CN113509189A

CN113509189A - Learning state monitoring method and related equipment thereof

Info

Publication number: CN113509189A
Application number: CN202110769337.9A
Authority: CN
Inventors: 胡铭铭; 梁华东; 李鑫
Original assignee: iFlytek Co Ltd
Current assignee: iFlytek Co Ltd
Priority date: 2021-07-07
Filing date: 2021-07-07
Publication date: 2021-10-19

Abstract

The application discloses a learning state monitoring method and related equipment thereof, wherein the method comprises the following steps: after the monitored object is determined to complete the test question to be evaluated, determining the fatigue score to be used of the monitored object according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated; and if the fatigue score to be used of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object is the fatigue state when the monitored object answers the test question to be evaluated, so that the real-time monitoring is carried out on the learning state of the monitored object.

Description

Learning state monitoring method and related equipment thereof

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a learning state monitoring method and related equipment.

Background

Learning has long been the focus of research. For students, the learning state is an important reason for influencing the learning efficiency and the achievement of the students. The students mainly do mental activities during learning, and the mental activities with high intensity or lasting for a long time bring uncomfortable states (such as fatigue states) to the students, the uncomfortable states can lead to the weakening of the learning ability of the students, the reduction of the learning efficiency and the psychological 'exhaustion', meanwhile, the students can also lead to emotional reactions such as tiredness and the like to the learning, and the learning effect of the students can be worse and worse after a long time.

However, how to monitor the learning state is still an urgent technical problem to be solved.

Disclosure of Invention

The embodiment of the present application mainly aims to provide a learning state monitoring method and related devices, which can monitor a learning state.

The embodiment of the application provides a learning state monitoring method, which comprises the following steps:

after the monitored object is determined to finish the test questions to be evaluated, determining the fatigue scores to be used of the monitored object according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object and the attribute information of the test questions to be evaluated; the electroencephalogram data to be used of the monitored object comprises electroencephalogram data appearing when the monitored object answers the test question to be evaluated; the eye movement data to be used of the monitored object comprises eye movement data which appears when the monitored object answers the test question to be evaluated; the answer information to be used of the monitored object comprises answer data of the monitored object aiming at the test question to be evaluated;

and if the to-be-used fatigue score of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object in answering the to-be-evaluated test question is a fatigue state.

In a possible embodiment, the process of determining the fatigue score to be used of the monitored object comprises:

determining a body fatigue score of the monitored object according to the electroencephalogram data to be used of the monitored object and the eye movement data to be used of the monitored object;

and determining the fatigue score to be used of the monitored object according to the body fatigue score of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated.

In one possible embodiment, the process of determining the physical fatigue score of the monitored subject includes:

performing feature extraction on the electroencephalogram data to be used of the monitored object to obtain electroencephalogram features to be used of the monitored object;

performing feature analysis on the eye movement data to be used of the monitored object to obtain the eye movement feature to be used of the monitored object;

performing factor analysis on the electroencephalogram characteristics to be used of the monitored object and the eye movement characteristics to be used of the monitored object to obtain at least one common factor and a weighting weight corresponding to the at least one common factor;

and carrying out weighted summation on the at least one common factor according to the weighted weight corresponding to the at least one common factor to obtain the body fatigue score of the monitored object.

In a possible embodiment, if the answer information to be used includes answer duration to be used and answer accuracy rate to be used, and the attribute information includes reference duration, determining the fatigue score to be used of the monitored object according to the body fatigue score of the monitored object, the answer information to be used of the monitored object, and the attribute information of the test question to be evaluated includes:

determining the reply duration score of the monitored object according to the ratio of the to-be-used reply duration of the monitored object to the reference duration of the to-be-evaluated test question; the answer duration to be used of the monitored object refers to the answer duration of the monitored object for the test question to be evaluated;

determining the response accuracy score of the monitored object according to the reciprocal of the to-be-used answer accuracy of the monitored object; the answer accuracy rate of the monitored object to be used refers to the answer accuracy rate of the monitored object to the test question to be evaluated;

and determining the fatigue score to be used of the monitored object according to the physical fatigue score of the monitored object, the response duration score of the monitored object and the response accuracy score of the monitored object.

In one possible embodiment, the determining the fatigue score to be used of the monitored subject according to the physical fatigue score of the monitored subject, the response duration score of the monitored subject and the response accuracy score of the monitored subject includes:

determining a fatigue score to be used of the monitored subject according to a product of the physical fatigue score of the monitored subject, the response duration score of the monitored subject and the response accuracy score of the monitored subject.

In one possible embodiment, the fatigue score threshold is generated according to sample brain electrical data of a sample object, sample eye movement data of the sample object, sample answer information of the sample object and attribute information of a sample test; the sample test questions and the test questions to be evaluated meet a preset association relationship; the sample electroencephalogram data of the sample object comprises electroencephalogram data which appears when the sample object answers the sample test question; the sample eye movement data for the sample object comprises eye movement data that occurs for the sample object in response to the sample question; the sample answer information of the sample object comprises answer data of the sample object aiming at the sample test question.

In a possible implementation manner, the meeting of the preset association relationship between the sample test questions and the test questions to be evaluated includes:

the test question difficulty of the sample test question is the same as the test question difficulty of the test question to be evaluated;

alternatively, the first and second electrodes may be,

the test question type of the sample test question is the same as the test question type of the test question to be evaluated;

alternatively, the first and second electrodes may be,

the test question type and the test question difficulty of the sample test question are respectively the same as the test question type and the test question difficulty of the test question to be evaluated;

alternatively, the first and second electrodes may be,

and the sample test questions are the test questions to be evaluated.

In a possible implementation, the generation process of the fatigue score threshold includes:

determining sample fatigue scores of the sample objects according to the sample electroencephalogram data of the sample objects, the sample eye movement data of the sample objects, the sample answer information of the sample objects and the attribute information of the sample test questions;

obtaining at least one personality score for the sample object;

determining the fatigue score threshold based on at least one personality score of the sample object and a sample fatigue score of the sample object.

In a possible implementation manner, if the number of personality scores is N and the number of sample objects is M, the determining the fatigue score threshold according to at least one personality score of the sample object and a sample fatigue score of the sample object includes:

obtaining a normal distribution mean value corresponding to the sample fatigue scores, a normal distribution standard deviation corresponding to the sample fatigue scores and a normal distribution graph corresponding to the sample fatigue scores according to the sample fatigue scores of the M sample objects; wherein M is a positive integer;

determining a normal distribution mean value corresponding to the nth personality score, a normal distribution standard deviation corresponding to the nth personality score and a normal distribution graph corresponding to the nth personality score according to the nth personality score of the M sample objects; wherein N is a positive integer, N is not more than N, and N is a positive integer;

and determining the fatigue score threshold according to the N personality scores of the M sample objects, the normal distribution graph corresponding to the sample fatigue score, the normal distribution mean value corresponding to the sample fatigue score, the normal distribution standard deviation corresponding to the sample fatigue score, the normal distribution graph corresponding to the N personality scores, the normal distribution mean value corresponding to the N personality scores and the normal distribution standard deviation corresponding to the N personality scores.

