Auxiliary use method and device for learning electronic product, equipment and medium

Abstract

The invention provides an auxiliary use method and device for learning electronic products, equipment and a medium. According to the method, under the condition that the use data of the user in the learning electronic product is obtained, the predicted learning time information of the user for each learning module is determined based on the obtained use data; the predicted learning time information can be used for indicating and predicting time required by the user to learn one to-be-learned content item in one learning module, so that time allocation recommendation information is determined based on the predicted learning time information of the user for each learning module; according to the method and the device, the learning time allocated on different learning modules by the user is recommended to the user through the time allocation recommendation information, so that the user can perform targeted learning on the content of each learning module according to the indication of the time allocation recommendation information, and the use efficiency of various learning resources in electronic learning products is improved.

Description

Technical Field

[0001] This specification relates to the field of computer technology, and in particular to a method, device, equipment, and medium for assisting the use of learning electronic products. Background Art

[0002] With the rapid development of science and technology, learning electronic products designed specifically to assist students in learning are emerging in an endless stream, such as e-readers, tablet computers, student mobile phones, etc. These products are designed to provide students with rich learning resources and diverse learning tools, and to improve students' learning efficiency and optimize their learning experience through intelligent means.

[0003] However, precisely because these electronic products provide rich learning resources and diverse learning tools, students often face difficulties in choosing when using them. It is difficult for them to accurately find the learning resources and learning tools that best suit their needs, making it difficult for them to conduct targeted learning, resulting in the advantages of these electronic learning products not being fully utilized. Summary of the Invention

[0004] To overcome the problems existing in the related art, this specification provides an auxiliary use method, device, equipment and medium for learning electronic products.

[0005] According to a first aspect of the embodiments of this specification, a method for assisting use of a learning electronic product is provided, the method comprising:

[0006] an acquisition unit, configured to acquire usage data of a user in a learning electronic product, wherein the usage data is used to indicate the learning performance of the user in each learning module;

[0007] a determining unit, configured to determine, based on the usage data, predicted learning time information of the user for each learning module, the predicted learning time information being used to indicate a predicted time required for the user to learn a content item to be learned in a learning module;

[0008] The determination unit is further used to determine time allocation recommendation information based on the user's predicted learning time information for each learning module, and the time allocation recommendation information is used to indicate the recommended learning time allocated to the user on different learning modules.

[0009] In some embodiments, the method further comprises:

[0010] determining user preference information of the user based on the usage data;

[0011] Based on the user preference information of the user and the resource tags of different learning resources under each learning module, learning resources corresponding to the resource tags matching the user preference information are pushed to the user.

[0012] In some embodiments, determining user preference information of the user based on the usage data includes:

[0013] Determining, based on the usage data, a parameter for evaluating the user's use of different learning resources under each learning module, the parameter for evaluating the user's use of different learning resources under each learning module, wherein the parameter for evaluating the user's completion efficiency of exercise questions under the different learning resources under each learning module, a parameter for evaluating the user's learning efficiency for the different learning resources under each learning module, and a parameter for evaluating the user's concentration during the learning process of the different learning resources under each learning module;

[0014] Based on the user's usage effect evaluation parameters for different learning resources under each learning module, a target learning resource whose usage effect evaluation parameters meet the sixth condition is determined, so as to determine the user preference information based on the resource tag of the target learning resource.

[0015] According to a second aspect of the embodiments of this specification, there is provided an auxiliary device for use of a learning electronic product, the device comprising:

[0016] an acquisition unit, configured to acquire usage data of a user in a learning electronic product, wherein the usage data is used to indicate the learning performance of the user in each learning module;

[0017] a determining unit, configured to determine, based on the usage data, predicted learning time information of the user for each learning module, the predicted learning time information being used to indicate a predicted time required for the user to learn a content item to be learned in a learning module;

[0018] The determination unit is further used to determine time allocation recommendation information based on the user's predicted learning time information for each learning module, and the time allocation recommendation information is used to indicate the recommended learning time allocated to the user on different learning modules.

[0019] According to a third aspect of the embodiments of this specification, a computing device is provided, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the operations performed by the auxiliary use method of learning electronic products as described in the first aspect above are implemented.

[0020] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, on which a program is stored, and the program is executed by a processor to perform the operations performed by the auxiliary use method of learning electronic products as described in the first aspect above.

[0021] According to a fifth aspect of the embodiments of this specification, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the operations performed by the auxiliary use method of learning electronic products as described in the first aspect above.

[0022] The technical solutions provided by the embodiments of this specification may have the following beneficial effects:

[0023] The embodiments of the present specification obtain the user's usage data in learning electronic products, and then determine the user's predicted learning time information for each learning module based on the obtained usage data. The predicted learning time information can be used to indicate the predicted time the user needs to spend learning a content item to be learned in a learning module, thereby determining time allocation recommendation information based on the user's predicted learning time information for each learning module, and recommending the user's learning time allocated to different learning modules through the time allocation recommendation information, so that the user can conduct targeted learning on the content of each learning module according to the indication of the time allocation recommendation information, thereby improving the utilization efficiency of various learning resources in electronic learning products.

[0024] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0026] Figure 1 This is a schematic diagram of an application scenario of an auxiliary use method of a learning electronic product according to an exemplary embodiment of this specification.

[0027] Figure 2 This is a flowchart of an auxiliary use method of a learning electronic product according to an exemplary embodiment of this specification.

[0028] Figure 3 This is a flowchart of a method for dynamically recommending exercise questions according to an exemplary embodiment of the present specification.

[0029] Figure 4 This is a block diagram of an auxiliary device for a learning electronic product according to an exemplary embodiment of this specification.

[0030] Figure 5 It is a structural diagram of a computing device according to an exemplary embodiment of the present specification. DETAILED DESCRIPTION

[0031] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatuses and methods consistent with certain aspects of this specification, as detailed herein.

[0032] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a," "the," and "the" used in this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0033] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, such information should not be limited to these terms. These terms are merely used to distinguish information of the same type from one another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information without departing from the scope of this specification. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to determining."

[0034] In the related art, there are many types of learning electronic products. Electronic learning products such as e-readers, tablets, and student mobile phones can provide students with a wealth of learning resources and enhance their learning experience through intelligent means. However, these electronic learning products contain a wide range of learning tools and resources, which can easily make it difficult for students to choose the appropriate learning tools and resources during use. Moreover, these electronic learning products may not be adjusted according to students' specific needs during use, resulting in the inability to fully realize the advantages of these learning electronic products. In addition, students may develop some unhealthy behavioral habits during the learning process, which may lead to health problems such as decreased vision.

[0035] In light of this, this manual aims to provide an auxiliary method for using learning electronic products. This method can recommend more appropriate learning content to students, thereby reducing the tendency for students to blindly select learning resources. It can also help students rationally allocate their study time across different subjects and improve their learning efficiency. Furthermore, it can correct students' bad study habits and help them develop good study habits through prompts, incentives, and attention-grabbing methods.

[0036] See also Figure 1 , Figure 1 This is a schematic diagram of an application scenario of an auxiliary use method of a learning electronic product according to an exemplary embodiment of this specification. Figure 1 As shown, the application scenario may be composed of a terminal device 101 and a computing device 102. The terminal device 101 may be a learning electronic product, such as, but not limited to, an e-reader, a tablet computer, a student mobile phone, a learning machine, a desktop computer, a portable computer, a notebook computer, a smart phone, a smart watch, or the like. Dedicated online learning software may be installed and run on the terminal device 101, so that users can use the terminal device 101 to achieve online learning based on a wealth of learning resources and learning tools. The computing device 102 may be, but not limited to, a server, multiple servers, a server cluster, a cloud computing platform, or the like. The computing device 102 may provide background support for the online learning software installed on the terminal device 101.

[0037] In some embodiments, a user can log in to the online learning software through the terminal device 101, thereby interacting with the computing device 102 through the terminal device 101 to obtain available learning resources and learning tools from the computing device 102, and then conduct online learning based on the obtained learning resources and learning tools. Furthermore, the usage data generated by the user when conducting online learning through the terminal device 101 can be fed back to the computing device so that the computing device can store the user's usage data to facilitate subsequent analysis of the user's usage habits, user preferences, etc.

[0038] In some embodiments, when the computing device provides learning resources and learning tools to the terminal device 101, it can also use the auxiliary use method of learning electronic products provided in this specification to determine auxiliary use information based on the user's usage data, and provide the determined auxiliary use information to the terminal device 101. The terminal device 101 can provide assistance to the user when selecting learning resources and learning tools, provide the user with suggestions on allocating time for learning, and promptly correct the user's bad learning habits based on the auxiliary use information provided by the computing device 102, so as to give full play to the advantages of learning electronic products.

[0039] The above is only an exemplary description of the application scenarios of this manual and does not constitute a limitation on the application scenarios of this manual. In more possible implementations, this manual can be applied to a variety of other processes using learning electronic products.

[0040] After introducing the application scenarios of the auxiliary use of the learning electronic product provided in this specification, the auxiliary use method of the learning electronic product provided in this specification is described in detail in combination with the embodiments of this specification.

[0041] See also Figure 2 , Figure 2 This is a flow chart of a method for assisting the use of a learning electronic product according to an exemplary embodiment of the present specification. Figure 2 As shown, the method includes the following steps:

[0042] Step 201: Obtain usage data of a user in a learning electronic product. The usage data is used to indicate the user's learning performance in each learning module.

[0043] In some embodiments, the user's usage data for a learning electronic product may be data generated during the user's learning process of each learning module in the learning electronic product, indicating the user's learning performance in each learning module. Optionally, the user's learning process of each learning module in the learning electronic product may include the user viewing learning resources for each content item to be learned in each learning module, the user completing exercises for each content item to be learned in each learning module, etc., but is not limited thereto.

[0044] Optionally, a learning module can be a subject, such as Chinese, mathematics, English, physics, etc., but not limited thereto. The content items to be learned in a learning module can be courses within the subject. For example, if the learning module is physics, the content items to be learned can be mechanics, electromagnetism, thermodynamics, quantum physics, etc., but not limited thereto. Learning resources for the content items to be learned can be, but not limited to, explanation videos, etc. There can be multiple exercises under the content items to be learned, each of which can correspond to at least one knowledge element, which can be, but not limited to, knowledge points or knowledge units within the course.

[0045] In some embodiments, usage data generated by users using learning electronic products within a target time period can be obtained, where the target time period can be a time period corresponding to any length of time before the current usage time, and this manual does not limit this.

[0046] In some embodiments, usage data can be used to indicate a user's learning performance in various learning modules, but is not limited thereto. Usage data can also be used to indicate certain characteristic information of the user and the user's usage of various learning modules. In other words, usage data can include, but is not limited to, user profile data, user behavior data, and health usage monitoring data.

[0047] Among them, user portrait data can be data extracted based on the user's registration information, including but not limited to user identification, user grade, user geographic location, etc. User behavior data can be data related to the user's learning situation and completion of exercises collected based on the user's use of learning electronic products. Optionally, user behavior data can include content item learning data and question practice data. Content item learning data can be used to indicate the relevant data generated by the user's learning of each content item to be learned in each learning module, and question practice data is used to indicate the relevant data generated by the user completing the exercises under each content item to be learned in each learning module, but is not limited to this. Health use detection data can be data related to the user's health status collected during the user's use of learning electronic products, including but not limited to emotional state detection, line of sight detection, distance, sitting posture detection and other data.

