Adaptive learning optimization method and system based on big data analysis

Abstract

The invention discloses an adaptive learning optimization method and system based on big data analysis, and relates to the technical field of machine learning, and the method comprises the following steps: deploying a computing node in a device of a learner, obtaining learning data, and configuring an edge computing gateway to transmit the processed learning data to a cloud computing center; analyzing the learning data through the cloud computing center to obtain the learning condition of the learner; based on the learning condition, a strategy optimization scheme is given. Through personalized learning path recommendation, a learner can master knowledge points more quickly, the exploration time in a poor field is shortened, targeted learning content pushing and practicing can help the learner to make progress in a course with poor scores, so that the overall academic score is improved, learning data is transmitted intermittently, and the learning efficiency is improved. The calculation amount of the whole equipment can be reduced, the analysis result is judged, and the accuracy of learning data and the precision of the analysis result are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of machine learning, and in particular to an adaptive learning optimization method and system based on big data analysis. Background Art

[0002] At present, traditional education only provides universal education for one class or one group, which is difficult to meet the personalized needs of students. At the same time, traditional education has a large demand for educational resources. In the current situation of insufficient educational resources, it is easy to produce uneven distribution of educational resources, which can easily lead to educational inequality. There is an urgent need for an adaptive learning method to recommend knowledge units suitable for students to learn and meet the personalized learning needs of different students. Summary of the invention

[0003] In view of the problems existing in the existing adaptive learning optimization and system based on big data analysis, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that the existing education cannot satisfy the one-to-one personalized learning method.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides an adaptive learning optimization method based on big data analysis, which comprises the following steps: Deploy computing nodes in learners’ devices, obtain learning data, and configure edge computing gateways to transmit processed learning data to the cloud computing center; Analyze learning data through cloud computing center to obtain learners' learning status; Based on the learning situation, a strategy optimization plan is given.

[0006] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, when the computing node acquires learning data, it includes data collection and data processing; The data collection steps include: collecting learning time data, collecting learning content data, and monitoring practice conditions; The method for collecting learning time data includes: classifying learning content according to courses, and recording a start timestamp when a learner opens a course; and recording an end timestamp when a learner ends a course; The method for collecting learning content data includes: setting a time interval, and capturing the corresponding learning course each time the learner views the learning course within the time interval; The method for monitoring the practice situation includes: recording the start timestamp each time the learner opens the practice questions; recording the close timestamp when the learner exits the practice questions; and calculating the correctness rate of the practice questions based on the practice results; The data processing steps include: cleaning the collected information, removing invalid or repeated content, and classifying it according to learning time data, learning content data, and practice status, and finally obtaining learning data.

[0007] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, when the edge computing gateway transmits the processed learning data to the cloud computing center, the transmission form is set to primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analysis information transmission, the type of learning data to be analyzed is set, and the learning data of this type is packaged, and then the packaged learning data is transmitted; When the transmission form is secondary analysis information transmission, the complete learning data is packaged and then the packaged learning data is transmitted; The rotation mode of the primary analysis information transmission and the secondary analysis information transmission is as follows: Set the learning data transmission cycle to X days, then complete the first-level analysis information transmission every X days, until the first-level analysis information transmission is completed Y times in a row, then perform a second-level analysis information transmission; Among them, when the transmission form is secondary analysis information transmission, primary analysis information transmission is not performed.

[0008] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, when the learning data is analyzed by the cloud computing center, it is determined that the transmission form is primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analytical information transmission, the data purpose is analyzed based on the transmitted learning data, and a data curve is formulated; When the transmission form is secondary analysis information transmission, obtain the time interval between the last secondary analysis information transmission and the current secondary analysis information transmission, and collect the analysis results and data purpose corresponding to the primary analysis information transmission within the time interval. Analyze the learning data of the secondary analysis information transmission according to the data purpose, and compare the two analysis results to obtain the analysis result error value, and then judge whether the analysis result error value is greater than the set error value. When it is greater than the set error value, it indicates that the analysis is abnormal, and a secondary judgment is made. When it is not greater than the set error value, it indicates that the analysis is normal, and the learner's learning situation is obtained according to the analysis result.