In a possible implementation, the determining of the fatigue score threshold includes:

determining test question scores corresponding to the nth personality score according to the nth personality score of the M sample objects, the normal distribution mean value corresponding to the sample fatigue score, the normal distribution standard deviation corresponding to the sample fatigue score, the normal distribution mean value corresponding to the nth personality score, the normal distribution standard deviation corresponding to the nth personality score and the correlation degree between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score; wherein N is a positive integer, N is not more than N, and N is a positive integer;

and determining the fatigue score threshold according to the N personality scores of the M sample objects and the test question scores corresponding to the N personality scores.

In one possible embodiment, the method further comprises:

after determining that the fatigue score to be used of the monitored object is higher than a fatigue score threshold value, generating reminding information and sending the reminding information to the monitored object; the reminding information is used for reminding the monitored object to take a rest.

The embodiment of the present application further provides a learning state monitoring device, including:

the system comprises a grading determination unit, a fatigue grading evaluation unit and a fatigue grading evaluation unit, wherein the grading determination unit is used for determining the fatigue grading to be used of a monitored object according to electroencephalogram data to be used of the monitored object, eye movement data to be used of the monitored object, answer information to be used of the monitored object and attribute information of a test question to be evaluated after the monitored object completes a test question to be evaluated; the electroencephalogram data to be used of the monitored object comprises electroencephalogram data appearing when the monitored object answers the test question to be evaluated; the eye movement data to be used of the monitored object comprises eye movement data which appears when the monitored object answers the test question to be evaluated; the answer information to be used of the monitored object comprises answer data of the monitored object aiming at the test question to be evaluated;

and the fatigue determining unit is used for determining that the learning state of the monitored object when answering the test question to be evaluated is a fatigue state if the fatigue score to be used of the monitored object is higher than a fatigue score threshold value.

An embodiment of the present application further provides a learning state monitoring system, the system includes: any one of the brain wave collecting device, the eye movement collecting device and the learning state monitoring device provided by the embodiment of the application; the brain wave acquisition equipment is used for acquiring electroencephalogram data to be used of a monitored object and sending the electroencephalogram data to be used of the monitored object to the learning state monitoring device; the eye movement acquisition equipment is used for acquiring the eye movement data to be used of the monitored object and sending the eye movement data to be used of the monitored object to the learning state monitoring device.

An embodiment of the present application further provides an apparatus, including: a processor, a memory, a system bus;

the processor and the memory are connected through the system bus;

the memory is used for storing one or more programs, and the one or more programs comprise instructions which, when executed by the processor, cause the processor to execute any implementation of the learning state monitoring method provided by the embodiment of the application.

The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a terminal device, the terminal device is enabled to execute any implementation of the learning state monitoring method provided in the embodiment of the present application.

The embodiment of the present application further provides a computer program product, and when the computer program product runs on a terminal device, the terminal device is enabled to execute any implementation manner of the learning state monitoring method provided by the embodiment of the present application.

Based on the technical scheme, the method has the following beneficial effects:

according to the technical scheme, after the monitored object is determined to complete the test question to be evaluated, determining the fatigue score to be used of the monitored object according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated; and if the fatigue score to be used of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object is the fatigue state when the monitored object answers the test question to be evaluated, so that monitoring is carried out aiming at the learning state of the monitored object.

Wherein, because the electroencephalogram data to be used of the monitored object includes the electroencephalogram data appearing when the monitored object answers the test question to be evaluated, the eye movement data to be used of the monitored object includes the eye movement data appearing when the monitored object answers the test question to be evaluated, and the answer information to be used of the monitored object includes the answer data of the monitored object for the test question to be evaluated, the fatigue score to be used of the monitored object determined based on the data can more accurately represent the learning state (such as whether in the fatigue state) of the monitored object when answering the test question to be evaluated, so that the fatigue score to be used can more accurately represent whether in the fatigue state when answering the test question to be evaluated, so that the learning state monitoring process based on the fatigue score to be used is more accurate, thereby being beneficial to improving the learning effect of the monitored object.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart of a learning status monitoring method according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a factor analysis provided in an embodiment of the present application;

fig. 3 is a schematic structural diagram of a learning status monitoring apparatus according to an embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a learning state monitoring system according to an embodiment of the present application.

Detailed Description

The inventors found in the study on the learning state that the learning state can be sometimes measured from a time perspective (for example, if a student has a learning period of 30 to 40 minutes, the student is determined to be in a fatigue state). However, when the students with different character characteristics face the same external environment change or are all in the same sudden pressure situation when the students with the same difficulty (or the same type of test questions or the same test questions) are faced under the same time requirement, the respective performances are different, so that the occurrence time points of the students with different character characteristics reaching the fatigue state are different. At this time, if the rest time points of the students are still determined according to the above time length (e.g., 30-40 minutes), it may cause fatigue of some students already in the learning process corresponding to the above time length. In addition, since the learning in the fatigue state is an ineffective learning (for example, in this state, the students may make mistakes in the mastered knowledge, and therefore perform wrong ineffective exercises), the "part of students" cannot continuously maintain efficient learning in the learning process corresponding to the above duration, and therefore the learning effect of the students is poor.

Based on the above findings, in order to solve the technical problems in the background art section, an embodiment of the present application provides a learning state monitoring method, including: after the monitored object is determined to complete the test question to be evaluated, determining the fatigue score to be used of the monitored object according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated; and if the fatigue score to be used of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object is the fatigue state when the monitored object answers the test question to be evaluated, so that monitoring is carried out aiming at the learning state of the monitored object.

In addition, the embodiment of the present application does not limit the execution subject of the learning state monitoring method, and for example, the learning state monitoring method provided by the embodiment of the present application may be applied to a data processing device such as a terminal device or a server. The terminal device may be a smart phone, a computer, a Personal Digital Assistant (PDA), a tablet computer, or the like. The server may be a stand-alone server, a cluster server, or a cloud server.

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all 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 application.

Method embodiment

Referring to fig. 1, the figure is a flowchart of a learning state monitoring method according to an embodiment of the present application.

The learning state monitoring method provided by the embodiment of the application comprises the following steps of S1-S2:

s1: after the monitored object is determined to complete the test question to be evaluated, determining the fatigue score to be used of the monitored object according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated.

The monitored object refers to a student needing to monitor the learning state.

The test questions to be evaluated refer to the test questions finished by the monitored object; in addition, the test questions to be evaluated are not limited in the embodiment of the present application, for example, in order to improve the monitoring real-time performance of the learning state, the test questions to be evaluated may refer to the test questions which are closest to the current time and completed by the monitored object. That is, in order to improve the monitoring real-time performance of the learning state, the learning state monitoring method provided in the embodiment of the present application may be immediately adopted to determine the learning state of the monitored object after each test question is completed by the monitored object, so as to reduce the learning duration of the monitored object in the fatigue state as much as possible, which is beneficial to improving the learning effect of the monitored object.

The electroencephalogram data to be used of the monitored object refers to electroencephalogram information which needs to be referred to when learning state monitoring is carried out on the monitored object; and the electroencephalogram data to be used of the monitored object comprises the electroencephalogram data which appears when the monitored object answers the test questions to be evaluated.

