Course learning data processing method and device and storage medium

Abstract

The embodiment of the invention provides a course learning data processing method and device and a storage medium, and aims to timely and accurately calculate a course section learning progress of a target course section in a target course and respond to a configuration operation of a course provider on the target course section in a learning progress statistical information configuration interface. And determining a first learning progress statistical object related to the learning duration and a second learning progress statistical object related to the learning achievement corresponding to the target course section under the plurality of course types. And according to the first learning progress statistical object and the second learning progress statistical object, acquiring learning behavior data generated when the target learning party learns the target course section in different course periods of the target course. And if new learning behavior data is collected, according to the collected learning behavior data, combining contents of a target course section repeatedly learned by a target learning party under various course types in different course periods, determining a first statistical value and a second statistical value corresponding to the first learning progress statistical object and the second learning progress statistical object, and calculating the course section learning progress of the target course section.

Description

Technical Field

[0001] This application relates to the field of Internet technology, and in particular to a method, device and storage medium for processing course learning data. Background Technology

[0002] With the development of internet technology, various learning platforms provide users with access to a wide range of courses. Users are no longer limited to offline learning; they can now study online directly on these platforms. Timely and accurate feedback on learning progress is provided to users as they learn through these platforms, allowing them to monitor their progress and enhancing their learning experience. Summary of the Invention

[0003] This application provides a method, device, and storage medium for processing course learning data, so as to provide timely and accurate feedback on learning progress to users.

[0004] In a first aspect, embodiments of this application provide a method for processing course learning data, the method comprising:

[0005] In response to the course provider's configuration operation on the learning progress statistics configuration interface for the target lessons included in the target course period, the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcome are determined for the target lessons under various course types; the target course period is any course period in the target course that contains the target lessons.

[0006] Based on the first learning progress statistics object and the second learning progress statistics object, collect learning behavior data generated by the target learner when learning the target lesson in different periods of the target course;

[0007] In response to the collection of new learning behavior data, based on the collected learning behavior data, the content of the target lesson in different course periods that is repeatedly learned by the target learner under the multiple course types is merged to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object;

[0008] Based on the first statistical value and the second statistical value, the learning progress of the corresponding lesson is determined.

[0009] Secondly, embodiments of this application provide a course learning data processing apparatus, the apparatus comprising:

[0010] The determination module is used to respond to the configuration operation of the course provider on the learning progress statistics information configuration interface for the target lessons included in the target course period of the target course, and to determine the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcome for the target lessons under various course types; the target course period is any course period of the target course that contains the target lessons;

[0011] The data collection module is used to collect learning behavior data generated by the target learner when learning the target lesson in different periods of the target course, based on the first learning progress statistics object and the second learning progress statistics object.

[0012] The processing module is configured to, in response to the collection of new learning behavior data, merge the content of the target lesson in different course periods that is repeatedly learned by the target learner under the multiple course types according to the collected learning behavior data, so as to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object; and determine the lesson learning progress corresponding to the target lesson according to the first statistical value and the second statistical value.

[0013] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a communication interface; wherein, the memory stores a computer program, and when the computer program is executed by the processor, the processor can at least implement the course learning data processing method as described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor of an electronic device, enables the processor to at least implement the course learning data processing method as described in the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product, including: a computer program or instructions, which, when executed by a processor of an electronic device, enable the processor to at least implement the course learning data processing method as described in the first aspect.

[0016] In the solution provided in this application embodiment, a target course includes multiple course periods, a course period includes multiple lesson segments, and a lesson segment corresponds to multiple course types, including but not limited to online video courses and offline practical courses. Different course periods of a target course may contain some identical or different lesson segments. In practical applications, for a specific lesson segment in a target course, a learner may study it in multiple different course periods containing that lesson segment, and the course types of the lessons they study may be the same or different. To address this complex course learning application scenario, this application embodiment provides a course learning data processing method to comprehensively process the learning data generated by the learner studying the target lesson segment in different course periods, in order to more accurately determine the learner's lesson segment learning progress.

