Preschool child personalized learning path planning method and system of online education system

Abstract

The invention discloses a preschool child personalized learning path planning method and system of an online education system, and relates to the technical field of electric digital data processing. The invention discloses a preschool child personalized learning path planning method of an online education system. The method comprises the following steps of performing continuity evaluation; evaluating resource browsing; and evaluating the learning completion degree. According to the invention, whether to perform learning data coherence adjustment is judged through the obtained coherence evaluation index, then whether to perform learning resource browsing adjustment is judged based on the obtained resource browsing evaluation index, and finally whether to perform learning completion adjustment is judged based on the obtained learning completion evaluation index. The effect of improving the timeliness of the online education system for planning the personalized learning path of the preschool children is achieved, and the problem that the timeliness of the online education system for planning the personalized learning path of the preschool children is low in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular 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 become an emerging force in the field of education, showing unprecedented vitality and potential. This education model breaks the time and space limitations of traditional education, and opens up a new learning world for learners with its high flexibility in time and complete freedom in location. In remote mountainous areas, educational resources are relatively scarce, and the updating and continuity of learning resources are crucial to improving the quality of education. Especially in the field of preschool children's education, online education systems can provide rich and diverse educational resources and learning materials to meet the diverse learning needs of children. Learners are no longer restricted to fixed classrooms and prescribed schedules, but can access the Internet anytime and anywhere. Personalized learning path planning refers to designing personalized learning paths for learners based on individual differences such as learners' knowledge, abilities, interests and learning habits. This planning method not only helps to improve learners' learning effects, allowing them to make greater progress in a short period of time, but also significantly improves their learning satisfaction.

[0003] Existing methods for planning personalized learning paths for children mainly collect and analyze various data of preschool children in the learning process, such as login information, learning history, answer records, etc. Educational institutions can build a learning portrait of each child and then develop personalized learning plans and learning paths for them.

[0004] For example, the invention patent with announcement number CN110569443B discloses an adaptive learning path planning system based on reinforcement learning, which includes three modules: environment simulation, strategy training and path planning. The whole process obtains the ability value of students at each moment based on the improved project reflection principle, simulates complex learning environment based on Markov decision process, and reasonably applies reinforcement learning algorithm combined with students' historical learning trajectory to train path planning strategy offline. Finally, learning path is adaptively planned for students online according to the trained strategy.

[0005] For example, the invention patent with publication number: CN112825071A discloses a learning path planning method, device, equipment and storage medium, including: obtaining a knowledge point set to be planned by the students to be planned, the learning behavior data of the students to be planned and the current learning mastery of each knowledge point in the knowledge point set of the students to be planned; obtaining the change in the mastery of each knowledge point of the students to be planned based on at least the learning behavior data and the current learning mastery; sorting each knowledge point according to the size of the change in the mastery to obtain a planned path for learning each knowledge point.

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

[0007] In the prior art, it is difficult for preschool children to focus on the same thing for a long time because their attention is easily distracted. This distraction requires constant changes in presentation methods and content when integrating learning resources, resulting in low timeliness in online education systems planning personalized learning paths for preschool children. Summary of the invention

[0008] The embodiments of the present application solve the problem of low timeliness of planning personalized learning paths for preschool children by online education systems in the prior art by providing a method and system for planning personalized learning paths for preschool children in an online education system, thereby improving the timeliness of planning personalized learning paths for preschool children by online education systems.

[0009] The embodiment of the present application provides a method for planning personalized learning paths for preschool children in an online education system, comprising the following steps: S1, obtaining a consistency evaluation index based on the acquired preschool children's learning data, and judging whether to adjust the consistency of the learning data based on the consistency evaluation index, wherein the consistency evaluation index is used to evaluate the consistency of learning resources; S2, obtaining a resource browsing evaluation index by combining the acquired consistency evaluation index that meets the resource consistency conditions and the preschool children's resource learning time data, and judging whether to adjust the learning resource browsing 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, obtaining a learning completion evaluation index by combining the acquired resource browsing evaluation index that meets the resource browsing conditions and the learning completion data, 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 children's completion of preset learning tasks.