The electroencephalogram data are not limited in the embodiment of the application, and for example, the electroencephalogram data can include the arrangement entropy characteristics of electroencephalogram alpha waves (8-13Hz), electroencephalogram theta waves (4-7Hz) and electroencephalogram delta waves (1-3Hz) and the like because research shows that the amplitude of the electroencephalogram alpha waves (8-13Hz), the amplitude of the electroencephalogram theta waves (4-7Hz) and the amplitude of the electroencephalogram delta waves (1-3Hz) are closely related to fatigue. The present embodiment is not limited to the manner of acquiring the "electroencephalogram data", and may be implemented using any existing or future-developed equipment capable of acquiring electroencephalograms, for example.

The eye movement data to be used of the monitored object refers to eye movement information which needs to be referred to when learning state monitoring is carried out on the monitored object; and the eye movement data to be used of the monitored object comprises eye movement data which appears when the monitored object answers the test question to be evaluated.

The embodiment of the present application does not limit "eye movement data", for example, it may specifically include: and (4) changing characteristics of eye movement indexes such as saccades, eye movement tracks, staring, pupils and winks. In addition, the embodiment of the present application also does not limit the manner of acquiring the "eye movement data", and for example, the acquisition may be performed by using an eye tracker. The eye tracker is a complex psychological precision instrument, and can record the change characteristics of related eye movement indexes such as saccades, eye movement tracks, staring, pupils, winks and the like when a person processes visual information based on a pupil-cornea reflection principle or a fixation point recording principle.

The to-be-used answer information of the monitored object refers to test question answer data which needs to be referred to when learning state monitoring is carried out on the monitored object; and the to-be-used answer information of the monitored object comprises answer data of the monitored object for the test question to be evaluated.

The embodiment of the present application does not limit "reply data", and for example, it may specifically include: response duration and/or response accuracy. Wherein the response time period is used to represent the time consumed by a student to respond to a test question. The response accuracy rate is used to indicate the scoring condition of a student for a test question. In addition, the embodiment of the present application does not limit the manner of obtaining the "response data", for example, if the monitored object responds to the test question to be evaluated on an answering system, the "response data" may be read from a system background (e.g., a server) corresponding to the answering system.

The attribute information of the test questions to be evaluated is used for describing the test question characteristics of the test questions to be evaluated. In addition, the embodiment of the present application does not limit "attribute information", and for example, the attribute information may include at least one of difficulty of test questions, types of test questions, and reference time length.

The reference time length is used for representing the time length of reference required when a test question is answered; the "reference time duration" is not limited in the embodiments of the present application, and for example, the reference time duration may refer to a standard response time duration of the test question, or may refer to a standard response time duration of a test question set (e.g., if the monitored object is responding to a test question set, and the test question set includes the test question to be evaluated, the "reference time duration of the test question to be evaluated" may refer to a standard response time duration of the test question set).

The fatigue score to be used of the monitored object is used for representing the fatigue degree of the monitored object when the monitored object answers the test question to be evaluated, so that the 'fatigue score to be used of the monitored object' can accurately represent the learning state of the monitored object when the monitored object answers the test question to be evaluated.

In addition, the embodiment of the present application does not limit the determination process of the fatigue score to be used of the monitored object, for example, in a possible implementation manner, the determination process may specifically include steps 11 to 12:

step 11: and determining the body fatigue score of the monitored object according to the electroencephalogram data to be used of the monitored object and the eye movement data to be used of the monitored object.

The physical fatigue score of the monitored object is used for representing the fatigue degree of the monitored object from the physical state when the monitored object responds to the test question to be evaluated.

In addition, the embodiment of step 11 is not limited in the examples of the present application, for example, in a possible implementation, step 11 may specifically include steps 111 to 114:

step 111: and performing feature extraction on the electroencephalogram data to be used of the monitored object to obtain the electroencephalogram features to be used of the monitored object.

The electroencephalogram feature to be used of the monitored object refers to an electroencephalogram feature (for example, a frequency spectrum, an energy spectrum, a power spectrum and the like) carried by the electroencephalogram data to be used of the monitored object, so that the electroencephalogram feature to be used of the monitored object can accurately represent the electroencephalogram state characteristics of the monitored object when the monitored object answers the test question to be evaluated.

The embodiment of the present invention is not limited to the implementation of step 111, and may be implemented, for example, by any existing or future method capable of extracting brain wave features from electroencephalogram data.

Step 112: and carrying out characteristic analysis on the eye movement data to be used of the monitored object to obtain the eye movement characteristic to be used of the monitored object.

The to-be-used eye movement feature of the monitored object refers to the eye movement features (such as the features of the eye jump average speed, the average fixation time, the main eye jump sequence parameter (the parameters of the amplitude, the frequency and the like of the eyes moving from the current fixation point to the next fixation point) and the like) carried by the above-mentioned "to-be-used eye movement data of the monitored object", so that the "to-be-used eye movement feature of the monitored object" can accurately represent the eye movement state characteristics of the monitored object when the monitored object replies to the test question to be evaluated.

The embodiment of the present application is not limited to the implementation of step 112, and for example, the present application may be implemented by any existing or future method capable of extracting the eye movement features from the eye movement data.

Step 113: and performing factor analysis on the electroencephalogram characteristics to be used of the monitored object and the eye movement characteristics to be used of the monitored object to obtain at least one common factor and a weighting weight corresponding to the at least one common factor.

In this embodiment of the application, after acquiring the to-be-used electroencephalogram feature of the monitored object and the to-be-used eye movement feature of the monitored object, factor analysis (as shown in fig. 2) may be directly performed on the features to obtain at least one common factor (e.g., common factor 1 to common factor H in fig. 2) and a weighting weight (e.g., weight 1 to weight H in fig. 2) corresponding to each common factor, so that a physical fatigue score of the monitored object can be determined based on the common factors and the weighting weights corresponding to the common factors. Wherein H is a positive integer.

Step 114: and carrying out weighted summation on the at least one common factor according to the weighted weight corresponding to the at least one common factor to obtain the physical fatigue score of the monitored object.

For example, as shown in fig. 2, if the "at least one common factor" includes a common factor 1, common factors 2, … …, and a common factor H, and the weighting weight corresponding to the common factor 1 is a weighting 1, the weighting weight corresponding to the common factor 2 is a weighting 2, … …, and the weighting weight corresponding to the common factor H is a weighting H, the physical fatigue score of the monitored subject is equal to the common factor 1 × the weighting 1+ the common factor 2 × the weighting 2+ … … + the common factor H × the weighting H.

Based on the related content in the step 11, after acquiring the electroencephalogram data to be used and the eye movement data to be used of the monitored object, body state features (such as electroencephalogram features, eye movement features and the like) can be extracted from the data; and calculating the physical fatigue score of the monitored object by referring to the physical state characteristics, so that the physical fatigue score can accurately represent the fatigue degree of the monitored object from the physical state when the monitored object responds to the test question to be evaluated, and the fatigue score to be used of the monitored object can be determined based on the physical fatigue score.

Step 12: and determining the fatigue score to be used of the monitored object according to the body fatigue score of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated.

The embodiment of the present application is not limited to the implementation manner of step 12, for example, if the to-be-used answer information includes a to-be-used answer duration and a to-be-used answer accuracy, and the attribute information includes a reference duration, step 12 may specifically include steps 121 to 123:

step 121: and determining the response time length score of the monitored object according to the ratio of the to-be-used response time length of the monitored object to the reference time length of the to-be-evaluated test question.

