time interval and the first percentage; determining a second valid behavior value of the first target category based on the product of the preset time interval and the second percentage; and determining a teaching behavior change trend graph according to the preset time interval, the first valid behavior value, and the second valid behavior value.

[0013] The method for generating dynamic analysis results provided in this embodiment can obtain specific first effective behavior values ​​and second effective behavior values ​​by multiplying the first time interval and the first percentage to determine the first effective behavior value of the first target category, and by multiplying the first time interval and the second percentage to determine the second effective behavior value of the first target category. The first effective behavior value and the second effective behavior value are used as quantitative indicators to generate a teaching behavior change trend chart, so that the performance of different categories in teaching activities can be intuitively reflected.

[0014] In an optional embodiment, after generating dynamic analysis results based on a preset time interval, teaching activity events of a first target category, teaching activity events of a second target category, and teaching activity events of a second category, the method includes: detecting whether the dynamic analysis results meet the preset analysis results; if the dynamic analysis results do not meet the preset analysis results, adjusting the total teaching activity events so that the dynamic analysis results meet the preset analysis results.

[0015] Dynamic analysis can reflect the status and effect of teaching activities in real time or near real time, so that teachers can get feedback quickly. In addition, once it is found that the dynamic analysis results do not meet the preset analysis results, the overall teaching activity events can be adjusted immediately, thereby reducing deviations and delays in the teaching process and ensuring that teaching activities are always carried out in the right direction.

[0016] In an optional embodiment, if the dynamic analysis results do not meet the preset analysis results, the total teaching activity events are adjusted so that the dynamic analysis results meet the preset analysis results, including: adjusting the teaching time in the total teaching activity events or splitting the learning courses in the total teaching activity events; and / or replacing the teaching activity events of the second target category in the total teaching activity events with the teaching activity events of the first target category.

[0017] The method for generating dynamic analysis results provided in this embodiment can improve the active learning process of the first target subject by adjusting the teaching time, splitting the courses and replacing the teaching activity events, so that the dynamic analysis results can meet the preset analysis results, thereby improving the learning efficiency of the first target subject.

[0018] In a second aspect, the present invention provides a device for generating dynamic analysis results, the device comprising: an acquisition module, used to acquire the total teaching activity events within a preset time interval; wherein the total teaching activity events include at least a first target main activity event and a second target main activity event; a classification processing module, used to classify the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and a second category; the first category includes multiple second categories, the teaching activity events of the second category include the first target main activity event and / or the second target main activity event, and the first category includes: a first target category and a second target category; a generation module, used to generate dynamic analysis results based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category.

[0019] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method for generating dynamic analysis results of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method for generating dynamic analysis results of the first aspect or any corresponding embodiment thereof.

[0021] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the method for generating dynamic analysis results of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 is a flow chart of a method for generating dynamic analysis results according to an embodiment of the present invention;

[0024] Figure 2 is a ST analysis curve diagram of different sampling time intervals according to an embodiment of the present invention;

[0025] Figure 3is a schematic diagram of independent sample nonparametric test results with different time intervals according to an embodiment of the present invention;

[0026] Figure 4 is a structural block diagram of a device for generating dynamic analysis results according to an embodiment of the present invention;

[0027] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0029] Based on relevant technologies, we can see that in today's education field, with the rapid development of information technology and the strengthening of the trend of education dataization, refined management and in-depth analysis of teaching activities have become important means to improve teaching quality and optimize the allocation of educational resources.

[0030] Current teaching analysis techniques often rely on students' regular test scores or teachers' subjective evaluations to determine whether students are in a highly efficient learning state.

[0031] However, the method of using regular test scores or teachers' subjective evaluation not only lags behind the real-time nature of the teaching process, but the teachers' subjective evaluation is also subjective to a certain extent, making it impossible to accurately determine the students' learning status.

[0032] Based on this, the present invention provides a method for generating dynamic analysis results. By presetting the total teaching activity events at a time interval, the timeliness and latestness of the total teaching activity events can be guaranteed, thereby ensuring that the first target subject activity events and the second target subject activity events receive equal attention, and more accurately presenting the changing rules in the teaching process. Moreover, based on the preset time interval, different categories of teaching activity events and the correlation analysis between them, the generated dynamic analysis results have a high degree of decision-making support, thereby accurately showing the current status and trends of teaching activities.