[0048] Step 202: Based on the usage data, determine the user's predicted learning time information for each learning module. The predicted learning time information is used to indicate the predicted time the user needs to spend learning a content item to be learned in a learning module.

[0049] In some embodiments, the user's learning ability for each learning module can be analyzed based on usage data indicating the user's learning performance in each learning module, so as to determine predicted learning time information that matches the user's learning ability for each learning module. The time predicted by the predicted learning time information that the user needs to spend learning a content item to be learned in a learning module is inversely proportional to the user's learning ability for the content item to be learned in the learning module.

[0050] Step 203: Based on the user's predicted learning time information for each learning module, determine time allocation recommendation information, where the time allocation recommendation information is used to indicate the learning time recommended to be allocated to different learning modules by the user.

[0051] In some embodiments, based on the user's predicted learning time information for each learning module, the learning time allocated by the user to different learning modules can be proportionally allocated to determine the time allocation recommendation information.

[0052] After introducing the basic implementation process of the present application, various non-limiting implementation methods of the present application are described in detail below.

[0053] In some embodiments, for step 201, when obtaining the user's usage data of learning electronic products, user portrait data, user behavior data (including content item learning data and question practice data) and health usage detection data, etc. can be obtained, so as to perform analysis in different dimensions based on the different data obtained to provide users with multi-dimensional auxiliary use.

[0054] In some embodiments, for step 202, when determining the user's predicted learning time information for each learning module based on usage data, the predicted learning time can be determined based on the acquired user behavior data (including content item learning data and question practice data).

[0055] In some embodiments, for any learning module, when determining the user's predicted learning time information for the learning module in step 202, the following steps may be performed:

[0056] Step 2021: Based on the usage data, determine the amount of information to be learned in the learning module and the user's learning performance evaluation parameters for the learning module.

[0057] Optionally, the learning performance evaluation parameters may include learning effect evaluation parameters and learning ability evaluation parameters. The learning effect evaluation parameters may be used to evaluate the user's learning effect on a content item to be learned under a certain learning module, such as whether the user has mastered the knowledge elements therein and whether the user can accurately complete related exercises based on the learned knowledge elements. The learning ability evaluation parameters may be used to evaluate the user's learning ability on a content item to be learned under a certain learning module, such as how long it takes to master the knowledge elements therein to ensure that the user can accurately complete related exercises.

[0058] Optionally, for the learning module, the content item learning data may include the number of content items to be learned corresponding to the learning module, the actual learning time of the user on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the number of repeated learning of each content item to be learned by the user in the learning module, but is not limited to this; the question practice data may include the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the question difficulty parameters under each content item to be learned in the learning module, and the time taken by the user to complete the practice questions of each content item to be learned in the learning module, but is not limited to this.

[0059] In one possible implementation, step 2021 may be implemented by determining the amount of information to be learned for the learning module and the user's learning performance evaluation parameters for the learning module based on the aforementioned usage data through the following steps:

[0060] Step 2021-1: Determine the amount of information to be learned in the learning module based on the required learning time for each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the question difficulty parameters under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

[0061] In one possible implementation, step 2021-1 may be implemented by the following steps:

[0062] Step 2021-1A: Based on the required learning time of each to-be-learned content item of the learning module, determine the total required learning time of all to-be-learned content items of the learning module.

[0063] In a possible implementation, the required learning time of each to-be-learned content item of the learning module may be accumulated to obtain the total required learning time of all to-be-learned content items of the learning module.

[0064] Step 2021-1B: Based on the question difficulty parameters under each to-be-learned content item of the learning module, determine the overall question difficulty coefficient of all to-be-learned content items of the learning module.

[0065] In a possible implementation, the difficulty parameters of the questions under the various content items to be learned in the learning module may be accumulated to obtain the overall difficulty coefficient of the questions of all the content items to be learned in the learning module.

[0066] Step 2021-1C, determine the amount of information to be learned in the learning module based on the total learning time required for all content items to be learned in the learning module, the overall difficulty coefficient of all content items to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

[0067] In one possible implementation, the first product of the total learning time required for all content items to be learned in the learning module and the overall question difficulty coefficient of all content items to be learned in the learning module can be determined, and the second product of the total number of practice questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module can be determined, thereby determining the ratio of the first product to the second product as the amount of information to be learned in the learning module.

[0068] Optionally, the amount of information to be learned by the learning module can be determined by the following formula (1):

[0069]

[0070] Among them, LIT represents the amount of information to be learned in the learning module, m represents the total number of exercises under all the content items to be learned in the learning module, n represents the number of content items to be learned corresponding to the learning module, and Vt k represents the required learning time for the content item k to be learned in this learning module, Indicates the total learning time required for all the content items to be learned in this learning module, dif f j Indicates the difficulty coefficient of the content item j to be learned in this learning module, Indicates the overall difficulty coefficient of all the content items to be learned in this learning module. That is the first product, and m×n represents the second product.

[0071] Step 2021-2: Determine the user's learning effect evaluation parameters for the learning module based on the number of exercises actually completed by the user under each content item to be learned in the learning module, the number of exercises correctly completed by the user under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

[0072] In one possible implementation, step 2021-2 may be implemented by the following steps:

[0073] Step 221-2A: Determine the user's completion accuracy rate for each content item to be learned in the learning module based on the number of practice questions actually completed by the user under each content item to be learned in the learning module and the number of practice questions correctly completed by the user under each content item to be learned in the learning module.

[0074] In one possible implementation, the ratio of the number of exercises correctly completed by the user under each to-be-learned content item in the learning module to the number of exercises actually completed by the user under each to-be-learned content item in the learning module can be determined as the user's correct completion rate for each to-be-learned content item in the learning module. That is, for any to-be-learned content item in the learning module, the ratio of the number of exercises correctly completed by the user under that to-be-learned content item to the number of exercises actually completed by the user under that to-be-learned content item can be determined as the user's correct completion rate for that to-be-learned content item.

[0075] Optionally, the correct completion rate of the user's questions under the to-be-learned content item i in the learning module can be determined by the following formula (2):

[0076]

[0077] Among them, Acc i Q represents the correct completion rate of the user's questions under the learning content item i in the learning module, r represents the number of exercises that the user has correctly completed under the learning content item i of the learning module, Q t Indicates the number of exercises the user has actually completed under the to-be-learned content item i of this learning module.

[0078] Step 221-2B: Based on the user's accuracy rate in completing questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module, determine the user's average accuracy rate in completing questions under each content item to be learned in the learning module as a parameter for evaluating the user's learning effect on the learning module.

[0079] In one possible implementation, the user's correct completion rates for questions under each content item to be learned in the learning module can be accumulated to determine the ratio of the accumulated result to the number of content items to be learned corresponding to the learning module, and the ratio can be used as the user's average correct completion rate for questions under each content item to be learned in the learning module. The determined average correct completion rate for questions can be used as a parameter for evaluating the user's learning effect on the learning module.

[0080] Optionally, the average correct completion rate of the user for each item of content to be learned in the learning module (i.e., the user's learning effect evaluation parameter for the learning module) can be determined by the following formula (3):

[0081]

[0082] Among them, EAcc represents the average correct rate of users completing questions under each learning content item in the learning module (i.e., the user's learning effect evaluation parameter for the learning module), n represents the number of learning content items corresponding to the learning module, Acc i Indicates the correct completion rate of the questions under the learning content item i in this learning module. It represents the cumulative result of the correct completion rate of the questions under each learning content item of this learning module by the user.

[0083] It should be noted that the learning effect evaluation parameter can, to a certain extent, indicate a user's mastery and proficiency in the knowledge within the learning module. Optionally, if the learning effect evaluation parameter is lower than a first parameter threshold, it can be considered that the user's learning effect on the learning module is poor, and the user can be advised to relearn the relevant content within the learning module. The first parameter threshold can be any value, and this specification does not limit the value of the first parameter threshold.

[0084] Step 2021-3: Determine the user's learning ability evaluation parameters for the learning module based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual learning time of the user on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module.

[0085] In one possible implementation, step 2021-3 may be implemented by the following steps:

[0086] Step 2021-3A, based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the time taken by the user to complete the practice questions under each content item to be learned in the learning module, determine the user's question completion efficiency evaluation parameters for the practice questions under the learning module.

[0087] In one possible implementation, step 2021-3A may be implemented by the following steps:

[0088] Step 1: Based on the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, and the total number of practice questions under each content item to be learned in the learning module, determine the effective completion rate of the user under each content item to be learned in the learning module.

[0089] Optionally, step 1 can be implemented by the following steps:

[0090] Step 1: Based on the number of exercises the user actually completes under each item of content to be learned in the learning module and the number of exercises the user correctly completes under each item of content to be learned in the learning module, determine the user's completion accuracy rate for each item of content to be learned in the learning module.

[0091] It should be noted that for the introduction to the implementation method of step 1, please refer to the introduction to step 2021-1A in the previous article, which will not be repeated here.

[0092] Step 2: Determine the user's completion rate for each content item to be learned in the learning module based on the number of exercises actually completed by the user under each content item to be learned in the learning module and the total number of exercises under each content item to be learned in the learning module.

[0093] In one possible implementation, the ratio of the number of practice questions actually completed by the user under each to-be-learned content item in the learning module to the total number of practice questions under each to-be-learned content item in the learning module can be determined as the user's completion rate for each to-be-learned content item in the learning module. That is, for any to-be-learned content item in the learning module, the ratio of the number of practice questions actually completed by the user under that to-be-learned content item to the total number of practice questions under that to-be-learned content item can be determined as the user's completion rate for that to-be-learned content item.

[0094] Optionally, the completion rate of the user's questions under the to-be-learned content item i in the learning module can be determined by the following formula (4):

[0095]

[0096] Among them, Fa i Q represents the completion rate of the user's questions under the learning content item i in the learning module, t represents the number of exercises that the user has actually completed under the learning content item i of the learning module, Q a Indicates the total number of exercises under the to-be-learned content item i of this learning module.

[0097] Step 3: Determine the effective completion rate of the user's questions under each content item to be learned in the learning module based on the user's correct completion rate of the questions under each content item to be learned in the learning module and the user's completion rate of the questions under each content item to be learned in the learning module.

[0098] In one possible implementation, the product of the user's correct completion rate for each content item to be learned in the learning module and the user's completion rate for each content item to be learned in the learning module can be determined as the user's effective completion rate for each content item to be learned in the learning module. That is, for any content item to be learned in the learning module, the product of the user's correct completion rate for the content item to be learned and the user's completion rate for the content item to be learned can be determined as the user's effective completion rate for the content item to be learned.

[0099] Step 2: Determine the user's completion efficiency for the exercises for each content item to be learned in the learning module based on the user's effective completion rate for the exercises for each content item to be learned in the learning module and the time the user takes to complete the exercises for each content item to be learned in the learning module.

[0100] In one possible implementation, the ratio of the user's effective completion rate for each to-be-learned content item in the learning module to the time it takes for the user to complete the practice questions for each to-be-learned content item in the learning module can be determined as the user's completion efficiency for the practice questions for each to-be-learned content item in the learning module. That is, for any to-be-learned content item in the learning module, the ratio of the user's effective completion rate for the to-be-learned content item to the time it takes for the user to complete the practice questions for that to-be-learned content item can be determined as the user's completion efficiency for the practice questions for that to-be-learned content item.

[0101] Step three: Based on the difficulty parameters of the questions under each content item to be learned in the learning module, the user's completion efficiency of the practice questions for each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, determine the user's completion efficiency evaluation parameters for the practice questions under the learning module.