[0010] Furthermore, 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, number of 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 represents the time a preschool child browses a single learning page within a preset time period; the number of page browsing times represents the number of times a preschool child browses 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 the preset time period and the timestamp 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.

[0011] Furthermore, 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, 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, the update rate compliance ratio is used to reflect the update rate compliance of the learning resource during the update process; combining 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 by the following steps: SS1, performing a ratio operation on 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 2 times the preset learning resource update frequency threshold to obtain the update frequency compliance ratio; the update rate compliance ratio is obtained by the following steps: AA1, performing a reciprocal 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 2 times the preset learning resource update rate threshold to obtain the update rate compliance ratio.

[0012] Furthermore, the constrained 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 rth 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 Indicates the update frequency ratio of learning resources in the rth preset time period, SLF r Indicates the update rate of the learning resource in the rth preset time period, DK rrepresents the average update bandwidth of the learning resource in the rth preset time period, ZS r represents the learning resource data packet capacity in the rth preset time period, ΔT r represents the average update time interval of the learning resource in the rth preset time period, PF 0 Indicates the preset learning resource update frequency threshold, SLF 0 represents the preset learning resource update rate threshold, τ1 represents the first consistency allocation weight, τ2 represents the second consistency allocation weight, and e represents a natural constant.

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

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

[0017] The embodiment of the present application provides a personalized learning path planning system for preschool children in an online education system, including a continuity evaluation module, a resource browsing evaluation module and a learning completion evaluation module: wherein the continuity evaluation module is used to obtain a continuity evaluation index according to the acquired preschool children's learning data, and judge whether to adjust the learning data consistency based on the continuity evaluation index, and the consistency evaluation index is used to evaluate the consistency of learning resources; the resource browsing evaluation module is used to combine the acquired continuity evaluation index that meets the resource consistency conditions 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, and 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 evaluation module is used to combine the acquired resource browsing evaluation index that meets the resource browsing conditions 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, and the learning completion evaluation index is used to evaluate the preschool children's completion of 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. Determine whether to adjust the consistency of learning data based on the obtained consistency evaluation index, then determine whether to adjust the learning resource browsing based on the obtained resource browsing evaluation index, and finally determine whether to adjust the learning completion based on the obtained learning completion evaluation index, thereby improving the reliability of the online education system in planning personalized learning paths for preschool children, and then improving the timeliness of the online education system in planning personalized learning paths for preschool children, effectively solving the problem of low timeliness of the online education system in planning personalized learning paths for preschool children in the prior art.

[0020] 2. The consistency evaluation index is obtained through the update frequency compliance ratio, the update rate compliance ratio, the first consistency allocation weight and the second consistency allocation weight. Then, the resource browsing evaluation index is obtained through the page dwell time compliance ratio, the session time compliance ratio and the consistency evaluation index that meets the resource consistency conditions. Finally, the learning completion evaluation index is obtained through the learning progress compliance ratio, the learning accuracy compliance ratio and the resource browsing evaluation index that meets the resource browsing conditions. This improves the accuracy of obtaining relevant data for personalized learning path planning, and further realizes the precise quantification of the timeliness of the online education system in planning personalized learning paths for preschool children.

