School student learning state monitoring method and system

Abstract

The invention relates to the technical field of student state monitoring, in particular to a school student learning state monitoring method and system. The method comprises the following steps: acquiring learning state monitoring data of students at school; analyzing a first learning state of the school student through the learning performance monitoring data, and identifying a student with an abnormal learning state and a student with a normal learning state according to an analysis result of the first learning state; combining the learning performance monitoring data, the physiological monitoring data and the behavioral performance monitoring data to obtain learning difficulty parameters; analyzing the physiological monitoring data, the behavior performance monitoring data and the social emotion monitoring data to obtain learning mental health parameters; according to the learning difficulty parameters and the learning mental health parameters, the learning state of the student with the abnormal learning state is judged. According to the method, the learning state of the student with the abnormal learning state is judged by analyzing the learning state monitoring data, the problem of efficiently and accurately identifying the abnormal student state is solved, and the intervention golden period of the abnormal student can be prevented from being missed.

Description

Technical Field

[0001] The present invention relates to the technical field of student status monitoring, and in particular to a method and system for monitoring the learning status of students at school. Background Art

[0002] Currently, students are facing increasing difficulties in learning and depression during their studies. Traditional monitoring methods rely primarily on teacher observation and student self-reporting, making them difficult to quantify. Furthermore, mental health screening relies on periodic questionnaires, which are subject to lags and subjectivity, leading to students with learning difficulties being mistaken for laziness. Some use wearable devices to monitor students' physiological data (such as heart rate and sleep quality) or to record academic performance through learning platforms. However, these single-dimensional data lacks multimodal correlation analysis, such as behavioral and social data, making it difficult to comprehensively and accurately determine whether students are experiencing learning difficulties or are prone to depression.

[0003] Therefore, how to efficiently and accurately identify the abnormal status of students and avoid missing the golden period of intervention for abnormal students is an urgent problem that needs to be solved. Summary of the Invention

[0004] In view of the shortcomings of existing methods and the needs of practical applications, in order to efficiently and accurately identify abnormal student status, so as to facilitate the guidance of abnormal students with learning difficulties and psychological depression during the golden period of intervention. On the one hand, the present invention provides a method for monitoring the learning status of students in school, comprising the following steps: Acquire learning status monitoring data of students in school, wherein the learning status monitoring data includes learning performance monitoring data, physiological monitoring data, behavioral performance monitoring data and social emotion monitoring data; analyze the first learning status of the students in school through the learning performance monitoring data, and identify students with abnormal learning status and students with normal learning status according to the analysis results of the first learning status; obtain learning difficulty parameters by combining the learning performance monitoring data, the physiological monitoring data and the behavioral performance monitoring data; obtain learning mental health parameters by analyzing the physiological monitoring data, the behavioral performance monitoring data and the social emotion monitoring data; and judge the learning status of the students with abnormal learning status according to the learning difficulty parameters and the learning mental health parameters.

[0005] The present invention analyzes learning difficulty parameters and learning mental health parameters by analyzing learning performance monitoring data, physiological monitoring data, behavioral performance monitoring data and social emotion monitoring data, and then judges the learning status of students with abnormal learning status, solving the problem of efficiently and accurately identifying the status of abnormal students, which is conducive to avoiding missing the golden period of intervention for abnormal students.

[0006] Optionally, analyzing the first learning status of the students using the learning performance monitoring data, and identifying students with abnormal learning status and students with normal learning status according to the analysis results of the first learning status, includes the following steps: The learning performance monitoring data is used to analyze the student's academic performance change rate, classroom enthusiasm change rate, and homework completion change rate; the first learning state is obtained by combining the academic performance change rate, classroom enthusiasm change rate, and homework completion change rate; and students with abnormal learning states and students with normal learning states are identified based on the analysis results of the first learning state. The present invention calculates the first learning state based on the academic performance change rate, classroom enthusiasm change rate, and homework completion change rate in the learning performance monitoring data, which is conducive to quickly determining students in normal states and students in abnormal states, narrowing the scope of identified students and thus accelerating the identification of types of abnormal states.

[0007] Optionally, the first learning state is obtained by combining the grade change rate, the class enthusiasm change rate, and the homework completion change rate, and satisfies the following formula: ,in, represents the first learning state, The weight coefficient representing the rate of change of grades, Indicates the rate of change of performance. represents the performance coefficient, The weight coefficient representing the rate of change of classroom enthusiasm, represents the rate of change of classroom enthusiasm, represents the classroom enthusiasm coefficient, The weight coefficient representing the change rate of job completion, represents the change rate of job completion, Indicates the task completion coefficient.