[0017] In the specific implementation process, firstly, in response to the course provider's configuration operation for the target lessons within the target course period in the learning progress statistics configuration interface, the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcomes are determined for each target lesson under various course types. The target course period is any course period containing the target lesson. Then, based on the first and second learning progress statistics objects, learning behavior data generated by the target learner during different course periods of the target course is collected. Next, in response to the collection of new learning behavior data, the content of the target lesson repeatedly studied by the target learner in different course periods under various course types is merged based on the collected learning behavior data to determine the first statistical value corresponding to the first learning progress statistics object and the second statistical value corresponding to the second learning progress statistics object. Finally, based on the first and second statistical values, the lesson learning progress corresponding to the target lesson is determined.

[0018] This solution allows for the customization of a first learning progress statistics object related to learning duration and a second learning progress statistics object related to learning outcomes (i.e., unrelated to learning duration) within the learning progress statistics configuration interface. This ensures that when collecting learning behavior data generated by the target learner during the learning of the target lesson, both time-related and time-independent learning behavior data are collected. Therefore, when calculating the lesson learning progress based on the collected learning behavior data and merging the content repeatedly studied by the target learner in different course periods under various course types, it no longer relies solely on the single factor of learning duration. Instead, it considers multiple time-related and time-independent factors. Consequently, it can determine the lesson learning progress that truly reflects the target learner's learning situation in the target lesson and effectively prevent potential "brushing" (attempting to skip lessons) behavior during the learning of the target lesson. Furthermore, based on the statistical object corresponding to the configured target lesson for calculating learning progress, and in the process of dynamically collecting learning behavior data corresponding to the target lesson as the learner's learning behavior occurs, the calculation of the lesson's learning progress is dynamically triggered when new learning behavior data is collected, ensuring the timeliness of the lesson's learning progress. In summary, this application provides a course learning data processing method that can calculate lesson learning progress in a timely and accurate manner. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic diagram of the hardware execution environment for a course learning data processing method provided in an embodiment of this application;

[0021] Figure 2 This is an application diagram of a cloud computing environment provided in an embodiment of this application;

[0022] Figure 3 A flowchart illustrating a course learning data processing method provided in this application embodiment;

[0023] Figure 4 A flowchart illustrating a learning behavior data collection method provided in this application embodiment;

[0024] Figure 5 A flowchart illustrating another learning behavior data acquisition method provided in this application embodiment;

[0025] Figure 6 A flowchart illustrating the process of determining a first statistical value and a second statistical value is provided for embodiments of this application.

[0026] Figure 7 A schematic diagram of a timeline provided for an embodiment of this application;

[0027] Figure 8 A flowchart illustrating another process for determining the first and second statistical values ​​provided in this application embodiment;

[0028] Figure 9 A flowchart illustrating another course learning data processing method provided in this application embodiment;

[0029] Figure 10 A flowchart illustrating yet another course learning data processing method provided in this application embodiment;

[0030] Figure 11 A schematic diagram of the structure of a course learning data processing device provided in an embodiment of this application;

[0031] Figure 12 To and Figure 11 The illustrated embodiment provides a schematic diagram of the electronic device corresponding to the course learning data processing device. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] It should be noted that, in the cases involving user information in the embodiments of this application, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse. In addition, the various models involved in this application (including but not limited to language models or large models) comply with relevant laws and standards.

[0034] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0035] With the development of internet technology, various learning platforms provide users with access to a wide range of courses. As a result, users' learning methods are no longer limited to offline learning; they can directly engage in online learning on these platforms, such as watching instructional videos. These learning platforms can take various forms, including but not limited to web applications, client applications, and mobile applications.

[0036] In practical applications, learning platforms are not limited to online theoretical courses; they can also support offline practical courses. For example, when a user studies a course offline, they can upload learning data such as study time and results to the learning platform. Thus, similar to calculating online learning progress, offline learning progress can also be calculated.

[0037] In this embodiment, the learning behavior data obtained by the learning platform is not limited to whether it is generated from online learning or offline learning. Whether it is generated in real time during online learning or uploaded to the learning platform after offline learning, it is considered as learning behavior data that the learning platform can obtain.

[0038] Timely and accurate feedback on learning progress provided to users during their course learning through the platform helps them stay informed about their progress and improves their learning experience. It's worth noting that learning progress is an important way to measure a user's knowledge acquisition and learning efficiency.

