WO2023116306A1 - Information processing method and apparatus, and readable storage medium and electronic device - Google Patents

Abstract

The present disclosure relates to an information processing method and apparatus, and a readable storage medium and an electronic device. The method comprises: acquiring first answer information from a preset number of answerers of a target question bank within a previous time period, and second answer information from the current answerer of the target question bank within the current time period; constructing a Bayesian network model by taking, as parameters to be estimated, initial learning capacities of answerers and question difficulties in an item response theory model, and successful learning effects and failed learning effects of the answerers for knowledge points involved in the target question bank as a performance factor analysis model; determining an estimated value of each of said parameters on the basis of the Bayesian network model and the first answer information; and determining the current capacity of the current answerer according to the estimated values and the second answer information. In this way, with the accumulation of answer exercises of an answerer for a target question bank, successful and failed learning effects are continuously updated, thereby realizing the function of dynamically tracking a change in the learning capacity of the answerer, and the method is suitable for dynamic tracking of the learning capacity of the answerer in an online exercise scenario.

Description

Information processing method, device, readable storage medium and electronic equipment

Cross References to Related Applications

This application claims the priority of the Chinese patent application with the application number 202111574789.8 and the title of the invention "information processing method, device, readable storage medium and electronic equipment" submitted on December 21, 2021. The entire content of the application is passed References are incorporated in this application.

technical field

The present disclosure relates to the technical field of information processing, and in particular, to an information processing method, device, readable storage medium, and electronic equipment.

Background technique

In online practice scenarios, answer data with a time-series nature is becoming more and more common. For such data, the ability to dynamically track real-time changes of answerers has an important role and significance in evaluating learning effects. At this stage, the ability of the respondent is mainly evaluated through models such as Item Response Theory (IRT), Performance Factor Analysis (PFA), Knowledge Tracing Model (KT) and other models, but none of them can dynamically Track changes in answerer abilities. Therefore, how to realize the dynamic tracking of the respondent's ability has become the focus of research.

Contents of the invention

This Summary is provided to introduce a simplified form of concepts that are described in detail later in the Detailed Description. This summary of the invention is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to be used to limit the scope of the claimed technical solution.

In a first aspect, the present disclosure provides an information processing method, including:

Obtain the first answer information of the preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period, wherein the current answerer is the preset number one of the respondents to the question;

With the respondent's initial learning ability in the item response theory model, the difficulty of the topic and the performance factor analysis model, the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank are Parameters to be estimated, construct a Bayesian network model;

determining an estimated value of each parameter to be estimated based on the Bayesian network model and the first answer information;

According to the estimated value of each parameter to be estimated and the second answer information, determine the current ability of the current answerer.

In a second aspect, the present disclosure provides an information processing device, including:

An acquisition module, configured to acquire the first answer information of a preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period, wherein the current answerer is one of the preset number of respondents;

A building block for analyzing the respondent's successful learning effect on each knowledge point involved in the target question bank in the item response theory model based on the respondent's initial learning ability, topic difficulty and performance factors , The failure learning effect is a parameter to be estimated, and a Bayesian network model is constructed;

A first determination module, configured to determine an estimated value of each parameter to be estimated based on the Bayesian network model obtained by the construction module and the first answer information obtained by the acquisition module;

The second determination module is configured to determine the current question answerer's current question mark according to the estimated value of each parameter to be estimated determined by the first determination module and the second answer information obtained by the acquisition module. ability.

In a third aspect, the present disclosure provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the method provided in the first aspect of the present disclosure are implemented.

In a fourth aspect, the present disclosure provides an electronic device, including:

a storage device on which a computer program is stored;

A processing device configured to execute the computer program in the storage device to implement the steps of the method provided in the first aspect of the present disclosure.

In the above-mentioned technical scheme, firstly, the respondent's initial learning ability, topic difficulty and performance factors in the item response theory model are used to analyze the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank. The effect is the parameter to be estimated, and the Bayesian network model is constructed; then, based on the constructed Bayesian network model and the preset number of respondents’ first answer information about the target question bank in the previous period, the value of each parameter to be estimated is determined. Estimated value; finally, according to the estimated value of each parameter to be estimated and the current answerer's second answer information about the target question bank in the current period, determine the current ability of the current answerer. In this way, as the respondent accumulates answering exercises for the target question bank, the respondent's success learning effect and failure learning effect on each knowledge point involved in the target question bank are continuously updated, and then the function of dynamically tracking the change of the answerer's learning ability is realized. Applicable Dynamic tracking of learners' learning ability in online practice scenarios. In addition, when determining the current ability of the current answerer, its initial learning ability is referred to, that is, the ability level that the current answerer has acquired before the current period is referred to. In this way, it is closer to the actual learning scene, so that the current answerer can be accurately evaluated current capabilities.

Other features and advantages of the present disclosure will be described in detail in the detailed description that follows.

