CN112435152B - Online learning investment dynamic evaluation method and system - Google Patents
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Abstract
The invention discloses an online learning input dynamic evaluation method and system, which divide online learning data into learning spaces with different granularities and learning time input characteristics with different dimensions, calculate the characteristics of each space granularity and time dimension input combination, construct space evaluation models with different dimensions and time evaluation models, construct a space input comprehensive model by taking the performance of students as a target value and output of the space granularity model as an input, construct a time input comprehensive model by taking the performance of the students as a target value and form an online learning input dynamic evaluation model by the models, comprehensively and deeply evaluate the online learning input of the students based on the dynamic evaluation model by acquiring online learning data of the students, know the learning state and dynamic evolution of the students in the air during different network learning, diagnose the learning problem, and better provide accurate teaching intervention and personalized learning support services according to the learning state and the optimization of course teaching and learning design.
Description
Technical Field
The invention relates to the technical field of online learning data analysis, in particular to a method and a system for dynamically evaluating online learning investment.
Background
Online teaching is becoming an important teaching mode more and more, and improving online learning investment and online teaching personalized service quality is always a major challenge facing most online education courses while online education develops rapidly.
In the online learning process of students, a learning platform stores a large amount of rich learner data information, valuable data are extracted from the data and analyzed, and the learning state, the learning preference, the learning resource utilization condition and the like of a learner can be better known from multiple angles. At present, in the aspects of data analysis and evaluation of online learning investment of learners, research lacks of an effective investment and evaluation framework on a curriculum level and an automatic method for revealing online curriculum learning investment quality and process, and dynamic evaluation of online learning investment cannot be effectively carried out.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defect that the online learning input dynamic evaluation cannot be effectively performed in the prior art, and therefore, a method and a system for online learning input dynamic evaluation are provided.
In order to achieve the purpose, the invention provides the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for dynamically evaluating an online learning investment, including the following steps:
dividing the acquired online learning data into learning space input characteristics with different granularities and learning time input characteristics with different dimensionalities, and calculating the characteristics of the combination of each space granularity input and each time dimensionality input;
establishing space evaluation models based on space granularity, wherein the input characteristic of each space evaluation model is the characteristic of each time dimension under the space granularity, and the characteristic of the preset representation student learning performance is used as a prediction index;
establishing time evaluation models based on time dimensions, wherein the input features of each time evaluation model are features of each space granularity under the time dimensions, and the features representing the learning performance of students are preset as prediction indexes;
the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a space input comprehensive model is constructed;
the output of each time dimension model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a time input comprehensive model is constructed;
the method comprises the steps of collecting real-time online learning data of students regularly to carry out feature calculation, forming indexes under input space granularity and time dimension, inputting the indexes into a space evaluation model and a time evaluation model correspondingly, generating prediction indexes of the space dimension and prediction indexes of the time dimension, inputting the prediction indexes of the space granularity into a space input comprehensive model, inputting the prediction indexes of the time dimension into a time input comprehensive model, carrying out final dynamic evaluation prediction, and forming comprehensive prediction indexes of the space granularity and comprehensive prediction indexes of the time dimension.
In one embodiment, the granularity of the learning space includes: course granularity, learning task granularity, learning page granularity, the spatial evaluation model that corresponds includes: a course model, a task model, and a page model.
In one embodiment, the dimension of learning time includes: duration, interval, moment, time sequence, dynamic trend, the corresponding time evaluation model includes: a duration model, an interval model, a time model, a timing model, and a trend model.
In one embodiment, the characteristics of each combination of spatial granularity investment and time dimension investment include:
the course investment is respectively combined with the characteristics of duration, interval, time sequence and dynamic trend investment;
the task investment is respectively combined with the characteristics of duration, interval, time sequence and dynamic trend investment;
the page investment is respectively combined with the characteristics of duration, time sequence and dynamic trend investment.
In one embodiment, the combined characteristics of the lesson investment and the duration investment include: the course investment total amount, the course learning concentration degree, the course learning distribution and the course learning speed; the combined characteristics of the course and interval plunges include: the course learning rhythm and the course learning intensity distribution; the combined characteristics of course investment and time investment comprise: delay in learning; the combined characteristics of course and time series investments include: the learning mode and the activity of the course are rich; the combined features of the lesson investment and the dynamic trend investment include: the course concentration degree variation trend, the course rhythm variation trend, the course distribution variation trend and the course learning speed variation trend;
the combined characteristics of the task investment and the duration investment comprise: the total amount of task learning investment, the degree of task learning concentration, the distribution of task investment and the speed of task learning; the combined characteristics of the mission investment and the interval investment include: task learning rhythm and task learning intensity distribution; the combined characteristics of the mission investment and the time investment include: task delay; the combined characteristics of the mission investment and the timing investment include: task participation model and behavior diversity; the combined features of mission investment and dynamic trend investment include: the change trend of concentration, the change trend of task rhythm, the change trend of task distribution and the change trend of task speed;
the combined characteristics of the page investment and the duration investment comprise: page immersion, page learning distribution and page learning speed; the combined features of page investing and timing investing comprise: attention circulation patterns, cognitive load volumes; the combined features of the page engagement and the dynamic trend engagement include: the method comprises the following steps of page immersion degree variation trend, page distribution variation trend and page learning speed variation trend.