[0008] Optionally, combining the learning performance monitoring data, the physiological monitoring data, and the behavioral performance monitoring data to obtain a learning difficulty parameter comprises the following steps: Based on the learning performance monitoring data, the learning performance parameters are analyzed; based on the physiological monitoring data and the behavioral performance monitoring data, the first influencing factor of learning performance and the second influencing factor of learning performance are analyzed respectively; and the learning performance parameters, the first influencing factor of learning performance, and the second influencing factor of learning performance are combined to obtain the learning difficulty parameter. The present invention calculates the learning difficulty parameter based on the learning performance parameters and the influencing factors, comprehensively considers multiple characteristic factors of learning difficulties, and can accurately characterize the degree of learning difficulties of students.

[0009] Optionally, the learning performance parameter is analyzed based on the learning performance monitoring data to satisfy the following formula: ,in, represents the learning performance parameter, represents the first learning state, represents the knowledge mastery entropy, represents the learning input-output ratio, Indicates the error rate of wrong question concepts.

[0010] Optionally, based on the physiological monitoring data, the first influencing factor of learning performance is analyzed to satisfy the following formula: ,in, It represents the first influencing factor of learning performance. Indicates the number of times your heart rate is greater than 110bpm while writing. represents the heart rate weight, Indicates the number of times you turn over more than 20 times at night. represents the weight of the number of turns, Indicates the number of times the number of steps taken during the break is less than 50. Indicates the weight of walking steps; Based on the behavioral performance monitoring data, the second influencing factor of learning performance is analyzed and satisfies the following formula: ,in, It represents the second most influential factor in learning performance. Indicates the number of behavioral performances, Indicates the The weight of the behavior, Indicates the The number of times this behavior appears in students, Indicates the The total number of times this behavior occurs in students with normal learning status in the class.

[0011] Optionally, analyzing the physiological monitoring data, the behavioral performance monitoring data, and the social emotion monitoring data to obtain learning mental health parameters includes the following steps: Based on the physiological monitoring data, the student's energy state value is calculated; based on the behavioral performance monitoring data, the student's first mental health factor is analyzed; through the social emotion monitoring data, the student's second mental health factor is obtained; and the learning mental health parameter is obtained by combining the energy state value, the first mental health factor, and the second mental health factor. The present invention calculates the learning mental health parameter using physiological monitoring data, behavioral performance monitoring data, and social emotion monitoring data, comprehensively considering multiple characteristic factors of mental health and accurately characterizing the student's mental health level.

[0012] Optionally, the combination of the energy state value, the first mental health factor, and the second mental health factor to obtain the learning mental health parameter satisfies the following formula: ,in, represents the learning mental health parameter, represents the first mental health factor, represents the second mental health factor, Indicates the energy status value.

[0013] Optionally, judging the learning status of the student with abnormal learning status according to the learning difficulty parameter and the learning mental health parameter comprises the following steps: Based on the learning difficulty parameters and the learning psychological health parameters of students with normal learning status, an abnormality judgment threshold is set; and based on the abnormality judgment threshold, the learning status of the student with abnormal learning status is judged. By fitting the data of normal students to generate an abnormality judgment threshold, and then judging the learning status of abnormal students, the present invention can efficiently and accurately determine whether a student is experiencing learning difficulties or a tendency towards psychological depression.

[0014] On the second aspect, in order to be able to efficiently execute the method for monitoring the learning status of students in school provided by the present invention, the present invention also provides a system for monitoring the learning status of students in school, including a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method for monitoring the learning status of students in school as described in the first aspect of the present invention. The system for monitoring the learning status of students in school of the present invention has a compact structure and stable performance, and can stably execute the method for monitoring the learning status of students in school provided by the present invention, further improving the overall applicability and practical application capabilities of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a method for monitoring the learning status of students in school provided by an embodiment of the present invention; Figure 2 This is a framework diagram of a system for monitoring the learning status of students in school provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0017] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0018] See also Figure 1 In order to identify abnormal student status efficiently and accurately, so as to facilitate the guidance of abnormal students with learning difficulties and psychological depression tendency during the golden period of intervention. The present invention provides a method for monitoring the learning status of students in school, such as Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Obtaining learning status monitoring data of students in school, wherein the learning status monitoring data includes learning performance monitoring data, physiological monitoring data, behavioral performance monitoring data, and social emotion monitoring data.

