Learning content cognition degree quantification and display method, electronic equipment and program product

Abstract

The invention relates to a learning content cognition degree quantification and display method, electronic equipment and a computer program product, and the method comprises the steps: receiving a first input which is an input of a query user for a learning state of a learning object library in an application; in response to the first input, displaying learning state data of the user for the learning object library, the learning state data including first state data, the first state data including a first time node corresponding to the first input and a first capability value of the user for the learning object library at the first time node; the ability value of the user represents the overall cognitive ability of the user for the learning object library formed on the basis of historical learning accumulation at the time node; the first ability value is determined based on a first cognition degree of the user for each learning object in the learning object library at a first time node; the cognition degree of the user for a learning object at a time node is determined based on learning data of the user for the learning object at the time node.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing for learning applications, and in particular to a method for quantifying and displaying the recognition of learning content, an electronic device, and a program product. Background Art

[0002] At present, with the development of Internet technology, various learning applications are widely used by people. Current learning applications only provide knowledge point learning and answering, and their functions are relatively simple. Summary of the invention

[0003] An object of the embodiments of the present disclosure is to provide a method for quantifying and displaying the recognition of learning content, an electronic device, and a computer program product.

[0004] According to a first aspect of the present disclosure, a method for quantifying and displaying the degree of recognition of learning content is provided, comprising:

[0005] Receive a first input; wherein the first input is an input for querying a user's learning status of a learning object library in an application, and the learning object library includes a plurality of learning objects;

[0006] In response to the first input, displaying the learning status data of the user for the learning object library;

[0007] The learning status data displayed includes first status data, the first status data includes a first time node corresponding to the first input, and a first ability value of the user for the learning object library at the first time node; the ability value of the user for the learning object library at a time node represents: the overall cognitive ability of the user for the learning object library formed at the time node based on historical learning accumulation;

[0008] The first ability value is determined based on the user's first awareness of each learning object in the learning object library at the first time node; the user's awareness of a learning object at a time node is determined based on the user's learning data of the learning object as of the time node.

[0009] In a possible implementation manner, the displayed learning status data further includes at least one second status data, where the second status data includes a second time node and a second capability value of the user for the learning object library at the second time node;

[0010] The second time node is before the first time node, and different second state data correspond to different second time nodes; the second ability value is determined based on the user's second cognition of each learning object in the learning object library at the second time node.

[0011] In a possible implementation manner, the displayed learning state data further includes state prediction data, and the state prediction data includes at least one of first prediction data, second prediction data, and third prediction data;

[0012] The first prediction data is compared with the first state data, the first prediction data includes the first time node and a first capability prediction value mapped to the first time node, and the first capability prediction value is determined based on the second state data;

[0013] The third prediction data includes a third time node and a third capability prediction value mapped to the third time node, and different third prediction data correspond to different third time nodes; the third time node is after the first time node, and the third capability prediction value is determined based on the first state data and the second state data;

[0014] The second prediction data indicates the length of time required for the user to reach the full capacity value corresponding to the learning object library from the first capacity value.

[0015] In a possible implementation, after displaying the learning status data of the user for the learning object library, the method further includes:

[0016] receiving a second input regarding the length of time;

[0017] The time length indicated by the second input is compared with the time length represented by the second prediction data, and learning parameters of a function module in the application that performs functions based on the learning object library are adjusted according to the comparison result.

[0018] In a possible implementation manner, before receiving the first input, the method further includes:

[0019] receiving a third input from a user requesting a target functional module of the application;

[0020] The application is provided with a first function module and a second function module, both of which are modules for performing a question-setting function based on the learning object library for users to answer, and the target function module is the first function module or the second function module; the first function module and the second function module correspond to different object ratio types when performing functions, and the object ratio type reflects the user's cognitive range of the learning object output by the function module;

[0021] In response to the third input, the target function module is executed based on the user's learning data set for the learning object library and the object matching type corresponding to the target function module; wherein the learning data set includes data entries for each learning object in the learning object library, and the data entries include: the user's learning data for each time of the corresponding learning object, and the degree of recognition determined based on the learning data of the corresponding learning object, and the learning data includes a learning time point and an answer result indicating whether the answer is correct.

[0022] In a possible implementation, the first functional module corresponds to a first ratio type, and the second functional module corresponds to a second ratio type; the recognition range reflected by the first ratio type is a first set range; the recognition range reflected by the second ratio type is a second set range; wherein the upper limit value of the second set range is greater than the upper limit value of the first set range, and the lower limit value of the second set range is greater than the lower limit value of the first set range.

[0023] In a possible implementation manner, the target functional module is the second functional module; and executing the target functional module includes:

[0024] Based on the user's invitation operation, generate invitation information for inviting others to participate in answering the learning object for sharing;

[0025] Obtaining a learning object output for the user based on the learning data set of the user and the object matching type corresponding to the target function module;

[0026] The learning object output for the user is associated with the invitation page linked to by the invitation information for output.