[0039] In related technologies, when calculating learning progress, the learning progress of a particular lesson is often determined directly by the ratio of the time already spent learning that lesson to the total time of the lesson. In scenarios with relatively simple course designs, this calculation method can reflect the user's learning progress to a certain extent. However, this method of calculating course learning progress has two problems. First, the indicator used to calculate learning progress is singular, involving only learning time. This means it cannot fully reflect the user's learning progress and cannot prevent users from slacking off on lessons. For example, if a user completes the entire duration of a lesson but scores zero on a quiz, in a skills-based course where the goal is for the user to master the knowledge in that lesson, it is obviously inaccurate to determine that the user's learning progress has reached 100% based solely on the learning time. Similarly, if a user repeatedly studies parts of a lesson, although their cumulative learning time reaches the duration of a lesson, they have not completed the entire lesson. In this case, determining that the user's learning progress has reached 100% based solely on the learning time is also inaccurate. Second, in scenarios with more complex course designs, such as the scenario involved in the embodiments of this application, simply adding up the learning time of each lesson cannot determine the accurate learning progress of each lesson.

[0040] The following is a brief description of the course design involved in the embodiments of this application.

[0041] In this embodiment, a course comprises multiple semesters, a semester comprises multiple lessons, and a lesson can correspond to various course types. A course refers to a systematic collection of teaching content designed to achieve a specific learning objective, typically revolving around a theme or subject, such as an introductory Python programming course or an advanced mathematics course. A semester is a specific implementation phase or batch of a course, representing a single course offering within a specific time period, such as an introductory Python programming course – semester 1 (starting in March 2025), an introductory Python programming course – semester 2 (starting in June 2025), etc. A lesson represents a specific teaching activity, such as a single lesson, explanation of a knowledge point, or a practice task, such as explanation of variables and data types, explanation of conditional and loop statements, etc. The course type of a lesson corresponds to the learning method of that lesson, including but not limited to: online video courses and offline practical courses. Online video courses can further include: online live video courses and online pre-recorded video courses.

[0042] Different course periods can contain the same or different lesson segments. For example, a course might have two periods: Period 1 and Period 2. Period 1 contains lesson segments A, B, and C; Period 2 contains lesson segments A, B, C, and D. To ensure users can access all the latest content of the course, users can study lessons from either Period 1 or Period 2 while learning through the platform. In this case, a user might study part of lesson segment A in Period 1 and then choose to study the remaining content from lesson segment A in Period 2, thus achieving complete learning of lesson segment A.

[0043] In this situation, if, as mentioned earlier, the learning progress of a lesson is directly determined by the ratio of the time spent learning a particular lesson to the total time of the lesson, the calculated learning progress will be incorrect. This is because, in terms of learning time, the user has only actually learned a portion of the content for both lesson A in lesson 1 and lesson A in lesson 2; however, in reality, the user has already completed the entire lesson A.

[0044] To address this, this application provides a course learning data processing method to calculate learning progress in scenarios where a course contains multiple semesters and a semester contains multiple lessons, ensuring the accuracy and timeliness of user learning progress calculation and greatly improving the user's learning experience and effectiveness.

[0045] In this embodiment of the application, the party that provides course content for users to learn on the learning platform is referred to as the course provider; and a user who learns the course content through the learning platform is referred to as the target learner.

[0046] The following description, in conjunction with the accompanying drawings, introduces and explains the course learning data processing scheme provided in the embodiments of this application.

[0047] Figure 1 A schematic diagram of the hardware execution environment for a course learning data processing method provided in this application embodiment is shown below. Figure 1 As shown, the hardware execution environment for learning data processing methods in this course can consist of a client device 101 and a server device 102, with the client device 101 and server device 102 communicating with each other. The server device 102 can be a cloud server from a cloud service provider. The client device 101 can be a laptop, tablet, PC, robot, etc.

[0048] The client device 101 runs a learning platform. Target learners can register or log in to their learning platform account on the client device 101, using a unique identifier to subscribe to or purchase various courses on the platform. Furthermore, the learning behavior data generated during the target learner's study of a course through the learning platform on the client device 101 is also linked to this unique identifier, facilitating accurate calculation of the target learner's learning progress later.