[0102] In one possible implementation, the average question difficulty parameter of each content item to be learned in the learning module can be determined based on the question difficulty parameter of each content item to be learned in the learning module, thereby determining the user's question completion efficiency evaluation parameter for the practice questions under the learning module based on the user's completion efficiency of the practice questions for each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module.

[0103] In a possible implementation, the difficulty parameters of the practice questions under the content items to be learned in the learning module may be arithmetic averaged to obtain the average difficulty parameter of the practice questions under the content items to be learned in the learning module.

[0104] That is, for any content item to be learned under the learning module, the difficulty parameters of the exercises under the content item to be learned can be arithmetic averaged to obtain the average difficulty parameter of the content item to be learned.

[0105] Optionally, for any content item to be learned under the learning module, the sum of the question difficulty parameters of each exercise question under the content item to be learned can be determined, thereby determining the ratio between the sum and the total number of exercise questions under the content item to be learned, so as to achieve the arithmetic average of the question difficulty parameters of each exercise question under the content item to be learned, and obtain the average question difficulty parameter under the content item to be learned.

[0106] In one possible implementation, the product of the average question difficulty parameter under each content item to be learned in the learning module and the user's completion efficiency of the practice questions for each content item to be learned in the learning module can be determined as the user's question completion efficiency evaluation parameter under each content item to be learned in the learning module, and then the question completion efficiency evaluation parameters of the user under each content item to be learned in the learning module are accumulated to determine the ratio between the cumulative result and the number of content items to be learned corresponding to the learning module, which is used as the user's question completion efficiency evaluation parameter for the practice questions under the learning module.

[0107] Optionally, the user's question completion efficiency evaluation parameter under the to-be-learned content item i of the learning module can be determined by the following formula (5):

[0108]

[0109] Among them, Ef i Indicates the user's completion efficiency evaluation parameter for the question under the learning content item i of the learning module, diff i Acc represents the average difficulty parameter of the learning content item i in the learning module. i Indicates the correct completion rate of the user's questions under the learning content item i in this learning module, Fa i Indicates the completion rate of the user's questions under the learning content item i in this learning module, Et i Indicates the time it takes for the user to complete the exercises for item i of the learning module. That is, the user's completion efficiency of the exercises for the learning content item i of the learning module.

[0110] Optionally, after determining the user's question completion efficiency evaluation parameters for each to-be-learned content item in the learning module using the above formula (5), the user's question completion efficiency evaluation parameters for the practice questions in the learning module can be determined using the following formula (6):

[0111]

[0112] Among them, EEf represents the user's evaluation parameter of the completion efficiency of the exercises in the learning module, Ef i It represents the user's evaluation parameter for the completion efficiency of the questions under the to-be-learned content item i of the learning module, and n represents the number of to-be-learned content items corresponding to the learning module.

[0113] It should be noted that the question completion efficiency evaluation parameter can, to a certain extent, indicate a user's mastery and proficiency in the knowledge within the learning module. Optionally, if the question completion efficiency evaluation parameter is lower than the second parameter threshold, it can be considered that the user's mastery of the knowledge elements within the learning module is not yet proficient enough, and the user can be recommended to complete more practice questions to consolidate their mastery of the knowledge elements. The second parameter threshold can be any value, and this specification does not limit the value of the second parameter threshold.

[0114] Step 2021-3B: Determine the user's learning efficiency evaluation parameters for the learning module based on the user's actual learning time on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module.

[0115] In one possible implementation, a repeated learning penalty coefficient can be determined based on the number of times the user repeatedly learns each content item to be learned in the learning module, and the user's learning efficiency evaluation parameters for the learning module can be determined based on the user's actual learning time on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the repeated learning penalty coefficient.

[0116] Optionally, when determining the repeated learning penalty coefficient based on the number of times the user repeatedly learns each content item to be learned in the learning module, the repeated learning penalty coefficient can be determined based on the relationship between the number of times the user repeatedly learns each content item to be learned in the learning module and the set threshold interval.

[0117] Optionally, if the number of times the user repeatedly studies each content item to be learned in the learning module is less than or equal to a first number threshold, the value of the repeated learning penalty coefficient can be determined to be a first value; if the number of times the user repeatedly studies each content item to be learned in the learning module is greater than the first number threshold, the value of the repeated learning penalty coefficient can be determined to be a second value.

[0118] Optionally, the first number threshold, the first value and the second value can all be arbitrary values. For example, the first number threshold can be 1, the first value can be 1, and the second value can be the product of the first number threshold and the set proportional coefficient. The set proportional coefficient can be any value. For example, the set proportional coefficient can be 0.8, but is not limited to this. This specification does not limit the values of the above parameter values.

[0119] Taking the first number threshold and the first value as 1 and the proportional coefficient as 0.8 as an example, the repeated learning penalty coefficient can be determined by the following formula (7):

[0120]

[0121] Among them, α represents the repeated learning penalty coefficient, and ln represents the number of times the user repeatedly learns a certain content item to be learned in the learning module.

[0122] Optionally, when determining the user's learning efficiency evaluation parameters for the learning module based on the user's actual learning time on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the repeated learning penalty coefficient, the ratio between the user's actual learning time on each content item to be learned in the learning module and the required learning time for each content item to be learned in the learning module can be determined, so as to determine the product of the repeated learning penalty coefficient and the ratio to obtain the user's learning efficiency evaluation parameters for each content item to be learned under the learning module, and then accumulate the user's learning efficiency evaluation parameters for each content item to be learned under the learning module to obtain the user's learning efficiency evaluation parameters for the learning module.

[0123] Optionally, the learning efficiency evaluation parameter of the user for the to-be-learned content item i under the learning module can be determined by the following formula (8):

[0124]

[0125] Among them, La i represents the learning efficiency evaluation parameter of the user for the content item i to be learned under the learning module, α represents the repeated learning penalty coefficient corresponding to the content item i to be learned under the learning module, which can be determined based on the number of repeated learning times of the content item i to be learned by the user in the learning module, and t r represents the actual learning time of the user on the content item i under the learning module, t n Indicates the required learning time for the content item i to be learned in this learning module.

[0126] Optionally, after determining the user's learning efficiency evaluation parameters for each to-be-learned content item under the learning module by using the above formula (8), the user's learning efficiency evaluation parameters for the learning module can be determined by using the following formula (9):

[0127]

[0128] Among them, LaT represents the user's learning efficiency evaluation parameter for this learning module, La i represents the user's learning efficiency evaluation parameter for the to-be-learned content item i under this learning module, and n represents the number of to-be-learned content items corresponding to this learning module.

[0129] It should be noted that the learning efficiency evaluation parameter can, to a certain extent, represent the user's learning ability for the learning module and the content items to be learned under the learning module. The smaller the learning efficiency evaluation parameter, the weaker the user's learning ability for the learning module and the content items to be learned under the learning module. Conversely, the larger the learning efficiency evaluation parameter, the stronger the user's learning ability for the learning module and the content items to be learned under the learning module.

[0130] Step 2021-3C: Determine the ratio of the user's question completion efficiency evaluation parameter for the exercise questions under the learning module to the user's learning efficiency evaluation parameter for the learning module as the user's learning ability evaluation parameter for the learning module.

[0131] In a possible implementation, the user's learning ability evaluation parameter for the learning module can be determined by the following formula (10):

[0132]

[0133] Among them, Ab represents the user's learning ability evaluation parameter for this learning module, EEf represents the user's question completion efficiency evaluation parameter for the exercise questions under this learning module, and LaT represents the user's learning efficiency evaluation parameter for this learning module.

[0134] It should be noted that this specification does not limit the execution order of step 2021-1, step 2021-2 and step 2021-3. The execution order of the above three steps can be adjusted as needed. In more possible implementation methods, at least two steps of step 2021-1, step 2021-2 and step 2021-3 can also be executed in parallel to improve processing efficiency.

[0135] Step 2022: Determine the user's predicted learning time information for the learning module based on the amount of information to be learned in the learning module, the user's learning performance evaluation parameters for the learning module, and the expected learning performance evaluation parameters under the learning module.

[0136] As mentioned above, the learning performance evaluation parameters may include learning effect evaluation parameters and learning ability evaluation parameters, and the expected learning performance evaluation parameters under a certain learning module may be the expected learning effect evaluation parameters under the learning module, which are used to indicate the learning effect expected to be achieved under the learning module.

[0137] In one possible implementation, step 2022 may be implemented by the following steps:

[0138] Step 2022-1: Determine the user's target learning time for the learning module based on the amount of information to be learned in the learning module and the user's learning ability evaluation parameters for the learning module.

[0139] In one possible implementation, the ratio between the amount of information to be learned in the learning module and the user's learning ability evaluation parameter for the learning module can be determined as the user's target learning time for the learning module.

[0140] Step 2022-2: Determine the user's learning effect deviation evaluation parameters for the learning module based on the expected learning performance evaluation parameters under the learning module and the user's learning effect evaluation parameters for the learning module.

[0141] In one possible implementation, the ratio between the expected learning performance evaluation parameter (i.e., the expected learning effect evaluation parameter) under the learning module and the user's learning effect evaluation parameter for the learning module can be determined as the user's learning effect deviation evaluation parameter for the learning module.

[0142] Optionally, the expected learning effect parameters under the learning module can be set by the user according to his or her own needs, but are not limited to this. They can also be obtained by the computing device based on a comprehensive evaluation of the learning status of registered users in the learning electronic products. This manual does not limit the setting method of the expected learning effect.

[0143] Optionally, the expected learning effect parameter under the learning module can be used to indicate the expected completion accuracy rate of the exercises for each to-be-learned content item under the learning module, but is not limited thereto.

[0144] It should be noted that this specification does not limit the execution order of step 2022-1 and step 2022-2. The execution order of the above two steps can be adjusted as needed. In more possible implementations, the above two steps can also be executed in parallel to improve processing efficiency.

[0145] Step 2022-3: perform weighted processing on the user's target learning time for the learning module based on the user's learning effect deviation evaluation parameter for the learning module to obtain the user's predicted learning time information for the learning module.

[0146] In one possible implementation, the product of the user's learning effect deviation evaluation parameter for the learning module and the user's target learning time for the learning module can be determined to achieve weighted processing of the target learning time based on the learning effect deviation evaluation parameter, thereby obtaining the user's predicted learning time information for the learning module.

[0147] Optionally, the above steps 2022-1 to 2022-3 can be implemented by the following formula (11):

[0148]

[0149] Among them, PTC represents the time required to learn a knowledge element in the learning module under the user's current learning ability (i.e., predicted learning time information), GAcc represents the expected learning performance evaluation parameter under the learning module (i.e., expected learning effect evaluation parameter), EAcc represents the user's learning effect evaluation parameter for the learning module, LIT represents the amount of information to be learned in the learning module, and Ab represents the user's learning ability evaluation parameter for the learning module.

[0150] It should be noted that the above embodiment is described by taking the example of determining the user's predicted learning time information for a learning module. The method of determining the user's predicted learning time information for other learning modules is the same as the above implementation method and will not be repeated here.

[0151] In some embodiments, after the user's predicted learning time information for each learning module is determined, time allocation recommendation information can be determined based on the user's predicted learning time information for each learning module in step 203 .

[0152] In some embodiments, in step 203, when determining the time allocation recommendation information based on the user's predicted learning time information for each learning module, it can be implemented as follows:

[0153] Based on the user's predicted learning time information for each learning module, the recommended time allocation ratio of the user on each learning module is determined, and then based on the user's historical usage time and the recommended time allocation ratio of the user on each learning module, the learning time allocated to the user on each learning module is determined to obtain time allocation recommendation information.