In one embodiment, the preset characteristics for representing the learning performance of the student comprise: achievement, completeness, adherence.
In one embodiment, each evaluation model, spatial input comprehensive model and time input comprehensive model in the spatial granularity and time dimension are constructed based on a neural network.
In a third aspect, an embodiment of the present invention provides an online learning investment dynamic evaluation system, including: the characteristic engineering module is used for dividing the acquired online learning data into learning space input characteristics with different granularities and learning time input characteristics with different dimensionalities and calculating the characteristics of the combination of each space granularity input and each time dimensionality input;
the model establishing module is used for establishing space evaluation models based on the space granularity, the input characteristics of each space evaluation model are the characteristics of each time dimension under the space granularity, and the characteristics representing the learning performance of students are preset as prediction indexes; establishing time evaluation models based on time dimensions, wherein the input features of each time evaluation model are features of each space granularity under the time dimensions, and the features representing the learning performance of students are preset as prediction indexes; the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a space input comprehensive model is constructed; the output of each time dimension model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a time input comprehensive model is constructed;
the dynamic index acquisition module is used for regularly acquiring real-time online learning data of students to perform feature calculation, forming indexes under input space granularity and time dimension, respectively and correspondingly inputting the indexes into a space evaluation model and a time evaluation model, generating prediction indexes of the space dimension and prediction indexes of the time dimension, finally inputting the prediction indexes of the space granularity into the space input comprehensive model, inputting the prediction indexes of the time dimension into the time input comprehensive model, performing final dynamic evaluation prediction, and forming comprehensive prediction indexes of the space granularity and comprehensive prediction indexes of the time dimension.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause a computer to execute the online learning investment dynamic evaluation method according to the first aspect of the embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides a computer device, including: the online learning input dynamic evaluation method comprises a memory and a processor, wherein the memory and the processor are mutually connected in a communication mode, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the online learning input dynamic evaluation method of the first aspect of the embodiment of the invention.
The technical scheme of the invention has the following advantages:
the invention provides an online learning investment dynamic evaluation method and system, which divide online learning data into learning space investment characteristics with different granularities and learning time investment characteristics with different dimensions, calculate the characteristics of the combination of each space granularity investment and time dimension investment, construct space evaluation models with different space granularities and time evaluation models with different time dimensions, construct a space investment comprehensive model by taking the characteristics of preset representation student learning performance as prediction indexes and taking the characteristics of preset representation student learning performance as the input of the output of each space granularity model, construct a time investment comprehensive model by taking the characteristics of preset representation student learning performance as the prediction indexes and form an online learning investment dynamic evaluation model with multi-granularity space and multi-dimensional time characteristics, acquire student online learning data regularly, perform comprehensive/deep evaluation on the student online learning investment based on the dynamic evaluation model, know the air learning state and dynamic evolution of students in different network learning processes, diagnose learning problems, provide accurate teaching intervention and personalized learning support services better and provide a teaching design basis for optimization of a teaching design.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart illustrating a specific example of a method for online learning engagement dynamic evaluation provided in an embodiment of the present invention;
FIG. 2 is a schematic diagram of an online learning investment dynamic evaluation model for constructing three-layer spatial multi-dimensional temporal features, provided in an embodiment of the present invention;
FIG. 3 is a block composition diagram of one particular example of an online learning engagement dynamic evaluation system provided in an embodiment of the present invention;
fig. 4 is a block diagram of a specific example of a computer device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Example 1
The embodiment of the invention provides a dynamic evaluation method for online learning investment, which comprises the following steps of:
step S1: and dividing the acquired online learning data into learning space input characteristics with different granularities and learning time input characteristics with different dimensionalities, and calculating the characteristics of the combination of each space granularity input and each time dimensionality input.