[0049] Unless otherwise specified in this embodiment, the client device 101 only has the learning platform account of the target learner logged in. During the learning progress calculation process, the acquired learning behavior data is the learning behavior data generated by the target learner through learning operations on the user terminal, and the calculated learning progress is the learning progress corresponding to the target learner.

[0050] In an optional embodiment, the execution process of the above-described course learning data processing method may be as follows: Client device 101 sends a course learning data processing request to server device 102 by calling the course learning data processing service provided by server device 102. This request includes the target lessons contained in the target course for which course learning progress statistics are currently to be performed. Server device 102, firstly, responds to the configuration operation of the course provider on the learning progress statistics information configuration interface for the target lessons contained in the target course's target period, and determines a first learning progress statistics object related to learning duration and a second learning progress statistics object related to learning outcomes for the target lessons under various course types. The target period is any period in the target course that contains the target lessons. Then, based on the first and second learning progress statistics objects, learning behavior data generated by the target learner learning the target lessons in different periods of the target course is collected. Subsequently, in response to the collection of new learning behavior data, based on the collected learning behavior data, the content of the target lesson repeatedly studied by the target learner under various course types in different course periods is merged to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object. Finally, based on the first and second statistical values, the lesson learning progress corresponding to the target lesson is determined, and the lesson learning progress corresponding to the target lesson is output to the client device 101. Thus, the client device 101 can display the lesson learning progress corresponding to the target lesson to the target learner through a preset display strategy.

[0051] Of course, the above-mentioned data processing methods can also be executed directly on the client device 101. The execution process is similar to that of the server device 102, and will not be described in detail here.

[0052] In practical applications, the aforementioned server-side device 102 can be a cloud server maintained by a cloud service provider—referred to as a computing node. In cases such as... Figure 2 The cloud computing environment shown may include several distributed deployments. Figure 2 The diagram illustrates compute nodes (201-1, 201-2, ...), each possessing processing resources such as computing and storage. In a cloud computing environment, multiple compute nodes can be organized to provide a specific service; conversely, a single compute node can provide one or more services. Figure 2The diagram illustrates services A, B, C, and D. In a cloud computing environment, these services can be provided via external service interfaces, which client device 101 calls to use the corresponding services. Service interfaces can take the form of Software Development Kits (SDKs) or Application Programming Interfaces (APIs).

[0053] The services described above are deployed using various virtualization technologies supported by cloud computing environments, such as virtual machine-based and container-based virtualization technologies. Taking container-based virtualization technology as an example, several containers corresponding to a service can be assembled into a container group (pod). For example... Figure 2 The illustrated service B can be configured with one or more pods, and each pod can include a proxy and one or more containers. The one or more containers in the pod are used to handle requests related to one or more corresponding functions of the service, and the proxy in the pod is used to control network functions related to the service, such as routing and load balancing.

[0054] During operation, executing a request from client device 101 may require invoking one or more services in the cloud computing environment, and executing one or more functions of one service may require invoking one or more functions of another service. For example... Figure 2 As shown, after receiving a request from client device 101, service A can call service B. Service B can request service D to perform one or more functions. In this embodiment of the application, cloud services for course learning data can be deployed in one or more computing nodes.

[0055] The execution process of the course learning data processing method provided in this application embodiment is described in detail below with reference to the accompanying drawings. This course learning data processing method can be executed by a computing node or client device 101 in the aforementioned cloud computing environment.

[0056] Figure 3 A flowchart of a course learning data processing method provided in this application embodiment is shown below. Figure 3 As shown, the steps 301 to 304 may be included as follows:

[0057] 301. In response to the course provider's configuration operation on the learning progress statistics configuration interface for the target course period containing the target lesson, determine the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcome for the target lesson under various course types.

[0058] In this embodiment of the application, the course for which the learning progress is to be calculated is called the target course, and the lesson in the target course for which the learning progress is to be calculated is called the target lesson. The target lesson can be a lesson that is common to multiple lessons in the target course, or it can be a lesson that is unique to a specific lesson in the target course.

[0059] To facilitate the collection of learning behavior data for calculating the learning progress of the target lesson, this application provides a learning progress statistics configuration interface.