[0019] In the embodiment, the learning performance monitoring data of the students in school is obtained through the school's academic management system and online learning platform, including learning indicator data such as the students' course grades, homework completion status, exam answering time, distribution of wrong questions, and classroom performance.

[0020] Obtain physiological monitoring data of students in school through wearable devices (such as smart bracelets and smart watches), including heart rate variability (HRV), galvanic skin response (EDA), body surface temperature, sleep duration, sleep quality, exercise steps, circadian activity rhythm and other physiological indicator data.

[0021] Through the deployed cameras, computer vision technology is used to identify students' behavioral performance monitoring data, including classroom participation behaviors such as the number of times they raise their hands, the length of time they focus, facial expressions (such as happiness, calmness, surprise, disgust, anger, sadness, frowning frequency, blinking frequency), body movements (such as yawning, stretching, playing with mobile phones, raising hands, talking, taking notes), sitting posture, head posture (such as lowering the head, raising the head, and turning the head back), and other behavioral performance indicator data.

[0022] Through the school's academic affairs management system and campus video equipment, we obtain social emotion indicator data such as the proportion of negative words in the text, the occurrence rate of negative emotional words (such as "tired" and "boring"), the length of time spent alone, social interaction entropy, the length of interaction between teachers and students / classmates, and the number of times people seek psychological help.

[0023] Furthermore, in some other embodiments, Z-score normalization is performed on numerical features (such as grades and duration), and word vectors are generated for text data using TF-IDF or BERT.

[0024] S2. Analyze the first learning status of the students using the learning performance monitoring data, and identify students with abnormal learning status and students with normal learning status based on the analysis results of the first learning status.

[0025] Specifically, analyzing the first learning status of the students using the learning performance monitoring data, and identifying students with abnormal learning status and students with normal learning status according to the analysis results of the first learning status, includes the following steps: S21. Analyze the student's academic performance change rate, class enthusiasm change rate, and homework completion rate change rate through the learning performance monitoring data.

[0026] The grade change rate is the ratio of the grade change to the previous grade. A positive and large grade change rate indicates that the student has made significant learning progress during this period, possibly due to appropriate learning methods, a serious attitude, or a significant improvement in their knowledge base. A negative and large grade change rate may indicate that the student has encountered difficulties during the learning process, such as inappropriate learning methods, difficulty understanding new knowledge, or an unhealthy learning attitude. It may also be due to external factors such as changes in the family environment or physical discomfort. Minimal grade fluctuations indicate a relatively stable learning state.

[0027] The change rate of classroom enthusiasm is determined based on the number of times a student speaks, the frequency of participating in group discussions, and the number of questions asked. The corresponding change rate is determined based on the number of times two consecutive times, and then the change rate is calculated based on the corresponding weights of the number of times a student speaks, the frequency of participating in group discussions, and the number of questions asked. A positive and large change rate indicates increased student interest in class. This may be due to the teacher's more engaging teaching methods, the students' own strong interest in the course content, or the students' increased sense of accomplishment in class. A negative change rate may indicate that the teacher's teaching methods are too boring, or that the students have encountered setbacks during the learning process, which has undermined their confidence and led to a loss of interest in class. A change rate close to zero indicates that student enthusiasm in class remains relatively stable.

[0028] Assignment completion is expressed as the ratio of the actual amount of homework completed to the expected amount. A positive rate of change indicates a more serious attitude toward homework and increased self-awareness and initiative in learning. This may be due to a better grasp of the content, enabling students to successfully complete the assignment, or a recognition of its importance, leading to increased effort. A negative rate of change indicates a decrease in the difficulty of the assignment, leading to more difficulties, or a change in the student's learning attitude, such as procrastination or laziness. A stable completion rate indicates a relatively stable student's homework completion.

[0029] S22. Combining the grade change rate, the class enthusiasm change rate, and the homework completion change rate, obtain the first learning status.

[0030] In an embodiment, the first learning state is obtained by combining the grade change rate, the class enthusiasm change rate, and the homework completion change rate, and satisfies the following formula: ,in, represents the first learning state, The weight coefficient representing the rate of change of grades, Indicates the rate of change of performance. It represents the performance coefficient, which is the ratio of the student's performance to the class average performance. The weight coefficient representing the rate of change of classroom enthusiasm, It represents the rate of change of classroom enthusiasm, that is, the ratio of students’ classroom enthusiasm to the class average classroom enthusiasm. represents the classroom enthusiasm coefficient, The weight coefficient representing the change rate of job completion, represents the change rate of job completion, It represents the homework completion coefficient, which is the ratio of the student's homework completion to the class average homework completion.