[0033] According to an embodiment of the present invention, an embodiment of a method for generating dynamic analysis results is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0034] In this embodiment, a method for generating dynamic analysis results is provided, which can be used in computer devices, such as computers, servers, etc. Figure 1 FIG. 1 is a flow chart of a method for generating dynamic analysis results according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0035] Step S101, obtaining the total teaching activity events within a preset time interval.

[0036] The preset time interval can be used to characterize the time for collecting total teaching activity events. Among them, the preset time interval can be 5s, or 6s, etc., which is not specifically limited here. The total teaching activity events can be used to characterize the collection of all teaching activity events in the teaching process. Among them, the total teaching activity events may include: teacher-student interaction, teacher lectures, etc., which are not specifically limited here. Specifically, the total teaching activity events can be directly obtained from the historical database, or the total teaching activity events at the preset time interval can be obtained by other means, etc., which are not specifically limited here and can be implemented by technical personnel in this field.

[0037] Preferably, the preset time interval is 5 seconds. Specifically, the preset time interval can be determined by performing a difference test on samples sampled at different time intervals from 1 second to 360 seconds using data statistical analysis software (such as IBM SPSS Statistics 26 software), and then setting the sampling time interval to the longest sampling time interval of 5 seconds without significant difference.

[0038] Preferably, each teaching activity event in the total teaching activity event can be encoded, for example: taking the form of multimedia, using sound, light and electricity, mobilizing multiple senses (PPT, video, etc., for vivid and intuitive teaching activity events) is encoded as Au; for another example: teachers lecturing and students listening are encoded as Le, etc., and no specific limitation is made here.

[0039] For example, taking the teaching activity time with a high average interaction frequency between teachers and students (4.5 times per minute) as an example, sampling is performed at different sampling time intervals and plotted to obtain ST analysis curves at different time intervals.

[0040] More specifically, Figure 2 The ST analysis curve diagram of different sampling time intervals provided in this embodiment is as follows: Figure 3 A schematic diagram of the results of the independent sample nonparametric test for different sampling time intervals provided in this embodiment.

[0041] Among them, combined Figure 2 and Figure 3As shown, the SPSS26 version was used to perform an independent sample nonparametric Kruskal-Wallis test on the values ​​of the ST curve coordinate points at different sampling time intervals and the values ​​of the ST curve coordinate points at 1s sampling time interval. It was found that when the horizontal axis (T axis) value was sampled at an interval of 6s, the progressive significance p = 0.048 (p < 0.05), which was significantly different from the horizontal axis data sampled at a time interval of 1s; it was found that when the vertical axis (S axis) value was sampled at an interval of 8s, the progressive significance p = 0.015 (p < 0.05), which was significantly different from the vertical axis data sampled at a time interval of 1s. It can be seen that when the number of teacher-student interactions is less than 6 seconds, the ST curve formed has no significant difference. Therefore, based on the principle of sampling economy, this embodiment sets the preset time interval to 5s.

[0042] Step S102, classify the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and second categories; the first category includes multiple second categories, the teaching activity events of the second category include first target subject activity events and / or second target subject activity events, and the first category includes: a first target category and a second target category.

[0043] The first target category and the second target category are relative and can be defined according to specific needs. For example, the first target category can be an active learning category, and the second category can be a passive learning category, etc., which are not specifically limited here.

[0044] The first target subject activity event can be used to represent the activity event of students, such as listening to lectures, reading, etc. The second target subject activity event can be used to represent the activity event of teachers, such as teaching, asking questions, etc. The first target subject activity event and the second target subject activity event as a whole can be used to represent the activity event of teacher-student interaction, etc., which is not specifically limited here.

[0045] The second category can be used to represent a more detailed classification result of the first category. The second category can be group discussion, project research, etc. in active learning activities; the second category can also be listening, reading, etc. in passive learning activities, which are not specifically limited here.

[0046] Specifically, the teaching activity events can be automatically classified using a machine learning algorithm, or the teaching activity events can be classified using a learning pyramid model, etc., which is not specifically limited here and can be implemented by those skilled in the art.

[0047] Step S103, generating a dynamic analysis result based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category.