[0009] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, the second judgment step includes: Determine whether the time interval between two analyses is greater than Day, when no greater than days, it indicates that the analysis is wrong, the data of the time interval will be deleted and invalidated, and the abnormal analysis will be reported; if it is greater than Days, divide the number of days exceeded by X days to obtain the number of analysis exceeded Z, and output the calculation formula to obtain the new error value; The calculation formula is expressed as: ; In the formula, Represented as the new error value, It represents the set error value, Z represents the number of exceeded analyses, and Y represents the number of analyses between two adjacent secondary analysis information transmissions; Continue to determine whether the error value of the analysis result is greater than the new error value. If it is greater, the data of the time interval is deleted and invalidated, and the abnormal analysis is reported. If it is not greater, it indicates that the analysis is normal, and the learner's learning situation is obtained based on the analysis results; Among them, N<Y.

[0010] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, the learning situation of the learner includes the change of learning time of each course, the change of learning completion ratio of each course, and the grade of each course; Among them, the change of learning time for each course is obtained through the analysis of learning time data; The changes in the learning completion rate of each course were obtained through the analysis of learning content data; The grade for each course is obtained through analysis of practice situations.

[0011] As a preferred solution of the adaptive learning optimization method based on big data analysis described in the present invention, the method for generating the strategy optimization solution includes: Get the course with the worst performance, and get the change in study time and the change in study completion ratio of the course. If the change in study time and the change in study completion ratio are on an upward trend, do not make any changes. If one or all of them are on a downward trend, increase the push content of the course. When a new course with the worst performance is generated, the content will be pushed to replace the new course.

[0012] In a second aspect, an embodiment of the present invention provides an adaptive learning optimization system based on big data analysis, which includes a data acquisition module, a data analysis module, and an information generation module; The data acquisition module is used to acquire learning data and preprocess the learning data; The data analysis module is used to analyze the learning data to obtain the learning situation; The information generation module is used to provide a strategy optimization solution.

[0013] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned adaptive learning optimization method based on big data analysis.

[0014] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned adaptive learning optimization method based on big data analysis is implemented.

[0015] The beneficial effects of the present invention are: Through personalized learning path recommendations, learners can master knowledge points faster and reduce the time spent on exploration in areas they are not good at. Targeted learning content push and practice can help learners make progress in courses where they have poor performance, thereby improving overall learning performance.

[0016] Intermittent transmission of learning data can reduce the amount of computing power of the entire device, and the settings of primary analysis information transmission and secondary analysis information transmission can judge the analysis results to ensure the correctness of the learning data and the accuracy of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 Flowchart of the adaptive learning optimization method based on big data analysis. DETAILED DESCRIPTION

[0018] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0019] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0021] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0022] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0023] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] Example: Refer to Figure 1 , which is the first embodiment of the present invention, and provides an adaptive learning optimization method based on big data analysis, comprising the following steps: S1. Deploy computing nodes in learners’ devices, obtain learning data, and configure edge computing gateways to transmit processed learning data to the cloud computing center.

[0025] In this embodiment, it is assumed that an online education platform wants to optimize its learning management system to provide a more personalized learning experience.

[0026] When the computing node acquires learning data, it includes data collection and data processing; The data collection steps include: collecting learning time data, collecting learning content data, and monitoring practice conditions; The method for collecting learning time data includes: classifying learning content according to courses, and recording a start timestamp when a learner opens a course; and recording an end timestamp when a learner ends a course; The method for collecting learning content data includes: setting a time interval, and capturing the corresponding learning course each time the learner views the learning course within the time interval; The method for monitoring the practice situation includes: recording the start timestamp each time the learner opens the practice questions; recording the close timestamp when the learner exits the practice questions; and calculating the correctness rate of the practice questions based on the practice results; The data processing steps include: cleaning the collected information, removing invalid or repeated content, and classifying it according to learning time data, learning content data, and practice status, and finally obtaining learning data.