[0031] S23. Identify students with abnormal learning status and students with normal learning status according to the analysis result of the first learning status.

[0032] Specifically, the students' first learning state change curve is drawn with the monitoring period as the horizontal axis and the first learning state as the vertical axis. Students whose first learning state change curve is downward and lower than the class average first learning state are identified as students with abnormal learning state, and the rest of the students are identified as normal students.

[0033] S3. Combining the learning performance monitoring data, the physiological monitoring data, and the behavioral performance monitoring data to obtain a learning difficulty parameter.

[0034] In an embodiment, combining the learning performance monitoring data, the physiological monitoring data, and the behavioral performance monitoring data to obtain a learning difficulty parameter comprises the following steps: S31. Analyze learning performance parameters based on the learning performance monitoring data.

[0035] Specifically, the learning performance parameters are analyzed based on the learning performance monitoring data to satisfy the following formula: ,in, represents the learning performance parameter, represents the first learning state, represents the knowledge mastery entropy, represents the learning input-output ratio, Indicates the error rate of wrong question concepts.

[0036] S32. Analyze the first influencing factor of learning performance and the second influencing factor of learning performance based on the physiological monitoring data and the behavioral performance monitoring data.

[0037] Specifically, based on the physiological monitoring data, the first influencing factor of learning performance is analyzed to satisfy the following formula: ,in, It represents the first influencing factor of learning performance. Indicates the number of times your heart rate is greater than 110bpm while writing. represents the heart rate weight, Indicates the number of times you turn over more than 20 times at night. represents the weight of the number of turns, Indicates the number of times the number of steps taken during the break is less than 50. Indicates the walking step weight.

[0038] Based on the behavioral performance monitoring data, the second influencing factor of learning performance is analyzed and satisfies the following formula: ,in, It represents the second most influential factor in learning performance. Indicates the number of behavioral performances, Indicates the The weight of the behavior, Indicates the The number of times the behavior appears in students, Indicates the The total number of times this behavior occurs in students with normal learning status in the class.

[0039] S33. Combining the learning performance parameter, the first influencing factor of learning performance, and the second influencing factor of learning performance, to obtain the learning difficulty parameter.

[0040] Specifically, the learning difficulty parameter is obtained by combining the learning performance parameter, the first influencing factor of learning performance, and the second influencing factor of learning performance, and satisfies the following formula: ,in, represents the learning difficulty parameter, represents the average first learning state, represents the learning performance parameter, It represents the first influencing factor of learning performance. It represents the second most influential factor in learning performance.

[0041] S4. Analyze the physiological monitoring data, the behavioral performance monitoring data, and the social emotion monitoring data to obtain learning mental health parameters.

[0042] In an embodiment, analyzing the physiological monitoring data, the behavioral performance monitoring data, and the social emotion monitoring data to obtain learning mental health parameters includes the following steps: S41. Calculate the student's energy status value based on the physiological monitoring data.

[0043] In the embodiment, each physiological data is first standardized to eliminate dimensional differences; then, in combination with the laws of chronobiology, time period weights are assigned according to physiological rhythms; finally, the standardized value of the indicator is multiplied by the time period weight to obtain the energy state value.

[0044] S42. Analyze the student's first mental health factor based on the behavioral performance monitoring data.

[0045] Specifically, frequency statistics are performed on all facial expressions and body movements in the behavioral performance monitoring data, and coefficient weights are preset for each behavioral performance, and then the first mental health factor is obtained through analysis.

[0046] S43. Obtain the student’s second mental health factor through the social emotion monitoring data.

[0047] Specifically, the student's second mental health factor is obtained through the social emotion monitoring data, which satisfies the following formula: ,in, represents the second mental health factor, Indicates the corresponding parameter weights, Indicates the proportion of negative words in the text, represents the occurrence rate of negative emotion words, Indicates the proportion of time spent alone, represents the social interaction entropy, Indicates the proportion of teacher-student / classmate interaction time, Indicates the frequency of seeking psychological help.

[0048] It should be understood that the social emotion monitoring data should be normalized before calculating the second mental health factor.