Description of drawings

The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale. In the attached picture:

Fig. 1 is a flowchart showing an information processing method according to an exemplary embodiment.

Fig. 2 is a flowchart showing an information processing method according to another exemplary embodiment.

Fig. 3 is a flowchart showing an information processing method according to another exemplary embodiment.

Fig. 4 is a block diagram of an information processing device according to an exemplary embodiment.

Fig. 5 is a block diagram of an electronic device according to an exemplary embodiment.

Detailed ways

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein; A more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for exemplary purposes only, and are not intended to limit the protection scope of the present disclosure.

It should be understood that the various steps described in the method implementations of the present disclosure may be executed in different orders, and/or executed in parallel. Additionally, method embodiments may include additional steps and/or omit performing illustrated steps. The scope of the present disclosure is not limited in this respect.

As used herein, the term "comprise" and its variations are open-ended, ie "including but not limited to". The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one further embodiment"; the term "some embodiments" means "at least some embodiments." Relevant definitions of other terms will be given in the description below.

It should be noted that concepts such as "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the sequence of functions performed by these devices, modules or units or interdependence.

It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more" multiple".

The names of messages or information exchanged between multiple devices in the embodiments of the present disclosure are used for illustrative purposes only, and are not used to limit the scope of these messages or information.

Fig. 1 is a flowchart showing an information processing method according to an exemplary embodiment. As shown in FIG. 1 , the above method includes S101 to S104.

In S101 , the first answer information of the preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period are acquired.

In the present disclosure, the current answerer is one of the preset number of answerers. The first answer information and the second answer information can include information such as answer questions and answer situations (such as answering correctly or incorrectly). In addition, the unit of day, week, quarter, etc. may be used as a time period.

In addition, the target question bank and the preset number of answerers can be selected according to actual needs. For example, it is possible to obtain the information on the first answer to the registered accounting question bank by accounting practitioners in city A last week, and the first answer information on subject four of the driver's license examiners in area B in the previous quarter.

In S102, take the respondent's initial learning ability in the item response theory model, the difficulty of the topic, and the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank in the performance factor analysis model to be estimated. Parameters to build a Bayesian network model.

In the present disclosure, the answerer is any one of the preset number of answerers mentioned above, and the learning effect represents the change speed of the answerer's ability in the process of accumulating learning times of the answerer.

In S103, the estimated value of each parameter to be estimated is determined based on the Bayesian network model and the first answer information.

In S104, the current ability of the current answerer is determined according to the estimated value of each parameter to be estimated and the second answer information.

In addition, it should be noted that the above S102 may be executed after the above S101 (as shown in FIG. 1 ), may also be executed simultaneously with the above S101, or may be executed before the above S101, which is not specifically limited in the present disclosure.

In the above-mentioned technical scheme, firstly, the respondent's initial learning ability, topic difficulty and performance factors in the item response theory model are used to analyze the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank. The effect is the parameter to be estimated, and the Bayesian network model is constructed; then, based on the constructed Bayesian network model and the preset number of respondents’ first answer information about the target question bank in the previous period, the value of each parameter to be estimated is determined. Estimated value; finally, according to the estimated value of each parameter to be estimated and the current answerer's second answer information about the target question bank in the current period, determine the current ability of the current answerer. In this way, as the respondent accumulates answering exercises for the target question bank, the respondent's success learning effect and failure learning effect on each knowledge point involved in the target question bank are continuously updated, and then the function of dynamically tracking the change of the answerer's learning ability is realized. Applicable Dynamic tracking of learners' learning ability in online practice scenarios. In addition, when determining the current ability of the current answerer, its initial learning ability is referred to, that is, the ability level that the current answerer has acquired before the current period is referred to. In this way, it is closer to the actual learning scene, so that the current answerer can be accurately evaluated current capabilities.

Exemplarily, the Bayesian network model is shown in the following equation (1):

in,

is the probability that the i-th answerer correctly answers the j-th question, y _ij =1; θ _i is the initial learning ability of the i-th answerer; b _j is the difficulty of the j-th question;

is a vector formed by the number of times the i-th answerer successfully learns each knowledge point involved in the target answer bank;

is a vector formed by the number of times the i-th answerer fails to learn at each knowledge point involved in the target answer bank;

is the successful learning effect of the ith answerer on each knowledge point involved in the target question bank;

is the failure learning effect of the ith answerer on each knowledge point involved in the target question bank;

is the distribution vector of the knowledge points involved in the jth topic; T is the transpose.

in,

as well as

All may be vectors of K*1, and K is the number of knowledge points involved in the target question bank.

In one embodiment,

It is equivalent to a binary vector, that is, the position that contains the knowledge point is 1, and the position that does not contain it is 0.