[0060] In practice, course providers can customize the target lessons included in the target course period of the target course in the learning progress statistics configuration interface to determine the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcomes for the target lessons under various course types.

[0061] Optionally, the target lesson period is any lesson period in the target course that contains the target lesson.

[0062] Optionally, the course types corresponding to the target lesson include, but are not limited to: online video courses and offline practical courses. Furthermore, online video courses may include: online live video courses and online pre-recorded video courses.

[0063] Optionally, the first and second learning progress statistical objects for different course types can be the same or different. The first learning progress statistical object can be, for example, the learning start time and learning end time, while the second learning progress statistical object includes at least one of the following: learning test results (e.g., exam scores, assessment scores), and learning evaluation results (e.g., teacher evaluations).

[0064] 302. Based on the first and second learning progress statistics subjects, collect learning behavior data generated by the target learners when learning target lessons in different periods of the target course.

[0065] The first and second learning progress statistics objects, which are configured by customization, can also be understood as learning behavior objects to be collected or recorded during the learning process of the target learner learning the target lesson.

[0066] In an optional embodiment, after determining the first learning progress statistics object and the second learning progress statistics object, code or software development kit (SDK) (commonly known as data tracking) can be added to the learning platform based on these two learning progress statistics objects. This is done by adding code or SDK to capture the learning behaviors generated by the target learner during the learning of the target lesson, which correspond to the first and second learning progress statistics objects respectively, and to record the learning behavior data corresponding to these learning behaviors.

[0067] As mentioned earlier, as long as a target course contains a target lesson within a specific term, the target learner can study the target lesson within that term. Therefore, the learning behavior data collected for the target learner and the target lesson is: the learning behavior data generated by the target learner studying the target lesson within different terms of the target course.

[0068] 303. In response to the collection of new learning behavior data, based on the collected learning behavior data, merge the content of the target lesson in different course periods that is repeatedly learned by the target learner under multiple course types, so as to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object.

[0069] It is understandable that as learners engage in new learning behaviors during the learning process of the target lesson, they will continuously generate new learning behavior data. When new learning behavior data is collected, it means that the learner's progress in learning the target lesson may change due to the influence of these new learning behaviors.

[0070] Based on this, in this embodiment of the application, in response to the collection of new learning behavior data, the calculation of the learning progress of the target lesson is triggered, thereby ensuring the timeliness of the calculation of the learning progress of the target lesson.

[0071] As an optional method for determining the learning progress of a target lesson, the following steps are taken: First, acquire the learning behavior data collected up to the current moment. Then, based on the correspondence between the collected learning behavior data and the first and second learning progress statistical objects, and with the goal of merging the content repeatedly learned by the target learner in different course periods across various course types, statistical calculations are performed on the learning behavior data corresponding to the first learning progress statistical object to determine a first statistical value. Similarly, statistical calculations are performed on the learning behavior data corresponding to the second learning progress statistical object to determine a second statistical value. Finally, based on the first and second statistical values, the learning progress of the target lesson is determined.

[0072] 304. Based on the first and second statistical values, determine the learning progress corresponding to the target lesson.

[0073] The first statistical value is the target learner's statistical learning time for the target lesson. Different moments in the statistical learning time correspond to different contents of the target lesson, representing the cumulative or distributed learning time of the target learner for different contents of the target lesson. The second statistical value is the target learner's statistical learning outcome for the target lesson, representing the target learner's level of understanding, mastery, or application ability of the content of the target lesson.

[0074] Based on this, the first statistical value and the second statistical value can together constitute a comprehensive evaluation basis for the learning process and learning effect, so as to determine the learning progress of the corresponding lesson for the target lesson.

[0075] In one optional embodiment, the calculation weights corresponding to the first statistical value and the second statistical value can be predefined. Then, the first statistical value and the second statistical value are weighted and summed based on the calculation weights. Further, the learning progress of the target lesson is determined based on the weighted summation result.

[0076] For ease of understanding, the following is... Figure 3 The course learning data processing method shown is illustrated with an example.