[0049] S44. Combining the energy state value, the first mental health factor, and the second mental health factor, obtain the learning mental health parameter.

[0050] Specifically, the combination of the energy state value, the first mental health factor, and the second mental health factor to obtain the learning mental health parameter satisfies the following formula: ,in, represents the learning mental health parameter, represents the first mental health factor, represents the second mental health factor, Indicates the energy status value.

[0051] S5. Determine the learning status of the student with abnormal learning status based on the learning difficulty parameter and the learning mental health parameter.

[0052] In an embodiment, judging the learning status of the student with abnormal learning status according to the learning difficulty parameter and the learning mental health parameter includes the following steps: S51. Setting an abnormality judgment threshold according to the learning difficulty parameter and the learning mental health parameter of a student with normal learning status.

[0053] Specifically, the minimum value of the learning difficulty parameter and the minimum value of the learning mental health parameter of students with normal learning status are set as abnormality judgment thresholds of the corresponding status.

[0054] S52: Determine the learning status of the student with abnormal learning status according to the abnormality judgment threshold.

[0055] In an embodiment, if the student's learning difficulty parameter is less than the learning difficulty parameter abnormal judgment threshold, the student's learning status is judged to be a learning difficulty status; if the student's learning mental health parameter is less than the learning mental health parameter abnormal judgment threshold, the student's learning status is judged to be a psychological depression tendency status.

[0056] Furthermore, when a student is judged to be experiencing learning difficulties or psychological depression, an early warning message will be automatically generated. The early warning message includes the student's basic information, the type of abnormal condition, relevant data evidence, and severity assessment. The early warning message will be sent to the student, the class teacher, the psychological counselor, and the parents. Different intervention measures will be recommended based on the early warning level, including: For low-level warnings, the class teacher will intervene through conversations, guidance on learning methods, etc.

[0057] For medium-level warnings, psychological counselors will be arranged to provide one-on-one psychological counseling, and personalized learning improvement plans will be developed in conjunction with subject teachers.

[0058] A high-level warning is issued, and parents are advised to take their children to professional medical institutions for diagnosis and treatment. The school will continue to follow up and provide support.

[0059] See also Figure 2 In an embodiment, in order to efficiently execute the method for monitoring the learning status of school students provided by the present invention, the present invention further provides a system for monitoring the learning status of school students, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory contains program instructions, which are used for the steps of the method for monitoring the learning status of school students. The system for monitoring the learning status of school students of the present invention has a compact structure and stable performance, and can stably execute the method for monitoring the learning status of school students of the present invention, further enhancing the overall applicability and practical application capabilities of the present invention.

[0060] In an embodiment, the processor may be a central processing unit (CPU), which may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The input device may be used to obtain data information. The output device may be used to output the results obtained by storing the program instructions contained in the computer program in the memory provided by the present invention. The memory may include a read-only memory and a random access memory, and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory.

[0061] In one possible implementation, the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function, etc.; the data storage area may store data created during use. In addition, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and processing hardware-based tasks.

[0062] The embodiment further provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for monitoring the learning status of students in school are implemented.

[0063] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., which can store program codes.

[0064] To sum up, the present invention analyzes learning difficulty parameters and learning mental health parameters by analyzing learning performance monitoring data, physiological monitoring data, behavioral performance monitoring data and social emotion monitoring data, and then judges the learning status of students with abnormal learning status, thereby solving the problem of efficiently and accurately identifying the status of abnormal students, which is conducive to avoiding missing the golden period of intervention for abnormal students.

[0065] Therefore, the present invention effectively overcomes various shortcomings of the prior art and has high industrial utilization value.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope described in the present invention.

Claims

1. A method for monitoring the learning status of students at school, characterized in that: The following steps are involved: Acquiring learning status monitoring data of students at school, wherein the learning status monitoring data includes learning performance monitoring data, physiological monitoring data, behavioral performance monitoring data, and social emotion monitoring data; analyzing a first learning status of the student using the learning performance monitoring data, and identifying students with abnormal learning status and students with normal learning status according to the analysis result of the first learning status; combining the learning performance monitoring data, the physiological monitoring data, and the behavioral performance monitoring data to obtain a learning difficulty parameter; Analyzing the physiological monitoring data, the behavioral performance monitoring data, and the social emotion monitoring data to obtain learning mental health parameters; The learning status of the student with abnormal learning status is judged according to the learning difficulty parameter and the learning mental health parameter.