[0027] In a possible implementation manner, before receiving the third input of the target function module requested to be applied by the user, the method further includes:

[0028] Determining that the learning data set of the user meets the set conditions for providing the target function module for the user;

[0029] A label control indicating the target functional module is provided on the application interface; wherein the third input is an input that triggers the label control.

[0030] In a possible implementation, the application is further provided with a third functional module, the third functional module provides a plurality of learning materials for establishing object connections, and the third functional module outputs a plurality of associated learning objects based on the learning materials;

[0031] Before receiving the third input of the target function module requested to be applied by the user, the method further includes:

[0032] receiving a fourth input of the user answering a learning object output by the learning material in the third functional module;

[0033] The data entry of the corresponding learning object is updated according to the fourth input.

[0034] In a possible implementation, executing the target function module includes:

[0035] Determine the cognitive range of the learning object to be output by the target functional module according to the object matching type corresponding to the target functional module and the learning data set, as well as the answer data of the user to the learning object output by the target functional module;

[0036] The answer data includes at least one of the answer accuracy rate and the answer result sequence.

[0037] In a possible implementation, the user's awareness of the learning object is updated based on an event in a set event set, wherein the set event set includes at least one of the following events:

[0038] First event: adding new learning data to the data entry of the learning object;

[0039] Second event: the time interval between the current time and the last update reaches the first set time;

[0040] The third event: the time interval between the current time and the last time learning data was added reaches the second set time length;

[0041] Fourth event: the user starts the application.

[0042] In a possible implementation, after executing the target function module, the method further includes:

[0043] The learning data set is updated based on the learning data generated by the user in the target function module.

[0044] According to another aspect of the present disclosure, an electronic device is provided, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the method according to any one of the first aspects under the control of the computer program.

[0045] According to another aspect of the present disclosure, a computer program product is provided, including a computer program or instructions, which implement the steps of any one of the methods described in the first aspect when executed by a processor.

[0046] According to an embodiment of the present disclosure, a query module for the user's learning status of the learning object library in the application is provided, which can track the learning status so that the user can understand the learning effect and improve the user experience. At the same time, the first ability value is determined based on the user's first recognition of each learning object in the learning object library at a first time node, and the user's recognition of a learning object at a time node is determined based on the user's learning data of the learning object as of the time node, so that the first ability value can more comprehensively and accurately reflect the user's ability value, thereby improving the effectiveness of learning status tracking.

[0047] Features and advantages of the embodiments of the present specification will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.

[0049] Figure 1 A schematic diagram showing a flow chart of a method for quantifying and displaying the degree of recognition of learning content according to some embodiments of the present disclosure;

[0050] Figure 2 A schematic diagram showing an application display interface according to some embodiments of the present disclosure is shown;

[0051] Figure 3 A schematic diagram of a text message display interface according to some embodiments of the present disclosure is shown;

[0052] Figure 4 A schematic diagram showing the relationship of learning status data provided according to some embodiments of the present disclosure;

[0053] Figure 5 A schematic diagram showing an application display interface according to some embodiments of the present disclosure is shown;

[0054] Figure 6 A schematic diagram showing the relationship between a first setting range and a second setting range provided according to some embodiments of the present disclosure is shown;

[0055] Figure 7 A structural block diagram of an electronic device according to some embodiments of the present disclosure is shown;

[0056] Figure 8 A structural block diagram of an electronic device according to some other embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0057] Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.

[0058] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the embodiments of the present specification and its application or uses.

[0059] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0060] It should be noted that all actions of acquiring signals, information or data in the embodiments of the present disclosure are performed in compliance with the corresponding data protection laws and policies of the country where the device is located, and with the authorization given by the owner of the corresponding device.

[0061] In the related art, after completing the answer to the knowledge point, the learning application only displays the answer result of this answer. After the user has studied for a period of time through the learning application, it is impossible to understand the degree of mastery of each knowledge point during this period, and it is impossible to display the learning effect of this period of study, which affects the customer experience. In order to solve the above technical problems, the embodiment of the present disclosure provides a method for quantifying and displaying the recognition of learning content, which can effectively track the learning status, so that the user can understand the learning effect and improve the user experience.

[0062] Figure 1 A flow chart of a method for quantifying and displaying the degree of cognition of learning content according to an embodiment of the present disclosure is shown. The method can be applied to an APP (application). The APP can be a client APP or a web APP. The learning category corresponding to the APP can be any of the following: literacy and reading, literary knowledge, mathematical thinking, encyclopedic knowledge, and foreign language.

[0063] according to Figure 1 As shown, the method includes steps S110 to S120.

[0064] Step S110, receiving a first input; wherein the first input is an input for querying a user's learning status for a learning object library in an application, and the learning object library includes a plurality of learning objects.