For example, the target question bank involves 5 knowledge points, namely knowledge point 1, knowledge point 2, knowledge point 3, knowledge point 4 and knowledge point 5, wherein the jth question involves knowledge point 1, knowledge point 3 and knowledge point 4 , then the distribution vector of the knowledge points involved in the jth topic

With the above implementation, the more knowledge points involved in the question, the higher the probability that the answerer will answer the question. In this way, the probability is directly related to the number of knowledge points involved in the question, resulting in low accuracy. Therefore, in another implementation manner, normalization processing is performed on the above-mentioned dichotomous vectors, and the distribution vector obtained after the normalization processing is determined as

Wherein, the sum of each element in the distribution vector obtained after the normalization processing is equal to 1. In this way, the probability will not increase due to the increase in the number of knowledge points involved in the topic, so that the accuracy of probability prediction can be improved.

For example, the target question bank involves 5 knowledge points, namely knowledge point 1, knowledge point 2, knowledge point 3, knowledge point 4 and knowledge point 5, wherein the jth question involves knowledge point 1, knowledge point 3 and knowledge point 4 , then the distribution vector of the knowledge points involved in the jth topic

In addition, the above distribution vectors can also be adjusted according to the importance of different knowledge points. Specifically, the element values at positions containing important knowledge points are relatively larger, and the element values at positions containing non-important knowledge points are relatively small. Thus reflecting the primary and secondary relationship of knowledge points.

The specific implementation manner of determining the estimated value of each parameter to be estimated based on the Bayesian network model and the first answer information in S103 will be described in detail below.

In one embodiment, based on the Bayesian network model and the first answer information, a Markov Chain Monte Carlo (MCMC) parameter estimation model can be used to determine the estimated value of each parameter to be estimated.

In another embodiment, based on the Bayesian network model and the first answer information, an expectation propagation (Expectation Propagation, EP) parameter estimation model can be used to determine the estimated value of each parameter to be estimated.

In yet another implementation manner, based on the Bayesian network model and the first answer information, a variational inference method may be used to determine the estimated value of each parameter to be estimated.

Specifically, it can be achieved through the following steps (1) to (3):

(1) For each parameter to be estimated, determine the prior distribution of the parameter to be estimated in the current period according to the approximate posterior distribution of the parameter to be estimated in the previous period.

(2) Based on the Bayesian network model, the first answer information and the prior distribution of each parameter to be estimated in the current period, the variational inference method is used to calculate the variational lower bound.

(3) The parameter estimation is performed with the maximization of the variational lower bound as the objective function, and the estimated value of each parameter to be estimated is obtained.

For example, parameter estimation may be performed with the maximization of the variational lower bound as the objective function, and an estimated value of each parameter to be estimated may be obtained by a stochastic gradient descent method. Since the specific implementation manner of using the stochastic gradient descent method to obtain the estimated value of each parameter to be estimated is well known to those skilled in the art, the present disclosure will not repeat it here.

In the above embodiment, according to the approximate posterior distribution of the parameter to be estimated in the previous period, the prior distribution of the parameter to be estimated in the current period is determined, which can make the variational inference method suitable for dynamic and continuous parameter estimation scenarios, And the amount of calculation and memory usage are better than the MCMC and EP parameter estimation methods mentioned above, and the estimated value of each parameter to be estimated can be estimated relatively accurately and quickly.

The specific implementation of determining the prior distribution of the parameter to be estimated in the current period based on the approximate posterior distribution of the parameter to be estimated in the previous period in the above step (1) will be described in detail below.

Specifically, it can be realized through various methods. In one embodiment, the approximate posterior distribution of the parameter to be estimated in the previous period can be determined as the prior distribution of the parameter to be estimated in the current period.

In another embodiment, the prior distribution of the parameter to be estimated in the current period can be determined by the following equation (2) according to the approximate posterior distribution of the parameter to be estimated in the previous period:

p(parameter)=(1-decay)*q _m (parameter)+decay*p(parameter) (2)

Among them, p(parameter) is the prior distribution of the parameter to be estimated in the current period; q _m (parameter) is the approximate posterior distribution of the parameter to be estimated in the previous period; decay is the weight coefficient.

In this embodiment, the prior distribution of the parameter to be estimated in the current period is not directly replaced by the approximate posterior distribution of the parameter to be estimated in the previous period, but the weighted average method is used to determine the parameter to be estimated The prior distribution in the current period, so that the approximate posterior distribution of the parameters to be estimated in the past period can gradually affect the update of the approximate posterior distribution of the current period and the future period, so that the initial stage (that is, the first few periods ) due to the lack of answer information and the influence of unstable posterior distribution estimation, thereby improving the accuracy of parameter estimation.

In the following step (2), based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period, the variational inference method is used to calculate the specific implementation of the variational lower bound. illustrate.

