Method and System for Planning Personalized Learning Paths for Preschool Children in an Online Education System

Abstract

The present invention discloses a method and system for planning personalized learning paths for preschool children in an online education system, which relates to the technical field of electrical digital data processing. The method for planning personalized learning paths for preschool children in the online education system includes the following steps: coherence assessment; resource browsing assessment; learning completion assessment. The present invention determines whether to adjust the coherence of learning data based on the obtained coherence assessment index, then determines whether to adjust the browsing of learning resources based on the obtained resource browsing assessment index, and finally determines whether to adjust the learning completion based on the obtained learning completion assessment index, achieving the effect of improving the timeliness of planning personalized learning paths for preschool children in the online education system, and solving the problem of low timeliness of planning personalized learning paths for preschool children in the online education system in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and particularly to a method and system for planning personalized learning paths for preschool children in an online education system. Background Art

[0002] With the continuous leap and innovation of Internet technology, online education platforms have become increasingly mature and gradually emerged as a new force in the education field, showing unprecedented vitality and potential. This education model breaks the time and space limitations of traditional education. With its high flexibility in time and complete freedom in location, it has opened up a brand-new learning world for learners. In remote mountainous areas, educational resources are relatively scarce, and the update and coherence of learning resources are crucial for improving the quality of education. Especially in the field of preschool children's education, an online education system can provide rich and diverse educational resources and learning materials to meet the diverse learning needs of children. Learners are no longer restricted to a fixed classroom and a prescribed schedule, but can access the Internet anytime and anywhere. Personalized learning path planning refers to designing personalized learning paths for learners based on their individual differences such as knowledge, ability, interest, and learning habits. This planning method not only helps to improve the learning effect of learners, enabling them to make greater progress in a short time, but also significantly enhances their learning satisfaction.

[0003] Existing methods for planning personalized learning paths for children mainly collect and analyze various data during the learning process of preschool children, such as login information, learning history, answer records, etc. Educational institutions can construct a learning profile for each child and then formulate a personalized learning plan and learning path for them.

[0004] For example, a patent for invention with the publication number CN110569443B discloses an adaptive learning path planning system based on reinforcement learning, including three modules: environment simulation, policy training, and path planning. Throughout the process, the ability value of the student at each moment is obtained according to the improved item response theory. Based on the Markov decision process, a complex learning environment is simulated, and the algorithm of reinforcement learning is reasonably applied to offline train the path planning strategy in combination with the student's historical learning trajectory. Finally, an adaptive learning path is planned online for the student according to the trained strategy.

[0005] For example, the learning path planning method, device, equipment and storage medium disclosed in the invention patent with the publication number of CN112825071A include: obtaining the knowledge point set to be path-planned for the student to be path-planned, the learning behavior data of the student to be path-planned, and the current learning mastery degree of each knowledge point in the knowledge point set of the student to be path-planned; obtaining the change amount of the mastery degree of each knowledge point of the student to be path-planned at least according to the learning behavior data and the current learning mastery degree; sorting each knowledge point according to the magnitude of the change amount of the mastery degree to obtain the planned path for learning each knowledge point.

[0006] However, in the process of implementing the technical solution of the present invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems:

[0007] In the prior art, due to the easy distraction of preschool children's attention, it is difficult to focus on the same thing for a long time. This distractibility of attention makes it necessary to constantly change the presentation methods and contents when integrating learning resources, resulting in the problem of low timeliness in planning the personalized learning path of the online education system for preschool children. Summary of the Invention

[0008] By providing a method and system for planning the personalized learning path of preschool children in an online education system, the embodiments of the present application solve the problem of low timeliness in planning the personalized learning path of the online education system for preschool children in the prior art, and achieve an improvement in the timeliness of planning the personalized learning path of the online education system for preschool children.

[0009] The embodiments of the present application provide a method for planning the personalized learning path of preschool children in an online education system, including the following steps: S1, obtaining a coherence evaluation index according to the obtained learning data of preschool children, and judging whether to adjust the coherence of the learning data based on the coherence evaluation index, where the coherence evaluation index is used to evaluate the coherence of learning resources; S2, combining the obtained coherence evaluation index that meets the resource coherence condition and the learning time data of preschool children for resources to obtain a resource browsing evaluation index, and judging whether to adjust the browsing of learning resources based on the resource browsing evaluation index, where the resource browsing evaluation index is used to evaluate the browsing of learning resources provided by the online education system by preschool children; S3, combining the obtained resource browsing evaluation index that meets the resource browsing condition and the learning completion degree data to obtain a learning completion degree evaluation index, and judging whether to adjust the learning completion degree based on the learning completion degree evaluation index, where the learning completion degree evaluation index is used to evaluate the completion of the preset learning tasks by preschool children.

[0010] Furthermore, the preschool children's learning data includes the average update bandwidth, the learning resource data packet capacity, and the average update time interval; the preschool children's resource learning time data includes the resource page viewing time, the number of page views, and the page viewing session time; the learning resource data packet capacity represents the size of the learning resource data packet within a preset time period; the resource page viewing time represents the time for a preschool child to view a single learning page within a preset time period; the number of page views represents the number of times a preschool child views a single learning page within a preset time period; the page viewing session time represents the difference between the time stamp corresponding to the last page viewed and the time stamp corresponding to the first page viewed by a preschool child within a preset time period; the learning completion data includes the number of completed learning tasks, the total number of learning tasks, and the number of qualified completed tasks.