[0021] 3. By judging whether the consistency evaluation index meets the resource consistency conditions, then judging whether the resource browsing evaluation index meets the resource browsing conditions, and finally judging whether the learning completion evaluation index meets the learning completion conditions, the dynamic adjustment of the timeliness of the online education system planning the personalized learning path for preschool children is realized, thereby realizing a comprehensive evaluation of the timeliness of the online education system planning the personalized learning path for preschool children. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0023] Figure 2 A statistical graph showing changes in the browsing page session time-session time matching ratio provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of a personalized learning path planning system for preschool children in an online education system provided in an embodiment of the present application;

[0025] Figure 4 An overall flow chart provided for an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application solve the problem of low timeliness of planning personalized learning paths for preschool children by online education systems in the prior art by providing a method and system for planning personalized learning paths for preschool children in an online education system. A consistency evaluation index is obtained by using the acquired learning data of preschool children, and whether to adjust the consistency of the learning data is determined based on the consistency evaluation index. Then, a resource browsing evaluation index is obtained by combining the acquired consistency evaluation index that meets the resource consistency conditions and the preschool children's resource learning time data. Based on the resource browsing evaluation index, it is determined whether to adjust the learning resource browsing. Finally, a learning completion evaluation index is obtained by combining the acquired resource browsing evaluation index that meets the resource browsing conditions and the learning completion data. Based on the learning completion evaluation index, it is determined whether to adjust the learning completion. This improves the timeliness of planning personalized learning paths for preschool children by online education systems.

[0027] The technical solution in the embodiment of the present application is to solve the problem of low timeliness of the above-mentioned online education system in planning personalized learning paths for preschool children. The overall idea is as follows:

[0028] Whether to adjust the consistency of learning data is determined by the obtained consistency evaluation index, and then whether to adjust the learning resource browsing is determined based on the obtained resource browsing evaluation index. Finally, whether to adjust the learning completion is determined based on the obtained learning completion evaluation index. This achieves the effect of improving the timeliness of the online education system in planning personalized learning paths for preschool children.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a flow chart of a method for planning a personalized learning path for preschool children in an online education system provided by an embodiment of the present application, the method comprising the following steps: S1, consistency evaluation: obtaining a consistency evaluation index according to the acquired preschool children's learning data, judging whether to adjust the learning data consistency based on the consistency evaluation index, the learning data consistency adjustment is used to adjust the consistency evaluation index to meet the resource consistency condition, and the consistency evaluation index is used to evaluate the consistency of learning resources; S2, resource browsing evaluation: combining the acquired 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 judging whether to adjust the learning data consistency based on the consistency evaluation index. The resource browsing evaluation index is used to determine whether to adjust the learning resource browsing. The learning resource browsing adjustment is used to adjust the resource browsing evaluation index to meet the resource browsing conditions. The resource browsing evaluation index is used to evaluate preschool children's browsing of the learning resources provided by the online education system; S3, learning completion evaluation: combining the resource browsing evaluation index that meets the resource browsing conditions and the learning completion data to obtain the learning completion evaluation index, and judging 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 conditions. The learning completion evaluation index is used to evaluate preschool children's completion of 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 of the resources. The coherence evaluation index indirectly affects the learning completion evaluation index. The more coherent the resources, the better they help preschool children understand and absorb knowledge, and the more helpful they are in completing learning tasks. If the learning completion evaluation index shows that the child's learning task completion is not high, the system may suggest adjusting the difficulty of the learning tasks (mathematics foundation, English foundation, art foundation, etc.) to ensure that the child can keep up with the learning progress and achieve the learning goals.

[0032] It should be supplemented that the learning data of preschool children include the average update bandwidth, the capacity of learning resource data packets, and the average update time interval; the resource learning time data of preschool children include the resource page browsing time, the number of page browsing times, and the page browsing session time; the learning resource data packet capacity indicates the size of the learning resource data packet within the preset time period; the resource page browsing time indicates the time that preschool children browse a single learning page within the preset time period; the number of page browsing times indicates the number of times preschool children browse a single learning page within the preset time period; the page browsing session time indicates the difference between the timestamp corresponding to the last page browsed by the preschool child within the preset time period and the timestamp 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] Furthermore, the specific process of obtaining the coherence evaluation index based on the obtained preschool children's learning data is as follows: obtaining the update frequency matching ratio (i.e., the PF in the restriction expression of the coherence evaluation index) r ), the update frequency compliance ratio is used to reflect the update frequency compliance of learning resources during the update process; the update rate compliance ratio (i.e., the SLF in the constraint expression of the coherence evaluation index) is obtained. r ), the update rate compliance ratio is used to reflect the update rate compliance of the learning resource during the update process; the consistency evaluation index is obtained by combining the update frequency compliance ratio, the update rate compliance ratio, the first consistency allocation weight and the second consistency allocation weight.