[0027] When the edge computing gateway transmits the processed learning data to the cloud computing center, the transmission form is set to primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analysis information transmission, the type of learning data to be analyzed is set, and the learning data of this type is packaged, and then the packaged learning data is transmitted; When the transmission form is secondary analysis information transmission, the complete learning data is packaged and then the packaged learning data is transmitted; The rotation mode of the primary analysis information transmission and the secondary analysis information transmission is as follows: Set the learning data transmission cycle to X days, then complete the first-level analysis information transmission every X days, until the first-level analysis information transmission is completed Y times in a row, then perform a second-level analysis information transmission; Wherein, when the transmission form is secondary analysis information transmission, primary analysis information transmission is not performed; In this embodiment, if X=1, Y=10 is set, the primary analysis information is transmitted once every day. When the 11th transmission is performed, it means that 10 primary analysis information transmissions are completed, and the secondary analysis information is transmitted. The primary analysis information is not transmitted this time, and the primary analysis information is transmitted again for the 12th time. And when the first-level analysis information is transmitted, it is packaged and transmitted according to the type of learning data, that is, it is packaged and transmitted according to the learning time data, learning content data or practice situation; All learning data is sent when the secondary analysis information is transmitted.

[0028] S2. Analyze the learning data through the cloud computing center to obtain the learners' learning status.

[0029] When analyzing learning data through the cloud computing center, determine whether the transmission form is primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analytical information transmission, the data purpose is analyzed based on the transmitted learning data, and a data curve is formulated; In this embodiment, the data purpose may be learning participation, course attractiveness, or learning efficiency, etc.

[0030] When the transmission form is secondary analysis information transmission, the time interval between the last secondary analysis information transmission and the current secondary analysis information transmission is obtained, and the analysis results and data purposes corresponding to the primary analysis information transmission within the time interval are collected. The learning data of the secondary analysis information transmission is analyzed according to the data purpose, and the two analysis results are compared to obtain the analysis result error value, and then it is determined whether the analysis result error value is greater than the set error value. If it is greater than the set error value, it indicates that the analysis is abnormal, and a secondary judgment is made. If it is not greater than the set error value, it indicates that the analysis is normal, and the learner's learning situation is obtained according to the analysis result; In this embodiment, the time interval between the last secondary analysis information transmission and the current secondary analysis information transmission is 10 days, and the analysis results and data purposes corresponding to the primary analysis information transmission within the 10 days are shown in Table 1; Table 1: Analysis results and data purpose corresponding to the first-level analysis information transmission within the time interval ; As shown in Table 1 , the data purposes corresponding to the first-level analysis information transmission within 10 days are: learning efficiency, course attractiveness, learning completeness, learning understanding, learner interest, and learning engagement; The learning data of the secondary analysis information transmission are analyzed through these six data purposes. For example, if the data purpose is that the analysis result of learning participation is 90%, the analysis of learning participation closest to this time is the 10th time (the result is 88%), and the set error value is 10%. The error values ​​of the two are less than the set error value, so it shows that the analysis is normal; For example, if the data objective is that the analysis result of the course attractiveness is 75%, and the analysis of the course attractiveness closest to this time is the 7th time (the result is 60%), the error values ​​of the two are greater than the set error value, thus indicating that the analysis is abnormal and a secondary judgment is made.