[0011] Furthermore, the specific process of obtaining the coherence evaluation index based on the acquired preschool children's learning data is as follows: Obtain the update frequency compliance ratio, which is used to reflect the compliance of the update frequency of learning resources during the update process; obtain the update rate compliance ratio, which is used to reflect the compliance of the update rate of learning resources during the update process; combine the update frequency compliance ratio, the update rate compliance ratio, the first coherence allocation weight, and the second coherence allocation weight to obtain the coherence evaluation index; the update frequency compliance ratio is obtained through the following steps: SS1, perform a ratio operation on the average update bandwidth and the learning resource data packet capacity to obtain the initial resource update frequency; SS2, perform a ratio operation on the sum of the initial resource update frequency and the preset learning resource update frequency threshold obtained from the database and twice the preset learning resource update frequency threshold to obtain the update frequency compliance ratio; the update rate compliance ratio is obtained through the following steps: AA1, perform a reciprocal operation on the average update time interval to obtain the initial resource update rate; AA2, perform a ratio operation on the sum of the initial resource update rate and the preset learning resource update rate threshold obtained from the database and twice the preset learning resource update rate threshold to obtain the update rate compliance ratio.

[0012] Furthermore, the limiting expression of the coherence evaluation index is as follows:

[0013]

[0014] In the formula, represents the coherence evaluation index of the learning resource in the r-th preset time period, r = 1, 2,..., d, r represents the number of the preset time period, d represents the total number of preset time periods, PF r represents the update frequency compliance ratio of the learning resource in the r-th preset time period, SLF r represents the update rate compliance ratio of the learning resource in the r-th preset time period, DK rDenote the average update bandwidth of learning resources in the r-th preset time period, ZS r Denote the learning resource data packet capacity of learning resources in the r-th preset time period, ΔT r Denote the average update time interval of learning resources in the r-th preset time period, PF 0 Denote the preset learning resource update frequency threshold, SLF 0 Denote the preset learning resource update rate threshold, τ1 represents the first coherence allocation weight, τ2 represents the second coherence allocation weight, and e represents the natural constant.

[0015] Furthermore, the specific process of combining the coherence evaluation index that meets the resource coherence condition and the preschool children's resource learning time data to obtain the resource browsing evaluation index is as follows: Obtain the initial average page stay time by performing a ratio operation on the resource page browsing time and the number of browsed pages; Obtain the page stay time compliance ratio by performing a ratio operation on the sum of the initial average page stay time and the preset stay time threshold obtained from the database and twice the preset stay time threshold; Obtain the session time compliance ratio by performing a ratio operation on the browsing page session time and the preset session time threshold obtained from the database; Combine the page stay time compliance ratio, the session time compliance ratio, and the coherence evaluation index that meets the resource coherence condition to obtain the resource browsing evaluation index.

[0016] Furthermore, the specific process of combining the resource browsing evaluation index that meets the resource browsing condition and the learning completion data to obtain the learning completion evaluation index is as follows: Obtain the initial learning progress by performing a ratio operation on the number of completed learning tasks and the total number of learning tasks; Obtain the learning progress compliance ratio by performing a ratio operation on the initial learning progress and the preset learning progress threshold obtained from the database; Obtain the initial learning correct rate by performing a ratio operation on the number of qualified completed tasks and the total number of learning tasks; Obtain the learning correct rate compliance ratio by using the initial learning correct rate and the preset learning correct rate threshold obtained from the database; Combine the learning progress compliance ratio, the learning correct rate compliance ratio, and the resource browsing evaluation index that meets the resource browsing condition to obtain the learning completion evaluation index.

[0017] The embodiment of the present application provides a personalized learning path planning system for preschool children in an online education system, including a coherence evaluation module, a resource browsing evaluation module, and a learning completion evaluation module: Among them, the coherence evaluation module is used to obtain a coherence evaluation index based on the acquired learning data of preschool children, and determine whether to adjust the coherence of learning data based on the coherence evaluation index. The coherence evaluation index is used to evaluate the coherence of learning resources; the resource browsing evaluation module is used to obtain a resource browsing evaluation index by combining the coherence evaluation index that meets the resource coherence condition and the preschool children's resource learning time data, and determine whether to adjust the learning resource browsing based on the resource browsing evaluation index. The resource browsing evaluation index is used to evaluate the browsing situation of preschool children on the learning resources provided by the online education system; the learning completion evaluation module is used to obtain a learning completion evaluation index by combining the resource browsing evaluation index that meets the resource browsing condition and the learning completion data, and determine whether to adjust the learning completion based on the learning completion evaluation index. The learning completion evaluation index is used to evaluate the completion situation of preschool children on the preset learning tasks.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. By judging whether to adjust the coherence of learning data based on the obtained coherence evaluation index, then judging whether to adjust the learning resource browsing based on the obtained resource browsing evaluation index, and finally judging whether to adjust the learning completion based on the obtained learning completion evaluation index, the reliability of the online education system for planning the personalized learning path of preschool children is improved, and then the timeliness of the online education system for planning the personalized learning path of preschool children is improved, effectively solving the problem of low timeliness of the online education system for planning the personalized learning path of preschool children in the prior art.

[0020] 2. The coherence evaluation index is obtained through the update frequency compliance ratio, the update rate compliance ratio, the first coherence allocation weight, and the second coherence allocation weight. Then, the resource browsing evaluation index is obtained through the page stay time compliance ratio, the session time compliance ratio, and the coherence evaluation index that meets the resource coherence condition. Finally, the learning completion evaluation index is obtained through the learning progress compliance ratio, the learning correct rate compliance ratio, and the resource browsing evaluation index that meets the resource browsing condition, thereby improving the accuracy of obtaining data related to personalized learning path planning, and then realizing the precise quantification of the timeliness of the online education system for planning the personalized learning path of preschool children.