[0154] Among them, the user's historical usage time can be determined based on the acquired user behavior data. For example, the user's usage time in each specified time period can be counted to determine the user's average usage time in the specified time period, and the determined average usage time is used as the user's historical usage time, but is not limited to this, and other methods can also be used to determine the user's historical usage time.

[0155] Optionally, the designated time period may be one week, but is not limited thereto. Other dimensions of time may also be used as the designated time period to achieve finer or coarser granularity analysis.

[0156] In some embodiments, the learning electronic product may also provide users with a variety of learning tools, and users may use these learning tools to assist themselves in learning better.

[0157] In some embodiments, the user's usage habit information for different learning tools may also be determined based on the user's usage time for each learning tool.

[0158] For example, the usage habit information corresponding to any learning tool can be divided into six types: using the learning tool every day, using the learning tool after N days, using the learning tool on a fixed day of the week, using the learning tool on weekdays, using the learning tool on weekends, and using the learning tool every week.

[0159] Optionally, if there is a record of the user using the learning tool every day, the user's usage habit information for the learning tool can be determined as using the learning tool every day; optionally, if the number of times the user uses the learning tool in a first time period is greater than a second number threshold, the user's usage habit information for the learning tool can be determined as using the learning tool at intervals of N days, wherein the length of the first time period can be N days, and the second number threshold can be any value; optionally, if the number of times the user uses the learning tool on a certain day of the week is greater than a third number threshold, the user's usage habit information for the learning tool can be determined as using the learning tool on a fixed day of the week, wherein the day can be any day of the week, and the third number threshold can be any value. any value; optionally, if the number of times the user uses the learning tool within the second time period is greater than a fourth number threshold, the user's usage habit information for the learning tool can be determined as using the learning tool on weekdays, wherein the second time period can be Monday to Friday, and the fourth number threshold can be an arbitrary value; optionally, if the number of times the user uses the learning tool within the third time period is greater than a fifth number threshold, the user's usage habit information for the learning tool can be determined as using the learning tool on weekends, wherein the third time period can be Saturday and Sunday, and the fifth number threshold can be an arbitrary value; optionally, if there is a record of the user using the learning tool every week, the user's usage habit information for the learning tool can be determined as using the learning tool every week.

[0160] It should be noted that the second number threshold, the third number threshold, the fourth number threshold and the fifth number threshold may be the same or different, and this specification does not limit this.

[0161] In addition, it should be noted that the above-mentioned usage habit information is not mutually exclusive. In more possible implementations, the user's usage habit information for a certain learning tool can also be a combination of at least two of the above-mentioned usage habit information. For example, the user's usage habit information for a certain learning tool can be that the learning tool is used on weekends and every week.

[0162] The above embodiment introduces the process of determining the time allocation recommendation information of the user on the learning electronic product by the computing device, so that the computing device can push the determined time allocation recommendation information to the user. The user can then allocate the learning time on different learning modules and different content items to be learned according to the instructions of the time allocation recommendation information when using the electronic learning product, thereby improving the user's efficiency in using the electronic learning product. In more possible implementations, suitable practice questions can also be recommended to the user based on the user's learning ability, and the recommended practice questions can be dynamically adjusted based on the user's response to the recommended practice questions. That is, this specification can also provide a dynamic practice question recommendation method, which dynamically determines the difficulty of questions suitable for the user based on the user's historical learning situation and combines the analysis of the user's real-time practice situation to dynamically adjust the number of questions the user practices, so as to achieve personalized practice for the user.

[0163] In some embodiments, usage data can also be used to indicate the user's completion status of practice questions in each learning module. Optionally, as described above, usage data may include user behavior data, and user behavior data may include question practice data. Question practice data is used to indicate the relevant data generated by the user completing the practice questions under each to-be-learned content item in each learning module. That is, question practice data can be used to indicate the user's completion status of practice questions in each learning module.

[0164] In some embodiments, the user's learning ability assessment level can be determined based on usage data indicating the user's completion of exercise questions in various learning modules, and then recommended exercise questions can be determined from multiple candidate exercise questions based on the user's learning ability assessment level and the difficulty levels of multiple candidate exercise questions, so as to push the recommended exercise questions to the user.

[0165] Optionally, when determining the user's learning ability assessment level based on usage data, the user's learning ability assessment level can be determined based on at least one of the user's average question completion accuracy rate under each to-be-learned content item in each learning module, the user's question completion efficiency assessment parameters for practice questions under each learning module, the user's learning efficiency assessment parameters for each learning module, and the user's learning ability assessment parameters for each learning module, which are determined based on usage data in the above embodiments.

[0166] Optionally, different parameter ranges can be set for different parameters, and different parameter ranges correspond to different learning ability assessment levels. For example, the learning ability assessment level can be set to four levels: poor, general, good, and excellent, where the parameter ranges corresponding to the learning ability assessment level "poor" can be: the average question completion accuracy is less than the first accuracy threshold, the question completion efficiency assessment parameter is less than the first question completion efficiency threshold, the learning efficiency assessment parameter is less than the first learning efficiency threshold, and the learning ability assessment parameter is less than the first ability assessment parameter threshold; the parameter ranges corresponding to the learning ability assessment level "general" can be: the average question completion accuracy is greater than or equal to the first accuracy threshold and less than the second accuracy threshold, the question completion efficiency assessment parameter is greater than or equal to the first question completion efficiency threshold and less than the second question completion efficiency threshold, the learning efficiency assessment parameter is greater than or equal to the first learning efficiency threshold and less than the second learning efficiency threshold, and the learning ability assessment parameter is greater than or equal to the first ability assessment parameter threshold Less than the second ability assessment parameter threshold; the parameter ranges corresponding to the learning ability assessment level of "good" can be: the average question completion accuracy is greater than or equal to the second accuracy threshold and less than the third accuracy threshold, the question completion efficiency assessment parameter is greater than or equal to the second question completion efficiency threshold and less than the third question completion efficiency threshold, the learning efficiency assessment parameter is greater than or equal to the second learning efficiency threshold and less than the third learning efficiency threshold, the learning ability assessment parameter is greater than or equal to the second ability assessment parameter threshold and less than the third ability assessment parameter threshold; the parameter ranges corresponding to the learning ability assessment level of "excellent" can be: the average question completion accuracy is greater than or equal to the third accuracy threshold, the question completion efficiency assessment parameter is greater than or equal to the third question completion efficiency threshold, the learning efficiency assessment parameter is greater than or equal to the third learning efficiency threshold, and the learning ability assessment parameter is greater than or equal to the third ability assessment parameter threshold.

[0167] Optionally, based on the parameter range to which the determined parameter belongs, the ability level that matches the parameter range to which the determined parameter belongs can be determined as the user's learning ability assessment level. It should be noted that if the parameter ranges to which different parameters belong correspond to different ability levels, any ability level corresponding to the parameter ranges to which different parameters belong can be used as the user's learning ability assessment level, or the ability level that appears the most times among the ability levels corresponding to the parameter ranges to which different parameters belong can be used as the user's learning ability assessment level, and so on. This specification does not limit this.

[0168] Optionally, each exercise question is set with a corresponding difficulty level, and different question difficulty levels can be adapted to different user learning ability assessment levels, so that after determining the user's learning ability assessment level, the exercise questions with difficulty levels adapted to the user's learning ability assessment level can be determined.

[0169] Optionally, the difficulty levels of the practice questions can be divided into three levels: 1, 2, and 3, which respectively indicate easy, medium, and difficult difficulty levels. If the user's learning ability assessment level is poor or average, practice questions with a difficulty level of 1 can be recommended to the user; if the user's learning ability assessment level is good, practice questions with a difficulty level of 2 can be recommended to the user; if the user's learning ability assessment level is excellent, practice questions with a difficulty level of 3 can be recommended to the user.

[0170] Optionally, if the user has no past usage data, a cold start strategy may be adopted to recommend exercises of a default difficulty level to the user. The default difficulty level may be difficulty level 2, but is not limited thereto.

[0171] Optionally, each candidate exercise question can correspond to a knowledge element in a to-be-learned content item in a learning module. Then, when determining recommended exercise questions from multiple candidate exercise questions based on the user's learning ability assessment level and the difficulty levels of multiple candidate exercise questions, it is also possible to determine the knowledge elements that the user has mastered based on usage data indicating the user's completion of exercise questions in each learning module, so as to eliminate the candidate exercise questions corresponding to the knowledge elements that the user has mastered from the multiple candidate exercise questions and obtain processed candidate exercise questions, thereby determining recommended exercise questions from the processed candidate exercise questions based on the user's learning ability assessment level and the difficulty levels of multiple candidate exercise questions.

[0172] Among them, multiple candidate practice questions can be divided into multiple groups, each group of candidate practice questions can include multiple candidate practice questions grouped according to the learning order of corresponding knowledge elements, and the multiple candidate practice questions in each group can be sorted according to the difficulty of the questions. Optionally, they can be sorted in ascending order of question difficulty or in descending order of question difficulty. This manual does not limit this.

[0173] Optionally, when determining recommended practice questions from multiple candidate practice questions, at least one group of practice questions corresponding to the knowledge elements that the user currently needs to practice can be determined from the multiple groups of practice questions, so as to eliminate the candidate practice questions corresponding to the knowledge elements that the user has mastered from the multiple candidate practice questions, thereby determining recommended practice questions from the multiple candidate practice questions in this at least one group of practice questions that are sorted according to the difficulty of the questions.

[0174] Optionally, before pushing recommended practice questions to the user, it is also possible to analyze based on the user's usage data whether the user has more than a fourth number threshold of incorrect practice questions that have not been specifically reviewed. If so, the above-mentioned incorrect practice questions can be recommended to the user first so that the user can re-practice the incorrect practice questions, and then the recommended practice questions can be pushed after the user's completion accuracy rate of the incorrect practice questions meets the requirements.

[0175] Optionally, before pushing recommended exercises to the user, the number of exercises required for the user to complete all recommended exercises can be estimated based on the number of knowledge elements corresponding to the recommended exercises and the user's learning ability assessment level, so as to estimate the user's practice progress.

[0176] Optionally, when estimating the number of times a user needs to practice all recommended practice questions based on the number of knowledge elements corresponding to the recommended practice questions and the user's learning ability assessment level, the calculation method used can be: required number of practices = [number of knowledge elements corresponding to the recommended practice questions * (total number of question difficulty levels - difficulty levels of recommended practice questions)] / number of practice questions each time, but is not limited to this.

[0177] In some embodiments, after recommended practice questions are pushed to the user, statistical information on the user's completion of the recommended practice questions can also be obtained. The completion statistical information includes at least the number of recommended practice questions completed by the user and the user's completion accuracy rate in the completed recommended practice questions, so as to dynamically adjust the recommended practice questions pushed to the user based on the completion statistical information.

[0178] Optionally, if the completion statistics satisfy a first condition, recommended exercises with a higher difficulty level may be pushed to the user. The completion statistics satisfying the first condition may include the number of recommended exercises completed by the user being greater than or equal to a second quantity threshold, and the user's completion accuracy rate for the completed recommended exercises being greater than or equal to a second accuracy threshold. However, the completion statistics may also include the number of recommended exercises correctly completed by the user consecutively and correctly, and so on. Specifically, if the second quantity threshold is K, the third quantity threshold is S, and the second accuracy threshold is P, recommended exercises with a higher difficulty level may be pushed to the user if the user correctly completes S recommended exercises consecutively or if the user achieves P completion accuracy after completing K recommended exercises.