[0031] The steps of secondary judgment include: Determine whether the time interval between two analyses is greater than Day, when no greater than days, it indicates that the analysis is wrong, the data of the time interval will be deleted and invalidated, and the abnormal analysis will be reported; if it is greater than Days, divide the number of days exceeded by X days to obtain the number of analysis exceeded Z, and output the calculation formula to obtain the new error value; The calculation formula is expressed as: ; In the formula, Represented as the new error value, It represents the set error value, Z represents the number of exceeded analyses, and Y represents the number of analyses between two adjacent secondary analysis information transmissions; Continue to determine whether the error value of the analysis result is greater than the new error value. If it is greater, the data of the time interval is deleted and invalidated, and the abnormal analysis is reported. If it is not greater, it indicates that the analysis is normal, and the learner's learning situation is obtained based on the analysis results; Where N<Y; In this embodiment, N is set to 3, that is, to determine whether the time interval between two analyses is greater than days. In this case, the time since the last analysis is 4 days, and the excess number of analyses is obtained, that is, the excess number of analyses is Z=4-3=1 times. Then, it is brought into the new error value formula to obtain the new error value. The calculation method is: The calculated result is 12.5%. The error value of 15% calculated by adding the analysis result 75% to the seventh analysis result 60% is still greater than the new error value 12.5%, indicating that the analysis is wrong. The data in this time interval is deleted and invalidated, and the abnormal analysis is reported.

[0032] S3. Based on the learning situation, provide a strategy optimization plan.

[0033] The learning situation of the learners includes the change of learning time of each course, the change of learning completion ratio of each course, and the grade of each course; Among them, the change of learning time for each course is obtained through the analysis of learning time data; The changes in the learning completion rate of each course were obtained through the analysis of learning content data; The grade for each course is obtained through analysis of practice situations.

[0034] The method for generating the strategy optimization solution includes: Get the course with the worst performance, and get the change in study time and the change in study completion ratio of the course. If the change in study time and the change in study completion ratio are on an upward trend, do not make any changes. If one or all of them are on a downward trend, increase the push content of the course. When a new course with the worst performance is generated, the content will be pushed to replace the new course.

[0035] In summary, through personalized learning path recommendations, learners can master knowledge points faster and reduce the time spent on exploration in areas they are not good at. Targeted learning content push and practice can help learners make progress in courses with poor performance, thereby improving overall learning performance.

[0036] Intermittent transmission of learning data can reduce the amount of computing power of the entire device, and the settings of primary analysis information transmission and secondary analysis information transmission can judge the analysis results to ensure the correctness of the learning data and the accuracy of the analysis results. Example

[0037] On the basis of the first embodiment, this embodiment further provides an adaptive learning optimization system based on big data analysis, including a data acquisition module, a data analysis module, and an information generation module; The data acquisition module is used to acquire learning data and preprocess the learning data; The data analysis module is used to analyze the learning data to obtain the learning situation; The information generation module is used to provide a strategy optimization solution.

[0038] This embodiment also provides a computer device, which is suitable for the case of an adaptive learning optimization method based on big data analysis, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the adaptive learning optimization method based on big data analysis proposed in the above embodiment.

[0039] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0040] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the adaptive learning optimization method based on big data analysis proposed in the above embodiment is implemented.

[0041] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0042] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An adaptive learning optimization method based on big data analysis, characterized in that: The following steps are included: Deploy computing nodes in learners’ devices, obtain learning data, and configure edge computing gateways to transmit processed learning data to the cloud computing center; Analyze learning data through cloud computing center to obtain learners' learning status; Based on the learning situation, a strategy optimization plan is given.

2. The adaptive learning optimization method based on big data analysis according to claim 1, characterized in that: When the computing node acquires learning data, it includes data collection and data processing; The data collection steps include: collecting learning time data, collecting learning content data, and monitoring practice conditions; The method for collecting learning time data includes: classifying learning content according to courses, and recording a start timestamp when a learner opens a course; and recording an end timestamp when a learner ends a course; The method for collecting learning content data includes: setting a time interval, and capturing the corresponding learning course each time the learner views the learning course within the time interval; The method for monitoring the practice situation includes: recording the start timestamp each time the learner opens the practice questions; recording the close timestamp when the learner exits the practice questions; and calculating the correctness rate of the practice questions based on the practice results; The data processing steps include: cleaning the collected information, removing invalid or repeated content, and classifying it according to learning time data, learning content data, and practice status, and finally obtaining learning data.