[0065] The first input may be an operation triggered by a user, or may be an operation automatically triggered by the application to report the learning status to the user.

[0066] When the first input is an operation triggered by a user, the display interface of the application displays an interactive control corresponding to the first input. When the user triggers the interactive control, the application receives the first input.

[0067] When the first input is an operation automatically triggered by the application to regularly report the learning status to the user, the first input may be any one of a link and a QR code. The first input may be received in the form of a text message or a communication via social media.

[0068] Step S120, in response to the first input, display the user's learning status data for the learning object library; wherein the displayed learning status data includes first status data, the first status data includes the first time node corresponding to the first input, and the first ability value of the user for the learning object library at the first time node; the ability value of the user for the learning object library at a time node represents: the user's overall cognitive ability of the learning object library formed at the time node based on historical learning accumulation; the first ability value is determined based on the user's first cognition of each learning object in the learning object library at the first time node; the user's cognition of a learning object at a time node is determined based on the user's learning data of the learning object as of the time node.

[0069] The learning object is related to the category of the application. When the application is a literacy and reading application, the learning object is Chinese character questions and / or reading comprehension questions. When the application is a literary knowledge and common sense application, the learning object is literary knowledge and common sense questions. When the application is a mathematical thinking question, the learning object is a calculation question and / or logical reasoning question. When the application is an encyclopedia knowledge application, the learning object is a science question. When the application is a foreign language application, the learning object is a word question.

[0070] The learning object library may consist of all learning objects in the application, or may consist of learning objects that have been answered at least once.

[0071] The learning data includes the learning time point and the answer result indicating whether the answer is correct. For example, the learning data can be represented in the following way: (t i , answer result), t i It represents the learning time point, and the answer result is right or wrong.

[0072] The learning data of a learning object as of a time node can be one or more. When the learning data of a learning object as of a time node is one, it can be expressed in the following way: (t i , answer result). When there are multiple learning data of a learning object ending at a time node, the following sequence can be used to represent it: [(t i1 , answer result), (t i2 , answer result),……(t in , answer results)].

[0073] For example, a user's awareness of a learning object at a certain point in time can be predicted using a machine learning model. The machine learning model is a prediction model trained based on a large number of training samples. Each training sample includes the learning data of a learning object and the corresponding awareness, which is manually annotated data, that is, the awareness is manually annotated based on the learning data of the learning object. When determining the awareness using a machine learning model, the learning data of a learning object is input into the machine learning model to obtain the corresponding awareness.

[0074] For another example, the user's recognition of a learning object at a certain time point can be determined based on the learning data and the forgetting curve. The forgetting curve reflects the memory law that the forgetting process is not uniform. After learning is completed, the initial forgetting speed is very fast, and then gradually slows down. In order to consolidate memory, review in time before forgetting occurs. Based on the memory law reflected by the forgetting curve, combined with the learning data of the learning object, the recognition of the learning object is predicted.

[0075] For another example, the user's recognition of a learning object at a time point can be determined based on the accuracy of the answer results in the learning data. For example, the learning data of a learning object as of a time point is [(t 1 , wrong), (t 2 , yes), (t 3 , yes), (t 4 , yes), (t 5 , correct)], the learning data includes 5 answer results, the accuracy rate of the 5 answer results is 80%, then the recognition degree of the learning object is 80%.

[0076] The capability value of the learning object library at a time node can be determined in the following manner: summing up the recognition of each learning object in the learning object library at the time node to obtain the total recognition as the capability value of the learning object library at the time node. For example, the learning object library includes 100 learning objects, and at a certain time node, a recognition can be obtained based on each learning object, so 100 recognitions can be obtained, and the sum of these 100 recognitions can be obtained to obtain a capability value.

[0077] After determining the cognition of each learning object, the cognition of each learning object can be binarized to obtain the processed cognition of each learning object, and the processed cognition of each learning object is used to determine the ability value of the learning object library. The binarization process is to compare the cognition of each learning object with a preset threshold. When the cognition of a learning object exceeds the preset threshold, the cognition of the corresponding learning object is determined to be 1, and when the cognition of a learning object does not exceed the preset threshold, the cognition of the corresponding learning object is determined to be 0.

[0078] The first state data displayed includes a first time node and a first capability value. The display mode of the first time node can be any one of a digital clock, an analog clock, a timeline, a date selector, and a calendar view. The display mode of the first capability value can be any one of a digital display, a progress bar, and a digital scrolling display. When a progress bar is used for display, the progress bar displays the ratio of the first capability value to the full capability value through the length of the fill bar. When a digital scrolling display is used, when the first capability value changes, it is displayed in a scrolling manner. The display mode of the first time node and the first capability value can be a default setting, or it can support user settings.