[0021] 3. By determining whether the coherence evaluation index meets the resource coherence condition, then determining whether the resource browsing evaluation index meets the resource browsing condition, and finally determining whether the learning completion evaluation index meets the learning completion condition, the dynamic adjustment of the timeliness of the personalized learning path planning for preschool children in the online education system is realized, and further, the timeliness of comprehensively evaluating the personalized learning path planning for preschool children in the online education system is realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the method for planning the personalized learning path of preschool children in the online education system provided by the embodiment of the present application;

[0023] Figure 2 It is a statistical chart of the change of the browsing page session time - session time compliance ratio provided by the embodiment of the present application;

[0024] Figure 3 It is a schematic structural diagram of the system for planning the personalized learning path of preschool children in the online education system provided by the embodiment of the present application;

[0025] Figure 4 It is the overall flowchart provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] In the embodiment of the present application, by providing a method and system for planning the personalized learning path of preschool children in the online education system, the problem of low timeliness of planning the personalized learning path of preschool children in the existing online education system is solved. By obtaining the coherence evaluation index of the learning data of preschool children, it is determined whether to adjust the coherence of the learning data based on the coherence evaluation index. Then, combining the coherence evaluation index that meets the resource coherence condition and the learning time data of preschool children's resources, the resource browsing evaluation index is obtained. Based on the resource browsing evaluation index, it is determined whether to adjust the learning resource browsing. Finally, combining the resource browsing evaluation index that meets the resource browsing condition and the learning completion data, the learning completion evaluation index is obtained. Based on the learning completion evaluation index, it is determined whether to adjust the learning completion, thus realizing the improvement of the timeliness of planning the personalized learning path of preschool children in the online education system.

[0027] The technical solution in the embodiment of the present application is to solve the problem of low timeliness of planning the personalized learning path of preschool children in the online education system. The general idea is as follows:

[0028] By determining whether to adjust the coherence of the learning data based on the obtained coherence evaluation index, then determining whether to adjust the learning resource browsing based on the obtained resource browsing evaluation index, and finally determining whether to adjust the learning completion based on the obtained learning completion evaluation index, the effect of improving the timeliness of planning the personalized learning path of preschool children in the online education system is achieved.

[0029] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0030] As Figure 1 shown, it is a flowchart of a method for planning personalized learning paths for preschool children in an online education system provided by an embodiment of the present application. The method includes the following steps: S1, Coherence Evaluation: Obtain a coherence evaluation index based on the acquired learning data of preschool children, and determine whether to adjust the learning data coherence based on the coherence evaluation index. The learning data coherence adjustment is used to adjust the coherence evaluation index to meet the resource coherence condition, and the coherence evaluation index is used to evaluate the coherence of learning resources; S2, Resource Browsing Evaluation: Obtain a resource browsing evaluation index by combining the acquired coherence evaluation index that meets the resource coherence condition and the preschool children's resource learning time data, and determine whether to adjust the learning resource browsing based on the resource browsing evaluation index. The learning resource browsing adjustment is used to adjust the resource browsing evaluation index to meet the resource browsing condition, and the resource browsing evaluation index is used to evaluate the browsing situation of preschool children on the learning resources provided by the online education system; S3, Learning Completion Evaluation: Obtain a learning completion evaluation index by combining the acquired resource browsing evaluation index that meets the resource browsing condition and the learning completion data, and determine whether to adjust the learning completion based on the learning completion evaluation index. The learning completion adjustment is used to adjust the learning completion evaluation index to meet the learning completion condition, and the learning completion evaluation index is used to evaluate the completion situation of preschool children on the preset learning tasks.

[0031] In this embodiment, the coherence evaluation index is a prerequisite for the resource browsing evaluation index. When the coherence of the learning resources reaches a certain standard (that is, the coherence evaluation index is not lower than the preset learning coherence threshold), the system will further consider the browsing situation of the resources. The coherence evaluation index indirectly affects the learning completion evaluation index. The higher the coherence of the resources, the more conducive it is for preschool children to better understand and absorb knowledge, and it is more conducive to the completion of learning tasks. If the learning completion evaluation index shows that the learning task completion degree of children is not high, the system may recommend adjusting the difficulty of learning tasks (such as basic mathematics, basic English, basic art, etc.) to ensure that children can keep up with the learning progress and achieve the learning goals.

[0032] It should be added that the learning data of preschool children includes the average update bandwidth, the capacity of the learning resource data packet, and the average update time interval; the learning time data of preschool children's resources includes the resource page viewing time, the number of viewed pages, and the viewing page session time; the capacity of the learning resource data packet represents the size of the learning resource data packet within a preset time period; the resource page viewing time represents the time for preschool children to view a single learning page within a preset time period; the number of viewed pages represents the number of times preschool children view a single learning page within a preset time period; the viewing page session time represents the difference between the time stamp corresponding to the last page viewed by preschool children within a preset time period and the time stamp corresponding to the first page; the learning completion data includes the number of completed learning tasks, the total number of learning tasks, and the number of qualified completed tasks.

[0033] Further, the specific process of obtaining the coherence evaluation index based on the obtained learning data of preschool children is as follows: Obtain the update frequency compliance ratio (i.e., PF in the limiting expression of the coherence evaluation index) r ), and the update frequency compliance ratio is used to reflect the compliance of the update frequency of learning resources during the update process; obtain the update rate compliance ratio (i.e., SLF in the limiting expression of the coherence evaluation index) r ), and the update rate compliance ratio is used to reflect the compliance of the update rate of learning resources during the update process; combine the update frequency compliance ratio, the update rate compliance ratio, the first coherence allocation weight, and the second coherence allocation weight to obtain the coherence evaluation index.

[0034] The update frequency compliance ratio is obtained through the following steps: SS1, perform a ratio operation on the average update bandwidth and the capacity of the learning resource data packet to obtain the initial resource update frequency; SS2, perform a ratio operation on the sum of the initial resource update frequency and the preset learning resource update frequency threshold obtained from the database and twice the preset learning resource update frequency threshold to obtain the update frequency compliance ratio.