Specifically, it can be realized in a variety of ways. In one implementation, a variational inference method can be used based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period. , to calculate the variational lower bound by the following equation (3):

Among them, ELBO is the variational lower bound;

is the vector formed by the initial ability of each respondent among the preset number of respondents;

is a vector composed of the difficulty of each question in the target question bank;

is a vector composed of each respondent's successful learning effect on each knowledge point involved in the target question bank;

is a vector composed of each respondent’s failure learning effect on each knowledge point involved in the target question bank; likelihood is a reconstruction likelihood function based on the variational posterior distribution, according to the Bayesian network model and each parameter to be estimated Sure;

for right

expectations;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution.

Exemplarily,

in,

for

The posterior joint distribution of ,

for right

The expectation of each parameter to be estimated,

Substituting into the above Bayesian network and calculating

In another implementation, based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period, the variational inference method can be used to calculate the variable by the following equation (4): Sub-boundary:

Among them, shrink and enhance are hyperparameters; g is equal to the product of the preset number and the number of questions corresponding to the first answer information; max is equal to the product of the total number of answerers on the target question bank and the number of questions contained in the target question bank.

In this embodiment, in the parameter estimation process, in combination with the characteristics of the Bayesian network model, shrink and enhance are set as hyperparameters, which can improve the accuracy of parameter estimation. In addition, due to the small number of answers and information in the initial stage, the initial learning ability and difficulty of the questions are mainly estimated. However, with the accumulation of answering exercises for the target question bank, the initial learning ability and difficulty of the questions are basically fixed. It can make the variational inference method more focused on estimating the two parameters to be estimated, the successful learning effect and the failure learning effect of each knowledge point involved in the target question bank, so as to improve the accuracy of parameter estimation. In the sub-lower bound ELBO, the KL divergence corresponding to the learned parameters to be estimated is enhanced, namely

and

In addition, in an implementation manner, the shrink of each time period is a preset value.

In another embodiment, the shrink of the initial period (that is, the first period) is a preset value, and shrink=1 in the subsequent period, so that the prior distribution of the current period already includes the parameters learned in the previous period after approximation The prior distribution, that is, the prior distribution that retains the future period contains the learned historical information, thereby improving the accuracy of parameter estimation.

The specific implementation manner of determining the current ability of the current answerer according to the estimated value of each parameter to be estimated and the second answer information in the above S104 will be described in detail below. Specifically, it can be achieved through the following steps:

First, according to the second answer information, determine the number of successful learning and the number of learning failures of the current answerer under each knowledge point involved in the topic corresponding to the second answer information.

Among them, the number of successful learning is the number of correct answers, and the number of learning failures is the number of wrong answers.

Then, according to the estimated value of each parameter to be estimated, the number of learning successes and the number of learning failures, determine the current ability of the current respondent.

Exemplarily, the current ability of the current respondent

Fig. 2 is a flowchart showing an information processing method according to another exemplary embodiment. As shown in FIG. 2 , the above method further includes S105.

In S105, according to the current ability of the current answerer, the difficulty of the candidate questions in the target question bank, and the Bayesian network model, the probability of the current answerer correctly answering the candidate questions is determined.

In this disclosure, from the above

Find the difficulty of the candidate questions in the test.

Specifically, the Bayesian network model can be simplified as:

In this way, by substituting the current ability of the current answerer and the difficulty of the candidate questions in the target question bank into the simplified Bayesian network model, the probability of the current answerer correctly answering the candidate questions is obtained. Since the current ability of the answerer and the difficulty of the candidate questions can be accurately evaluated, the accuracy of the probability prediction can be guaranteed.

In addition, the questions can be automatically pushed to the answerers according to the above probability. Specifically, as shown in FIG. 3 , the above method further includes S106.

In S106, if the probability of the current answerer correctly answering the candidate question satisfies the preset condition, the candidate question is pushed to the current answerer.

In an implementation manner, the preset condition may be that the above probability is within a preset probability range, for example, 0.5-0.9.

In another embodiment, the preset condition may be that the entropy value of the candidate topic is greater than the preset threshold, where the entropy value of the candidate topic H=-plogp-(1-p)log(1-p), where p For the above probability, that is

According to the principle of maximum entropy, the greater the entropy value of a candidate question, the more information the answerer can obtain by practicing the question. Therefore, when H is greater than the preset threshold, the candidate question is pushed to the current answerer.

Fig. 4 is a block diagram of an information processing device according to an exemplary embodiment. As shown in Figure 4, the device 400 includes:

The acquisition module 401 is used to acquire the first answer information of the preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period, wherein the current answerer is one of the preset number of respondents;

The building block 402 is used to analyze the respondent's successful learning of each knowledge point involved in the target question bank with the respondent's initial learning ability, topic difficulty and performance factors in the item response theory model The effect and failure learning effect are the parameters to be estimated, and the Bayesian network model is constructed;

The first determination module 403 is configured to determine the estimated value of each parameter to be estimated based on the Bayesian network model obtained by the construction module 402 and the first answer information obtained by the acquisition module 401 ;

The second determination module 404 is configured to determine the current answer according to the estimated value of each parameter to be estimated determined by the first determination module 403 and the second answer information acquired by the acquisition module 401 the current capabilities of the