[0079] Figure 2 This is a schematic diagram of an interface of an APP corresponding to the method for quantifying and displaying the degree of cognition of learning content provided by an embodiment of the present invention. Figure 2 As shown, the application interface currently displays multiple learning materials and an interactive control "query". When the user clicks to trigger the interactive control "query", the application interface displays the first state data.

[0080] Figure 3 The first input is pushed to the user in the form of a text message. Figure 3 As shown, the first input is a link. When the user triggers the link, the page displaying the first state data can be jumped to.

[0081] The method for quantifying and displaying the awareness of learning content provided by the embodiment of the present invention provides a query module for the user's learning status of the learning object library in the application, which can track the learning status so that the user can understand the learning effect and improve the user experience. At the same time, the first ability value is determined based on the user's first awareness of each learning object in the learning object library at the first time node, and the user's awareness of a learning object at a time node is determined based on the user's learning data of the learning object as of the time node, so that the first ability value can more comprehensively and accurately reflect the user's ability value, thereby improving the effectiveness of learning status tracking.

[0082] In one embodiment, the user's awareness of the learning object is updated based on the events in the set event set. The set event set includes at least one of the following events: a first event: adding new learning data to the data entry of the learning object; a second event: the time interval between the current time and the last update reaches a first set time length; a third event: the time interval between the current time and the last addition of learning data reaches a second set time length; a fourth event: the user starts the application. The data entry of the learning object includes: the user's learning data for each time of the corresponding learning object, and the awareness determined based on the learning data of the corresponding learning object, the learning data including the learning time point and the answer result indicating whether the answer is correct. In this way, the real-time update of the awareness of the learning object can be achieved, making the data determined based on the awareness of the learning object more accurate.

[0083] In one embodiment, the displayed learning status data also includes at least one second status data, the second status data includes a second time node, and a second ability value of the user for the learning object library at the second time node; the second time node is before the first time node, and different second status data correspond to different second time nodes; the second ability value is determined based on the user's second cognition of each learning object in the learning object library at the second time node.

[0084] The method for determining the second state data may be the same as the method for determining the first state data, and will not be elaborated herein.

[0085] In order to facilitate the understanding of the first state data and the second state data, combined Figure 4 For detailed description. Figure 4 As shown in the figure, the horizontal axis t represents the time node, and the vertical axis N represents the capability value. i 、N i ) represents the time node t i and the first capability value N i The first state data composed of. Point (t i-1 、N i-1 ) represents the time node t i-1 and the second capability value N i-1 A second state data composed of. Point (t i-2 、N i-2 ) represents the time node t i-2 and the second capability value N i-2 Another second state data composed of.

[0086] The second state data displayed includes the second time node and the second capability value of the user for the learning object library at the second time node. The display mode of the second time node can refer to the display mode of the first time node, and the display mode of the second capability value can refer to the display mode of the first capability value, which will not be described in detail here.

[0087] In one embodiment, the displayed learning status data also includes status prediction data, and the status prediction data includes at least one of first prediction data, second prediction data and third prediction data; wherein, the first prediction data is compared with the first status data, the first prediction data includes a first time node, and a first capability prediction value mapped to the first time node, and the first capability prediction value is determined based on the second status data; the third prediction data includes a third time node, and a third capability prediction value mapped to the third time node, and different third prediction data correspond to different third time nodes; the third time node is after the first time node, and the third capability prediction value is determined based on the first status data and the second status data; the second prediction data represents: the length of time required for the user to reach the full capability value corresponding to the learning object library from the first capability value.

[0088] The first capability prediction value is determined based on the second state data, specifically based on a forgetting curve, and the first capability prediction value is obtained using the second state data.

[0089] The third capability prediction value is determined based on the first state data and the second state data, specifically, the change trend of the capability value is determined according to the first state data and the second state data, and the third capability prediction value is obtained according to the change trend of the capability value. The change trend of the capability value can be determined using a fitting algorithm. The fitting algorithm is to find an optimal function or curve that can best describe or predict the change trend of the capability value. The fitting algorithm can be a least squares method or a gradient descent method.

[0090] In order to facilitate the understanding of the first ability prediction value and the third ability prediction value, combined Figure 4 Point (t i 、N i ') represents the time node t i and the first ability prediction value N i 'The first prediction data composed of the first capability prediction value N i ' is based on Figure 4 The two second state data shown are determined. Point (t i+1 、N i+1 ') represents the time node t i+1 and the second ability prediction value N i+1 The third prediction data is based on Figure 4The first state data and two second state data shown are determined. The N in the point (t', N) represents the full capacity value corresponding to the learning object library, and t' represents the time node corresponding to the user's capacity value reaching the full capacity value. The second prediction data is based on t' and t i Sure.

[0091] In one embodiment, the full capacity value corresponding to the learning object library is that the sum of the recognition of each learning object in the learning object library exceeds a first preset threshold. The first preset threshold can be set according to demand.