[0035] The update rate compliance ratio is obtained through the following steps: AA1, perform a reciprocal operation on the average update time interval to obtain the initial resource update rate; AA2, perform a ratio operation on the sum of the initial resource update rate and the preset learning resource update rate threshold obtained from the database and twice the preset learning resource update rate threshold to obtain the update rate compliance ratio; the first coherence allocation weight and the second coherence allocation weight are obtained from the database, and the first coherence allocation weight is used to reflect the influence degree of the update frequency of learning resources during the update process on the coherence evaluation index; the second coherence allocation weight is used to reflect the influence degree of the update rate of learning resources during the update process on the coherence evaluation index.

[0036] Among them, the limiting expression of the coherence evaluation index is as follows:

[0037]

[0038] In the formula, represents the coherence evaluation index of the learning resource in the r-th preset time period, where r = 1, 2,..., d, r represents the number of the preset time period, and d represents the total number of the preset time periods, PF r represents the update frequency compliance ratio of the learning resource in the r-th preset time period, SLF r represents the update rate compliance ratio of the learning resource in the r-th preset time period, DK r represents the average update bandwidth of the learning resource in the r-th preset time period, ZS r represents the learning resource data packet capacity of the learning resource in the r-th preset time period, ΔT r represents the average update time interval of the learning resource in the r-th preset time period, PF 0 represents the preset learning resource update frequency threshold, SLF 0 represents the preset learning resource update rate threshold, τ1 represents the first coherence allocation weight, τ2 represents the second coherence allocation weight, and e represents the natural constant.

[0039] In this embodiment, the uploading, downloading, and updating conditions of the learning resource data packets are monitored by a network traffic analyzer, so as to obtain the average update bandwidth, the learning resource data packet capacity, and the average update time interval within the preset time period. The resource page browsing time, the number of browsed pages, and the browsing page session time are recorded through the back-end log of the web page, and the number of completed learning tasks, the total number of learning tasks, and the number of qualified completed tasks are recorded through the online education system.

[0040] Specifically, in this embodiment, the first coherence allocation weight and the second coherence allocation weight are respectively the weights corresponding to the update frequency and the update rate in the database. The first coherence allocation weight is the value corresponding to the influence degree of the update frequency on the coherence evaluation index, and the second coherence allocation weight is the value corresponding to the influence degree of the update rate on the coherence evaluation index. When in use, they can be directly obtained from the database, and their corresponding relationships are preset. For example, the update frequency and the update rate form a mapping set with the weights corresponding to the preset update frequency and update rate in the database, and the real-time update frequency and update rate are input into the mapping set to obtain the weights corresponding to the update frequency and the update rate. The mapping relationship therein can be a one-to-one or many-to-one relationship, and the value range in this embodiment is 0 - 1.

[0041] The aforementioned database is a database established before the design of the personalized learning path planning method for preschool children in the online education system provided by the embodiments of the present application, and is used to store various set data, including but not limited to page browsing time, resource update time, resource update rate, etc. Various values therein are directly set by technicians. For example, the preset learning resource update rate threshold is represented by the average value of the learning resource update rate in the historical time period in the database, and the preset learning resource update frequency threshold is represented by the average value of the learning resource update frequency in the historical time period in the database.

[0042] It should be understood that the algorithm of this embodiment comprehensively analyzes preschool children's learning data to obtain a coherence evaluation index. The preschool children's learning data in the algorithm of this embodiment does not exist independently and has mutual relevance. The larger the average update bandwidth, it may mean that the learning resources can be updated faster, thereby shortening the average update time interval, maintaining higher coherence, and thus resulting in an increase in the coherence evaluation index. At the same time, the size of the learning resource data packet capacity will also affect the update efficiency. Larger data packets may take longer to transmit or update. The shorter the update time interval means that the learning resources can be updated more frequently, thereby maintaining higher coherence. The parameters of the algorithm of this embodiment need to jointly consider the impact on the results at the same time. Specifically, in remote mountainous areas, due to the remote geographical location and poor network conditions, the learning resources are updated slowly and the coherence is insufficient. The coherence of the learning resources is evaluated by monitoring the coherence evaluation index.

[0043] Specifically, assume that the update frequency compliance ratio PF r ranges from 0.6 to 1, the update rate compliance ratio SLF r ranges from 0.6 to 1, the first coherence allocation weight τ1 is 0.5, and the second coherence allocation weight τ2 is 0.5. As shown in Table 1, it is a statistical table of the changes in the coherence evaluation index:

[0044] Table 1 Statistical table of changes in coherence evaluation index

[0045]

[0046] As can be seen from the above table, as the update frequency compliance ratio PF r and the update rate compliance ratio SLF r gradually increase, the coherence evaluation index also gradually increases, indicating that the coherence of the learning resources is gradually improved, realizing the accurate quantification of the coherence of the learning resources, and further improving the timeliness of the online education system to plan the personalized learning path for preschool children.

[0047] Further, the specific process of determining whether to perform learning data coherence adjustment based on the coherence evaluation index is as follows: In the first step, it is determined whether the coherence evaluation index meets the resource coherence condition. If so, no learning data coherence adjustment is performed; otherwise, the second step is executed. In the second step, incremental data transmission is performed. When the monitored coherence evaluation index meets the resource coherence condition, the learning data coherence adjustment is stopped; otherwise, the third step is executed. Incremental data transmission means reducing the occupancy of network bandwidth through incremental synchronization methods. In the third step, stream processing is performed. When the monitored coherence evaluation index meets the resource coherence condition, the learning data coherence adjustment is stopped; otherwise, an alarm prompt is sent to the preset personnel. Stream processing means reducing the delay during data update through stream data processing methods. The resource coherence condition means that the coherence evaluation index is not lower than the preset learning coherence threshold obtained from the database.