[0077] Assume the target course includes Course 1 and Course 2. Course 1 includes Lesson A, Lesson B, and Lesson C; Course 2 includes Lesson A, Lesson B, Lesson C, and Lesson D. The target lesson is Lesson A, and the course type corresponding to Lesson A includes online video courses and offline practical courses. The course provider configures Lesson A in Course 1 through the learning progress statistics configuration interface to determine that, for both online video courses and offline practical courses, the first learning progress statistics related to learning duration are: learning start time and learning end time, and the second learning progress statistics related to learning outcomes are: learning test results and learning evaluation results.

[0078] Based on the above assumptions, when the target learner studies Lesson A in Course Period 1, if the video type of Lesson A currently being studied by the target learner is an online video course, then the learning behavior data generated by the target learner in studying Lesson A, which is an online video course, will be collected based on the first learning progress statistics object (i.e., learning start time and learning end time) and the second learning progress statistics object (i.e., learning test results and learning evaluation results) corresponding to Lesson A in the online video course. If the video type of Lesson A currently being studied by the target learner is an offline practical course, then the learning behavior data generated by the target learner in studying Lesson A, which is an offline practical course, will be collected based on the first learning progress statistics object (i.e., learning start time and learning end time) and the second learning progress statistics object (i.e., learning test results and learning evaluation results) corresponding to Lesson A in the offline practical course.

[0079] Similar to the above methods, when the target learner studies Lesson A in the second semester of course period 2, learning behavior data corresponding to online video courses and learning behavior data corresponding to offline practical courses can also be collected, which will not be elaborated here.

[0080] Suppose that new learning behavior data is collected at the current moment, and the learning behavior data collected up to the current moment for the target learner learning course A includes: learning behavior data 1 corresponding to lesson A of online video course in course period 1, learning behavior data 2 corresponding to lesson A of offline practical course in course period 1, learning behavior data 3 corresponding to lesson A of online video course in course period 2, and learning behavior data 4 corresponding to lesson A of offline practical course in course period 2.

[0081] Subsequently, on the one hand, from learning behavior data 1 to learning behavior data 4, the learning start time and learning end time corresponding to the first learning progress statistical object are determined, and the determined learning start time and learning end time are merged to determine the first statistical value corresponding to the first learning progress statistical object. On the other hand, from learning behavior data 1 to learning behavior data 4, the learning test results and learning evaluation results corresponding to the second learning progress statistical object are determined, and the determined learning test results and learning evaluation results are merged to determine the second statistical value corresponding to the second learning progress statistical object.

[0082] Finally, based on the first and second statistical values, the learning progress of the target learner in lesson A is determined.

[0083] In summary, this solution, through customizing the learning progress statistics configuration interface, allows for the collection of both time-related and time-independent learning behavior data when gathering learning behavior data from the target learner for the target lesson. This is achieved by configuring a first learning progress statistics object related to learning duration and a second learning progress statistics object related to learning outcomes (i.e., unrelated to learning duration) for the target lesson. Consequently, when calculating the lesson's learning progress by merging content repeatedly studied by the target learner across different course types in various semesters based on the collected learning behavior data, the solution no longer relies solely on the single factor of learning duration. Instead, it considers multiple time-related and time-independent factors. This approach not only determines the lesson's learning progress that truly reflects the target learner's learning of the target lesson but also effectively prevents potential instances of students simply "cheat" during the learning process. Furthermore, based on the statistical object corresponding to the configured target lesson for calculating learning progress, and in the process of dynamically collecting learning behavior data corresponding to the target lesson as the learner's learning behavior occurs, the calculation of the lesson's learning progress is dynamically triggered when new learning behavior data is collected, ensuring the timeliness of the lesson's learning progress. In summary, this application provides a course learning data processing method that can calculate lesson learning progress in a timely and accurate manner.

[0084] Figure 3 The provided method for processing course learning data explains the calculation process for the learning progress of any lesson within the target course, from the perspective of lesson segments. In fact, after calculating the learning progress of each lesson segment in the target course, it is further possible to calculate the learning progress of the target learner in the target course from the perspective of the entire course.