In an embodiment of the present invention, the granularity of the learning space includes: course granularity, learning task granularity, learning page granularity, the dimension of learning time includes: duration, interval, time, timing, dynamic trend, each feature of a combination of spatial granularity investment and time dimension investment comprising: the course investment is respectively combined with the combination characteristics of duration, interval, time sequence and dynamic trend investment; the task investment is respectively combined with the characteristics of duration, interval, time sequence and dynamic trend investment; the page investment is respectively combined with the characteristics of duration, time sequence and dynamic trend investment.
The characteristics of the combination of the investment and time dimensions for each spatial granularity are represented by the following table:
it should be noted that the task type is a common task, such as watching video, performing test, discussing forum, and performing job; when speed analysis is carried out, the completion rate of the discussion task is difficult to judge, and the completion rate is replaced by the number of posted total words. Pages in the page types are classified according to media forms, page interactivity and page connectivity, specifically, the media forms comprise video pages, text pages, various media and the like, and the interactivity comprises interactive components and belongs to interactive pages; the connective pages contain hyperlinks, and can tag the pages according to the characteristic conditions of the pages in the three aspects.
The analysis content included in the above space granularity and time dimension, and the characteristics of the combination of the investment of each space granularity and the time dimension in the above table are only used as examples, and in practical application, the analysis content is reasonably set according to the final target evaluation content, and the corresponding characteristics are obtained by using a corresponding calculation method, and the following three-dimensional space and multi-dimensional time characteristic calculation table may be specifically referred to.
Step S2: and establishing space evaluation models based on the space granularity, wherein the input characteristics of each space evaluation model are the characteristics of each time dimension under the space granularity, and the characteristics which are preset to represent the learning performance of students are used as prediction indexes.
And step S3: and establishing time evaluation models based on the time dimension, wherein the input characteristics of each time evaluation model are the characteristics of each space granularity under the time dimension, and the characteristics representing the learning performance of students are preset as prediction indexes.
The embodiment of the invention presets the characteristics for representing the learning performance of students, and comprises the following steps: the score, the completion degree and the adherence degree are expanded and set according to the specific performance in practice. Based on the content to be analyzed in the spatial granularity and the time dimension, the spatial evaluation model includes: a course model, a task model, and a page model. The time evaluation model comprises: a duration model, an interval model, a time model, a timing model, and a trend model.
The input features of each model in the spatial evaluation model are features of each time dimension under the spatial granularity, and the performance of students is generally used as a prediction index. The input of the course model is five characteristics of duration, interval, time sequence and trend under the course input granularity; the input of the task model is five types of characteristics of duration, interval, moment, time sequence and trend under the task input granularity; the input of the page model is five types of characteristics of duration, interval, time sequence and trend under the task attention input granularity.
The input features of each model in the time evaluation model are features of each space granularity under the time dimension, and the performance of students is generally used as a prediction index. The input of the duration model is a plurality of characteristics under the course input granularity, the task input granularity and the page attention input granularity; the input of the interval model is a plurality of characteristics under the course input granularity, the task input granularity and the page attention input granularity; the input of the time model is a plurality of characteristics under the course input granularity, the task input granularity and the page attention input granularity; the input of the time sequence model is a plurality of characteristics under the course input granularity, the task input granularity and the page attention input granularity; the input of the trend model is a plurality of characteristics under the course input granularity, the task input granularity and the page attention input granularity.
And step S4: and the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and the constructed space input comprehensive model is constructed.
Step S5: and the output of each time dimension model is used as input, the characteristics representing the learning performance of the students are preset as prediction indexes, and the constructed time input comprehensive model is constructed.
In this embodiment, each evaluation model, the spatial input comprehensive model, and the temporal input comprehensive model in the spatial granularity and the temporal dimension are constructed based on a neural network, for example, based on an LSTM network structure, which is only used as an example and is not limited thereto, and in other embodiments, other deep learning models may be used. The embodiment of the invention takes the output of three models of space granularity as input, takes the characteristics which are preset to represent the learning performance of students as prediction indexes, and constructs a space input comprehensive model; and (3) taking the output of the five models of the time dimension as input, taking the characteristics which are preset to represent the learning performance of the students as prediction indexes, and constructing a time input comprehensive model.
Step S6: the method comprises the steps of collecting real-time online learning data of students periodically to carry out feature calculation, forming indexes under input space granularity and time dimension, inputting the indexes into a space evaluation model and a time evaluation model correspondingly, generating prediction indexes of the space dimension and prediction indexes of the time dimension, inputting the prediction indexes of the space granularity into the space input comprehensive model, inputting the prediction indexes of the time dimension into the time input comprehensive model, carrying out final dynamic evaluation prediction, and forming comprehensive prediction indexes of the space granularity and comprehensive prediction indexes of the time dimension.