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

[0035] The update rate conformity ratio is obtained through the following steps: AA1, performing a reciprocal 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 a preset learning resource update rate threshold obtained from a database and twice the preset learning resource update rate threshold to obtain the update rate conformity ratio; the first consistency allocation weight and the second consistency allocation weight are obtained from the database, the first consistency allocation weight is used to reflect the degree of influence of the update frequency of the learning resource during the update process on the consistency evaluation index; the second consistency allocation weight is used to reflect the degree of influence of the update rate of the learning resource during the update process on the consistency evaluation index.

[0036] Among them, the restricted 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 rth 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 Indicates the update frequency ratio of learning resources in the rth preset time period, SLF r Indicates the update rate of the learning resource in the rth preset time period, DK r represents the average update bandwidth of the learning resource in the rth preset time period, ZS r represents the learning resource data packet capacity in the rth preset time period, ΔT r represents the average update time interval of the learning resource in the rth preset time period, PF 0 Indicates the preset learning resource update frequency threshold, SLF 0 represents the preset learning resource update rate threshold, τ1 represents the first consistency allocation weight, τ2 represents the second consistency allocation weight, and e represents a natural constant.

[0039] In this embodiment, the upload, download and update of the learning resource data packet are monitored by a network traffic analyzer to obtain the average update bandwidth, learning resource data packet capacity and average update time interval within a preset time period. The resource page browsing time, the number of page browsing times and the page browsing session time are recorded through the back-end log of the web page browsing. 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 consistency allocation weight and the second consistency allocation weight are weights corresponding to the update frequency and update rate in the database, respectively. The first consistency allocation weight is a numerical value corresponding to the degree of influence of the update frequency on the consistency evaluation index, and the second consistency allocation weight is a numerical value corresponding to the degree of influence of the update rate on the consistency evaluation index. When used, they can be directly obtained from the database, and the corresponding relationship is preset. For example, the update frequency and update rate form a mapping set with the weights corresponding to the update frequency and update rate preset 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 update rate. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, the value range is 0-1.

[0041] The aforementioned database is a database for storing various types of setting data established before the design of the personalized learning path planning method for preschool children in the online education system provided in the embodiment of the present application. The database includes but is not limited to page browsing time, resource update time and resource update rate, etc., and the various numerical values ​​therein are directly set by technical personnel. 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 combines the comprehensive analysis of preschool children's learning data to obtain the coherence evaluation index. The preschool children's learning data in the algorithm of this embodiment does not exist independently, but has mutual correlation. The larger the average update bandwidth, the faster the learning resources can be updated, thereby shortening the average update time interval and maintaining a higher coherence, which leads to an increase in the coherence evaluation index. At the same time, the size of the learning resource data packet capacity will also affect the efficiency of the update. The larger the data packet, the longer it may take to transmit or update. The shorter the update time interval means that the learning resources can be updated more frequently, thereby maintaining a higher coherence. The parameters of the algorithm of this embodiment need to consider the impact on the results together. Specifically, in remote mountainous areas, due to the remote geographical location and poor network conditions, the learning resources are updated slowly and lack coherence. The coherence of the learning resources is evaluated by monitoring the coherence evaluation index.