3. The adaptive learning optimization method based on big data analysis according to claim 2, characterized in that: When the edge computing gateway transmits the processed learning data to the cloud computing center, the transmission form is set to primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analysis information transmission, the type of learning data to be analyzed is set, and the learning data of this type is packaged, and then the packaged learning data is transmitted; When the transmission form is secondary analysis information transmission, the complete learning data is packaged and then the packaged learning data is transmitted; The rotation mode of the primary analysis information transmission and the secondary analysis information transmission is as follows: Set the learning data transmission cycle to X days, then complete the first-level analysis information transmission every X days, until the first-level analysis information transmission is completed Y times in a row, then perform a second-level analysis information transmission; Among them, when the transmission form is secondary analysis information transmission, primary analysis information transmission is not performed.

4. The adaptive learning optimization method based on big data analysis according to claim 3, characterized in that: When analyzing learning data through the cloud computing center, determine whether the transmission form is primary analysis information transmission or secondary analysis information transmission; When the transmission form is primary analytical information transmission, the data purpose is analyzed based on the transmitted learning data, and a data curve is formulated; When the transmission form is secondary analysis information transmission, obtain the time interval between the last secondary analysis information transmission and the current secondary analysis information transmission, and collect the analysis results and data purpose corresponding to the primary analysis information transmission within the time interval. Analyze the learning data of the secondary analysis information transmission according to the data purpose, and compare the two analysis results to obtain the analysis result error value, and then judge whether the analysis result error value is greater than the set error value. When it is greater than the set error value, it indicates that the analysis is abnormal, and a secondary judgment is made. When it is not greater than the set error value, it indicates that the analysis is normal, and the learner's learning situation is obtained according to the analysis result.

5. The adaptive learning optimization method based on big data analysis according to claim 4, characterized in that: The steps of the secondary judgment include: Determine whether the time interval between two analyses is greater than Day, when no greater than days, it indicates that the analysis is wrong, the data of the time interval will be deleted and invalidated, and the abnormal analysis will be reported; if it is greater than Days, divide the number of days exceeded by X days to obtain the number of analysis exceeded Z, and output the calculation formula to obtain the new error value; The calculation formula is expressed as: ; In the formula, Represented as the new error value, It represents the set error value, Z represents the number of exceeded analyses, and Y represents the number of analyses between two adjacent secondary analysis information transmissions; Continue to determine whether the error value of the analysis result is greater than the new error value. If it is greater, the data of the time interval is deleted and invalidated, and the abnormal analysis is reported. If it is not greater, it indicates that the analysis is normal, and the learner's learning situation is obtained based on the analysis results; Among them, N<Y.

6. The adaptive learning optimization method based on big data analysis according to claim 5, characterized in that: The learning situation of the learners includes the change of learning time of each course, the change of learning completion ratio of each course, and the grade of each course; Among them, the change of learning time for each course is obtained through the analysis of learning time data; The changes in the learning completion rate of each course were obtained through the analysis of learning content data; The grade for each course is obtained through analysis of practice situations.

7. The adaptive learning optimization method based on big data analysis according to claim 6, characterized in that: The method for generating the strategy optimization solution includes: Get the course with the worst performance, and get the change in study time and the change in study completion ratio of the course. If the change in study time and the change in study completion ratio are on an upward trend, do not make any changes. If one or all of them are on a downward trend, increase the push content of the course. When a new course with the worst performance is generated, the content will be pushed to replace the new course.

8. An adaptive learning optimization system based on big data analysis, based on the adaptive learning optimization method based on big data analysis according to any one of claims 1 to 7, characterized in that: It includes a data acquisition module, a data analysis module, and an information generation module; The data acquisition module is used to acquire learning data and preprocess the learning data; The data analysis module is used to analyze the learning data to obtain the learning situation; The information generation module is used to provide a strategy optimization solution.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the adaptive learning optimization method based on big data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the adaptive learning optimization method based on big data analysis described in any one of claims 1 to 7 are implemented.

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