[0092] In one embodiment, each learning object in the learning object library corresponds to a full recognition level. The full recognition level corresponding to a learning object at a time node is when the accuracy rate of the answer results in the learning data of a learning object as of a time node reaches a second preset threshold, and the second preset threshold can be set according to demand. The full capacity value corresponding to the learning object library is the capacity value corresponding to when the recognition level of each learning object in the learning object library reaches the full recognition level. The full capacity value obtained in this way can ensure the user's mastery of each learning object and avoid the problem of poor mastery of individual or a few learning objects.

[0093] The length of time required for the user to reach the full capacity value corresponding to the learning object library from the first capacity value can be determined based on the forgetting curve.

[0094] The length of time required for the user to reach the full capacity value corresponding to the learning object library from the first capacity value can be determined based on the change trend of the capacity value determined above.

[0095] The first prediction data includes a first time node and a first capability prediction value. The third prediction data includes a third time node and a third capability prediction value. The second prediction data is a time length value. The display mode of the third time node can refer to the display mode of the first time node, and the display modes of the first capability prediction value, the third capability prediction value, and the second prediction data can refer to the display mode of the first capability value, which will not be described in detail here.

[0096] In one embodiment, the displayed learning status data also includes all learning data of each learning object as of a time node. When the learning data of a learning object as of a time node is one, the following method can be used to represent the learning object name: (t i , answer result). When there are multiple learning data of a learning object ending at a time node, the following sequence can be used to represent the learning object name: [(t i1 , answer result), (t i2 , answer result),……(t in, answer results)], so that users can intuitively understand the learning status of each learning object.

[0097] In one embodiment, after displaying the user's learning status data for the learning object library, the method also includes: receiving a second input regarding the length of time; comparing the length of time indicated by the second input with the length of time represented by the second prediction data, and adjusting the learning parameters of the functional modules in the application that perform functions based on the learning object library according to the comparison results.

[0098] The display interface of the application displays an interactive control corresponding to the second input. When the user triggers the interactive control, an input window for the time length is displayed. In response to a time length import instruction performed on the window, the imported time length is received. The time length can be displayed in the input window in any of the following ways, for example, a drop-down menu, an input box, or a pop-up window.

[0099] The learning parameters include at least one of the duration of a single learning session, the number of learning objects, and the object matching type. The object matching type reflects the cognitive range of the learning object. If the left and right boundary values ​​of the cognitive range of the learning object are both high, it means that the learning object has a high correct answer rate. If the left and right boundary values ​​of the cognitive range of the learning object are both low, it means that the learning object has a low correct answer rate.

[0100] When the time length indicated by the second input is shorter than the time length represented by the second prediction data, the duration of a single learning session can be increased, and / or the number of questions for a single learning session can be increased, and / or the object matching type can be adjusted to obtain learning objects with a higher rate of correct answers, so that the user's ability value can reach the full ability value faster, thereby improving the user experience.

[0101] When the time length indicated by the second input is longer than the time length represented by the second predicted data, the duration of a single learning session can be reduced, and / or the number of questions for a single learning session can be reduced, and / or the object matching type can be adjusted to obtain learning objects with a lower answer accuracy rate, so that users can slow down the pace of learning according to their own needs and improve user experience.

[0102] In one embodiment, before receiving the first input, the method also includes: receiving a third input of a target function module requested by the user for application; wherein the application is provided with a first function module and a second function module, both of which are modules for executing a question-setting function based on a learning object library for users to answer, and the target function module is the first function module or the second function module; the first function module and the second function module correspond to different object ratio types when executing functions, and the object ratio type reflects the user's range of cognition of the learning object output by the function module; in response to the third input, the target function module is executed based on the user's learning data set for the learning object library and the object ratio type corresponding to the target function module; wherein the learning data set includes data entries for each learning object in the learning object library, and the data entries include: each learning data of the user for the corresponding learning object, and the cognition determined based on the learning data of the corresponding learning object, and the learning data includes a learning time point and an answer result indicating whether the answer is correct.

[0103] The display interface of the application displays an interactive control corresponding to the third input. When the user triggers the interactive control, the display interface corresponding to the target functional module is entered.

[0104] For example, the target functional module is the first functional module, and the first functional module is used for review by the user. Figure 5 As shown, the application interface displays an interactive control "Review". When the user clicks on the interactive control "Review", the first functional module is executed. Executing the first functional module specifically means entering the display interface corresponding to the first functional module, which displays the learning objects output by the first functional module, and the user can review based on the learning objects output by the first functional module.

[0105] For example, the target functional module is the second functional module, and the second functional module is used for users to perform PK. Figure 5 As shown, the application interface displays an interactive control "PK". When the user clicks on the interactive control "PK", the second functional module is executed. Executing the second functional module specifically means entering the display interface corresponding to the second functional module, which displays the learning objects output by the second functional module, and the user can perform PK based on the learning objects output by the second functional module.