In the above-mentioned technical scheme, firstly, the respondent's initial learning ability, topic difficulty and performance factors in the item response theory model are used to analyze the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank. The effect is the parameter to be estimated, and the Bayesian network model is constructed; then, based on the constructed Bayesian network model and the preset number of respondents’ first answer information about the target question bank in the previous period, the value of each parameter to be estimated is determined. Estimated value; finally, according to the estimated value of each parameter to be estimated and the current answerer's second answer information about the target question bank in the current period, determine the current ability of the current answerer. In this way, as the respondent accumulates answering exercises for the target question bank, the respondent's success learning effect and failure learning effect on each knowledge point involved in the target question bank are continuously updated, and then the function of dynamically tracking the change of the answerer's learning ability is realized. Applicable Dynamic tracking of learners' learning ability in online practice scenarios. In addition, when determining the current ability of the current answerer, its initial learning ability is referred to, that is, the ability level that the current answerer has acquired before the current period is referred to. In this way, it is closer to the actual learning scene, so that the current answerer can be accurately evaluated current capabilities.

Optionally, the first determining module 403 is configured to use a Markov chain Monte Carlo parameter estimation model to determine the value of each parameter to be estimated based on the Bayesian network model and the first answer information. estimated value.

Optionally, the first determining module 403 is configured to determine an estimated value of each parameter to be estimated by using an expected propagation parameter estimation model based on the Bayesian network model and the first answer information.

Optionally, the first determination module 403 is configured to determine the estimated value of each parameter to be estimated by using a variational inference method based on the Bayesian network model and the first answer information.

Optionally, the first determining module 403 includes:

The first determining submodule is configured to, for each of the parameters to be estimated, determine the prior distribution of the parameter to be estimated in the current period according to the approximate posterior distribution of the parameter to be estimated in the previous period;

A calculation submodule, configured to calculate a variational lower bound by using a variational inference method based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period;

The estimation sub-module is used to perform parameter estimation with maximization of the variational lower bound as the objective function, and obtain an estimated value of each parameter to be estimated.

Optionally, the first determination submodule is configured to determine the prior distribution of the parameter to be estimated in the current period by the above equation (2) according to the approximate posterior distribution of the parameter to be estimated in the previous period. test distribution.

Optionally, the calculation submodule is configured to use a variational inference method based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period , to compute the variational lower bound by the above equation (4).

Optionally, shrink=1.

Optionally, the second determining module 404 includes:

The second determination sub-module is used to determine the number of times of successful learning and the number of times of learning failure of the current answerer under each knowledge point involved in the topic corresponding to the second answer information according to the second answer information;

The third determining submodule is used to determine the current ability of the current answerer according to the estimated value of each parameter to be estimated, the number of successful learning and the number of failed learning.

Optionally, the Bayesian network model is the above equation (1).

Optionally,

is the distribution vector obtained after normalization.

Optionally, the device 400 also includes:

The third determination module is configured to determine the probability of the current answerer correctly answering the candidate questions according to the current ability of the current answerer, the difficulty of the candidate questions in the target question bank, and the Bayesian network model.

Optionally, the device 400 also includes:

A push module, configured to push the candidate questions to the current answerer if the probability satisfies a preset condition.

The present disclosure also provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the above-mentioned information processing method provided by the present disclosure are realized.

Referring now to FIG. 5 , it shows a schematic structural diagram of an electronic device (such as a terminal device or a server) 600 suitable for implementing an embodiment of the present disclosure. The terminal equipment in the embodiment of the present disclosure may include but not limited to such as mobile phone, notebook computer, digital broadcast receiver, PDA (personal digital assistant), PAD (tablet computer), PMP (portable multimedia player), vehicle terminal (such as mobile terminals such as car navigation terminals) and fixed terminals such as digital TVs, desktop computers and the like. The electronic device shown in FIG. 5 is only an example, and should not limit the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 5, an electronic device 600 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 601, which may be randomly accessed according to a program stored in a read-only memory (ROM) 602 or loaded from a storage device 608. Various appropriate actions and processes are executed by programs in the memory (RAM) 603 . In the RAM 603, various programs and data necessary for the operation of the electronic device 600 are also stored. The processing device 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to the bus 604 .

Typically, the following devices can be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; including, for example, a liquid crystal display (LCD), speaker, vibration an output device 607 such as a computer; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While FIG. 5 shows electronic device 600 having various means, it should be understood that implementing or having all of the means shown is not a requirement. More or fewer means may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product, which includes a computer program carried on a non-transitory computer readable medium, where the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from storage means 608, or from ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the methods of the embodiments of the present disclosure are performed.

It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

In some embodiments, the client and the server can communicate using any currently known or future network protocols such as HTTP (HyperText Transfer Protocol, Hypertext Transfer Protocol), and can communicate with digital data in any form or medium The communication (eg, communication network) interconnections. Examples of communication networks include local area networks ("LANs"), wide area networks ("WANs"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network of.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device, or may exist independently without being incorporated into the electronic device.