[0048] In this embodiment, the preset learning coherence threshold is represented by the average value of the coherence evaluation indexes in the historical time period in the database. By using the incremental synchronization method, only the data that has changed since the last synchronization is transmitted. The system will compare the local data with the remote data to determine which data is new or has been modified. Only the data identified as new or modified will be transmitted, rather than the entire data set, to reduce the occupancy of network bandwidth. The stream processing method can process data almost in real time and minimize the data processing time. In a big data environment, the amount of data generated by the data source may be very large, including a lot of noise data or data that is useless for the current analysis. The stream processing method can perform a preliminary screening of these data streams, improving the availability of the data related to learning path planning and further enhancing the timeliness of the online education system for planning the personalized learning path of preschool children.

[0049] Further, the specific process of obtaining the resource browsing evaluation index by combining the obtained coherence evaluation index that meets the resource coherence condition and the preschool child resource learning time data is as follows: The initial average page stay time is obtained through ratio operation processing of the resource page browsing time and the number of browsed pages. The initial average page stay time is used to reflect the average degree of the stay time when the preschool child browses the page within the preset time period. The page stay time compliance ratio (i.e., FTL in the limit expression of the resource browsing evaluation index) is obtained through ratio operation processing of the sum of the initial average page stay time and the preset stay time threshold obtained from the database and twice the preset stay time threshold. r ) The page stay time compliance ratio is used to reflect the compliance of the stay time when the preschool child browses the page within the preset time period. The session time compliance ratio (i.e., FHH in the limit expression of the resource browsing evaluation index) is obtained through ratio operation processing of the browsing page session time and the preset session time threshold obtained from the database. r);The session time compliance ratio is used to reflect the compliance of the conversation time when preschool children browse pages within a preset time period; the resource browsing evaluation index is obtained by combining the page stay time compliance ratio, the session time compliance ratio, and the coherence evaluation index that meets the resource coherence condition.

[0050] Among them, the method for obtaining the resource browsing evaluation index is as follows:

[0051]

[0052] In the formula, represents the resource browsing evaluation index of preschool children in the r-th preset time period, r = 1, 2,..., d, r represents the number of the preset time period, d represents the total number of preset time periods, FTL r represents the page stay time compliance ratio of preschool children in the r-th preset time period, FHH r represents the session time compliance ratio of preschool children in the r-th preset time period, represents the coherence evaluation index that meets the resource coherence condition in the r-th preset time period, TT r represents the resource page browsing time of preschool children in the r-th preset time period, CS r represents the number of times of browsing pages by preschool children in the r-th preset time period, ΔHT r represents the browsing page session time of preschool children in the r-th preset time period, TL 0 represents the preset stay time threshold, FHH 0 represents the preset session time threshold, and e represents the natural constant.

[0053] In this embodiment, the preset stay time threshold is represented by the average value of the stay time in the historical time period in the database, and the preset session time threshold is represented by the average value of the session time in the historical time period in the database.

[0054] It should be understood that the algorithm in this embodiment comprehensively analyzes the resource browsing evaluation index by combining the coherence evaluation index and the preschool children's resource learning time data. The coherence evaluation index and the preschool children's resource learning time data in the algorithm of this embodiment do not exist independently and are interrelated. When the learning resource has a high degree of coherence, that is, when the coherence evaluation index increases, children are more likely to understand and absorb information, which may increase the browsing time and frequency of the resource. The longer the resource page browsing time, usually means that children have a greater interest in the page content or a deeper understanding, which may lead to an increase in the number of times of browsing the page, helping to improve the resource browsing evaluation index. The parameters of the algorithm in this embodiment need to jointly consider the impact on the result at the same time. Specifically, in remote mountainous areas, the high coherence of learning resources is crucial for improving children's learning effects. When the learning resource has a high degree of coherence, that is, when the coherence evaluation index increases, children are more likely to understand and absorb information, which may increase the browsing time and frequency of the resource.

[0055] Specifically, assume the browsing page session time ΔHT r ranges from 5 to 15 (min), and the preset session time threshold FHH 0 is 10 (min). As Figure 2 shown, it is a statistical chart of the change of the browsing page session time - session time compliance ratio provided by the embodiment of the present application. It can be seen from Figure 2 that as the browsing page session time gradually increases, the session time compliance ratio also gradually increases, indicating that the attention concentration time of preschool children is relatively improved, and the compliance of the staying time when preschool children browse the page is also gradually improved, realizing the accurate quantification of the compliance of the staying time when evaluating preschool children's browsing of the page, and further improving the timeliness of the online education system to plan the personalized learning path of preschool children.

[0056] Furthermore, the specific process of judging whether to adjust the learning resource browsing based on the resource browsing evaluation index is as follows: Q1, judge whether the resource browsing evaluation index meets the resource browsing condition. If so, no learning resource browsing adjustment is performed; otherwise, execute Q2; Q2, send a prompt to the preset person to increase the types of learning resources. When the monitored resource browsing evaluation index meets the resource browsing condition, stop the learning resource browsing adjustment; otherwise, execute Q3; Q3, send a prompt to the preset person to change the learning resource display form. When the monitored resource browsing evaluation index meets the resource browsing condition, stop the learning resource browsing adjustment; otherwise, send an alarm prompt to the preset person. Changing the learning resource display form is used to improve the attractiveness of the browsing page through video explanations, audio courseware, and interactive charts; the resource browsing condition means that the resource browsing evaluation index is not lower than the preset browsing threshold obtained from the database.

[0057] In this embodiment, the preset browsing threshold is represented by the average value of the resource browsing evaluation index in the historical time period in the database. By increasing the types of learning resources, it is possible to meet the needs of preschool children with different learning styles and interests, improve their learning interest and participation. The types of learning resources include game activities, music education, art education, math enlightenment, English enlightenment, etc. By changing the display form of learning resources, such as using video explanations, audio courseware, interactive charts, etc., the attractiveness of the resources can be improved, attracting the attention of preschool children. Different display forms of learning resources can meet the needs of different learning styles, such as visual, auditory, and hands-on learners. The diverse display forms help to achieve personalized learning, providing a customized learning experience according to each child's preferences and abilities; improving the timeliness of the online education system for planning the personalized learning path of preschool children.