[0048] The dynamic analysis results can be used to characterize the teaching activity event data based on the preset event interval, and the evaluation results of the teaching activity status and effect generated by statistical and analytical methods. Specifically, statistics are performed on teaching activity events of various categories, such as calculating the number of occurrences, duration, number of participants and other indicators of various activities, and dynamic analysis results are generated based on these indicators. It is also possible to combine machine learning and data mining technology to automatically identify patterns and trends in teaching activities, predict future teaching effects, generate dynamic analysis results, etc., which are not specifically limited here and can be implemented by those skilled in the art.

[0049] The method for generating dynamic analysis results provided in this embodiment can ensure the timeliness and latestness of the total teaching activity events through the total teaching activity events at preset time intervals, thereby ensuring that the first target subject activity events and the second target subject activity events receive equal attention, and more accurately present the changing rules in the teaching process. In addition, based on the preset time intervals, different categories of teaching activity events and the correlation analysis between them, the generated dynamic analysis results have a high degree of decision-making support, thereby accurately showing the current status and trends of teaching activities.

[0050] In an optional embodiment, in the above step S103, based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category, a dynamic analysis result is generated, including:

[0051] Step a1: based on the teaching activity events of the second category, determine the first percentage of each teaching activity event of the second category to the total teaching activity events.

[0052] The teaching activity events of the second category can be used to characterize the teaching activity events of the subcategories further subdivided within the first category, such as group discussions, experiments, case studies, etc. The first percentage is used to characterize the proportion of a teaching activity event of a second category to the total teaching activity events. Among them, the first percentage can be determined by the ratio of the number of teaching activity events of the second category to the total number of teaching activity events. It is also possible to give different weights to different categories of teaching activity events because they differ in importance, and then perform weighted calculations to obtain the first percentage, etc., which is not specifically limited here and can be implemented by those skilled in the art.

[0053] Step a2, based on the teaching activity events of the first target category and the teaching activity events of the second target category, determine a second percentage of the teaching activity events of the first target category to the teaching activity events of the second target category.

[0054] The teaching activity events of the first target category may include: active learning activities (such as group discussions, project research). The teaching activity events of the second target category may include: passive learning activities (such as listening, reading), etc. The second percentage can be used to characterize the proportion of teaching activity events of a first target category to the second target category. Among them, please refer to the above step a1 for the determination method of the second percentage, and no further details will be given here.

[0055] Step a3, generating teaching behavior analysis results based on the preset time interval, the first percentage and the second percentage.

[0056] Specifically, the analysis results of the first percentage and the second percentage are presented in the form of a text report, describing in detail the current status and trend of the teaching behavior. It is also possible to combine the information of the preset time interval and use a chart (such as a time series chart, a radar chart) to show the trend of the teaching behavior over time, etc., which is not specifically limited here and can be implemented by those skilled in the art.

[0057] Specifically, the above step a3 includes:

[0058] Step a31, determining the first valid behavior value of each second category based on the product of the preset time interval and the first percentage.

[0059] The first effective behavior value can be used to represent the value of the effective behavior amount of each second category teaching activity event calculated based on the preset time interval and the first percentage. For example, if the preset time interval is 5s and the first percentage is 20%, then the value of the effective behavior amount is 100%.

[0060] Step a32, determining a second effective behavior value of the first target category based on the product of the preset time interval and the second percentage.

[0061] The second effective behavior value can be used to represent a value calculated based on a preset time interval and a second percentage, reflecting the effective behavior amount of the first target category teaching activity event.

[0062] Step a33, determining a teaching behavior change trend graph according to a preset time interval, the first valid behavior value, and the second valid behavior value.

[0063] Specifically, a time series graph can be drawn with time as the horizontal axis and the first valid behavior value and the second valid behavior value as the vertical axis. By comparing the trends of the two curves, the changing trend of the teaching behavior can be analyzed. It is also possible to consider the teaching behaviors of multiple categories or indicators at the same time, and a stacked chart can be used to show their relative contributions and changing trends within a preset time interval. It is also possible to more intuitively show the cumulative effect of data changes over time by using a method similar to a time series graph but using area to represent the amount of data. It is also possible to use the interactive functions provided by the data analysis software or platform to adjust the time range, filter categories, etc. as needed to obtain a more detailed or customized teaching behavior change trend graph, etc., which is not specifically limited here and can be implemented by those skilled in the art.