[0106] In one embodiment, the first functional module corresponds to a first ratio type, and the second functional module corresponds to a second ratio type; the recognition range reflected by the first ratio type is a first set range; the recognition range reflected by the second ratio type is a second set range; wherein, the upper limit value of the second set range is greater than the upper limit value of the first set range, and the lower limit value of the second set range is greater than the lower limit value of the first set range.

[0107] according to Figure 6 As shown, the cognitive range of the learning object output by the first functional module is the first set range, and [P 1 , P 2 The range of the learning object's cognition output by the second functional module is the second setting range, represented by [P 3 , P 4 ]express. Figure 6 Three situations of the first setting range and the second setting range are shown. In the first situation, there is no overlap between the first setting range and the second setting range, that is, P 2 <P 3 In the second case, the first set range and the second set range have overlapping boundary points, that is, P 2 , P 3 The third case is that the first setting range and the second setting range partially overlap, that is, P 3 <P 2 .

[0108] When the first function module is used for review by the user and the second function module is used for PK by the user, when the second function module is executed, the learning object output has a high degree of recognition, that is, the learning object with a high accuracy rate of the user's answer, so that when PK with the opponent, the accuracy rate of the user's answer can be guaranteed, and the user's self-confidence and satisfaction during PK can be enhanced. When the first function module is executed, the learning object output has a low degree of recognition, that is, the learning object with a low accuracy rate of the user's answer, which helps the user to fill in the gaps in learning and help the user fully master the learning objects in the learning object library.

[0109] In one embodiment, the target function module is the second function module. Executing the target function module specifically includes: based on the user's invitation operation, generating invitation information for inviting others to participate in answering the learning object for sharing; obtaining the object matching type corresponding to the user's learning data set and the target function module, and outputting the learning object for the user; associating the learning object output for the user to the invitation page linked to the invitation information for output.

[0110] When the target functional module is the second functional module, the recognition range reflected by the object ratio type corresponding to the second functional module is the second set range. A learning object satisfying the second set range is obtained from the learning data set as the learning object output for the user.

[0111] The user triggers the invitation operation through the "Invite" button or link on the display interface corresponding to the second functional module, generates invitation information, and sends the invitation information to the invitee in any of the following sharing methods, such as social media, email, and text message. The invitee can trigger the invitation information and enter the invitation page linked to the invitation information. The invitation interface can output the learning object output by the second functional module, and the invitee can compete with the inviter based on the learning object output by the second functional module.

[0112] It should be noted that, in response to each third input, based on the user's learning data set for the learning object library and the object ratio type corresponding to the target function module, a group of learning objects will be determined, and the group of learning objects will be used as the learning objects output by the target function module. When executing the target function module, the group of learning objects can be unchanged or dynamically adjusted while being output.

[0113] When executing the target function module and the group of learning objects is dynamic, in one embodiment, executing the target function module specifically includes: determining the cognitive range of the learning object to be output by the target function module according to the object ratio type and learning data set corresponding to the target function module, and the user's answer data for the learning object output by the target function module; wherein the answer data includes at least one of the answer accuracy rate and the answer result sequence. In this way, the learning object output by the target function module can be adjusted in real time, and the user's confidence in answering can be enhanced.

[0114] The object ratio type corresponding to the target function module reflects the cognitive range of the learning object output by the target function module. When the answer data of the learning object output by the target function module appears at least one of the following situations, for example, the correct rate of the answer of the output learning object is less than the first threshold, and the number of consecutive wrong answers of the output learning object exceeds the second threshold, the cognitive range of the learning object to be output by the target function module is adjusted. The adjustment mentioned here refers to increasing the upper and lower limits of the cognitive range of the learning object. In addition, the number of consecutive wrong answers of the learning object is determined according to the sequence of answer results.

[0115] For example, according to the object ratio type corresponding to the target function module, the cognitive range of the learning object output by the target function module is determined to be [P m , P n ]. When executing the target function module, the cognition of the learning object currently output is within the cognition range [P m , P n ] When the correct rate of the answer of the output learning object is less than the first threshold, and / or the number of consecutive wrong answers of the output learning object exceeds the second threshold, the recognition range of the learning object to be output by the target functional module is determined to be [P' m , P'n ], where P' m >P m , P' n >P n This improves the recognition of the learning object to be output by the target functional module, which can ensure the accuracy of the user's subsequent answers and enhance the user's self-confidence.

[0116] In one embodiment, before receiving the third input of the target function module requested by the user to apply, the method also includes: determining that the user's learning data set meets the set conditions for providing the target function module to the user; providing a label control indicating the target function module on the application interface; wherein the third input is the input that triggers the label control.

[0117] The learning data set includes data entries for each learning object in the learning object library. The data entries include: each learning data of the user for the corresponding learning object, and the recognition level determined based on the learning data of the corresponding learning object. The learning data includes the learning time point and the answer result indicating whether the answer is correct.