[0085] In one optional embodiment, the number of lessons included in different semesters of the target course can be summarized first to determine the multiple lessons corresponding to the target course. Figure 3 In the illustrated embodiment, the target lesson is any one of multiple lesson lessons. Then, for the target learner, through... Figure 3 The method illustrated in the embodiment calculates the learning progress for each of the multiple lesson segments. Then, based on the learning progress for each of the multiple lesson segments, the learning progress for the target course is determined. Finally, in response to the learning progress viewing operation triggered by the target learner for the target lesson segment, the learning progress for the target lesson segment and the learning progress for the target course are output.

[0086] Optionally, the progress of each lesson and the progress of the course can be expressed as a percentage or other similar form.

[0087] For example, following the example above, after summarizing the class periods contained in period 1 and period 2 of the target course, the identified class periods corresponding to the target course are: class period A, class period B, class period C, and class period D. In simpler terms, the summarized class periods corresponding to the target course are the union of the class periods contained in the various periods of the target course.

[0088] For the target learner, assuming lesson A corresponds to lesson learning progress A, lesson B to lesson learning progress B, lesson C to lesson learning progress C, and lesson D to lesson learning progress D, then the learning progress corresponding to the target course is determined based on lesson learning progress A, lesson learning progress B, lesson learning progress C, and lesson learning progress D. For example, the learning progress of lesson learning progress A, lesson learning progress B, lesson learning progress C, and lesson learning progress D can be summed and averaged to determine the learning progress corresponding to the target course.

[0089] Finally, in response to the learning progress viewing operation triggered by the target learner for any of the lesson segments A, B, C, and D (i.e., the target lesson segment), the learning progress of the target lesson segment and the learning progress of the target course are output, so that the target learner can view the learning progress of the target lesson segment in the target course and the learning progress of the target course through the client device.

[0090] This solution calculates the learning progress of each lesson and each course, and outputs these progresses to the target learner, thus making the learning progress transparent. This allows the target learner to keep track of their own progress and improves their learning experience.

[0091] The above provides an overall overview of the course learning data processing method provided in the embodiments of this application. Next, the collection of learning behavior data corresponding to the target learner, as well as the calculation of the first statistical value, the second statistical value, and the learning progress of each lesson, will be explained in turn.

[0092] As mentioned earlier, course providers can determine the learning behavior data to be collected from the target learner's learning of the target lesson by customizing the first and second learning progress statistics objects. In this embodiment, the process of collecting learning behavior data from the target learner's learning of the target lesson at different course periods is illustrated using the example of "the course types corresponding to the target lesson are: online video courses and offline practical courses; online video courses include: online live video courses and online recorded video courses; the first learning progress statistics object includes: learning start time and learning end time; and the second learning progress statistics object includes: learning test results and learning evaluation results." However, this is not limited to this.

[0093] Figure 4 A flowchart of a learning behavior data collection method provided in this application embodiment is shown below. Figure 4 As shown, it may include the following steps:

[0094] 401. For any online video course segment under any term of the target course, in response to the target learner's viewing operation of the video segment corresponding to the target segment, obtain the first time value corresponding to the learning start time and the second time value corresponding to the learning end time.

[0095] 402. In response to the target learner's online response to the online test content corresponding to the target lesson, obtain the first learning test result corresponding to the online test content, as well as the first learning evaluation result corresponding to the target learner uploaded by the instructor through the terminal device.

[0096] 403. The first time value, the second time value, the first learning test result, and the first learning evaluation result are used as the learning behavior data generated by the target learner in the target lesson of the online video course in any course period.

[0097] This application embodiment describes the process of collecting learning behavior data corresponding to the target lesson of an online video course in any lesson period (hereinafter referred to as lesson period i) of the target course.

[0098] Understandably, regardless of whether the target lesson is an online live video course or an online pre-recorded video course, the learner can learn the content of the target lesson by playing and watching the corresponding lesson video.

[0099] For each playback action by the target learner, there are corresponding start and end times. These start and end times are relative to the time of the lesson video segment. For example, a playback action might start at the 10th second of the lesson video and end at the 10th minute and 30th second.

[0100] Based on this, optionally, in response to the target learner's viewing operation of the lesson video corresponding to the target lesson, the playback start time can be used as the first time value corresponding to the learning start time, and the playback end time can be used as the second time value corresponding to the learning end time, so as to complete the collection of learning behavior data related to the first learning progress statistics object.