[0064] The method for generating dynamic analysis results provided by the present embodiment quantifies the proportion of each second-category teaching activity event to the total teaching activity event and the proportion of the first target category teaching activity event to the second target category teaching activity event, so that the analysis results of teaching behavior are more intuitive and easy to understand, thereby facilitating the discovery of key issues and trends in the teaching process, and providing specific digital support for subsequent decision-making. In addition, by considering the preset time interval, the teaching behavior analysis results are time-sensitive. By regularly analyzing the teaching behavior data in different time periods, the changing trend of teaching behavior can be tracked, and potential teaching problems can be discovered and responded to in a timely manner.

[0065] In an optional implementation, in the above step S102, the total teaching activity events are classified to obtain teaching activity events of various categories, including:

[0066] Step b1, classify the total teaching activity events through the learning pyramid to determine the teaching activity events of the first target category and the teaching activity events of the second target category; wherein the first target category is used to represent the active learning category, and the second target category is used to represent the passive learning category.

[0067] The learning pyramid can be used to represent a learning theory model, which specifically demonstrates the impact of different learning methods (such as listening, reading, discussion, practice, etc.) on learners' memory retention rate. The first target category can be used to represent the active learning category, including those teaching activity events that can encourage learners to actively participate, think and apply. The second target category can be used to represent the passive learning category, including those teaching activity events that mainly rely on teachers' lectures and learners' passive acceptance. Specifically, the computer device can obtain each teaching activity event through the encoding and recognition of each teaching activity event.

[0068] Step b2: determining the teaching activity events of the second category corresponding to the first target category based on the first target category and the preset first association relationship.

[0069] The first association relationship can be used to characterize the rules or standards for the relationship between the first target category (active learning) and the second category of teaching activity events. The second category of teaching activity events can be used to characterize specific teaching activity events that are further subdivided within the first target category, such as group discussions, case analysis, project research, etc. Specifically, the computer device defines which teaching activity events belong to the second category under the first target category based on the historical association relationship database. Data analysis tools can also be used to perform cluster analysis or association rule mining on teaching activity events, automatically discover the association relationship between the first target category and the second category, etc., which is not specifically limited here and can be implemented by those skilled in the art.

[0070] Step b3: determining the second category of teaching activity events corresponding to the second target category based on the second target category and the preset second association relationship.

[0071] The second association relationship can be used to characterize the rules or standards of the relationship between the second target category (passive learning) and the second category teaching activity events. The second category of teaching activity events can be used to characterize specific teaching activity events that are further subdivided within the second target category. The specific determination method can refer to the above step b2, and no further elaboration is given here.

[0072] Preferably, the first association relationship and the second association relationship can be based on the association relationship obtained by ST analysis. The specific steps are as follows: Use ST analysis to record the S behavior and T behavior sequences in the teaching activity events. Classify S behavior and T behavior into active learning mode and passive learning mode respectively. Analyze which time periods in the teaching process are mainly passive learning and which time periods are mainly active learning. According to the learning pyramid theory, predict the learning effect of students under different learning methods. Combined with the results of ST analysis, evaluate whether the actual teaching effect is consistent with the prediction of the learning pyramid. According to the analysis results of the association relationship, adjust the teaching strategy, increase the proportion of active learning, and reduce the proportion of passive learning. Improve students' learning effect and participation by changing the ratio of teacher behavior and student behavior.

[0073] Specifically, Table 1 shows a mapping table based on the ST analysis method and the learning pyramid.

[0074] Table 1

[0075]

[0076] Specifically, as shown in Table 1, the percentage of effective behaviors of teachers’ teaching behaviors (T) and students’ learning behaviors (S) for different learning styles is formed by dividing the proportion of teachers’ teaching behaviors (T) and students’ learning behaviors (S) according to the knowledge retention rate of the learning pyramid theory. The second category is divided into the second category of the first target category and the second category of the second target category.

[0077] The method for generating dynamic analysis results provided in this embodiment clearly divides teaching activity events into two categories: active learning and passive learning through the learning pyramid, and can accurately determine the teaching activity events of each category through the classification of the learning pyramid, the first target category and the preset first association relationship, and the second target category and the preset second association relationship.

[0078] In an optional embodiment, the above method further includes:

[0079] Step c1, detecting whether the dynamic analysis result meets the preset analysis result.