According to the online learning investment dynamic evaluation method provided by the embodiment of the invention, as shown in fig. 2, an online learning investment dynamic evaluation model based on three-layer space multi-dimensional time characteristics is formed by ten models including three models of space dimensions, five models of time dimensions, a time investment comprehensive evaluation model and a space investment comprehensive model, online learning investment of students can be comprehensively evaluated based on the dynamic evaluation model by acquiring online learning data of the students, learning states of the students can be known favorably, learning contents can be optimized according to the learning states, and personalized online teaching services are better provided for the students.
Example 2
An embodiment of the present invention provides an online learning investment dynamic evaluation system, as shown in fig. 3, including:
the characteristic engineering module 1 is used for dividing the acquired online learning data into learning space input characteristics with different granularities and learning time input characteristics with different dimensionalities and calculating the characteristics of the combination of each space granularity input and each time dimensionality input; this module performs the method described in step S1 of embodiment 1, and is not described here.
The model establishing module 2 is used for establishing space evaluation models based on space granularity, the input characteristic of each space evaluation model is the characteristic of each time dimension under the space granularity, and the characteristic of the preset representation student learning performance is used as a prediction index; establishing time evaluation models based on time dimensions, wherein the input features of each time evaluation model are features of each space granularity under the time dimensions, and the features representing the learning performance of students are preset as prediction indexes; the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a space input comprehensive model is constructed; the output of each time dimension model is used as input, the characteristics representing the learning performance of students in a preset mode are used as prediction indexes, and a time input comprehensive model is constructed; this module performs the method described in steps S2 to S5 in embodiment 1, and is not described here.
The dynamic index acquisition module 3 is used for regularly acquiring real-time online learning data of students to perform feature calculation, forming each index under input space granularity and time dimension, respectively and correspondingly inputting the indexes into a space evaluation model and a time evaluation model, generating a prediction index of the space dimension and a prediction index of the time dimension, finally inputting the prediction index of the space granularity into a space input comprehensive model, inputting the prediction index of the time dimension into a time input comprehensive model, performing final dynamic evaluation prediction, and forming a comprehensive prediction index of the space granularity and a comprehensive prediction index of the time dimension; this module executes the method described in step S6 in embodiment 1, which is not described here.
The online learning investment dynamic evaluation system provided by the embodiment of the invention is characterized in that an online learning investment dynamic evaluation model based on three-layer space multi-dimensional time characteristics is formed by ten models including three models of space dimensions, five models of time dimensions, a time investment comprehensive evaluation model and a space investment comprehensive model, online learning investment of students can be comprehensively evaluated based on the dynamic evaluation model by acquiring online learning data of the students, learning states of the students can be known favorably, learning contents can be optimized according to the learning states, and personalized online teaching services are better provided for the students.
Example 3
An embodiment of the present invention provides a computer device, as shown in fig. 4, the device may include a processor 51 and a memory 52, where the processor 51 and the memory 52 may be connected by a bus or in another manner, and fig. 4 takes the connection by the bus as an example.
The memory 52, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions/modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 52, that is, implements the online learning investment dynamic evaluation method in the above method embodiment 1.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 52, and when executed by the processor 51, perform the online learning investment dynamic evaluation method in embodiment 1.
The details of the computer device can be understood by referring to the corresponding related description and effects in embodiment 1, which are not described herein.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by a computer program that instructs the relevant hardware to perform the processes, and the computer program may be stored in a computer readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications of the invention may be made without departing from the spirit or scope of the invention.
Claims (10)
1. A dynamic evaluation method for online learning investment is characterized by comprising the following steps:
dividing the acquired online learning data into learning space input features with different granularities and learning time input features with different dimensionalities, and calculating the combined features of each space granularity input and each time dimensionality input;
establishing space evaluation models based on the space granularity, wherein the input characteristics of each space evaluation model are the characteristics of each time dimension under the space granularity, and the characteristics representing the learning performance of students are preset as prediction indexes;
establishing time evaluation models based on time dimensions, wherein the input characteristics of each time evaluation model are characteristics under each space granularity under the time dimensions, and the characteristics representing the learning performance of students are preset and used as prediction indexes;
the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a space input comprehensive model is constructed;
the output of each time dimension model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a time input comprehensive model is constructed;
the method comprises the steps of collecting real-time online learning data of students regularly to carry out feature calculation, forming indexes under input space granularity and time dimension, inputting the indexes into a space evaluation model and a time evaluation model correspondingly, generating prediction indexes of the space dimension and prediction indexes of the time dimension, inputting the prediction indexes of the space granularity into a space input comprehensive model, inputting the prediction indexes of the time dimension into a time input comprehensive model, carrying out final dynamic evaluation prediction, and forming comprehensive prediction indexes of the space granularity and comprehensive prediction indexes of the time dimension.