2. The method for monitoring the learning status of students in school according to claim 1, characterized in that: Analyzing the first learning status of the students using the learning performance monitoring data, and identifying students with abnormal learning status and students with normal learning status according to the analysis results of the first learning status, includes the following steps: Analyzing the student's academic performance change rate, classroom enthusiasm change rate, and homework completion rate change rate based on the learning performance monitoring data; Combining the grade change rate, the class enthusiasm change rate, and the homework completion change rate to obtain the first learning status; Students with abnormal learning status and students with normal learning status are identified according to the analysis result of the first learning status.

3. The method for monitoring the learning status of students in school according to claim 2, characterized in that: The first learning state is obtained by combining the grade change rate, the class enthusiasm change rate, and the homework completion change rate, and satisfies the following formula: ,in, represents the first learning state, The weight coefficient representing the rate of change of grades, Indicates the rate of change of performance. represents the performance coefficient, The weight coefficient representing the rate of change of classroom enthusiasm, represents the rate of change of classroom enthusiasm, represents the classroom enthusiasm coefficient, The weight coefficient representing the change rate of job completion, represents the change rate of job completion, Indicates the completion coefficient of the task.

4. The method for monitoring the learning status of students in school according to claim 1, characterized in that: The step of combining the learning performance monitoring data, the physiological monitoring data, and the behavioral performance monitoring data to obtain a learning difficulty parameter comprises the following steps: analyzing learning performance parameters based on the learning performance monitoring data; Analyzing a first influencing factor of learning performance and a second influencing factor of learning performance based on the physiological monitoring data and the behavioral performance monitoring data; The learning difficulty parameter is obtained by combining the learning performance parameter, the first influencing factor of learning performance, and the second influencing factor of learning performance.

5. The method for monitoring the learning status of students in school according to claim 4, characterized in that: The learning performance parameters are analyzed based on the learning performance monitoring data to satisfy the following formula: ,in, represents the learning performance parameter, represents the first learning state, represents the knowledge mastery entropy, represents the learning input-output ratio, Indicates the error rate of wrong question concepts.

6. The method for monitoring the learning status of students in school according to claim 4, characterized in that: Based on the physiological monitoring data, the first influencing factor of learning performance is analyzed and satisfies the following formula: ,in, It represents the first influencing factor of learning performance. Indicates the number of times your heart rate is greater than 110bpm while writing. represents the heart rate weight, Indicates the number of times you turn over more than 20 times at night. represents the weight of the number of turns, Indicates the number of times the number of steps taken during the break is less than 50. Indicates the weight of walking steps; Based on the behavioral performance monitoring data, the second influencing factor of learning performance is analyzed and satisfies the following formula: ,in, It represents the second most influential factor in learning performance. Indicates the number of behavioral performances, Indicates the The weight of the behavior, Indicates the The number of times this behavior appears in students, Indicates the The total number of times this behavior occurs in students with normal learning status in the class.

7. The method for monitoring the learning status of students in school according to claim 1, characterized in that: The analyzing the physiological monitoring data, the behavioral performance monitoring data, and the social emotion monitoring data to obtain learning mental health parameters includes the following steps: Calculating the student's energy status value based on the physiological monitoring data; Analyzing the student's first mental health factor based on the behavioral performance monitoring data; Obtaining the student's second mental health factor through the social emotion monitoring data; The learning mental health parameter is obtained by combining the energy state value, the first mental health factor and the second mental health factor.

8. The method for monitoring the learning status of students in school according to claim 7, characterized in that: The learning mental health parameter is obtained by combining the energy state value, the first mental health factor, and the second mental health factor, and satisfies the following formula: ,in, represents the learning mental health parameter, represents the first mental health factor, represents the second mental health factor, Indicates the energy status value.

9. The method for monitoring the learning status of students in school according to claim 1, characterized in that: Judging the learning status of the student with abnormal learning status according to the learning difficulty parameter and the learning mental health parameter includes the following steps: Setting an abnormality judgment threshold according to the learning difficulty parameter and the learning mental health parameter of students with normal learning status; The learning status of the student with abnormal learning status is judged according to the abnormality judgment threshold.

10. A system for monitoring the learning status of students at school, characterized in that: The system for monitoring the learning status of students in school includes: an input device, an output device, a processor, and a memory. The input device, output device, processor, and memory are interconnected. The memory includes program instructions, and the program instructions are used to execute the method for monitoring the learning status of students in school as described in any one of claims 1-9.

Images