[0179] Among them, the second quantity threshold, the third quantity threshold and the second accuracy threshold can all be arbitrary values, and this specification does not limit this.

[0180] Optionally, if the recommended practice questions pushed to the user are of the highest difficulty level, practice summary information can be generated based on the user's completion statistics to summarize information such as the accuracy rate of the practice, the number of knowledge elements passed in the practice, etc., but is not limited to this.

[0181] Optionally, if the completion statistics do not meet the first condition, recommended exercises with a lower difficulty level may be pushed to the user. The reason for the completion statistics not meeting the first condition may be that the number of recommended exercises completed by the user is less than the second quantity threshold, or that the number of recommended exercises completed by the user is greater than or equal to the second quantity threshold, but the user's completion accuracy rate for the completed recommended exercises is less than the second accuracy threshold, but this is not limited to the above. Specifically, if the second quantity threshold is K, the third quantity threshold is S, and the second accuracy threshold is P, then if the user has not completed K recommended exercises, or if the user's completion accuracy rate after completing K recommended exercises does not reach P, recommended exercises with a lower difficulty level may be pushed to the user.

[0182] Optionally, if the recommended practice questions pushed to the user are of the lowest difficulty level and the number of practice questions completed by the user reaches a first threshold, practice summary information can be generated based on the user's completion statistics to summarize information such as the accuracy rate of the practice, the number of knowledge elements passed in the practice, etc., but is not limited to this.

[0183] Optionally, if the recommended practice questions pushed to the user are of the lowest difficulty level and the user's completion accuracy in the completed recommended practice questions is lower than the first accuracy threshold, learning resources associated with the knowledge elements corresponding to the recommended practice questions that the user completed incorrectly can be pushed so that the user can continue to learn relevant knowledge based on the pushed learning resources.

[0184] The first quantity threshold and the first accuracy threshold can both be arbitrary values, and this specification does not limit them.

[0185] See also Figure 3 , Figure 3 This is a flowchart of a method for recommending dynamic exercises according to an exemplary embodiment of the present specification. Figure 3 As shown, the method may include the following steps:

[0186] Step 301: Evaluate the user's learning ability evaluation level based on the user's usage data.

[0187] Optionally, the user's practice records can be obtained from the user's historical practice records, and by calculating their recent average performance, their abilities can be divided into four learning ability assessment levels: poor, average, good, and excellent.

[0188] Step 302: Based on the user's usage data, determine whether the user has any knowledge elements that have not been practiced in the current learning module.

[0189] Optionally, the question practice records of the same learning module can be obtained from the historical records, and the mastery of the relevant knowledge points can be recorded to determine whether the user has any knowledge elements that have not been practiced in the current learning module.

[0190] Step 303: Acquire candidate practice questions associated with all knowledge elements of the current learning module, group the acquired candidate practice questions based on the knowledge elements corresponding to the acquired candidate practice questions, and sort the candidate practice questions in each group according to the difficulty level of the questions.

[0191] Optionally, the difficulty levels of the practice questions can be divided into three levels: 1, 2, and 3, corresponding to easy, medium, and difficult difficulty levels of the questions, respectively.

[0192] Step 304: Decision on the difficulty level of the recommended questions based on the user's learning ability assessment level.

[0193] Optionally, if the user's learning ability assessment level is average or below, you can start practicing questions from difficulty level 1; if the user's learning ability assessment level is good, you can start practicing questions from difficulty level 2; if the user's learning ability assessment level is excellent, you can start practicing questions from difficulty level 3.

[0194] Optionally, if the user has no history, a cold start strategy may be adopted, and the user may practice questions starting from difficulty level 2 by default.

[0195] Step 305: Recommend questions based on the candidate practice questions corresponding to the knowledge elements that the user has not practiced and the difficulty level decision results of the recommended questions.

[0196] Optionally, based on the candidate practice questions corresponding to the knowledge elements that the user has not practiced and the decision result of the difficulty level of the recommended questions, K candidate practice questions can be randomly selected from the candidate practice questions with the difficulty level of the recommended questions under the knowledge elements that the user has not practiced as recommended practice questions.

[0197] Step 306: Calculate the number of exercises required to complete all exercises in the current learning module based on the number of knowledge elements to be practiced and the user's learning ability assessment level, so as to estimate the user's practice progress.

[0198] Step 307: Push the practice questions and obtain the user's practice results.

[0199] Optionally, before pushing the practice questions, you can first analyze whether the user has more than N wrong questions that have not been reviewed based on the user's historical records. If so, the user will re-practice the wrong questions before this practice.

[0200] Step 308: Determine whether the exercise is passed.

[0201] Optionally, during the practice process, if the accuracy rate reaches P after practicing S or K questions continuously, it can be considered that the questions with difficulty level K under the current knowledge element have passed the test. At this time, it can be further determined whether the practice end conditions are met. Optionally, if the number of practice questions has not reached the maximum number of practice questions M, step 309 can be executed. Conversely, if the number of practice questions has reached the maximum number of practice questions M, step 311 can be executed.

[0202] Optionally, if the accuracy rate does not reach P after practicing K questions, it can be considered that the question with difficulty level K under the current knowledge element has failed the test, and step 310 can be executed.

[0203] Step 309: Determine whether to increase the difficulty level.

[0204] Optionally, if there is a difficulty level higher than difficulty level K and the maximum practice difficulty set by the user has not been reached, the difficulty level of the practice questions can be increased to K+1 so that the user can continue to practice based on questions of difficulty level K+1; conversely, if there is no difficulty level higher than difficulty level K or difficulty level K is already the maximum practice difficulty set by the user, step 311 can be executed.

[0205] Step 310: Determine whether to use the mandatory policy.

[0206] Alternatively, if a mandatory policy is set, the practice can be interrupted and the user can try to reduce the difficulty of the current knowledge element so that the user can continue to practice based on a lower difficulty question. Alternatively, if the current difficulty is already the lowest difficulty, step 312 can be executed.

[0207] Optionally, if a non-compulsory policy is set, the user may be allowed to continue with subsequent practice. Optionally, before the user continues with subsequent practice, it may be determined whether the number of failed practiced questions has reached an interruption threshold. If the number of failed practiced questions has reached the interruption threshold, the practice may be interrupted and step 312 may be executed. Conversely, if the number of failed practiced questions has not reached the interruption threshold, it may be determined whether the number of practiced questions has reached a threshold M. If the number of practiced questions has reached the threshold M, step 311 may be executed. Conversely, if the number of practiced questions has not reached the threshold M, the user may continue with subsequent practice.

[0208] Step 311: Practice summary.

[0209] Optionally, information such as the accuracy rate of the exercise, the number of knowledge elements that have been practiced, etc. can be summarized.

[0210] Optionally, it is also possible to determine through the practice summary whether the user has knowledge elements that need to be relearned (knowledge elements that have not been practiced can be regarded as knowledge elements that need to be learned). If so, step 312 can be executed.

[0211] Step 312: Push learning materials of related knowledge elements.

[0212] Through the above embodiment, practice questions that match the user's learning ability can be recommended to the user based on the user's previous practice situation, so that the user can start practicing based on the questions that match his or her learning ability. Moreover, during the practice process, the difficulty of the next question and the number of practice questions can be dynamically adjusted according to the difficulty of the question and the answer situation, thereby realizing personalized practice and improving the user's practice efficiency.

[0213] In some embodiments, the solutions provided in this specification can also be used to promptly correct users' bad behavior habits.

[0214] In some embodiments, the user's behavioral habit information can be determined based on usage data, so that interactive information can be pushed to the user if the user's behavioral habit information meets specific behavioral habit tendencies. The interactive information can be used to guide the user based on the user's behavioral habits.

[0215] Optionally, the usage data may include the number of times the user performs the first operation when viewing the learning resources of each learning module, the number of times the user performs the second operation when completing the exercises under the content to be learned items of each learning module, the number of exercises actually completed by the user under the content to be learned items of each learning module, the number of exercises correctly completed by the user under the content to be learned items of each learning module, whether the user has re-learned the knowledge elements corresponding to the exercises that were not completed correctly under the content to be learned items of each learning module, the number of exercises correctly completed when the user re-completes the exercises that were not completed correctly under the content to be learned items of each learning module, the number of times the user's line of sight deviates when viewing the learning resources of each learning module, the length of time the user views the learning resources of each learning module, etc., but is not limited to these.

[0216] The first operation may be a fast-forward operation, and the second operation may be a skip operation, but is not limited thereto.

[0217] In some embodiments, determining the user's behavioral habit information based on usage data can be achieved through at least one of the following methods:

[0218] In one possible implementation, if the number of times the user performs the first operation when viewing the learning resources of each learning module, and the question completion accuracy determined based on the number of practice questions actually completed by the user under the content to be learned in each learning module and the number of practice questions correctly completed by the user under the content to be learned in each learning module meet the second condition, then it can be determined that the user's behavioral habit information meets the first behavioral habit tendency.

[0219] Optionally, the second condition may be that the number of times the user performs the first operation when viewing the learning resources of each learning module is greater than or equal to the first number threshold, and the user's accuracy in completing questions under the content to be learned in each learning module is less than or equal to a fourth accuracy threshold, but is not limited to this.

[0220] The first number threshold and the fourth accuracy threshold can both be arbitrary values, and this specification does not limit them.

[0221] In one possible implementation, if the number of times the user performs the second operation when completing the practice questions under the to-be-learned content items of each learning module, and the question completion accuracy rate determined based on the number of practice questions actually completed by the user under the to-be-learned content items of each learning module and the number of practice questions correctly completed by the user under the to-be-learned content items of each learning module meet the third condition, then it can be determined that the user's behavioral habit information meets the first behavioral habit tendency;

[0222] Optionally, the third condition may be that the number of times the user performs the second operation when completing the practice questions under the content to be learned in each learning module is greater than or equal to the second number threshold, and the user's completion accuracy rate of the questions under the content to be learned in each learning module is less than or equal to the fifth accuracy threshold, but is not limited to this.

[0223] The second number threshold and the fifth accuracy threshold can both be arbitrary values, and this specification does not limit them.

[0224] It should be noted that the first behavioral habit tendency can be used to indicate that the user is relatively impatient, but is not limited thereto.

[0225] In one possible implementation, if whether the user has relearned the knowledge elements corresponding to the exercises that were not completed correctly under the content to be learned in each learning module, the number of exercises that the user has not completed correctly under the content to be learned in each learning module, and the number of exercises that the user has completed correctly when recompleting the exercises that were not completed correctly under the content to be learned in each learning module meet the fourth condition, it can be determined that the user's behavioral habit information meets the second behavioral habit tendency.

[0226] Optionally, the fourth condition may be that the user has not re-learned the knowledge elements corresponding to the practice questions that have not been completed correctly under the content to be learned in each learning module, and the number of practice questions correctly completed by the user when re-completing the practice questions that have not been completed correctly under the content to be learned in each learning module is greater than or equal to the fourth quantity threshold, but is not limited to this.

[0227] The fourth quantity threshold value can be any value, and this specification does not limit it.

[0228] It should be noted that the second behavioral habit tendency can be used to indicate that the user is relatively careless, but is not limited thereto.

[0229] In one possible implementation, if the user's viewing time of the learning resources of each learning module and the number of times the user's vision deviates when viewing the learning resources of each learning module meet the fifth condition, it can be determined that the user's behavioral habit information meets the third behavioral habit tendency.