The answer time length to be used of the monitored object is used for representing the time consumed by the monitored object to answer the test question to be evaluated.

The reference time length of the test question to be evaluated refers to the time length which needs to be referred when the answer time length grading is carried out on the test question to be evaluated; and the reference time period may be preset. It should be noted that the relevant content of "reference duration" is referred to above.

The response time length score of the monitored object is used for representing the fatigue degree of the monitored object on the response time length when responding to the test question to be evaluated; and the positive correlation is formed between the response time length score of the monitored object and the to-be-used answer time length of the monitored object.

In addition, the embodiment of the present application does not limit the determination process of the "reply duration score of the monitored object", and for example, the determination process may specifically include: and determining the ratio of the answer duration to be used of the monitored object to the reference duration of the test question to be evaluated as the answer duration score of the monitored object.

Step 122: and determining the response accuracy score of the monitored object according to the reciprocal of the to-be-used answer accuracy of the monitored object.

The answer accuracy rate of the monitored object to be used refers to the answer accuracy rate of the monitored object to the test question to be evaluated.

The response accuracy score of the monitored object is used for representing the fatigue degree of the monitored object from the response score when responding to the test question to be evaluated; and the negative correlation is formed between the answer accuracy score of the monitored object and the answer accuracy rate to be used of the monitored object.

In addition, the embodiment of the present application does not limit the determination process of the "response accuracy score of the monitored object", and for example, the determination process may specifically include: and determining the reciprocal of the to-be-used answer accuracy of the monitored object as the answer accuracy score of the monitored object.

Step 123: and determining the fatigue score to be used of the monitored object according to the physical fatigue score of the monitored object, the response time length score of the monitored object and the response accuracy score of the monitored object.

Example 1, step 123 may specifically include: and determining the fatigue score to be used of the monitored object according to the product of the physical fatigue score of the monitored object, the response duration score of the monitored object and the response accuracy score of the monitored object.

It can be seen that after the physical fatigue score of the monitored object, the response duration score of the monitored object and the response accuracy score of the monitored object are obtained, the product of the physical fatigue score of the monitored object, the response duration score of the monitored object and the response accuracy score of the monitored object may be referred to determine the fatigue score to be used of the monitored object (as shown in formula (1), the product of the physical fatigue score and the response duration score of the monitored object may be directly determined as the fatigue score to be used of the monitored object).

P_score＝z×(t_use/T_refer)/acc_score (1)

In the formula, P_scoreRepresenting a fatigue score to be used of the monitored object; z represents the physical fatigue score of the monitored subject; t is t_useRepresenting the answering time length to be used of the monitored object; t is_referRepresenting the reference time length of the test questions to be evaluated; acc (acrylic acid)_scoreAnd expressing the answer accuracy to be used of the monitored object.

Example 2, step 123 may specifically include: and determining the fatigue score to be used of the monitored object according to the sum of the physical fatigue score of the monitored object, the response time length score of the monitored object and the response accuracy score of the monitored object.

It can be seen that after the physical fatigue score of the monitored object, the response duration score of the monitored object, and the response accuracy score of the monitored object are obtained, the sum of the three values may be referred to determine the fatigue score to be used of the monitored object (for example, the sum of the three values may be directly determined as the fatigue score to be used of the monitored object).

Based on the related content of S1, after the monitored object completes the test question to be evaluated, the fatigue score to be used of the monitored object may be determined according to the electroencephalogram data and the eye movement data of the monitored object appearing when the monitored object answers the test question to be evaluated, the answer data of the monitored object for the test question to be evaluated, and the attribute information of the test question to be evaluated, so that the fatigue score to be used may accurately represent the fatigue degree of the monitored object when the monitored object answers the test question to be evaluated, and it may be possible to subsequently determine whether the monitored object is in a fatigue state based on the fatigue score to be used.

S2: and if the to-be-used fatigue score of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object is the fatigue state when the monitored object answers the to-be-evaluated test question.

Wherein the fatigue score threshold is used for describing a boundary between a fatigue state and a non-fatigue state; the determination method of the fatigue score threshold is not limited in the embodiments of the present application, and for example, the fatigue score threshold may be preset.

In some cases, to further improve the accuracy of the fatigue score threshold, the test questions with different attribute information may correspond to different fatigue score thresholds. Based on this, the present application provides a possible implementation manner of the learning status monitoring method, and before S2, the method further includes: and searching a fatigue scoring threshold corresponding to the attribute information of the test question to be assessed from the first mapping relation. The first mapping relation comprises the corresponding relation between the attribute information of the test question to be evaluated and the fatigue scoring threshold value corresponding to the attribute information of the test question to be evaluated.

It can be seen that the first mapping relationship is used to record fatigue scoring thresholds corresponding to different attribute information (e.g., test question difficulties and/or test question types), so in order to further improve the accuracy of the fatigue scoring thresholds, after obtaining the attribute information of the test question to be evaluated, the fatigue scoring threshold corresponding to the attribute information of the test question to be evaluated may be searched from the first mapping relationship (e.g., if the first mapping relationship is used to record fatigue scoring thresholds corresponding to different test question difficulties, the fatigue scoring threshold corresponding to the test question difficulty of the test question may be searched from the first mapping relationship; for example, if the first mapping relationship is used to record fatigue scoring thresholds corresponding to different test question types, the fatigue scoring threshold corresponding to the test question types of the test question may be searched from the first mapping relationship; for example, if the first mapping relationship is used to record different two tuples (test question difficulties, test question type), the fatigue score threshold corresponding to the binary group (test question difficulty of the test question to be evaluated, test question type of the test question to be evaluated) can be searched from the first mapping relation, so that the fatigue score threshold can be subsequently utilized to compare with the fatigue score to be used of the monitored object.

In some cases, to further improve the accuracy of the fatigue score threshold, different test questions may correspond to different fatigue score thresholds. Based on this, the present application provides a possible implementation manner of the learning status monitoring method, and before S2, the method further includes: and searching a fatigue score threshold corresponding to the test question to be evaluated from the second mapping relation. The second mapping relation comprises a corresponding relation between the test questions to be evaluated and fatigue scoring thresholds corresponding to the test questions to be evaluated.

Therefore, in order to further improve the accuracy of the fatigue score threshold, the fatigue score threshold corresponding to the test question to be evaluated can be searched from the second mapping relationship after the test question to be evaluated is obtained, so that the fatigue score threshold can be subsequently used for comparing with the fatigue score to be used of the monitored object.

In the embodiment of the present application, the manner of acquiring each fatigue score threshold in the first mapping relationship or the second mapping relationship is not limited, and may be set in advance, for example. For another example, the generation may be performed based on some sample data in advance, and for convenience of understanding, the generation process of a fatigue score threshold is described as an example below.

As an example, the fatigue score threshold may be generated in advance according to sample brain electrical data of a sample object, sample eye movement data of the sample object, sample answer information of the sample object, and attribute information of a sample test question.

Wherein, the sample object refers to a student which needs reference when generating a fatigue score threshold value; in addition, the number of sample objects is not limited in the embodiments of the present application, and for example, the number of sample objects may be M, where M is a positive integer.

The sample test questions refer to the test questions finished by the sample object; and the sample test question and the test question to be evaluated meet a preset association relationship.