[0058] Further, the specific process of obtaining the learning completion evaluation index by combining the resource browsing evaluation index and the learning completion data that meet the resource browsing conditions is as follows: The initial learning progress is obtained by performing a ratio operation on the number of completed learning tasks and the total number of learning tasks; the initial learning progress is used to reflect the progress of preschool children in completing the preset learning tasks within the preset time period; the learning progress compliance ratio (i.e., FJD in the limit expression of the learning completion evaluation index) is obtained by performing a ratio operation on the initial learning progress and the preset learning progress threshold obtained from the database. r ) The learning progress compliance ratio reflects the compliance of the progress of preschool children in completing the preset learning tasks within the preset time period; the initial learning correct rate is obtained by performing a ratio operation on the number of qualified completed tasks and the total number of learning tasks; the initial learning correct rate is used to reflect the correctness of preschool children in completing the preset learning tasks within the preset time period; the learning correct rate compliance ratio (i.e., FZQ in the limit expression of the learning completion evaluation index) is obtained by the initial learning correct rate and the preset learning correct rate threshold obtained from the database. r ) The learning correct rate compliance ratio is used to reflect the compliance of the correctness of preschool children in completing the preset learning tasks within the preset time period; the learning completion evaluation index is obtained by combining the learning progress compliance ratio, the learning correct rate compliance ratio, and the resource browsing evaluation index that meet the resource browsing conditions.

[0059] Among them, the method for obtaining the learning completion evaluation index is as follows:

[0060]

[0061] In the formula, represents the learning completion evaluation index of preschool children in the r-th preset time period, r = 1, 2,..., d, r represents the number of the preset time period, and d represents the total number of preset time periods, FJD rIt indicates the learning progress of preschool children in the rth preset time period, FZQ r It indicates the learning accuracy rate of preschool children in the rth preset time period. represents the resource browsing evaluation index that meets the resource browsing condition in the rth preset time period, WC r represents the number of learning tasks completed by preschool children in the rth preset time period, ZR r It represents the total number of learning tasks for preschool children in the rth preset time period, ZCR r represents the number of qualified preschool children who complete the task in the rth preset time period, FJD 0 Indicates the preset learning progress threshold, FZQ 0 represents the preset learning accuracy threshold, and e represents a natural constant.

[0062] In this embodiment, the preset learning progress threshold is represented by the average value of the learning progress in the historical time period in the database, and the preset learning accuracy threshold is represented by the average value of the learning accuracy in the historical time period in the database.

[0063] It should be understood that the algorithm of this embodiment combines the resource browsing evaluation index and the learning completion data for comprehensive analysis to obtain the resource browsing evaluation index. In the algorithm of this embodiment, the resource browsing evaluation index and the learning completion data do not exist independently, but are interrelated. A higher resource browsing evaluation index usually means that children have a higher degree of attention and interest in resources, which may promote them to participate more actively in learning tasks, thereby improving learning completion. An increase in the number of completed learning tasks usually means that children have made progress in their learning progress. The more learning tasks have been completed, the greater the chance and possibility of learners completing qualified tasks. This helps to improve the learning completion evaluation index. The increase in the number of qualified tasks completed indicates that children have made progress in learning quality, which helps to improve the learning completion evaluation index. The parameters of the algorithm of this embodiment need to consider the impact on the results together; the precise quantification of the completion of preschool children's preset learning tasks is achieved; and the timeliness of the online education system planning of personalized learning paths for preschool children is improved. Specifically, in remote mountainous areas, the limited educational resources and the particularity of children's learning environment make it particularly important to accurately quantify the learning completion of preschool children. By implementing the algorithm of this embodiment, we can achieve accurate quantification of the completion of preschool children's learning tasks, and then plan a more timely and personalized learning path.

[0064] Further, the specific process of determining whether to adjust the learning completion degree based on the learning completion degree evaluation index is as follows: W1, determine whether the learning completion degree evaluation index meets the learning completion degree condition. If so, no learning completion degree adjustment is performed; otherwise, execute W2; W2, send a prompt to the preset personnel to change the preset learning task content. When the monitored learning completion degree evaluation index meets the learning completion degree condition, stop the learning completion degree adjustment; otherwise, execute W3; W3, set the priority of the learning tasks. When the monitored learning completion degree evaluation index meets the learning completion degree condition, stop the learning completion degree adjustment; otherwise, send an alarm prompt to the preset personnel. The learning task priority setting means that the preset personnel divide the preset learning tasks through the Eisenhower matrix; the learning completion degree condition means that the learning completion degree evaluation index is not lower than the preset completion threshold obtained from the database.

[0065] In this embodiment, the preset completion threshold is represented by the average value of the learning completion degree evaluation index in the historical time period in the database. By changing the learning task content, which includes basic mathematics, basic English, basic art, etc., it can ensure that the learning tasks are more in line with the interests and needs of preschool children, thereby improving their learning motivation and participation. The preset personnel use the Eisenhower matrix to prioritize the preset learning tasks, manually mark the urgent tasks and postponed tasks, and push the urgent tasks to preschool children first. By sorting the learning tasks by priority, it can ensure that the key learning goals (i.e., urgent tasks) receive attention and resources, thus helping to achieve these goals; it improves the timeliness of the online education system to plan the personalized learning path for preschool children.