[0043] Specifically, assuming that the update frequency meets the PF r The range is 0.6-1, and the update rate conforms to the SLF r The range is 0.6-1, the first coherence allocation weight τ1 is 0.5, and the second coherence allocation weight τ2 is 0.5, as shown in Table 1, which is a statistical table of changes in the coherence evaluation index:

[0044] Table 1 Statistics of changes in the coherence evaluation index

[0045]

[0046] As can be seen from the table above, as the update frequency meets the PF r and update rate conforms to SLF r The coherence assessment index It also gradually increases, which means that the continuity of learning resources is gradually improved, and the accurate quantification of the continuity of learning resources can be achieved, thereby improving the timeliness of the online education system in planning personalized learning paths for preschool children.

[0047] Furthermore, the specific process of judging whether to adjust the consistency of learning data based on the consistency evaluation index is as follows: the first step is to judge whether the consistency evaluation index meets the resource consistency condition. If so, the consistency adjustment of learning data is not performed, otherwise the second step is executed; the second step is to perform incremental data transmission. When the monitored consistency evaluation index meets the resource consistency condition, the consistency adjustment of learning data is stopped, otherwise the third step is executed. Incremental data transmission means reducing the network bandwidth occupancy through the incremental synchronization method; the third step is to perform stream processing. When the monitored consistency evaluation index meets the resource consistency condition, the consistency adjustment of learning data is stopped, otherwise an alarm is sent to the preset personnel. Stream processing means reducing the delay in data update through the streaming data processing method; the resource consistency condition means that the consistency evaluation index is not lower than the preset learning consistency threshold obtained from the database.

[0048] In this embodiment, the preset learning continuity threshold is represented by the average value of the continuity evaluation index of the historical time period in the database. The incremental synchronization method is adopted to transmit only the data that has changed since the last synchronization. The system compares the local data with the remote data to determine which data is new or has been modified. Only those data identified as new or modified will be transmitted, rather than the entire data set, to reduce network bandwidth usage; the stream processing method can process data almost in real time and minimize data processing time. In a big data environment, the amount of data generated by the data source may be large, which contains a lot of noise data or data that is useless for the current analysis. The stream processing method can perform preliminary screening of these data streams; the availability of data related to learning path planning is improved, thereby achieving the improvement of the timeliness of the online education system in planning personalized learning paths for preschool children.

[0049] Furthermore, 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 children's resource learning time data 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 initial average page dwell time is used to reflect the average degree of dwell time of preschool children when browsing pages within a preset time period; the page dwell time compliance ratio (i.e., the FTL in the restriction expression of the resource browsing evaluation index) 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. r ); the page dwell time compliance ratio is used to reflect the compliance of the dwell time of preschool children when browsing the page within the preset time period; the session time compliance ratio (i.e., FHH in the restriction expression of the resource browsing evaluation index) is obtained by performing ratio calculation on 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 the page within the preset time period; the resource browsing evaluation index is obtained by combining the page dwell time compliance ratio, the session time compliance ratio and the coherence evaluation index that meets the resource coherence conditions.

[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 rth 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 It indicates the proportion of preschool children’s page dwell time in the rth preset time period, FHH r It represents the conversation time compliance ratio of preschool children in the rth preset time period, Indicates the consistency evaluation index that satisfies the resource consistency condition in the rth preset time period, TT r represents the time that preschool children browse resource pages in the rth preset time period, CS r represents the number of pages browsed by preschool children in the rth preset time period, ΔHT r represents the browsing session time of preschool children in the rth preset time period, TL 0 Indicates the preset dwell time threshold, FHH 0 represents the preset session time threshold, and e represents a natural constant.

[0053] In this embodiment, the preset residence time threshold is represented by the average value of the residence 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 of this embodiment combines the coherence evaluation index and the preschool children's resource learning time data for comprehensive analysis to obtain the resource browsing evaluation index. In the algorithm of this embodiment, the coherence evaluation index and the preschool children's resource learning time data are not independent, but have mutual correlation. When the learning resources have 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 number of resources. The longer the resource page browsing time usually means that the child is more interested in the page content or the deeper the understanding, which may lead to an increase in the number of page browsing times, which helps to improve the resource browsing evaluation index. The parameters of the algorithm of this embodiment need to consider the impact on the results together. Specifically, in remote mountainous areas, the high coherence of learning resources is crucial to improving children's learning effects. When the learning resources have a high degree of coherence, that is, the coherence evaluation index increases, children are more likely to understand and absorb information, which may increase the browsing time and number of resources.