In addition, the embodiment of the present application is not limited to the above-mentioned "preset association relationship", for example, the "preset association relationship" may include any one of the following relationships:

relation 1: the test difficulty of the sample test questions is the same as that of the test questions to be evaluated.

Relation 2: the test question type of the sample test question is the same as the test question type of the test question to be evaluated.

Relation 3: the test question type and the test question difficulty of the sample test question are respectively the same as the test question type and the test question difficulty of the test question to be evaluated.

Relationship 4: the sample test questions are to-be-evaluated test questions.

In addition, the determination process of the "preset association relationship" is not limited in the embodiment of the present application, for example, in a possible implementation manner, the "preset association relationship" may be determined according to a monitoring accuracy requirement in a current application scenario; it may specifically include: if the monitoring accuracy requirement under the current application scene comprises the accuracy of test question difficulty level, determining that all test questions share the same fatigue scoring threshold under the same test question difficulty, and determining that the preset association relation comprises that the test question difficulty of the sample test question is the same as the test question difficulty of the test question to be assessed; if the monitoring accuracy requirement under the current application scene comprises the accuracy reaching the test question type level, all the test questions belonging to the same test question type can be determined to share the same fatigue scoring threshold value, so that the test question type of the sample test question including the preset association relation can be determined to be the same as the test question type of the test question to be assessed; if the monitoring accuracy requirement under the current application scene is that the test question level accuracy is achieved, it can be determined that each test question corresponds to different fatigue scoring thresholds, so that the preset association relation can be determined to include that the sample test question is a test question to be assessed.

Therefore, the relation 1 can be used for constructing a first mapping relation in which fatigue score threshold values corresponding to different test question difficulties are recorded; the relation 2 can be used for constructing a first mapping relation recorded with fatigue score threshold values corresponding to different test question types; the relation 3 can be used for constructing a first mapping relation for recording fatigue score threshold values corresponding to different binary groups (test question difficulty and test question types); the above "relation 4" may be used to construct a second mapping relation in which fatigue score thresholds corresponding to different test questions are recorded.

The sample electroencephalogram data of the sample object refers to electroencephalogram information which needs to be referred to when the learning state of the sample object is monitored by using sample test questions; and the sample brain electrical data of the sample object may include brain electrical data that appears when the sample object answers the sample test question. Please refer to S1 above for the relevant content of the "electroencephalogram data".

The sample eye movement data of the sample object refers to eye movement information which needs to be referred to when the learning state of the sample object is monitored by using a sample test question; and the sample eye movement data for the sample object includes eye movement data that occurs when the sample object answers the sample question. It should be noted that the content of the "eye movement data" is referred to as S1 above.

The sample answer information of the sample object refers to test question answer data which needs to be referred to when the learning state of the sample object is monitored by using sample test questions; and the sample answer information of the sample object comprises the answer data of the sample object aiming at the sample test question. It should be noted that the relevant content of the "response data" is referred to above as S1.

In addition, the embodiment of the present application is not limited to the generation process of the "fatigue score threshold", for example, in one possible implementation, if the number of the sample objects is M, the generation process of the "fatigue score threshold" may specifically include steps 21 to 23:

step 21: and determining the sample fatigue score of the mth sample object according to the sample electroencephalogram data of the mth sample object, the sample eye movement data of the mth sample object, the sample answer information of the mth sample object and the attribute information of the mth sample test question. Wherein m is a positive integer; m is less than or equal to M, and M is a positive integer.

The sample fatigue score of the mth sample object is used for representing the fatigue degree of the mth sample object in answering the sample test question, so that the 'sample fatigue score of the mth sample object' can accurately represent the learning state of the mth sample object in answering the sample test question.

In addition, the determination process of the "sample fatigue score of the mth sample object" may be implemented by any one of the above embodiments of the determination process of the "fatigue score to be used of the monitored object", and it is sufficient to replace the "monitored object" by the "mth sample object" and the "fatigue score to be used" by the "sample fatigue score" in any one of the above embodiments of the determination process of the fatigue score to be used of the monitored object.

Step 22: obtaining at least one personality score of the mth sample object; wherein m is a positive integer; m is less than or equal to M, and M is a positive integer.

And at least one personality score of the mth sample object is used for describing the personality characteristics of the mth sample object in at least one personality dimension.

In addition, the examples of the present application do not limit "at least one personality", and may specifically include, for example, the personality measured by the five personality questionnaire (i.e., patency, responsibility, extroversion, hommization, and neutral quality), the personality measured by the Kate 16 personality questionnaire (i.e., race, clever, stability, strength, excitement, identity, dare, sensitivity, suspicion, hallucinationality, cause, apprehension, experiment, independence, discipline, and stress), and the personality measured by the Essenberg questionnaire (i.e., inside-out, neutral, and neutral qualities).

In addition, the embodiment of step 22 is not limited in this application, and for example, it may specifically include: at least one personality score of the mth sample object is determined based on measurements of the mth sample object on a preset personality measurement questionnaire (e.g., the wufeng questionnaire, the cartel 16 personality factor questionnaire, the asecker personality questionnaire, etc.).

Step 23: determining a fatigue score threshold based on the sample fatigue scores of the M sample objects and the at least one personality score of the M sample objects.

As an example, if the number of personality scores is N, step 23 may specifically include steps 231 to 233:

step 231: and obtaining a normal distribution mean value corresponding to the sample fatigue scores, a normal distribution standard deviation corresponding to the sample fatigue scores and a normal distribution graph corresponding to the sample fatigue scores according to the sample fatigue scores of the M sample objects.

The embodiment of the present application is not limited to the implementation of step 231, and for the convenience of understanding, the following description is made with reference to two examples.

Example 1, step 231 may specifically include steps 31-33:

step 31: and determining a normal distribution mean value corresponding to the sample fatigue scores according to the mean value among the sample fatigue scores of the M sample objects.

In the embodiment of the application, after the sample fatigue scores of the M sample objects are obtained, an average value among the sample fatigue scores of the M sample objects may be calculated first; then, the normal distribution mean corresponding to the sample fatigue score is determined according to the average value (for example, the average value can be directly determined as the normal distribution mean corresponding to the sample fatigue score).

Step 32: and determining normal distribution standard deviation corresponding to the sample fatigue scores according to the variance among the sample fatigue scores of the M sample objects.

In the embodiment of the application, after the sample fatigue scores of M sample objects are obtained, the variance among the sample fatigue scores of the M sample objects is calculated; then, the normal distribution standard deviation corresponding to the sample fatigue score is determined according to the average value (for example, the variance can be directly determined as the normal distribution standard deviation corresponding to the sample fatigue score).

Step 33: and constructing a normal distribution graph corresponding to the sample fatigue score according to the normal distribution mean value corresponding to the sample fatigue score and the normal distribution standard deviation corresponding to the sample fatigue score.

In this embodiment of the application, after the normal distribution mean corresponding to the sample fatigue score and the normal distribution standard deviation corresponding to the sample fatigue score are obtained, the normal distribution graph corresponding to the sample fatigue score may be constructed according to the normal distribution mean corresponding to the sample fatigue score and the normal distribution standard deviation corresponding to the sample fatigue score, so that the normal distribution graph may perform data distribution according to the normal distribution mean corresponding to the sample fatigue score and the normal distribution standard deviation corresponding to the sample fatigue score.