[0066] Such as Figure 3As shown in the figure, it is a schematic structural diagram of a personalized learning path planning system for preschool children in the online education system provided by the embodiments of the present application. The personalized learning path planning system for preschool children in the online education system provided by the embodiments of the present application includes: a coherence evaluation module, a resource browsing evaluation module, and a learning completion evaluation module. Among them, the coherence evaluation module is used to obtain a coherence evaluation index based on the acquired learning data of preschool children, and judge whether to adjust the learning data coherence based on the coherence evaluation index. The learning data coherence adjustment is used to adjust the coherence evaluation index to meet the resource coherence condition. The coherence evaluation index is used to evaluate the coherence of learning resources. The resource browsing evaluation module is used to combine the acquired coherence evaluation index that meets the resource coherence condition and the preschool children's resource learning time data to obtain a resource browsing evaluation index, and judge whether to adjust the learning resource browsing based on the resource browsing evaluation index. The learning resource browsing adjustment is used to adjust the resource browsing evaluation index to meet the resource browsing condition. The resource browsing evaluation index is used to evaluate the browsing situation of the learning resources provided by the online education system by preschool children. The learning completion evaluation module is used to combine the acquired resource browsing evaluation index that meets the resource browsing condition and the learning completion data to obtain a learning completion evaluation index, and judge whether to adjust the learning completion based on the learning completion evaluation index. The learning completion adjustment is used to adjust the learning completion evaluation index to meet the learning completion condition. The learning completion evaluation index is used to evaluate the completion situation of preschool children for the preset learning tasks.

[0067] As Figure 4 shown in the figure, it is the overall flowchart provided by the embodiments of the present application. The coherence evaluation module, the resource browsing evaluation module, and the learning completion evaluation module cooperate with each other, enabling the online education system to respond to the learning needs and progress of preschool children in real time and realize the dynamic adjustment of the personalized learning path. This personalized learning experience can not only improve the learning effect, but also keep children highly engaged and interested during the learning process, thus promoting their all-round development. It aims to provide a personalized learning path planning for preschool children. By continuously evaluating and adjusting the coherence, browsing situation, and completion degree of learning resources, the system can adapt to the learning needs and progress of preschool children, thereby improving the learning effect and learning experience; it realizes the improvement of the timeliness of the online education system in planning the personalized learning path for preschool children.

[0068] In summary, the embodiments of the present application determine whether to perform coherence adjustment of learning data based on the obtained coherence evaluation index, then determine whether to perform browsing adjustment of learning resources based on the obtained resource browsing evaluation index, and finally determine whether to perform completion adjustment of learning based on the obtained learning completion evaluation index, thereby improving the reliability of planning personalized learning paths for preschool children in the online education system, and further improving the timeliness of planning personalized learning paths for preschool children in the online education system, effectively solving the problem of low timeliness of planning personalized learning paths for preschool children in the online education system in the prior art.

[0069] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0071] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device that implements the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1 one or more of the blocks or multiple blocks.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the functions specified in Figure 1 one or more of the processes or multiple processes and / or blocks Figure 1Steps of the functions specified in one or more boxes.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for planning personalized learning paths for preschool children in an online education system, characterized in that: The following steps are involved: S1, obtaining a coherence evaluation index according to the acquired preschool children's learning data, and judging whether to adjust the coherence of the learning data based on the coherence evaluation index, wherein the coherence evaluation index is used to evaluate the coherence of the learning resources; S2, combining the obtained coherence evaluation index satisfying the resource coherence condition and the preschool children's resource learning time data to obtain a resource browsing evaluation index, and judging whether to make a learning resource browsing adjustment based on the resource browsing evaluation index, wherein the resource browsing evaluation index is used to evaluate the preschool children's browsing of the learning resources provided by the online education system; S3, combining the resource browsing evaluation index that meets the resource browsing condition and the learning completion data to obtain a learning completion evaluation index, and judging whether to adjust the learning completion based on the learning completion evaluation index, wherein the learning completion evaluation index is used to evaluate the preschool child's completion of a preset learning task; The specific process of obtaining the coherence evaluation index based on the acquired preschool children's learning data is as follows: Obtaining an update frequency compliance ratio, where the update frequency compliance ratio is used to reflect the update frequency compliance of the learning resource during the update process; Obtaining an update rate compliance ratio, where the update rate compliance ratio is used to reflect the update rate compliance of the learning resource during the update process; Obtaining a coherence evaluation index by combining the update frequency coincidence ratio, the update rate coincidence ratio, the first coherence allocation weight, and the second coherence allocation weight; The update frequency coincidence ratio is obtained by the following steps: SS1, calculate the ratio of the average update bandwidth and the learning resource data packet capacity to obtain the initial resource update frequency; SS2, performing a ratio operation on the sum of the initial resource update frequency and the preset learning resource update frequency threshold obtained from the database and twice the preset learning resource update frequency threshold to obtain an update frequency compliance ratio; The update rate coincidence ratio is obtained by the following steps: AA1, perform the inverse operation on the average update time interval to obtain the initial resource update rate; AA2, performing a ratio operation on the sum of the initial resource update rate and the preset learning resource update rate threshold obtained from the database and twice the preset learning resource update rate threshold to obtain an update rate compliance ratio; The constrained expression of the coherence evaluation index is as follows: ； ； ； In the formula, represents the coherence evaluation index of the learning resource in the rth preset time period, , r represents the number of the preset time period, d represents the total number of preset time periods, It indicates the update frequency of learning resources in the rth preset time period. It indicates the update rate of learning resources in the rth preset time period. represents the average update bandwidth of the learning resource in the rth preset time period, represents the learning resource data packet capacity in the rth preset time period, represents the average update time interval of the learning resource in the rth preset time period, Indicates the preset learning resource update frequency threshold. Indicates the preset learning resource update rate threshold. represents the first coherence allocation weight, represents the second coherence allocation weight, and e represents a natural constant.