[0055] Specifically, assuming that the page browsing session time ΔHT r The range is 5-15 (min), and the preset session time threshold is FHH 0 is 10 (min), such as Figure 2 As shown, it is a statistical diagram of the changes in the browsing page session time-session time matching ratio provided by the embodiment of the present application, which is composed of Figure 2 It can be seen that as the page browsing session time gradually increases, the session time compliance ratio also gradually increases, which means that the attention span of preschool children is relatively improved, and the compliance of the stay time of preschool children when browsing the page is also gradually improved. The accurate quantification of the compliance of the stay time of preschool children when browsing the page is achieved, and then the timeliness of the online education system in planning personalized learning paths for preschool children is improved.

[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, judging whether the resource browsing evaluation index meets the resource browsing condition, if so, no adjustment is made to the learning resource browsing, otherwise Q2 is executed; Q2, sending a prompt to the preset personnel to increase the types of learning resources, when the monitored resource browsing evaluation index meets the resource browsing condition, stop adjusting the learning resource browsing, otherwise Q3 is executed; Q3, sending a prompt to the preset personnel to change the learning resource display form, when the monitored resource browsing evaluation index meets the resource browsing condition, stop adjusting the learning resource browsing, otherwise an alarm is sent to the preset personnel to change the learning resource display form to improve the attractiveness of the browsing page through video explanations, audio courseware, and interactive charts; the resource browsing condition indicates 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 of 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, and improve the learning interest and participation of preschool children. The types of learning resources include game activities, music education, art education, mathematics 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 resources can be improved to attract the attention of preschool children. Different learning resource display forms can meet the needs of different learning styles, such as visual, auditory and hands-on learners. Diversified display forms help to achieve personalized learning, provide customized learning experiences according to the preferences and abilities of each child, and realize the improvement of the timeliness of the online education system in planning personalized learning paths for preschool children.

[0058] Furthermore, 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 a preset time period; the learning progress compliance ratio (i.e., the FJD in the restriction 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 preschool children in completing the preset learning tasks within the preset time period; 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 initial learning accuracy is used to reflect the correctness of the preschool children in completing the preset learning tasks within the preset time period; the learning accuracy compliance ratio (i.e., FZQ in the restricted expression of the learning completion evaluation index) is obtained by the initial learning accuracy and the preset learning accuracy threshold obtained from the database r ); the learning accuracy compliance ratio is used to reflect the correct compliance 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 accuracy compliance ratio and the resource browsing evaluation index that meets the resource browsing conditions.

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

[0060]

[0061] In the formula, It represents the learning completion evaluation index of preschool children in the rth 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, 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] Furthermore, the specific process of judging whether to adjust the learning completion degree based on the learning completion evaluation index is as follows: W1, judging whether the learning completion evaluation index meets the learning completion condition. If so, no learning completion adjustment is made, otherwise W2 is executed; W2, sending a prompt to the preset personnel to change the preset learning task content. When the monitored learning completion evaluation index meets the learning completion condition, the learning completion adjustment is stopped, otherwise W3 is executed; W3, setting the learning task priority. When the monitored learning completion evaluation index meets the learning completion condition, the learning completion adjustment is stopped, otherwise an alarm prompt is sent to the preset personnel. The learning task priority setting indicates that the preset personnel divide the preset learning tasks through the Eisenhower matrix; the learning completion condition indicates that the learning completion 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 evaluation index of the historical time period in the database. By changing the content of the learning tasks, which include mathematics foundation, English foundation, art foundation, etc., it can be ensured 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 urgent tasks and deferred tasks, and push urgent tasks to preschool children first. By prioritizing the learning tasks, it can be ensured that key learning goals (i.e., urgent tasks) receive attention and resources, which helps to achieve these goals; the timeliness of the online education system in planning personalized learning paths for preschool children is improved.