The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device: obtains the first answer of the preset number of answerers on the target question bank in the previous period information and the current answerer's second answer information about the target question bank in the current period, wherein the current answerer is one of the preset number of answerers; the answerer in the item response theory model The respondent's initial learning ability, topic difficulty, and performance factor analysis model include the successful learning effect and failure learning effect of each knowledge point involved in the target question bank as parameters to be estimated, and a Bayesian network model is constructed. ; Based on the Bayesian network model and the first answer information, determine the estimated value of each parameter to be estimated; according to the estimated value of each parameter to be estimated and the second answer information, determine the estimated value Describe the current abilities of the current respondent.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, or combinations thereof, including but not limited to object-oriented programming languages—such as Java, Smalltalk, C++, and Includes conventional procedural programming languages - such as "C" or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, using an Internet service provider to connected via the Internet).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code that contains one or more logical functions for implementing specified executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified functions or operations , or may be implemented by a combination of dedicated hardware and computer instructions.

The modules involved in the embodiments described in the present disclosure may be implemented by software or by hardware. Among them, the name of the module does not constitute a limitation of the module itself under certain circumstances. For example, the obtaining module can also be described as "obtaining the first answer information and A module of the second answer information of the current answerer on the target question bank in the current period".

The functions described herein above may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), System on Chips (SOCs), Complex Programmable Logical devices (CPLDs), etc.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

According to one or more embodiments of the present disclosure, Example 1 provides an information processing method, including: obtaining the first answer information of a preset number of respondents in the previous period about the target question bank and the current respondent's information about the target question bank in the current period The second answer information of the target question bank, wherein, the current answerer is one of the preset number of answerers; the initial learning ability, difficulty level and The successful learning effect and the failure learning effect of the respondent in the performance factor analysis model on each knowledge point involved in the target question bank are parameters to be estimated, and a Bayesian network model is constructed; based on the Bayesian network model and the first answer information to determine the estimated value of each parameter to be estimated; according to the estimated value of each parameter to be estimated and the second answer information, determine the current ability of the current answerer.

According to one or more embodiments of the present disclosure, Example 2 provides the method of Example 1, wherein the estimated value of each parameter to be estimated is determined based on the Bayesian network model and the first answer information, The method includes: determining the estimated value of each parameter to be estimated by using a variational inference method based on the Bayesian network model and the first answer information.

According to one or more embodiments of the present disclosure, Example 3 provides the method of Example 1. Based on the Bayesian network model and the first answer information, the variational inference method is used to determine each of the estimated The estimated value of the parameter includes: for each parameter to be estimated, according to the approximate posterior distribution of the parameter to be estimated in the previous period, determining the prior distribution of the parameter to be estimated in the current period; based on the The Bayesian network model, the first answer information and the prior distribution of each parameter to be estimated in the current period, using the variational inference method to calculate the variational lower bound; maximize the variational lower bound Parameter estimation is performed for the objective function, and an estimated value of each parameter to be estimated is obtained.

According to one or more embodiments of the present disclosure, Example 4 provides the method of Example 3, wherein the parameter to be estimated in the current period is determined according to the approximate posterior distribution of the parameter to be estimated in the previous period Prior distributions, including:

According to the approximate posterior distribution of the parameter to be estimated in the previous period, the prior distribution of the parameter to be estimated in the current period is determined by the following formula, including:

p(parameter)=(1-decay)*q _m (parameter)+decay*p(parameter)

Wherein, p(parameter) is the prior distribution of the parameter to be estimated in the current period; q _m (parameter) is the approximate posterior distribution of the parameter to be estimated in the previous period; decay is a weight coefficient.

According to one or more embodiments of the present disclosure, Example 5 provides the method of Example 3, wherein based on the Bayesian network model, the first answer information and each of the parameters to be estimated in the current period The prior distribution of , using the variational inference method to calculate the variational lower bound, including:

Based on the Bayesian network model, the first answer information and the prior distribution of each parameter to be estimated in the current period, the variational inference method is used to calculate the variational lower bound by the following formula:

Wherein, ELBO is the variational lower bound;

is a vector of initial abilities for each said respondent;

is a vector formed by the difficulty of each question in the target question bank;

is a vector formed by each answerer's successful learning effect on each knowledge point involved in the target question bank;

is a vector composed of each respondent's failure learning effect on each knowledge point involved in the target question bank; likelihood is a reconstruction likelihood function based on the variational posterior distribution, according to the Bayesian network model and each of the parameters to be estimated; shrink and enhance are hyperparameters; g is equal to the product of the number of questions corresponding to the preset number and the first answer information; max is equal to the total number of answerers about the target question bank The product of the number of questions contained in the target question bank;

for right

expectations;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution;

for

The KL divergence of the approximate posterior distribution of and its prior distribution.