Based on the related contents in the above steps 31 to 33, after the sample fatigue scores of the M sample objects are obtained, the normal distribution mean value corresponding to the sample fatigue scores and the normal distribution standard deviation corresponding to the sample fatigue scores may be determined according to the sample fatigue scores of the M sample objects; and then, the normal distribution graph corresponding to the sample fatigue scores is constructed by referring to the two normal distribution parameters, so that the normal distribution graph can accurately show the normal distribution state of the sample fatigue scores of the M sample objects.

Example 2, step 231 may specifically include steps 41 to 42:

step 41: and performing normal distribution fitting on the sample fatigue scores of the M sample objects to obtain a normal distribution graph corresponding to the sample fatigue scores.

It should be noted that, the embodiment of the present application is not limited to the implementation of "fitting the normal distribution" in step 41, and may be implemented by any method that can fit the normal distribution from some data, which is currently available or will appear in the future.

Step 42: and determining a normal distribution mean value corresponding to the sample fatigue score and a normal distribution standard deviation corresponding to the sample fatigue score according to the normal distribution graph corresponding to the sample fatigue score.

Based on the related contents of the above steps 41 to 42, after the sample fatigue scores of the M sample objects are obtained, normal distribution fitting may be performed on the sample fatigue scores of the M sample objects to obtain a normal distribution graph corresponding to the sample fatigue scores, so that the normal distribution graph can accurately represent the normal distribution state of the sample fatigue scores of the M sample objects; and extracting a normal distribution mean value corresponding to the sample fatigue score and a normal distribution standard deviation corresponding to the sample fatigue score from the normal distribution graph.

Based on the related content of the above step 232, after the sample fatigue scores of the M sample objects are obtained, the normal distribution mean corresponding to the sample fatigue scores, the normal distribution standard deviation corresponding to the sample fatigue scores, and the normal distribution graph corresponding to the sample fatigue scores may be analyzed from the sample fatigue scores of the M sample objects, so that the fatigue score threshold may be determined based on these data in the following.

Step 232: according to the nth personality score of the M sample objects, determining a normal distribution mean value corresponding to the nth personality score, a normal distribution standard deviation corresponding to the nth personality score and a normal distribution graph corresponding to the nth personality score; wherein N is a positive integer, N is less than or equal to N, and N is a positive integer.

Example 1, step 232 may specifically include steps 51 to 53:

step 51: and determining a normal distribution mean value corresponding to the nth personality score according to the mean value among the nth personality scores of the M sample objects.

In the embodiment of the application, after the nth personality scores of the M sample objects are obtained, an average value between the nth personality scores of the M sample objects may be calculated first; then, the normal distribution mean corresponding to the nth personality score is determined according to the average (for example, the average may be directly determined as the normal distribution mean corresponding to the nth personality score).

Step 52: and determining the normal distribution standard deviation corresponding to the nth personality score according to the variance among the nth personality scores of the M sample objects.

In the embodiment of the application, after the nth personality scores of the M sample objects are obtained, the variance between the nth personality scores of the M sample objects is calculated; and then, determining a normal distribution standard deviation corresponding to the nth personality score according to the average value (for example, the variance can be directly determined as the normal distribution standard deviation corresponding to the nth personality score).

Step 53: and constructing a normal distribution graph corresponding to the nth personality score according to the normal distribution mean value corresponding to the nth personality score and the normal distribution standard deviation corresponding to the nth personality score.

In this embodiment of the application, after the normal distribution mean corresponding to the nth personality score and the normal distribution standard deviation corresponding to the nth personality score are obtained, the normal distribution graph corresponding to the nth personality score may be constructed according to the normal distribution mean corresponding to the nth personality score and the normal distribution standard deviation corresponding to the nth personality score, so that the normal distribution graph performs data distribution according to the normal distribution mean corresponding to the nth personality score and the normal distribution standard deviation corresponding to the nth personality score.

Based on the related contents in the above steps 51 to 53, after the nth personality score of the M sample objects is obtained, the normal distribution mean corresponding to the nth personality score and the normal distribution standard deviation corresponding to the nth personality score may be determined according to the nth personality score of the M sample objects; and then, constructing a normal distribution graph corresponding to the nth personality score by referring to the two normal distribution parameters, so that the normal distribution graph can accurately represent the normal distribution state of the nth personality score of the M sample objects.

Example 2, step 232 may specifically include steps 61 to 62:

step 61: and performing normal distribution fitting on the nth personality score of the M sample objects to obtain a normal distribution graph corresponding to the nth personality score.

It should be noted that, the embodiment of the present application is not limited to the implementation of "fitting a normal distribution" in step 61, and may be implemented by any method that can fit a normal distribution map from some data, which is currently available or will appear in the future.

Step 62: and determining a normal distribution mean value corresponding to the nth personality score and a normal distribution standard deviation corresponding to the nth personality score according to the normal distribution graph corresponding to the nth personality score.

Based on the related contents in the above steps 61 to 62, after the nth personality score of the M sample objects is obtained, a normal distribution fitting may be performed on the nth personality score of the M sample objects to obtain a normal distribution map corresponding to the nth personality score, so that the normal distribution map can accurately represent the normal distribution state of the nth personality score of the M sample objects; and extracting a normal distribution mean value corresponding to the nth personality score and a normal distribution standard deviation corresponding to the nth personality score from the normal distribution graph.

Based on the above-mentioned related content of step 232, after the nth personality score of the M sample objects is obtained, the normal distribution mean corresponding to the nth personality score, the normal distribution standard deviation corresponding to the nth personality score, and the normal distribution graph corresponding to the nth personality score may be analyzed from the nth personality score of the M sample objects, so that the fatigue score threshold may be determined based on these data in the following. Wherein N is a positive integer, N is less than or equal to N, and N is a positive integer.

Step 233: determining a fatigue score threshold according to the N personality scores of the M sample objects, the normal distribution graph corresponding to the sample fatigue score, the normal distribution mean value corresponding to the sample fatigue score, the normal distribution standard deviation corresponding to the sample fatigue score, the normal distribution graph corresponding to the N personality scores, the normal distribution mean value corresponding to the N personality scores and the normal distribution standard deviation corresponding to the N personality scores.

The embodiment of the present application is not limited to the implementation of step 233, and for example, it may specifically include steps 71 to 72:

step 71: and determining test question scores corresponding to the nth personality score according to the nth personality score of the M sample objects, the normal distribution mean value corresponding to the sample fatigue score, the normal distribution standard deviation corresponding to the sample fatigue score, the normal distribution mean value corresponding to the nth personality score, the normal distribution standard deviation corresponding to the nth personality score and the correlation degree between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score. Wherein N is a positive integer, N is less than or equal to N, and N is a positive integer.

The "degree of correlation between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score" is used to indicate the correlation between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score.

In the embodiment of the present application, the determination process of the "degree of correlation between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score" is not limited, and for example, the distance between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score may be calculated by using js divergence (Jensen-Shannon divergence); then, the reciprocal of the distance between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score is determined as the correlation degree between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score.

The test question score corresponding to the nth personality score is used to represent the learning state (e.g., fatigue degree) presented on the nth personality score by the above-mentioned "M sample objects" in response to the sample test questions.