[0080] The preset analysis results can be used to characterize the expected analysis result standards or thresholds. Specifically, taking the active learning category and the passive learning category as an example, the more the active learning category exceeds the passive learning category, the better the effective learning effect of the students. Specifically, the upper and / or lower thresholds can be set for the first target category and the second target category respectively, and it is checked whether the dynamic analysis results fall within these threshold ranges. Complex judgment logic can also be defined by using a rule engine. These logics can specifically represent the association and mutual influence between the first target category and the second target category, etc., which are not specifically limited here and can be implemented by those skilled in the art.

[0081] Step c2: if the dynamic analysis result does not meet the preset analysis result, adjust the total teaching activity events so that the dynamic analysis result meets the preset analysis result.

[0082] Specifically, the above step c2 includes: step c21 and / or step c22. In step c21, the teaching time in the total teaching activity event is adjusted or the learning courses in the total teaching activity event are split.

[0083] The teaching duration can be used to indicate the length of a single teaching activity (such as a class). Learning courses: can be used to represent a series of related teaching activities or learning units aimed at imparting specific knowledge or skills. Specifically, when the computer device identifies that the dynamic analysis results do not meet the preset analysis results, it can divide the original learning course or teaching activity event into smaller parts or units, or adjust the duration of a single teaching activity. Specifically, it can include reducing the duration of a single teaching activity or increasing the duration of a single teaching activity.

[0084] Step c22: Replace the teaching activity events of the second target category in the total teaching activity events with the teaching activity events of the first target category.

[0085] The specific process of replacing the teaching activity events of the second target category with the teaching activity events of the first target category can be: adjusting the activity events that take the form of multimedia, use sound, light and electricity, and mobilize multiple senses (PPT, video, etc., for vivid and intuitive teaching) to the teaching activity events of the first target category, thereby increasing the proportion of teaching activity events of the first target category relative to the teaching activity events of the second target category.

[0086] Preferably, in the passive learning category, try to use more efficient learning behaviors to replace less efficient learning behaviors, such as replacing simple teacher lecturing behaviors with multimedia audio-visual behaviors, replacing student reading behaviors with teacher demonstration behaviors, etc.; in the active learning category, try to allocate more time to student presentation behaviors, etc., which are not specifically limited here and can be implemented by technical personnel in this field.

[0087] The method for generating dynamic analysis results provided in this embodiment can improve the active learning process of the first target subject by adjusting the teaching time, splitting the courses and replacing the teaching activity events, so that the dynamic analysis results can meet the preset analysis results, thereby improving the learning efficiency of the first target subject.

[0088] In the present embodiment, a device for generating dynamic analysis results is also provided, and the device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceived.

[0089] This embodiment provides a device for generating dynamic analysis results, such as Figure 4 As shown, it includes: an acquisition module 401, which is used to acquire the total teaching activity events within a preset time interval; wherein the total teaching activity events at least include a first target main activity event and a second target main activity event; a classification processing module 402, which is used to classify the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and a second category; the first category includes multiple second categories, and the teaching activity events of the second category include the first target main activity event and / or the second target main activity event, and the first category includes: a first target category and a second target category; a generation module 403, which is used to generate dynamic analysis results based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category.

[0090] In an optional embodiment, the generation module 403 includes: a first determination unit, used to determine, based on the teaching activity events of the second category, the first percentage of the teaching activity events of each second category in the total teaching activity events; a second determination unit, used to determine, based on the teaching activity events of the first target category and the teaching activity events of the second target category, the second percentage of the teaching activity events of the first target category in the teaching activity events of the second target category; a first generation unit, used to generate the teaching behavior analysis results based on a preset time interval, the first percentage and the second percentage.

[0091] In an optional embodiment, the classification processing module 402 includes: a third determination unit, used to classify the total teaching activity events through the learning pyramid, and determine the teaching activity events of the first target category and the teaching activity events of the second target category; wherein the first target category is used to represent the active learning category, and the second target category is used to represent the passive learning category; a fourth determination unit, used to determine the teaching activity events of the second category corresponding to the first target category based on the first target category and a preset first association relationship; a fifth determination unit, used to determine the teaching activity events of the second category corresponding to the second target category based on the second target category and a preset second association relationship.

[0092] In an optional embodiment, the first generating unit includes: a first determination subunit, used to determine the first valid behavior value of each second category based on the product of a preset time interval and a first percentage; a second determination subunit, used to determine the second valid behavior value of the first target category based on the product of a preset time interval and a second percentage; and a third determination subunit, used to determine a teaching behavior change trend graph according to the preset time interval, the first valid behavior value and the second valid behavior value.