According to one or more embodiments of the present disclosure, Example 6 provides the method of Example 5, shrink=1.

According to one or more embodiments of the present disclosure, Example 7 provides the method of Example 1, wherein the current ability of the current answerer is determined according to the estimated value of each of the parameters to be estimated and the second answer information , including: according to the second answer information, determine the number of successful learning and the number of learning failures of the current answerer under each knowledge point involved in the topic corresponding to the second answer information; The estimated value of the parameter to be estimated, the number of successful learning and the number of failed learning determine the current ability of the current answerer.

According to one or more embodiments of the present disclosure, Example 8 provides the method of Example 1, and the Bayesian network model is:

in,

is the probability that the i-th answerer correctly answers the j-th question, y _ij =1; θ _i is the initial learning ability of the i-th answerer; b _j is the difficulty of the j-th question;

is a vector formed by the number of times the i-th answerer successfully learns each knowledge point involved in the target answer bank;

is a vector formed by the number of times the i-th answerer fails to learn at each knowledge point involved in the target answer bank;

is the successful learning effect of the ith answerer on each knowledge point involved in the target question bank;

is the failure learning effect of the ith answerer on each knowledge point involved in the target question bank;

is the distribution vector of the knowledge points involved in the jth topic.

According to one or more embodiments of the present disclosure, Example 9 provides the method of Example 8,

is the distribution vector obtained after normalization.

According to one or more embodiments of the present disclosure, Example 10 provides the method of any one of Example 1-Example 9, the method further includes: according to the current ability of the current answerer and the candidate questions in the target question bank The difficulty of the question and the Bayesian network model determine the probability that the current answerer answers the candidate question correctly.

According to one or more embodiments of the present disclosure, Example 11 provides the method of Example 10, the method further comprising: if the probability satisfies a preset condition, pushing the candidate question to the current answerer.

According to one or more embodiments of the present disclosure, Example 12 provides an information processing device, including: an acquisition module, configured to acquire the first answer information and the current answer of a preset number of answerers on the target question bank in the previous period The respondent's second answer information about the target question bank in the current period, wherein the current respondent is one of the preset number of respondent; a building block for responding to the item in the theoretical model The respondent's initial learning ability, topic difficulty and performance factor analysis model, the successful learning effect and failure learning effect of the respondent on each knowledge point involved in the target question bank are parameters to be estimated, and a Bayesian network is constructed. model; a first determination module, configured to determine an estimated value of each parameter to be estimated based on the Bayesian network model obtained by the construction module and the first answer information obtained by the acquisition module; The second determination module is configured to determine the current question answerer's current question mark according to the estimated value of each parameter to be estimated determined by the first determination module and the second answer information obtained by the acquisition module. ability.

According to one or more embodiments of the present disclosure, Example 13 provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processing device, the steps of the method in any one of Examples 1-11 are provided.

According to one or more embodiments of the present disclosure, Example 14 provides an electronic device, including: a storage device, on which a computer program is stored; a processing device, configured to execute the computer program in the storage device, to Implement the steps of any one of the methods in Examples 1-11.

The above description is only a preferred embodiment of the present disclosure and an illustration of the applied technical principle. Those skilled in the art should understand that the disclosure scope involved in this disclosure is not limited to the technical solution formed by the specific combination of the above-mentioned technical features, but also covers the technical solutions formed by the above-mentioned technical features or Other technical solutions formed by any combination of equivalent features. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

In addition, while operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or performed in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while the above discussion contains several specific implementation details, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims. Regarding the apparatus in the foregoing embodiments, the specific manner in which each module executes operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims (14)

1. An information processing method, characterized by comprising:

   Obtain the first answer information of the preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period, wherein the current answerer is the preset number one of the respondents to the question;

   With the respondent's initial learning ability in the item response theory model, the difficulty of the topic and the performance factor analysis model, the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank are Parameters to be estimated, construct a Bayesian network model;

   determining an estimated value of each parameter to be estimated based on the Bayesian network model and the first answer information;

   According to the estimated value of each parameter to be estimated and the second answer information, the current ability of the current answerer is determined.
2. The method according to claim 1, wherein the determining the estimated value of each parameter to be estimated based on the Bayesian network model and the first answer information includes:

   Based on the Bayesian network model and the first answer information, a variational inference method is used to determine an estimated value of each parameter to be estimated.
3. The method according to claim 2, wherein, based on the Bayesian network model and the first answer information, using a variational inference method to determine the estimated value of each of the parameters to be estimated includes:

   For each parameter to be estimated, according to the approximate posterior distribution of the parameter to be estimated in the previous period, determine the prior distribution of the parameter to be estimated in the current period;

   calculating a variational lower bound by using a variational inference method based on the Bayesian network model, the first answer information, and the prior distribution of each parameter to be estimated in the current period;