[0066] like Figure 3As shown, it is a structural schematic diagram of the personalized learning path planning system for preschool children of the online education system provided in the embodiment of the present application. The personalized learning path planning system for preschool children of the online education system provided in the embodiment of the present application includes: a consistency evaluation module, a resource browsing evaluation module and a learning completion evaluation module: wherein the consistency evaluation module is used to obtain a consistency evaluation index according to the acquired preschool children's learning data, and judge whether to adjust the learning data consistency based on the consistency evaluation index, and the learning data consistency adjustment is used to adjust the consistency evaluation index to meet the resource consistency condition, and the consistency evaluation index is used to evaluate the consistency of the learning resources; the resource browsing evaluation module is used to combine the acquired consistency evaluation index that meets the resource consistency condition and the preschool children's learning data to obtain a consistency evaluation index. The resource browsing evaluation index is obtained from the children's resource learning time data, and it is determined whether to make learning resource browsing adjustments 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 conditions. The resource browsing evaluation index is used to evaluate preschool children's browsing of 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 obtained resource browsing evaluation index that meets the resource browsing conditions and the learning completion data, and it is determined whether to make learning completion adjustments 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 conditions. The learning completion evaluation index is used to evaluate preschool children's completion of preset learning tasks.

[0067] like Figure 4 As shown, it is the overall flow chart provided by the embodiment of the present application. The coherence evaluation module, the resource browsing evaluation module and the learning completion evaluation module cooperate with each other, so that the online education system can respond to the learning needs and progress of preschool children in real time, and realize the dynamic adjustment of personalized learning paths. This personalized learning experience can not only improve the learning effect, but also allow children to maintain a high sense of participation and interest in the learning process, thereby promoting their all-round development. It aims to provide personalized learning path planning for preschool children. By constantly evaluating and adjusting the coherence, browsing status and completion of learning resources, the system can adapt to the learning needs and progress of preschool children, thereby improving learning effects and learning experience; it realizes the improvement of the timeliness of the online education system planning personalized learning paths for preschool children.

[0068] To summarize, the embodiment of the present application determines whether to adjust the consistency of learning data based on the obtained consistency evaluation index, and then determines whether to adjust the learning resource browsing based on the obtained resource browsing evaluation index, and finally determines whether to adjust the learning completion based on the obtained learning completion evaluation index, thereby achieving an improvement in the reliability of the online education system in planning the personalized learning path for preschool children, and then achieving an improvement in the timeliness of the online education system in planning the personalized learning path for preschool children, effectively solving the problem of low timeliness of the online education system in planning the personalized learning path for preschool children in the prior art.

[0069] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may 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 flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for 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 may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted 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 equivalents, 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 conditions 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 completion of preschool children on preset learning tasks.

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 2, characterized in that: 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.

4. The method for planning personalized learning paths for preschool children in an online education system according to claim 3, characterized in that: 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 = 1, 2, ..., d, r represents the number of the preset time period, d represents the total number of preset time periods, PF r Indicates the update frequency ratio of learning resources in the rth preset time period, SLF r Indicates the update rate of the learning resource in the rth preset time period, DK r represents the average update bandwidth of the learning resource in the rth preset time period, ZS r represents the learning resource data packet capacity in the rth preset time period, ΔT r represents the average update time interval of the learning resource in the rth preset time period, PF 0 Indicates the preset learning resource update frequency threshold, SLF 0 represents the preset learning resource update rate threshold, τ1 represents the first consistency allocation weight, τ2 represents the second consistency allocation weight, and e represents a natural constant.

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 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.

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 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.

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 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.

8. 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.

9. 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.

10. The personalized learning path planning system for preschool children in the online education system is characterized by: It includes the coherence assessment module, resource browsing assessment module and learning completion assessment 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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