[0118] The setting condition may be at least one of the following: the learning data of each learning object in the user's learning data set exceeds a first threshold, the recognition degree of each learning object in the user's learning data set exceeds a second threshold, and the sum of the recognition degrees of each learning object in the user's learning data set exceeds a third threshold. When the above setting conditions are met, a label control indicating the target function module is provided on the application interface. The label control may be Figure 5 The interactive controls "Review" and Figure 5 At least one of the interactive controls "PK" is shown.

[0119] In one embodiment, the application is further provided with a third functional module, which provides a plurality of learning materials for establishing object connections, and the third functional module outputs a plurality of associated learning objects based on the learning materials.

[0120] The form of learning materials can be at least one of the following: text content, video, and audio.

[0121] In this embodiment, a learning object may be a knowledge point generated based on a learning material and available for users to answer. It should be noted that a learning object may also be a knowledge point generated independently of any learning material and available for users to answer.

[0122] In this embodiment, before receiving the third input of the target function module requested by the user, the method also includes: receiving the fourth input of the user answering the learning object output by the learning material in the third function module; and updating the data entry of the corresponding learning object according to the fourth input.

[0123] The data entry of the learning object includes: each learning data of the user for the corresponding learning object, and the recognition degree determined based on the learning data of the corresponding learning object. The learning data includes the learning time point and the answer result indicating whether the answer is correct.

[0124] For example, the application interface currently displays multiple learning materials. When the user triggers a learning material by clicking, the application interface begins to display the learning material. The learning object can be output after the learning material is displayed, or it can be output along with the display of the learning material. When outputting the learning object, the application interface displays an interactive control corresponding to the fourth input. The interactive control corresponding to the fourth input can be displayed in any of the following ways, for example, a drop-down menu, an input box, a pop-up window, a list view, and a picture view. The user can answer the learning object through the interactive control corresponding to the fourth input to generate a learning data of the corresponding learning object. Update the data entry of the corresponding learning object according to the learning data of the corresponding learning object.

[0125] In one embodiment, after executing the target function module, the method further includes: updating the learning data set based on the learning data generated by the user in the target function module, so as to increase the learning data in the learning data set so that the data determined based on the learning data in the learning data set is more accurate.

[0126] The present disclosure also provides an electronic device for implementing any of the above method embodiments. Figure 7 The structure block diagram of the electronic device 700 according to some embodiments is shown. The electronic device 700 may be a PC, a workstation, a notebook computer, a server, etc., which is not limited here.

[0127] like Figure 7 As shown, the electronic device 700 includes a processor 710 and a memory 720 for storing instructions executable by the processor 710. The processor 710 is configured to implement the quantification and display method of learning content recognition according to any embodiment of the present disclosure when executing the instructions stored in the memory 720.

[0128] The processor 710 is used to execute computer instructions, which can be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 720 includes, for example, ROM (read-only memory), RAM (random access memory), a non-volatile memory such as a hard disk, etc., which are not limited here.

[0129] Figure 8 FIG. 2 shows a structural block diagram of an electronic device according to some other embodiments. Figure 8As shown, the electronic device 800 may include a display device 830, an interface device 840, a communication device 850, an input device 860, etc. in addition to a processor 810 and a memory 820.

[0130] The interface device 840 includes, for example, a USB interface, a bus interface, a network interface, etc. The communication device 850 can, for example, perform wired or wireless communication. The communication device 850 may include at least one short-range communication module, such as any module for short-range wireless communication based on short-range wireless communication protocols such as Hilink protocol, WiFi (IEEE 802.11 protocol), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, LiFi, etc. The communication device 850 may also include a remote communication module, such as any module for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The display device 830 can display learning status data. The input device 860 may include a touch screen, a keyboard, a mouse, a microphone, a camera, etc., which are not limited here.

[0131] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and equipment embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0132] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0133] The embodiments of the present specification may be devices, methods and / or computer program products. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the embodiments of the present specification.

[0134] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0135] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0136] The computer program instructions for executing the operation of the embodiments of this specification may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, executed as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA) may be personalized by utilizing the state information of a computer-readable program instruction, and the electronic circuit may execute a computer-readable program instruction, thereby realizing various aspects of the embodiments of this specification.

[0137] Various aspects of the embodiments of the present specification are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (equipment) and computer program products according to the embodiments of the present specification. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0138] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0139] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0140] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of this specification. In this regard, each box in the flowchart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0141] The embodiments of the present specification have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for quantifying and displaying the degree of recognition of learning content, characterized in that: include: Receive a first input; wherein the first input is an input for querying a user's learning status of a learning object library in an application, and the learning object library includes a plurality of learning objects; In response to the first input, displaying the learning status data of the user for the learning object library; The learning status data displayed includes first status data, the first status data includes a first time node corresponding to the first input, and a first ability value of the user for the learning object library at the first time node; the ability value of the user for the learning object library at a time node represents: the overall cognitive ability of the user for the learning object library formed at the time node based on historical learning accumulation; The first ability value is determined based on the user's first awareness of each learning object in the learning object library at the first time node; the user's awareness of a learning object at a time node is determined based on the user's learning data of the learning object as of the time node.