[0230] Optionally, the ratio of the number of times a user's gaze wanders while viewing learning resources in each learning module to the user's viewing time for each learning module can be determined, thereby determining that the user's behavioral habit information in a learning module for which the ratio is less than or equal to a third parameter threshold satisfies the third behavioral habit tendency. That is, the fifth condition can be that the ratio of the number of times a user's gaze wanders while viewing learning resources in each learning module to the user's viewing time for each learning module is less than or equal to the third parameter threshold, but is not limited to this.

[0231] The third parameter threshold value may be any value, which is not limited in this specification.

[0232] It should be noted that the third behavioral habit tendency can be used to indicate that the user's concentration is low, but is not limited thereto.

[0233] In some embodiments, when it is determined that the user meets any one or more of the above behavioral habit tendencies, corresponding interactive information can be pushed to the user to guide the user to adjust the bad behavioral habits in a timely manner.

[0234] In one possible implementation, when the user's behavioral habit information meets the first behavioral habit tendency and / or the third behavioral habit tendency, first interaction information can be pushed to the user, and the first interaction information can be interactive content that can attract the user's attention.

[0235] For example, when the user's behavioral habit information meets the first behavioral habit tendency and / or the third behavioral habit tendency (that is, the user is more impatient and / or less focused), interesting interaction points or interactive questions can be pushed to the user to add more interesting interaction points in the learning process or to guide the user with interesting questions to arouse curiosity, so as to keep the user patient and focused as much as possible.

[0236] In a possible implementation, when the user's behavioral habit information meets the second behavioral habit tendency, second interaction information may be pushed to the user, and the second interaction information may be used to prompt the user to check completed exercises.

[0237] For example, when the user's behavioral habit information meets the second behavioral habit tendency (that is, the user is relatively careless), a reminder to do the questions carefully can be added during the practice process to prompt the user to check after completing the practice questions.

[0238] In some embodiments, the solution provided in this specification can also be used to recommend learning resources that the user may prefer.

[0239] In some embodiments, the user's user preference information can be determined based on usage data, and based on the user's user preference information and the resource tags of different learning resources under each learning module, learning resources corresponding to resource tags matching the user preference information can be pushed to the user.

[0240] Optionally, when determining the user preference information of the user based on usage data, the user's usage effect evaluation parameters for different learning resources under each learning module can be determined based on the usage data, and thus, based on the user's usage effect evaluation parameters for different learning resources under each learning module, the target learning resource whose usage effect evaluation parameters meet the sixth condition can be determined, so as to determine the user preference information based on the resource tag of the target learning resource.

[0241] Optionally, the usage effect evaluation parameters may include at least one of the user's completion efficiency evaluation parameters for practice questions under different learning resources of each learning module, the user's learning efficiency evaluation parameters for different learning resources of each learning module, and the user's concentration evaluation parameters during the learning process of different learning resources of each learning module, but are not limited thereto.

[0242] It should be noted that the method for determining the user's completion efficiency evaluation parameters for exercise questions under different learning resources of each learning module and the user's learning efficiency evaluation parameters for different learning resources of each learning module can be found in the previous article and will not be repeated here.

[0243] The user's concentration evaluation parameter during the learning process of different learning resources in each learning module can be determined based on the user's viewing time of the learning resources in each learning module and the number of times the user's gaze strayed while viewing the learning resources in each learning module. For example, the ratio of the number of times the user's gaze strayed while viewing the learning resources in each learning module to the user's viewing time of the learning resources in each learning module can be determined as the user's concentration evaluation parameter during the learning process of different learning resources in each learning module, but the present invention is not limited thereto.

[0244] Optionally, the usage effect evaluation parameter satisfies the sixth condition, which may be that the usage effect evaluation parameter satisfies a corresponding threshold. For example, the usage effect evaluation parameter satisfies the sixth condition, which may be that the user's evaluation parameter for completing the exercise questions under the learning resource is greater than or equal to the fourth parameter threshold, or the user's learning efficiency evaluation parameter for the learning resource is greater than or equal to the fifth parameter threshold, or the user's concentration evaluation parameter during the learning process of the learning resource is less than or equal to the sixth parameter threshold.

[0245] Among them, the fourth parameter threshold, the fifth parameter threshold and the sixth parameter threshold can be any value, and this specification does not limit this. Optionally, the fourth parameter threshold, the fifth parameter threshold and the sixth parameter threshold can be determined based on the user's usage effect evaluation parameters for all learning resources, but are not limited to this. For example, the average value of the user's question completion efficiency evaluation parameters for practice questions under different learning resources of all learning modules can be determined as the fourth parameter threshold; the average value of the user's learning efficiency evaluation parameters for different learning resources of all learning modules can be determined as the fifth parameter threshold; the average value of the user's concentration evaluation parameters in the learning process of different learning resources of all learning modules can be determined as the sixth parameter threshold.

[0246] It should be noted that for target learning resources whose usage effect evaluation parameters meet the sixth condition, it can be considered that users prefer them more, and each learning resource is set with a corresponding resource label (such as serious, lively, academic, etc.), so that the resource style that users may prefer can be determined based on the resource label of the target learning resource, so as to recommend learning resources of the corresponding style to users.

[0247] Corresponding to the aforementioned method embodiments, this specification also provides embodiments of an apparatus and a computing device to which the apparatus is applied.

[0248] See also Figure 4 , Figure 4 This is a block diagram of an auxiliary device for using a learning electronic product according to an exemplary embodiment of the present specification. Figure 4 As shown, the device includes:

[0249] An acquisition unit 401 is configured to acquire usage data of a user on a learning electronic product, wherein the usage data is used to indicate the user's learning performance in various learning modules;

[0250] A determining unit 402 is configured to determine, based on the usage data, predicted learning time information of the user for each learning module, the predicted learning time information being used to indicate a predicted time required for the user to learn a content item to be learned in a learning module;

[0251] The determining unit 402 is further configured to determine time allocation recommendation information based on the user's predicted learning time information for each learning module, wherein the time allocation recommendation information is configured to indicate the recommended learning time allocated to the user for different learning modules.

[0252] In some embodiments, for any learning module, the determining unit 402, when determining the user's predicted learning time information for the learning module based on the usage data, is configured to:

[0253] determining, based on the usage data, an amount of information to be learned of the learning module and a learning performance evaluation parameter of the user for the learning module;

[0254] Based on the amount of information to be learned in the learning module, the user's learning performance evaluation parameters for the learning module, and the expected learning performance parameters under the learning module, the user's predicted learning time information for the learning module is determined.

[0255] In some embodiments, the usage data includes at least the required learning time for each content item to be learned in the learning module, the number of content items to be learned corresponding to the learning module, the total number of practice questions under each content item to be learned in the learning module, and the difficulty parameter of the questions under each content item to be learned in the learning module;

[0256] The determining unit 402, when used to determine the amount of information to be learned by the learning module based on the usage data, is configured to:

[0257] The amount of information to be learned in the learning module is determined based on the required learning time for each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the question difficulty parameters under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

[0258] In some embodiments, the determining unit 402, when determining the amount of information to be learned for the learning module based on the required learning time for each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the question difficulty parameter under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, is configured to:

[0259] Determining the total learning time required for all content items to be learned in the learning module based on the learning time required for each content item to be learned in the learning module;

[0260] Determining an overall difficulty coefficient of all content items to be learned in the learning module based on the difficulty parameters of the content items under each content item to be learned in the learning module;

[0261] The amount of information to be learned in the learning module is determined based on the total learning time required for all content items to be learned in the learning module, the overall difficulty coefficient of all content items to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

[0262] In some embodiments, the determining unit 402, when determining the amount of information to be learned for the learning module based on the total learning time required for all content items to be learned in the learning module, the overall difficulty coefficient of all content items to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, is configured to:

[0263] Determining a first product of the total learning time required for all content items to be learned in the learning module and the overall difficulty coefficient of all content items to be learned in the learning module, and determining a second product of the total number of practice questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module;

[0264] A ratio of the first product to the second product is determined as the amount of information to be learned by the learning module.

[0265] In some embodiments, the user's learning performance evaluation parameters for the learning module include at least the user's learning effect evaluation parameters for the learning module;

[0266] The usage data includes at least the number of exercises actually completed by the user under each to-be-learned content item of the learning module, the number of exercises correctly completed by the user under each to-be-learned content item of the learning module, and the number of to-be-learned content items corresponding to the learning module;

[0267] The determining unit 402, when used to determine the learning performance evaluation parameter of the user for the learning module based on the usage data, is configured to:

[0268] Based on the number of exercise questions actually completed by the user under each content item to be learned in the learning module, the number of exercise questions correctly completed by the user under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, the learning effect evaluation parameters of the user on the learning module are determined.

[0269] In some embodiments, the determining unit 402, when determining the user's learning effect evaluation parameter for the learning module based on the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, is configured to:

[0270] Determining the user's accuracy rate for completing each of the content items to be learned in the learning module based on the number of practice questions actually completed by the user under each of the content items to be learned in the learning module and the number of practice questions correctly completed by the user under each of the content items to be learned in the learning module;

[0271] Based on the user's correct completion rate of questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module, the user's average correct completion rate of questions under each content item to be learned in the learning module is determined as a parameter for evaluating the user's learning effect on the learning module.

[0272] In some embodiments, the user's learning performance evaluation parameters for the learning module include at least the user's learning ability evaluation parameters for the learning module;

[0273] The usage data at least includes difficulty parameters of questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual study time of the user on each content item to be learned in the learning module, the required study time for each content item to be learned in the learning module, and the number of times the user repeatedly studies each content item to be learned in the learning module;

[0274] The determining unit 402, when used to determine the learning performance evaluation parameter of the user for the learning module based on the usage data, is configured to:

[0275] Based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual learning time of the user on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module, the learning ability evaluation parameters of the user for the learning module are determined.

[0276] In some embodiments, the determination unit 402, when determining the learning ability assessment parameters of the user for the learning module based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions for each content item to be learned in the learning module, the actual study time of the user on each content item to be learned in the learning module, the required study time for each content item to be learned in the learning module, and the number of repeated studies of each content item to be learned in the learning module by the user, is configured to:

[0277] Determining a question completion efficiency evaluation parameter of the user for the practice questions under the learning module based on a question difficulty parameter under each content item to be learned in the learning module, a number of practice questions actually completed by the user under each content item to be learned in the learning module, a number of practice questions correctly completed by the user under each content item to be learned in the learning module, a total number of practice questions under each content item to be learned in the learning module, and a time taken by the user to complete the practice questions under each content item to be learned in the learning module;

[0278] Determining a learning efficiency evaluation parameter of the user for the learning module based on the actual learning time of the user on each content item to be learned in the learning module, the required learning time of each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module;

[0279] Determine a ratio of the user's question completion efficiency evaluation parameter for the exercise questions under the learning module to the user's learning efficiency evaluation parameter for the learning module as the user's learning ability evaluation parameter for the learning module.