[0093] In an optional embodiment, the above-mentioned device also includes: a detection module, which is used to detect whether the dynamic analysis result meets the preset analysis result; and an adjustment module, which is used to adjust the total teaching activity event if the dynamic analysis result does not meet the preset analysis result, so that the dynamic analysis result meets the preset analysis result.

[0094] In an optional embodiment, the adjustment module includes: an adjustment and splitting unit, used to adjust the teaching time in the total teaching activity event or split the learning courses in the total teaching activity event; and / or, a replacement unit, used to replace the teaching activity events of the second target category in the total teaching activity event with the teaching activity events of the first target category.

[0095] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0096] The device for generating dynamic analysis results in this embodiment is presented in the form of a functional unit, where the functional unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0097] The embodiment of the present invention also provides a computer device having the above Figure 4 The device for generating dynamic analysis results is shown.

[0098] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0099] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0100] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0101] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0102] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0103] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0104] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0105] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0106] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for generating dynamic analysis results, characterized in that: The method comprises: Get the total teaching activity events in a preset time interval; Classify the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and second categories; the first category includes multiple second categories, the teaching activity events of the second category include first target subject activity events and / or second target subject activity events, and the first category includes: a first target category and a second target category; A dynamic analysis result is generated based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category.

2. The method for generating dynamic analysis results according to claim 1, characterized in that: The generating of dynamic analysis results based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category includes: Based on the teaching activity events of the second category, determining a first percentage of each teaching activity event of the second category to the total teaching activity events; Based on the teaching activity events of the first target category and the teaching activity events of the second target category, determining a second percentage of the teaching activity events of the first target category to the teaching activity events of the second target category; A teaching behavior analysis result is generated based on the preset time interval, the first percentage and the second percentage.

3. The method for generating dynamic analysis results according to claim 1, characterized in that: The total teaching activity events are classified to obtain teaching activity events of various categories, including: Classifying the total teaching activity events through the learning pyramid to determine the teaching activity events of the first target category and the teaching activity events of the second target category; wherein the first target category is used to represent the active learning category, and the second target category is used to represent the passive learning category; Based on the first target category and the preset first association relationship, determining a second category of teaching activity events corresponding to the first target category; Based on the second target category and the preset second association relationship, a second category of teaching activity events corresponding to the second target category is determined.

4. The method for generating dynamic analysis results according to claim 2, characterized in that: The generating of the teaching behavior analysis result based on the preset time interval, the first percentage and the second percentage includes: determining a first valid behavior value of each of the second categories based on a product of the preset time interval and the first percentage; determining a second valid behavior value of the first target category based on a product between the preset time interval and the second percentage; According to the preset time interval, the first valid behavior value and the second valid behavior value, a teaching behavior change trend graph is determined.

5. The method for generating dynamic analysis results according to claim 1, characterized in that: After generating a dynamic analysis result based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category, the method includes: Detecting whether the dynamic analysis result meets the preset analysis result; If the dynamic analysis result does not meet the preset analysis result, the total teaching activity event is adjusted so that the dynamic analysis result meets the preset analysis result.

6. The method for generating dynamic analysis results according to claim 5, characterized in that: If the dynamic analysis result does not meet the preset analysis result, adjusting the total teaching activity events so that the dynamic analysis result meets the preset analysis result includes: Adjusting the teaching time in the total teaching activity event or splitting the learning courses in the total teaching activity event; and / or, The teaching activity events of the second target category in the total teaching activity events are replaced with the teaching activity events of the first target category.

7. A device for generating dynamic analysis results, characterized in that: The device comprises: An acquisition module, used for acquiring total teaching activity events at a preset time interval; wherein the total teaching activity events at least include a first target subject activity event and a second target subject activity event; A classification processing module, used for classifying the total teaching activity events to obtain teaching activity events of various categories; wherein the categories include at least two first categories and second categories; the first category includes multiple second categories, the teaching activity events of the second category include first target subject activity events and / or second target subject activity events, and the first category includes: a first target category and a second target category; A generation module is used to generate a dynamic analysis result based on the preset time interval, the teaching activity events of the first target category, the teaching activity events of the second target category, and the teaching activity events of the second category.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for generating dynamic analysis results according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method for generating dynamic analysis results according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the method for generating dynamic analysis results according to any one of claims 1 to 6.

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