2. The method according to claim 1, characterized in that: The learning status data displayed further includes at least one second status data, wherein the second status data includes a second time node and a second capability value of the user for the learning object library at the second time node; The second time node is before the first time node, and different second state data correspond to different second time nodes; the second ability value is determined based on the user's second cognition of each learning object in the learning object library at the second time node.

3. The method according to claim 2, characterized in that The learning state data displayed further includes state prediction data, and the state prediction data includes at least one of first prediction data, second prediction data, and third prediction data; The first prediction data is compared with the first state data, the first prediction data includes the first time node and a first capability prediction value mapped to the first time node, and the first capability prediction value is determined based on the second state data; The third prediction data includes a third time node and a third capability prediction value mapped to the third time node, and different third prediction data correspond to different third time nodes; the third time node is after the first time node, and the third capability prediction value is determined based on the first state data and the second state data; The second prediction data indicates the length of time required for the user to reach the full capacity value corresponding to the learning object library from the first capacity value.

4. The method according to claim 3, characterized in that After displaying the learning status data of the user for the learning object library, the method further includes: receiving a second input regarding the length of time; The time length indicated by the second input is compared with the time length represented by the second prediction data, and learning parameters of a function module in the application that performs functions based on the learning object library are adjusted according to the comparison result.

5. The method according to claim 1, characterized in that Before receiving the first input, the method further includes: receiving a third input from a user requesting a target functional module of the application; The application is provided with a first function module and a second function module, both of which are modules for performing a question-setting function based on the learning object library for users to answer, and the target function module is the first function module or the second function module; the first function module and the second function module correspond to different object ratio types when performing functions, and the object ratio type reflects the user's cognitive range of the learning object output by the function module; In response to the third input, the target function module is executed based on the user's learning data set for the learning object library and the object matching type corresponding to the target function module; wherein the learning data set includes data entries for each learning object in the learning object library, and the data entries include: the user's learning data for each time of the corresponding learning object, and the degree of recognition determined based on the learning data of the corresponding learning object, and the learning data includes a learning time point and an answer result indicating whether the answer is correct.

6. The method according to claim 5, characterized in that The first functional module corresponds to a first ratio type, and the second functional module corresponds to a second ratio type; the recognition range reflected by the first ratio type is a first setting range; the recognition range reflected by the second ratio type is a second setting range; wherein the upper limit value of the second setting range is greater than the upper limit value of the first setting range, and the lower limit value of the second setting range is greater than the lower limit value of the first setting range.

7. The method according to claim 6, characterized in that The target functional module is the second functional module; and executing the target functional module includes: Based on the user's invitation operation, generate invitation information for inviting others to participate in answering the learning object for sharing; Obtaining a learning object output for the user based on the learning data set of the user and the object matching type corresponding to the target function module; The learning object output for the user is associated with the invitation page linked to by the invitation information for output.

8. The method according to claim 5, characterized in that Before receiving the third input of the target function module requested to be applied by the user, the method further includes: Determining that the learning data set of the user meets the set conditions for providing the target function module for the user; A label control indicating the target functional module is provided on the application interface; wherein the third input is an input that triggers the label control.

9. The method according to claim 5, characterized in that The application is further provided with a third functional module, the third functional module providing a plurality of learning materials for establishing object connections, the third functional module outputting a plurality of associated learning objects based on the learning materials; Before receiving the third input of the target function module requested to be applied by the user, the method further includes: receiving a fourth input of the user answering a learning object output by the learning material in the third functional module; The data entry of the corresponding learning object is updated according to the fourth input.

10. The method according to claim 5, characterized in that The executing the target function module comprises: Determine the cognitive range of the learning object to be output by the target functional module according to the object matching type corresponding to the target functional module and the learning data set, as well as the answer data of the user to the learning object output by the target functional module; The answer data includes at least one of the answer accuracy rate and the answer result sequence.

11. The method according to claim 5, characterized in that The user's awareness of the learning object is updated based on an event in a set event set, wherein the set event set includes at least one of the following events: First event: adding new learning data to the data entry of the learning object; Second event: the time interval between the current time and the last update reaches the first set time; The third event: the time interval between the current time and the last time learning data was added reaches the second set time length; Fourth event: the user starts the application.

12. The method according to claim 5, characterized in that After executing the target function module, the method further includes: The learning data set is updated based on the learning data generated by the user in the target function module.

13. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the method according to any one of claims 1 to 12 under the control of the computer program.

14. A computer program product, comprising a computer program or instructions, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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