In addition, the present example does not limit the implementation manner of step 71, and for the convenience of understanding, the following description is made with reference to two examples.

For example, if the test question score corresponding to the nth personality score only includes one numerical value, step 71 may be implemented using formula (2).

In the formula (I), the compound is shown in the specification,

showing the test question score corresponding to the nth personality score; u shape_zRepresenting a normal distribution mean value corresponding to the fatigue score of the sample; sd_zRepresenting normal distribution standard deviation corresponding to the sample fatigue score;

an nth personality score representing an mth sample object;

representing the normal distribution mean value corresponding to the nth personality score;

representing the normal distribution standard deviation corresponding to the nth personality score; d_nThe correlation degree between the normal distribution graph corresponding to the sample fatigue score and the normal distribution graph corresponding to the nth personality score is shown.

For example two, if the test question score corresponding to the nth personality score includes M numerical values, step 71 may be implemented using equation (3).

In the formula (I), the compound is shown in the specification,

indicates the mth number of the test question scores corresponding to the nth personality score (i.e., the test question score corresponding to the nth personality score includes

)；U_zRepresenting a normal distribution mean value corresponding to the fatigue score of the sample; sd_zRepresenting normal distribution standard deviation corresponding to the sample fatigue score;

an nth personality score representing an mth sample object;

Based on the related content in step 71, after obtaining the normal distribution mean corresponding to the sample fatigue score, the normal distribution standard deviation corresponding to the sample fatigue score, the normal distribution mean corresponding to the nth personality score, the normal distribution standard deviation corresponding to the nth personality score, and the normal distribution map corresponding to the nth personality score, the test question score corresponding to the nth personality score may be determined by referring to these information and the nth personality score of the M sample objects, so that the test question score corresponding to the nth personality score may accurately indicate the fatigue degree exhibited by the "M sample objects" in the nth personality score when the sample test questions are answered.

Step 72: and determining a fatigue score threshold according to the N personality scores of the M sample objects and the test question scores corresponding to the N personality scores.

The embodiment of step 72 is not limited to the embodiment of the present application, and for the convenience of understanding, the following description is made with reference to two examples.

Example one: if the test question corresponding to the nth personality score is scored as above

Step 72 may specifically be implemented using equation (4).

In the formula, P_thresholdRepresents a fatigue score threshold; (ii) a

An nth personality score representing an mth sample object;

and showing the test question score corresponding to the nth personality score.

Example two, if the test question score corresponding to the nth personality score comprises

Step 72 may specifically be implemented using equation (5).

In the formula, P_thresholdRepresents a fatigue score threshold; (ii) a

An nth personality score representing an mth sample object;

and the m-th numerical value in the test question score corresponding to the n-th personality score is shown.

Based on the related content of the fatigue score threshold, the fatigue score threshold can accurately represent the learning state (e.g., fatigue degree) of the student in the normal state (i.e., not reaching the fatigue state) when answering the test question to be evaluated, so that the fatigue score threshold can be used as a reference value to monitor the learning state of the student.

It can be seen that after the fatigue score to be used of the monitored object is obtained, if the fatigue score to be used of the monitored object is higher than a fatigue score threshold (e.g., a fatigue score threshold corresponding to the test question difficulty of the test question to be evaluated, a fatigue score threshold corresponding to the test question type of the test question to be evaluated, a fatigue score threshold corresponding to a binary group (the test question difficulty of the test question to be evaluated, the test question type of the test question to be evaluated), or a fatigue score threshold corresponding to the test question to be evaluated), it indicates that the fatigue degree of the monitored object presented when the test question to be evaluated is answered is higher, so that the learning state of the monitored object when the test question to be evaluated is answered can be determined to be the fatigue state; if the fatigue score to be used of the monitored object is not higher than the fatigue score threshold, it indicates that the fatigue degree of the monitored object in the reply of the test question to be evaluated is low, so that it can be determined that the learning state of the monitored object in the reply of the test question to be evaluated is a normal state.

In addition, in order to further improve the learning effect of the monitored object, after it is determined that the to-be-used fatigue score of the monitored object is higher than the fatigue score threshold, the reminding information is generated and sent to the monitored object, so that the reminding information is used for reminding the monitored object to take a rest.

Therefore, after the fatigue score to be used of the monitored object is determined to be higher than the fatigue score threshold value, the fatigue degree of the monitored object in answering the test question to be evaluated can be determined to be higher, so that the learning ability of the monitored object can be determined to be reduced, the monitored object can be reminded to have a rest by means of the reminding information, the monitored object can have a rest in time, the monitored object can be prevented from carrying out invalid learning in the fatigue state as far as possible, and the learning effect of the monitored object is improved.

The present embodiment does not limit the sending method of the "reminder information", and may specifically be, for example, displaying via a display screen, sending via a short message, sending via a mail, or the like.

Based on the related contents of S1 to S2, it can be known that, with the learning state monitoring method provided in the embodiment of the present application, after the monitored object is determined to complete the test question to be evaluated, the fatigue score to be used of the monitored object is determined according to the electroencephalogram data to be used of the monitored object, the eye movement data to be used of the monitored object, the answer information to be used of the monitored object, and the attribute information of the test question to be evaluated; and if the fatigue score to be used of the monitored object is higher than the fatigue score threshold value, determining that the learning state of the monitored object is the fatigue state when the monitored object answers the test question to be evaluated, so that monitoring is carried out aiming at the learning state of the monitored object.

Wherein, the electroencephalogram data to be used of the monitored object comprises the electroencephalogram data which appears when the monitored object answers the test question to be evaluated, the eye movement data to be used of the monitored object comprises the eye movement data which appears when the monitored object answers the test question to be evaluated, and the to-be-used answer information of the monitored object comprises the answer data of the monitored object aiming at the test question to be evaluated, so that the fatigue score to be used of the monitored object determined based on the data can more accurately represent the learning state of the monitored object when the monitored object replies to the test question to be evaluated, so that the fatigue score to be used can more accurately represent whether the monitored object has a fatigue state or not when answering the test question to be evaluated, therefore, the learning state monitoring process based on the fatigue score to be used is more accurate, and the learning effect of the monitored object is improved.

Based on the learning state monitoring method provided by the above method embodiment, the embodiment of the present application further provides a learning state monitoring device, which is explained and explained below with reference to the accompanying drawings.

Device embodiment

The embodiment of the apparatus introduces a learning status monitoring apparatus, and please refer to the above method embodiment for related contents.

Referring to fig. 3, the figure is a schematic structural diagram of a learning state monitoring device according to an embodiment of the present application.

The learning state monitoring device 300 provided in the embodiment of the present application includes:

the score determining unit 301 is configured to determine a fatigue score to be used of the monitored object according to electroencephalogram data to be used of the monitored object, eye movement data to be used of the monitored object, answer information to be used of the monitored object, and attribute information of the test question to be evaluated after the monitored object is determined to complete the test question to be evaluated; the electroencephalogram data to be used of the monitored object comprises electroencephalogram data appearing when the monitored object answers the test question to be evaluated; the eye movement data to be used of the monitored object comprises eye movement data which appears when the monitored object answers the test question to be evaluated; the answer information to be used of the monitored object comprises answer data of the monitored object aiming at the test question to be evaluated;

a fatigue determining unit 302, configured to determine that the learning state of the monitored object in answering the test question to be evaluated is a fatigue state if the fatigue score to be used of the monitored object is higher than a fatigue score threshold.