2. The method for planning personalized learning paths for preschool children in an online education system according to claim 1, characterized in that: The preschool children's learning data includes average update bandwidth, learning resource data packet capacity, and average update time interval; The preschool children's resource learning time data includes resource page browsing time, page browsing times and page browsing session time; The learning resource data packet capacity represents the size of the learning resource data packet within a preset time period; The resource page browsing time refers to the time that the preschool child browses a single learning page within a preset time period; The number of page views indicates the number of times a preschool child views a single learning page within a preset time period; The page browsing session time represents the difference between the timestamp corresponding to the last page browsed by the preschool child within a preset time period and the timestamp corresponding to the first page browsed; The learning completion data includes the number of completed learning tasks, the total number of learning tasks and the number of qualified completed tasks.

3. The method for planning personalized learning paths for preschool children in an online education system according to claim 1, characterized in that: The specific process of judging whether to adjust the coherence of learning data based on the coherence evaluation index is as follows: The first step is to determine whether the consistency evaluation index meets the resource consistency condition. If so, no learning data consistency adjustment is performed. Otherwise, the second step is executed. The second step is to transmit incremental data. When the monitored consistency evaluation index meets the resource consistency condition, the learning data consistency adjustment is stopped. Otherwise, the third step is executed. The third step is to perform stream processing. When the monitored consistency evaluation index meets the resource consistency condition, the learning data consistency adjustment is stopped. Otherwise, an alarm prompt is sent to the preset personnel. The resource consistency condition indicates that the consistency evaluation index is not lower than a preset learning consistency threshold obtained from a database.

4. The method for planning personalized learning paths for preschool children in an online education system according to claim 2, characterized in that: The specific process of combining the obtained coherence evaluation index that meets the resource coherence condition with the preschool children's resource learning time data to obtain the resource browsing evaluation index is as follows: The initial average page dwell time is obtained by performing a ratio operation on the resource page browsing time and the number of page browsing times; The page dwell time compliance ratio is obtained by performing a ratio operation on the sum of the initial average page dwell time and the preset dwell time threshold obtained from the database and twice the preset dwell time threshold; The session time compliance ratio is obtained by performing a ratio operation on the browsing page session time and the preset session time threshold obtained from the database; The resource browsing evaluation index is obtained by combining the page dwell time compliance ratio, the session time compliance ratio and the consistency evaluation index that meets the resource consistency conditions.

5. The method for planning personalized learning paths for preschool children in an online education system according to claim 1, characterized in that: The specific process of judging whether to adjust the learning resource browsing based on the resource browsing evaluation index is as follows: Q1, determine whether the resource browsing evaluation index meets the resource browsing conditions. If so, no learning resource browsing adjustment is performed. Otherwise, Q2 is executed; Q2, send a reminder to the preset personnel to add the types of learning resources. When the monitored resource browsing evaluation index meets the resource browsing conditions, stop adjusting the learning resource browsing. Otherwise, execute Q3; Q3, send a reminder to the preset personnel to change the learning resource display format. When the monitored resource browsing evaluation index meets the resource browsing conditions, stop adjusting the learning resource browsing. Otherwise, send an alarm reminder to the preset personnel; The resource browsing condition indicates that the resource browsing evaluation index is not lower than a preset browsing threshold obtained from a database.

6. The method for planning personalized learning paths for preschool children in an online education system according to claim 2, characterized in that: The specific process of combining the resource browsing evaluation index that meets the resource browsing condition and the learning completion data to obtain the learning completion evaluation index is as follows: The initial learning progress is obtained by performing a ratio operation on the number of completed learning tasks and the total number of learning tasks; The learning progress compliance ratio is obtained by performing a ratio operation on the initial learning progress and the preset learning progress threshold obtained from the database; The initial learning accuracy is obtained by performing a ratio operation on the number of qualified completed tasks and the total number of learning tasks; The learning accuracy compliance ratio is obtained by using the initial learning accuracy and the preset learning accuracy threshold obtained from the database; The learning completion evaluation index is obtained by combining the learning progress compliance ratio, the learning accuracy compliance ratio and the resource browsing evaluation index that meets the resource browsing conditions.

7. The method for planning personalized learning paths for preschool children in an online education system according to claim 1, characterized in that: The specific process of determining whether to adjust the learning completion degree based on the learning completion degree evaluation index is as follows: W1, determine whether the learning completion evaluation index meets the learning completion conditions. If so, no learning completion adjustment is performed. Otherwise, W2 is executed; W2, send a reminder to the preset personnel to change the preset learning task content. When the monitored learning completion evaluation index meets the learning completion condition, stop adjusting the learning completion, otherwise execute W3; W3, setting the priority of learning tasks. When the monitored learning completion evaluation index meets the learning completion condition, stop adjusting the learning completion. Otherwise, send an alarm to the preset personnel. The learning completion condition indicates that the learning completion evaluation index is not lower than a preset completion threshold obtained from a database.

8. The personalized learning path planning system for preschool children in the online education system is characterized by: A method for planning a personalized learning path for preschool children in an online education system for executing any one of claims 1 to 7, comprising a continuity evaluation module, a resource browsing evaluation module, and a learning completion evaluation module: The consistency evaluation module is used to obtain a consistency evaluation index based on the acquired preschool children's learning data, and determine whether to adjust the consistency of the learning data based on the consistency evaluation index. The consistency evaluation index is used to evaluate the consistency of the learning resources. The resource browsing evaluation module is used to combine the obtained consistency evaluation index that meets the resource consistency condition and the preschool children's resource learning time data to obtain a resource browsing evaluation index, and judge whether to make a learning resource browsing adjustment based on the resource browsing evaluation index. The resource browsing evaluation index is used to evaluate the preschool children's browsing of the learning resources provided by the online education system; The learning completion assessment module is used to obtain a learning completion assessment index by combining the resource browsing assessment index that meets the resource browsing conditions and the learning completion data, and to determine whether to make a learning completion adjustment based on the learning completion assessment index. The learning completion assessment index is used to assess the completion of preschool children on preset learning tasks.

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