[0280] In some embodiments, the determination unit 402, when determining a parameter for evaluating the user's completion efficiency of the practice questions in the learning module based on a difficulty parameter of each content item to be learned in the learning module, the number of practice questions actually completed by the user in each content item to be learned in the learning module, the number of practice questions correctly completed by the user in each content item to be learned in the learning module, the total number of practice questions in each content item to be learned in the learning module, and the time taken by the user to complete the practice questions in each content item to be learned in the learning module, is configured to:

[0281] Determining the effective completion rate of the user's questions under each content item to be learned in the learning module based on the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, and the total number of practice questions under each content item to be learned in the learning module;

[0282] Determining the user's completion efficiency of the practice questions for each content item to be learned in the learning module based on the user's effective completion rate of the questions under each content item to be learned in the learning module and the time taken by the user to complete the practice questions for each content item to be learned in the learning module;

[0283] Based on the difficulty parameters of the questions under each content item to be learned in the learning module, the user's completion efficiency of the practice questions for each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module, determine the user's completion efficiency evaluation parameters for the practice questions under the learning module.

[0284] In some embodiments, the user's learning performance evaluation parameter for the learning module includes the user's learning effect evaluation parameter for the learning module and the user's learning ability evaluation parameter for the learning module; the expected learning performance parameter under the learning module is used to indicate the learning effect expected to be achieved under the learning module;

[0285] The determining unit 402, when used to determine the user's predicted learning time information for the learning module based on the amount of information to be learned in the learning module, the user's learning performance evaluation parameter for the learning module, and the expected learning performance parameter under the learning module, is configured to:

[0286] Determining a target learning time for the user for the learning module based on the amount of information to be learned in the learning module and a learning ability evaluation parameter of the user for the learning module;

[0287] Determining a learning effect deviation evaluation parameter of the user for the learning module based on an expected learning performance evaluation parameter under the learning module and a learning effect evaluation parameter of the user for the learning module;

[0288] The user's target learning time for the learning module is weighted based on the learning effect deviation evaluation parameter of the user for the learning module to obtain the user's predicted learning time information for the learning module.

[0289] In some embodiments, the usage data is further used to indicate the completion status of the user's exercises in each learning module;

[0290] The determining unit 402 is further configured to determine the user's learning ability assessment level based on the usage data;

[0291] The determining unit 402 is further configured to determine recommended practice questions from the plurality of candidate practice questions based on the user's learning ability assessment level and the difficulty levels of the plurality of candidate practice questions;

[0292] The device further comprises:

[0293] A push unit is used to push the recommended practice questions to the user.

[0294] In some embodiments, each candidate exercise question corresponds to a knowledge element in a to-be-learned content item in a learning module, and the determining unit 402 is further configured to determine the knowledge element that the user has mastered based on the usage data;

[0295] The device further comprises:

[0296] a processing unit for eliminating candidate practice questions corresponding to the knowledge elements already mastered by the user from the plurality of candidate practice questions to obtain processed candidate practice questions, and determining recommended practice questions from the processed candidate practice questions based on the user's learning ability assessment level and the difficulty levels of the plurality of candidate practice questions.

[0297] In some embodiments, the acquisition unit 401 is further configured to acquire statistical information on the user's completion of the recommended practice questions, the completion statistical information including at least the number of recommended practice questions completed by the user and the completion accuracy rate of the user among the completed recommended practice questions;

[0298] The device further comprises:

[0299] An adjustment unit is used to dynamically adjust the recommended practice questions pushed to the user based on the completion status statistical information.

[0300] In some embodiments, the adjustment unit, when used to dynamically adjust the recommended practice questions pushed to the user based on the completion status statistical information, is used for any of the following:

[0301] If the completion status statistical information meets the first condition, push recommended practice questions with a higher difficulty level to the user;

[0302] When the completion status statistical information does not meet the first condition, recommended practice questions with a lower difficulty level are pushed to the user.

[0303] In some embodiments, the apparatus further comprises:

[0304] a generating unit configured to generate practice summary information based on the user's completion status statistics if the recommended practice problems pushed to the user are of the highest difficulty level, or if the recommended practice problems pushed to the user are of the lowest difficulty level and the number of practice problems completed by the user reaches a first threshold;

[0305] The push unit is further configured to push learning resources associated with the knowledge elements corresponding to the recommended practice questions that the user incorrectly completed, if the recommended practice questions pushed to the user are of the lowest difficulty level and the user's completion accuracy in the completed recommended practice questions is lower than a first accuracy threshold.

[0306] In some embodiments, the determining unit 402 is further configured to determine the user's behavioral habit information based on the usage data;

[0307] The push unit is further configured to push interaction information to the user when the user's behavior habit information meets a specific behavior habit tendency, and the interaction information is used to guide the user based on the behavior habit.

[0308] In some embodiments, the usage data includes at least the number of times the user performs a first operation when viewing the learning resources of each learning module, the number of times the user performs a second operation when completing exercises under the content to be learned items of each learning module, the number of exercises actually completed by the user under the content to be learned items of each learning module, the number of exercises correctly completed by the user under the content to be learned items of each learning module, whether the user has relearned the knowledge elements corresponding to the exercises that were not completed correctly under the content to be learned items of each learning module, the number of exercises correctly completed when the user recompleted the exercises that were not completed correctly under the content to be learned items of each learning module, the number of times the user's line of sight was diverted when viewing the learning resources of each learning module, and the viewing time of the user for the learning resources of each learning module;

[0309] The determining unit 402, when used to determine the user's behavioral habit information based on the usage data, is used for at least one of the following:

[0310] If the number of times the user performs the first operation when viewing the learning resources of each learning module, and the question completion accuracy rate determined based on the number of practice questions actually completed by the user under the to-be-learned content items of each learning module and the number of practice questions correctly completed by the user under the to-be-learned content items of each learning module meet the second condition, then it is determined that the user's behavioral habit information meets the first behavioral habit tendency;

[0311] If the number of times the user performs the second operation when completing the practice questions under the content to be learned items of each learning module, and the question completion accuracy rate determined based on the number of practice questions actually completed by the user under the content to be learned items of each learning module and the number of practice questions correctly completed by the user under the content to be learned items of each learning module meet the third condition, then it is determined that the user's behavioral habit information meets the first behavioral habit tendency;

[0312] If the user has relearned the knowledge elements corresponding to the exercises that were not correctly completed under the content to be learned in each learning module, the number of exercises that were not correctly completed by the user under the content to be learned in each learning module, and the number of exercises that were correctly completed when the user re-completed the exercises that were not correctly completed under the content to be learned in each learning module meet the fourth condition, then it is determined that the user's behavioral habit information meets the second behavioral habit tendency;

[0313] If the user's viewing time of the learning resources of each learning module and the number of times the user's gaze is diverted when viewing the learning resources of each learning module meet the fifth condition, it is determined that the user's behavioral habit information meets the third behavioral habit tendency.

[0314] In some embodiments, the push unit, when used to push interaction information to the user when the user's behavioral habit information meets a specific behavioral habit tendency, is used to do at least one of the following:

[0315] When the user's behavior habit information satisfies the first behavior habit tendency and / or the third behavior habit tendency, pushing first interaction information to the user, where the first interaction information is interactive content that can attract the user's attention;

[0316] In a case where the user's behavior habit information satisfies a second behavior habit tendency, second interaction information is pushed to the user, where the second interaction information is used to prompt the user to check completed exercises.

[0317] In some embodiments, the determining unit 402 is further configured to determine user preference information of the user based on the usage data;

[0318] The push unit is further configured to push learning resources corresponding to resource tags matching the user preference information to the user based on the user preference information of the user and resource tags of different learning resources under each learning module.

[0319] In some embodiments, the determining unit 402, when used to determine the user preference information of the user based on the usage data, is used to:

[0320] Determining, based on the usage data, a parameter for evaluating the user's use of different learning resources under each learning module, the parameter for evaluating the user's use of different learning resources under each learning module, wherein the parameter for evaluating the user's completion efficiency of exercise questions under the different learning resources under each learning module, a parameter for evaluating the user's learning efficiency for the different learning resources under each learning module, and a parameter for evaluating the user's concentration during the learning process of the different learning resources under each learning module;

[0321] Based on the user's usage effect evaluation parameters for different learning resources under each learning module, a target learning resource whose usage effect evaluation parameters meet the sixth condition is determined, so as to determine the user preference information based on the resource tag of the target learning resource.

[0322] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0323] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this specification. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0324] This application also provides a computing device, see Figure 5 , Figure 5 This is a schematic diagram of a computing device according to an exemplary embodiment of the present specification. Figure 5 As shown, the computing device includes a processor 510, a memory 520, and a network interface 530. The memory 520 is used to store computer instructions that can be executed on the processor 510. The processor 510 is used to implement the auxiliary use method of the learning electronic product provided in any embodiment of the present application when executing the computer instructions. The network interface 530 is used to implement input and output functions. In more possible implementations, the computing device may also include other hardware, which is not limited in this application.

[0325] The present application also provides a computer-readable storage medium, which can be in various forms. For example, in different examples, the computer-readable storage medium can be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, storage drive (such as a hard disk drive), solid-state drive, any type of storage disk (such as a CD, DVD, etc.), or similar storage medium, or a combination thereof. In particular, the computer-readable medium can also be paper or other suitable medium capable of printing programs. A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, the auxiliary use method of the learning electronic product provided in any embodiment of the present application is implemented.

[0326] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the auxiliary use method of the learning electronic product provided in any embodiment of the present application.

[0327] It should be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, apparatus, computing device, computer-readable storage medium, or computer program product. Thus, one or more embodiments of this specification may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0328] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the embodiments corresponding to the computing device are generally similar to the method embodiments, so the description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0329] The foregoing description of specific embodiments of this specification is provided. Other embodiments are within the scope of this application. In some cases, the actions or steps described herein may be performed in an order different from that shown in the embodiments and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0330] The embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. The embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of a data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver device for execution by an auxiliary use device of a learning electronic product. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0331] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0332] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer does not necessarily have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0333] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0334] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of the sub-combination.

[0335] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0336] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of this application. In some cases, the actions described in this application can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0337] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the present invention and practicing the invention claimed herein. This specification is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of this specification and include common knowledge or customary techniques in the art that are not claimed herein. That is, this specification is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof.

[0338] The above description is only an optional embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for assisting the use of a learning electronic product, characterized in that: The method comprises: Acquire usage data of a user in a learning electronic product, wherein the usage data is used to indicate the user's learning performance in various learning modules; Determining, based on the usage data, predicted learning time information of the user for each learning module, the predicted learning time information being used to indicate a predicted time required for the user to learn a content item to be learned in a learning module; Based on the predicted learning time information of the user for each learning module, time allocation recommendation information is determined, and the time allocation recommendation information is used to indicate the recommended learning time allocated to different learning modules for the user.

2. The method according to claim 1, characterized in that For any learning module, determining the user's predicted learning time information for the learning module based on the usage data includes: determining, based on the usage data, an amount of information to be learned of the learning module and a learning performance evaluation parameter of the user for the learning module; Based on the amount of information to be learned in the learning module, the user's learning performance evaluation parameters for the learning module, and the expected learning performance parameters under the learning module, the user's predicted learning time information for the learning module is determined.

3. The method according to claim 2, characterized in that The usage data includes at least the learning time required for each to-be-learned content item of the learning module, the number of to-be-learned content items corresponding to the learning module, the total number of practice questions under each to-be-learned content item of the learning module, and the difficulty parameter of the questions under each to-be-learned content item of the learning module; Determining the amount of information to be learned by the learning module based on the usage data includes: The amount of information to be learned in the learning module is determined based on the required learning time for each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the question difficulty parameters under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

4. The method according to claim 3, characterized in that The determining of the amount of information to be learned in the learning module based on the required learning time for each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the difficulty parameter of the questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module includes: Determining the total learning time required for all content items to be learned in the learning module based on the learning time required for each content item to be learned in the learning module; Determining an overall difficulty coefficient of all content items to be learned in the learning module based on the difficulty parameters of the content items under each content item to be learned in the learning module; The amount of information to be learned in the learning module is determined based on the total learning time required for all content items to be learned in the learning module, the overall difficulty coefficient of all content items to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module.