In a possible implementation, the score determining unit 301 includes:

the first determining subunit is used for determining a body fatigue score of the monitored object according to the electroencephalogram data to be used of the monitored object and the eye movement data to be used of the monitored object;

and the second determining subunit is used for determining the fatigue score to be used of the monitored object according to the body fatigue score of the monitored object, the answer information to be used of the monitored object and the attribute information of the test question to be evaluated.

In a possible implementation manner, the first determining subunit is specifically configured to: performing feature extraction on the electroencephalogram data to be used of the monitored object to obtain electroencephalogram features to be used of the monitored object; performing feature analysis on the eye movement data to be used of the monitored object to obtain the eye movement feature to be used of the monitored object; performing factor analysis on the electroencephalogram characteristics to be used of the monitored object and the eye movement characteristics to be used of the monitored object to obtain at least one common factor and a weighting weight corresponding to the at least one common factor; and carrying out weighted summation on the at least one common factor according to the weighted weight corresponding to the at least one common factor to obtain the body fatigue score of the monitored object.

In a possible implementation, the second determining subunit includes:

a third determining subunit, configured to determine, if the to-be-used answer information includes a to-be-used answer duration and a to-be-used answer accuracy, and the attribute information includes a reference duration, a score of the response duration of the monitored object according to a ratio between the to-be-used answer duration of the monitored object and the reference duration of the test question to be evaluated; the answer duration to be used of the monitored object refers to the answer duration of the monitored object for the test question to be evaluated;

the fourth determining subunit is used for determining the response accuracy score of the monitored object according to the reciprocal of the to-be-used answer accuracy of the monitored object; the answer accuracy rate of the monitored object to be used refers to the answer accuracy rate of the monitored object to the test question to be evaluated;

a fifth determining subunit, configured to determine a to-be-used fatigue score of the monitored object according to the physical fatigue score of the monitored object, the response duration score of the monitored object, and the response accuracy score of the monitored object.

In a possible implementation manner, the fifth determining subunit is specifically configured to: determining a fatigue score to be used of the monitored subject according to a product of the physical fatigue score of the monitored subject, the response duration score of the monitored subject and the response accuracy score of the monitored subject.

alternatively, the first and second electrodes may be,

and the sample test questions are the test questions to be evaluated.

obtaining at least one personality score for the sample object;

In a possible implementation manner, if the number of personality scores is N and the number of sample objects is M, the determining of the fatigue score threshold includes:

In a possible implementation, the learning state monitoring apparatus 300 further includes:

the information reminding unit is used for generating reminding information after determining that the fatigue score to be used of the monitored object is higher than a fatigue score threshold value, and sending the reminding information to the monitored object; the reminding information is used for reminding the monitored object to take a rest.

Based on the related content of the learning status monitoring apparatus 300, as shown in fig. 4, an embodiment of the present application further provides a learning status monitoring system 400, where the system 400 includes: any one of the brain wave acquisition device 401, the eye movement acquisition device 402, and the learning state monitoring apparatus 300 according to the embodiment of the present application; the brain wave acquisition device 401 is configured to acquire electroencephalogram data to be used of a monitored object and send the electroencephalogram data to be used of the monitored object to the learning state monitoring apparatus 300; the eye movement collecting device 402 is configured to collect the eye movement data to be used of the monitored object and send the eye movement data to be used of the monitored object to the learning state monitoring apparatus 300.

Further, an embodiment of the present application further provides a learning state monitoring device, including: a processor, a memory, a system bus;

the processor and the memory are connected through the system bus;

the memory is used for storing one or more programs, and the one or more programs comprise instructions which, when executed by the processor, cause the processor to execute any one of the implementation methods of the learning state monitoring method.

Further, an embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are run on a terminal device, the instructions cause the terminal device to execute any implementation method of the above learning state monitoring method.

Further, an embodiment of the present application further provides a computer program product, which when running on a terminal device, causes the terminal device to execute any implementation method of the above learning state monitoring method.

As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above embodiment methods can be implemented by software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.

It should be noted that, in the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments may be referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.

It is further noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A learning state monitoring method, the method comprising:

2. The method according to claim 1, wherein the determination of the fatigue score to be used for the monitored object comprises:

3. The method of claim 2, wherein the process of determining the physical fatigue score of the monitored subject comprises:

4. The method according to claim 2, wherein if the answer information to be used includes answer duration to be used and answer accuracy to be used, and the attribute information includes a reference duration, the determining the fatigue score to be used of the monitored object according to the physical fatigue score of the monitored object, the answer information to be used of the monitored object, and the attribute information of the test question to be evaluated comprises:

5. The method according to claim 4, wherein determining the fatigue score to be used of the monitored subject based on the physical fatigue score of the monitored subject, the response duration score of the monitored subject, and the response accuracy score of the monitored subject comprises:

6. The method of claim 1, wherein the fatigue score threshold is generated from sample brain electrical data of a sample object, sample eye movement data of the sample object, sample answer information of the sample object, and attribute information of a sample test; the sample test questions and the test questions to be evaluated meet a preset association relationship; the sample electroencephalogram data of the sample object comprises electroencephalogram data which appears when the sample object answers the sample test question; the sample eye movement data for the sample object comprises eye movement data that occurs for the sample object in response to the sample question; the sample answer information of the sample object comprises answer data of the sample object aiming at the sample test question.

7. The method according to claim 6, wherein the sample test questions and the test questions to be evaluated satisfy a preset association relationship, comprising:

alternatively, the first and second electrodes may be,

and the sample test questions are the test questions to be evaluated.

8. The method of claim 6, wherein the generating of the fatigue score threshold comprises:

obtaining at least one personality score for the sample object;

9. The method of claim 8, wherein if the number of personality scores is N and the number of sample objects is M, said determining the fatigue score threshold based on at least one personality score of the sample object and a sample fatigue score of the sample object comprises:

10. The method of claim 9, wherein the determining of the fatigue score threshold comprises:

11. The method of claim 1, further comprising:

12. A learning state monitoring apparatus, comprising:

13. A learning state monitoring system, the system comprising: a brain wave collecting device, an eye movement collecting device, and the learning state monitoring apparatus of claim 12; the brain wave acquisition equipment is used for acquiring electroencephalogram data to be used of a monitored object and sending the electroencephalogram data to be used of the monitored object to the learning state monitoring device; the eye movement acquisition equipment is used for acquiring the eye movement data to be used of the monitored object and sending the eye movement data to be used of the monitored object to the learning state monitoring device.

14. An apparatus, characterized in that the apparatus comprises: a processor, a memory, a system bus;

the processor and the memory are connected through the system bus;

the memory is for storing one or more programs, the one or more programs comprising instructions, which when executed by the processor, cause the processor to perform the method of any of claims 1 to 11.

15. A computer-readable storage medium having stored therein instructions which, when run on a terminal device, cause the terminal device to perform the method of any one of claims 1 to 11.

16. A computer program product, characterized in that it, when run on a terminal device, causes the terminal device to perform the method of any one of claims 1 to 11.