2. The online learning investment dynamic evaluation method according to claim 1, wherein the granularity of the learning space comprises: course granularity, learning task granularity and learning page granularity, and the corresponding spatial evaluation model comprises: a course model, a task model, and a page model.
3. The online learning engagement dynamic evaluation method according to claim 1 or 2, wherein the dimension of learning time includes: duration, interval, moment, time sequence, dynamic trend, the corresponding time evaluation model includes: a duration model, an interval model, a time model, a timing model and a trend model.
4. The method according to claim 3, wherein the characteristics of each combination of spatial granularity investment and time dimension investment comprises:
the course investment is respectively combined with the combination characteristics of duration, interval, time sequence and dynamic trend investment;
the task investment is respectively combined with the characteristics of duration, interval, time sequence and dynamic trend investment;
the page investment is respectively combined with the characteristics of duration, time sequence and dynamic trend investment.
5. The online learning investment dynamic evaluation method according to claim 4,
the combined characteristics of course and duration investments include: the course investment total amount, the course learning concentration degree, the course learning distribution and the course learning speed; the combined characteristics of the course and interval plunges include: the course learning rhythm and the course learning intensity distribution; the combined characteristics of course investment and time investment comprise: delay in learning; the combined characteristics of course and time series investments include: the learning mode and the activity of the course are rich; the combined features of the lesson investment and the dynamic trend investment include: the course concentration degree variation trend, the course rhythm variation trend, the course distribution variation trend and the course learning speed variation trend;
the combined characteristics of the task investment and the duration investment comprise: task input allocation, task learning concentration degree, total task learning input amount and task learning speed; the combined characteristics of the mission investment and the interval investment include: task learning rhythm and task learning intensity distribution; the combined characteristics of the mission investment and the time investment comprise: task delay; the combined features of mission investment and timing investment include: task participation model and behavior diversity; the combined features of mission investment and dynamic trend investment include: concentration variation trend, task rhythm variation trend, task distribution variation trend and task speed variation trend;
the combined characteristics of the page investment and the duration investment comprise: page immersion, page learning distribution and page learning speed; the combined features of page throw and timing throw include: attention circulation patterns, cognitive load amounts; the combined features of page engagement and dynamic trend engagement include: the method comprises the following steps of page immersion degree variation trend, page distribution variation trend and page learning speed variation trend.
6. The on-line learning input dynamic evaluation method according to claim 1, wherein the presetting of characteristics for characterizing the learning performance of students comprises: achievement, completion, adherence.
7. The online learning investment dynamic evaluation method according to claim 1, wherein each evaluation model, the spatial investment integration model and the time investment integration model in the spatial granularity and the time dimension are constructed based on a neural network.
8. An online learning investment dynamic evaluation system, comprising:
the characteristic engineering module is used for dividing the acquired online learning data into learning space input characteristics with different granularities and learning time input characteristics with different dimensionalities and calculating the characteristics of the combination of each space granularity input and each time dimensionality input;
the model establishing module is used for establishing space evaluation models based on the space granularity, the input characteristics of each space evaluation model are the characteristics of each time dimension under the space granularity, and the characteristics representing the learning performance of students are preset as prediction indexes; establishing time evaluation models based on time dimensions, wherein the input characteristics of each time evaluation model are characteristics under each space granularity under the time dimensions, and the characteristics representing the learning performance of students are preset and used as prediction indexes; the output of each space granularity model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a space input comprehensive model is constructed; the output of each time dimension model is used as input, the characteristics representing the learning performance of students are preset as prediction indexes, and a time input comprehensive model is constructed;
the dynamic index acquisition module is used for regularly acquiring real-time online learning data of students to perform feature calculation, forming indexes under input space granularity and time dimension, respectively and correspondingly inputting the indexes into a space evaluation model and a time evaluation model, generating prediction indexes of the space dimension and prediction indexes of the time dimension, finally inputting the prediction indexes of the space granularity into the space input comprehensive model, inputting the prediction indexes of the time dimension into the time input comprehensive model, performing final dynamic evaluation prediction, and forming comprehensive prediction indexes of the space granularity and comprehensive prediction indexes of the time dimension.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions for causing the computer to execute the online learning investment dynamic evaluation method according to any one of claims 1 to 7.
10. 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 computer instructions to perform the online learning input dynamic evaluation method according to any one of claims 1 to 7.
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