5. The method according to claim 4, characterized in that Determining the amount of information to be learned in the learning module based on the total learning time required for all content items to be learned in the learning module, the overall difficulty coefficient of all content items to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module includes: Determining a first product of the total learning time required for all content items to be learned in the learning module and the overall difficulty coefficient of all content items to be learned in the learning module, and determining a second product of the total number of practice questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module; A ratio of the first product to the second product is determined as the amount of information to be learned by the learning module.

6. The method according to claim 2, characterized in that The user's learning performance evaluation parameters for the learning module at least include the user's learning effect evaluation parameters for the learning module; The usage data includes at least the number of exercises actually completed by the user under each to-be-learned content item of the learning module, the number of exercises correctly completed by the user under each to-be-learned content item of the learning module, and the number of to-be-learned content items corresponding to the learning module; Determining a learning performance evaluation parameter of the user for the learning module based on the usage data includes: Based on the number of exercise questions actually completed by the user under each content item to be learned in the learning module, the number of exercise questions correctly completed by the user under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module, the learning effect evaluation parameters of the user on the learning module are determined.

7. The method according to claim 6, characterized in that The determining of the user's learning effect evaluation parameters for the learning module based on the number of exercises actually completed by the user under each content item to be learned in the learning module, the number of exercises correctly completed by the user under each content item to be learned in the learning module, and the number of content items to be learned corresponding to the learning module includes: Determining the user's accuracy rate for completing each of the content items to be learned in the learning module based on the number of practice questions actually completed by the user under each of the content items to be learned in the learning module and the number of practice questions correctly completed by the user under each of the content items to be learned in the learning module; Based on the user's correct completion rate of questions under each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module, the user's average correct completion rate of questions under each content item to be learned in the learning module is determined as a parameter for evaluating the user's learning effect on the learning module.

8. The method according to claim 2, characterized in that The user's learning performance evaluation parameters for the learning module at least include the user's learning ability evaluation parameters for the learning module; The usage data at least includes difficulty parameters of questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual study time of the user on each content item to be learned in the learning module, the required study time for each content item to be learned in the learning module, and the number of times the user repeatedly studies each content item to be learned in the learning module; Determining a learning performance evaluation parameter of the user for the learning module based on the usage data includes: Based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual learning time of the user on each content item to be learned in the learning module, the required learning time for each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module, the learning ability evaluation parameters of the user for the learning module are determined.

9. The method according to claim 8, characterized in that Determining the learning ability assessment parameters of the user for the learning module based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, the time taken by the user to complete the practice questions of each content item to be learned in the learning module, the actual study time of the user on each content item to be learned in the learning module, the required study time for each content item to be learned in the learning module, and the number of repeated studies of the user on each content item to be learned in the learning module, includes: Determining a question completion efficiency evaluation parameter of the user for the practice questions under the learning module based on a question difficulty parameter under each content item to be learned in the learning module, a number of practice questions actually completed by the user under each content item to be learned in the learning module, a number of practice questions correctly completed by the user under each content item to be learned in the learning module, a total number of practice questions under each content item to be learned in the learning module, and a time taken by the user to complete the practice questions under each content item to be learned in the learning module; Determining a learning efficiency evaluation parameter of the user for the learning module based on the actual learning time of the user on each content item to be learned in the learning module, the required learning time of each content item to be learned in the learning module, and the number of times the user repeatedly learns each content item to be learned in the learning module; Determine a ratio of the user's question completion efficiency evaluation parameter for the exercise questions under the learning module to the user's learning efficiency evaluation parameter for the learning module as the user's learning ability evaluation parameter for the learning module.

10. The method according to claim 9, characterized in that The determining of the evaluation parameters of the user's completion efficiency of the practice questions under the learning module based on the difficulty parameters of the questions under each content item to be learned in the learning module, the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, the total number of practice questions under each content item to be learned in the learning module, and the time taken by the user to complete the practice questions under each content item to be learned in the learning module includes: Determining the effective completion rate of the user's questions under each content item to be learned in the learning module based on the number of practice questions actually completed by the user under each content item to be learned in the learning module, the number of practice questions correctly completed by the user under each content item to be learned in the learning module, and the total number of practice questions under each content item to be learned in the learning module; Determining the user's completion efficiency of the practice questions for each content item to be learned in the learning module based on the user's effective completion rate of the questions under each content item to be learned in the learning module and the time taken by the user to complete the practice questions for each content item to be learned in the learning module; Based on the difficulty parameters of the questions under each content item to be learned in the learning module, the user's completion efficiency of the practice questions for each content item to be learned in the learning module and the number of content items to be learned corresponding to the learning module, determine the user's completion efficiency evaluation parameters for the practice questions under the learning module.

11. The method according to claim 2, characterized in that The user's learning performance evaluation parameters for the learning module include the user's learning effect evaluation parameters for the learning module and the user's learning ability evaluation parameters for the learning module; the expected learning performance parameters under the learning module are used to indicate the learning effect expected to be achieved under the learning module; The determining, based on the amount of information to be learned in the learning module, the user's learning performance evaluation parameter for the learning module, and the expected learning performance parameter under the learning module, information about the user's predicted learning time for the learning module includes: Determining a target learning time for the user for the learning module based on the amount of information to be learned in the learning module and a learning ability evaluation parameter of the user for the learning module; Determining a learning effect deviation evaluation parameter of the user for the learning module based on an expected learning performance evaluation parameter under the learning module and a learning effect evaluation parameter of the user for the learning module; The user's target learning time for the learning module is weighted based on the learning effect deviation evaluation parameter of the user for the learning module to obtain the user's predicted learning time information for the learning module.

12. The method according to claim 1, characterized in that The usage data is also used to indicate the completion status of the user's exercises in each learning module. The method further includes: Determining a learning ability assessment level of the user based on the usage data; determining recommended practice questions from the plurality of candidate practice questions based on the user's learning ability assessment level and the difficulty levels of the plurality of candidate practice questions; Push the recommended practice questions to the user.

13. The method according to claim 12, characterized in that Each candidate practice question corresponds to a knowledge element in a to-be-learned content item in a learning module. When determining a recommended practice question from the plurality of candidate practice questions based on the user's learning ability assessment level and the difficulty levels of the plurality of candidate practice questions, the method further includes: Determining knowledge elements that the user has mastered based on the usage data; Eliminate candidate practice questions corresponding to knowledge elements that the user has mastered from the multiple candidate practice questions to obtain processed candidate practice questions, and determine recommended practice questions from the processed candidate practice questions based on the user's learning ability assessment level and the difficulty levels of the multiple candidate practice questions.

14. The method according to claim 12, characterized in that After pushing the recommended practice questions to the user, the method further includes: Obtaining statistical information on the user's completion of the recommended practice questions, the statistical information including at least the number of recommended practice questions completed by the user and the completion accuracy rate of the user among the completed recommended practice questions; Based on the completion status statistical information, the recommended practice questions pushed to the user are dynamically adjusted.

15. The method according to claim 14, characterized in that The dynamically adjusting the recommended practice questions pushed to the user based on the completion status statistical information includes any of the following: If the completion status statistical information meets the first condition, push recommended practice questions with a higher difficulty level to the user; When the completion status statistical information does not meet the first condition, recommended practice questions with a lower difficulty level are pushed to the user.

16. The method according to claim 15, characterized in that The method further comprises: If the recommended practice questions pushed to the user are of the highest difficulty level, or if the recommended practice questions pushed to the user are of the lowest difficulty level and the number of practice questions completed by the user reaches a first threshold, generating practice summary information based on the user's completion status statistics; If the recommended practice questions pushed to the user are of the lowest difficulty level and the user's completion accuracy in the completed recommended practice questions is lower than a first accuracy threshold, then the learning resources associated with the knowledge elements corresponding to the recommended practice questions that the user incorrectly completed are pushed.

17. The method according to claim 1, wherein The method further comprises: Determining the user's behavioral habit information based on the usage data; In the case where the user's behavioral habit information meets a specific behavioral habit tendency, interactive information is pushed to the user, and the interactive information is used to guide the user based on the behavioral habit.

18. The method according to claim 17, characterized in that The usage data at least includes the number of times the user performs a first operation when viewing the learning resources of each learning module, the number of times the user performs a second operation when completing exercises under the content to be learned items of each learning module, the number of exercises actually completed by the user under the content to be learned items of each learning module, the number of exercises correctly completed by the user under the content to be learned items of each learning module, whether the user has re-learned the knowledge elements corresponding to the exercises that were not completed correctly under the content to be learned items of each learning module, the number of exercises correctly completed when the user re-completed the exercises that were not completed correctly under the content to be learned items of each learning module, the number of times the user's line of sight was diverted when viewing the learning resources of each learning module, and the viewing time of the user for the learning resources of each learning module; Determining the user's behavior habit information based on the usage data includes at least one of the following: If the number of times the user performs the first operation when viewing the learning resources of each learning module, and the question completion accuracy rate determined based on the number of practice questions actually completed by the user under the to-be-learned content items of each learning module and the number of practice questions correctly completed by the user under the to-be-learned content items of each learning module meet the second condition, then it is determined that the user's behavioral habit information meets the first behavioral habit tendency; If the number of times the user performs the second operation when completing the practice questions under the content to be learned items of each learning module, and the question completion accuracy rate determined based on the number of practice questions actually completed by the user under the content to be learned items of each learning module and the number of practice questions correctly completed by the user under the content to be learned items of each learning module meet the third condition, then it is determined that the user's behavioral habit information meets the first behavioral habit tendency; If the user has relearned the knowledge elements corresponding to the exercises that were not correctly completed under the content to be learned in each learning module, the number of exercises that were not correctly completed by the user under the content to be learned in each learning module, and the number of exercises that were correctly completed when the user re-completed the exercises that were not correctly completed under the content to be learned in each learning module meet the fourth condition, then it is determined that the user's behavioral habit information meets the second behavioral habit tendency; If the user's viewing time of the learning resources of each learning module and the number of times the user's gaze is diverted when viewing the learning resources of each learning module meet the fifth condition, it is determined that the user's behavioral habit information meets the third behavioral habit tendency.

19. The method according to claim 18, characterized in that Pushing interaction information to the user when the user's behavior habit information meets a specific behavior habit tendency includes at least one of the following: When the user's behavior habit information satisfies the first behavior habit tendency and / or the third behavior habit tendency, pushing first interaction information to the user, where the first interaction information is interactive content that can attract the user's attention; In a case where the user's behavior habit information satisfies a second behavior habit tendency, second interaction information is pushed to the user, where the second interaction information is used to prompt the user to check completed exercises.

20. An auxiliary device for use of a learning electronic product, characterized in that: The device comprises: an acquisition unit, configured to acquire usage data of a user in a learning electronic product, wherein the usage data is used to indicate the learning performance of the user in each learning module; a determining unit, configured to determine, based on the usage data, predicted learning time information of the user for each learning module, the predicted learning time information being used to indicate a predicted time required for the user to learn a content item to be learned in a learning module; The determination unit is further used to determine time allocation recommendation information based on the user's predicted learning time information for each learning module, and the time allocation recommendation information is used to indicate the recommended learning time allocated to the user on different learning modules.

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