   Parameter estimation is performed with maximization of the variational lower bound as an objective function to obtain an estimated value of each parameter to be estimated.
4. The method according to claim 3, wherein the determining the prior distribution of the parameter to be estimated in the current period according to the approximate posterior distribution of the parameter to be estimated in the previous period comprises:

   According to the approximate posterior distribution of the parameter to be estimated in the previous period, the prior distribution of the parameter to be estimated in the current period is determined by the following formula, including:

   p(parameter)=(1-decay)*q _m (parameter)+decay*p(parameter)

   Wherein, p(parameter) is the prior distribution of the parameter to be estimated in the current period; q _m (parameter) is the approximate posterior distribution of the parameter to be estimated in the previous period; decay is a weight coefficient.
5. The method according to claim 3, characterized in that, based on the Bayesian network model, the first answer information and the prior distribution of each parameter to be estimated in the current period, using variable Fractional inference methods to calculate variational lower bounds, including:

   Based on the Bayesian network model, the first answer information and the prior distribution of each parameter to be estimated in the current period, the variational inference method is used to calculate the variational lower bound by the following formula:

   

   Wherein, ELBO is the variational lower bound;

   

   is a vector of initial abilities for each said respondent;

   

   is a vector formed by the difficulty of each question in the target question bank;

   

   is a vector formed by each answerer's successful learning effect on each knowledge point involved in the target question bank;

   

   is a vector composed of each respondent's failure learning effect on each knowledge point involved in the target question bank; likelihood is a reconstruction likelihood function based on the variational posterior distribution, according to the Bayesian network model and each of the parameters to be estimated; shrink and enhance are hyperparameters; g is equal to the product of the number of questions corresponding to the preset number and the first answer information; max is equal to the total number of answerers about the target question bank The product of the number of questions contained in the target question bank;

   

   for right

   

   expectations;

   

   for

   

   The KL divergence of the approximate posterior distribution of and its prior distribution;

   

   for

   

   The KL divergence of the approximate posterior distribution of and its prior distribution;

   

   for

   

   The KL divergence of the approximate posterior distribution of and its prior distribution;

   

   for

   

   The KL divergence of the approximate posterior distribution of and its prior distribution.
6. The method according to claim 5, characterized in that shrink=1.
7. The method according to claim 1, wherein the determining the current ability of the current answerer according to the estimated value of each parameter to be estimated and the second answer information includes:

   According to the second answer information, determine the number of successful learning and the number of learning failures of the current answerer for each knowledge point involved in the topic corresponding to the second answer information;

   The current ability of the current answerer is determined according to the estimated value of each parameter to be estimated, the number of successful learning and the number of failed learning.
8. The method according to claim 1, wherein the Bayesian network model is:

   

   in,

   

   is the probability that the i-th answerer correctly answers the j-th question, y _ij =1; θ _i is the initial learning ability of the i-th answerer; b _j is the difficulty of the j-th question;

   

   is a vector formed by the number of times the i-th answerer successfully learns each knowledge point involved in the target answer bank;

   

   is a vector formed by the number of times the i-th answerer fails to learn at each knowledge point involved in the target answer bank;

   

   is the successful learning effect of the ith answerer on each knowledge point involved in the target question bank;

   

   is the failure learning effect of the ith answerer on each knowledge point involved in the target question bank;

   

   is the distribution vector of the knowledge points involved in the jth topic.
9. The method according to claim 8, characterized in that,

   

   is the distribution vector obtained after normalization.
10. The method according to any one of claims 1-9, further comprising:

    According to the current ability of the current answerer, the difficulty of the candidate questions in the target question bank, and the Bayesian network model, the probability of the current answerer correctly answering the candidate questions is determined.
11. The method according to claim 10, characterized in that the method further comprises:

    If the probability satisfies the preset condition, the candidate question is pushed to the current answerer.
12. An information processing device, characterized in that it includes:

    An acquisition module, configured to acquire the first answer information of a preset number of answerers on the target question bank in the previous period and the second answer information of the current answerer on the target question bank in the current period, wherein the current answerer is one of the preset number of respondents;

    The building block is used to use the respondent's initial learning ability, topic difficulty and performance factors in the item response theory model to analyze the respondent's successful learning effect and failure learning effect on each knowledge point involved in the target question bank Construct a Bayesian network model for the parameters to be estimated;

    A first determination module, configured to determine an estimated value of each parameter to be estimated based on the Bayesian network model obtained by the construction module and the first answer information obtained by the acquisition module;

    The second determination module is configured to determine the current question answerer's current question mark according to the estimated value of each parameter to be estimated determined by the first determination module and the second answer information obtained by the acquisition module. ability.
13. A computer-readable medium, on which a computer program is stored, wherein, when the program is executed by a processing device, the steps of the method according to any one of claims 1-11 are realized.
14. An electronic device, characterized in that it comprises:

    a storage device on which a computer program is stored;

    A processing device, configured to execute the computer program in the storage device, so as to realize the steps of the method according to any one of claims 1-11.

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