[0101] For target lessons that are part of online video courses, in order to verify the learning outcomes of the target learners on the content of the target lessons, that is, to verify whether the target learners have mastered the content of the target lessons, learning behavior data related to the second learning progress statistics object can also be collected.

[0102] Optionally, online test content corresponding to the target lesson can be set up, such as an online lesson exam. The target learner can answer the online test content online. Furthermore, the first learning test result for the target learner can be determined by the corresponding answer result (such as the online lesson exam score). The learning outcomes of the target learner can also be evaluated by the instructor, such as a high accuracy rate in answering questions, to determine the first learning evaluation result for the target learner. The instructor can upload the first learning evaluation result for the target learner through a terminal device.

[0103] Optionally, in response to the target learner's online response to the online test content corresponding to the target lesson, the system can obtain the first learning test result corresponding to the online test content, as well as the first learning evaluation result corresponding to the target learner uploaded by the instructor through the terminal device. Of course, the method of obtaining the first learning test result and the first learning evaluation result is not limited to this. For example, it can also obtain the first learning test result corresponding to the online test content and the first learning evaluation result in response to the instructor's upload operation of the first learning evaluation result corresponding to the target learner through the terminal device.

[0104] In the specific implementation process, optionally, the learning behaviors generated by the target learner during the learning of the target lesson can be captured by pre-added code or SDK in the learning platform, such as watching the lesson video corresponding to the target lesson, answering the online test content corresponding to the target lesson, etc., thereby triggering the collection of the first and second time values ​​related to the first learning progress statistics object, as well as the first learning test results and the first learning evaluation results related to the second learning progress statistics object.

[0105] Finally, the first time value, the second time value, the first learning test result, and the first learning evaluation result are used as the learning behavior data generated by the target learner in learning the target lesson of the online video course under lesson i.

[0106] In this embodiment of the application, for any target lesson in an online video course within a semester, learning behaviors such as watching or answering questions triggered by the target learning policy for that target lesson are identified, and the corresponding learning behavior data is collected, thereby achieving dynamic and real-time collection of learning behavior data corresponding to the target lesson in the online video course within that semester.

[0107] Figure 5 A flowchart of another learning behavior data collection method provided in the embodiments of this application is shown below. Figure 5 As shown, it may include the following steps:

[0108] 501. For any offline practical course segment under any term of the target course, in response to multiple check-in operations triggered by the target learner, determine the third time value corresponding to the learning start time and the fourth time value corresponding to the learning end time based on the operation time corresponding to the multiple check-in operations.

[0109] 502. In response to the target learner's upload of the learning test results corresponding to the offline operation content, obtain the second learning test results corresponding to the offline operation content, as well as the second learning evaluation results corresponding to the target learner uploaded by the instructor through the terminal device.

[0110] 503. The third time value, the fourth time value, the second learning test result, and the second learning evaluation result are used as the learning behavior data generated by the target learner in any course period when learning the target lesson of the offline practical course.

[0111] This application embodiment describes the process of collecting learning behavior data corresponding to the target lesson of the offline practical course in any course period (hereinafter referred to as course period j) of the target course.

[0112] Offline practical courses, as the name suggests, are hands-on courses that require learners to complete in a physical teaching setting. Offline practical courses typically include, but are not limited to, hands-on experiments, skills training, and equipment operation, emphasizing the improvement of learners' practical skills and problem-solving abilities through face-to-face instruction and on-site practice.

[0113] To facilitate the collection of learning behavior data for target lessons in offline practical courses, in this embodiment of the application, the target learner can upload the learning behavior data to the learning platform so that the server-side device can collect it in a timely manner.

[0114] Optionally, for target lessons in the course period j of the target course that are offline practical courses, the learning platform can provide check-in methods such as QR code scanning, so that the target learner can determine the start and end time of learning by checking in at the beginning and end of the learning session.

[0115] In practice, learners can check in multiple times during the learning of the target lesson. In response to these multiple check-in actions, the time of the learner's first check-in is used as the third time value corresponding to the start of learning, and the time of the learner's last check-in is used as the fourth time value corresponding to the end of learning. This completes the collection of learning behavior data related to the first learning progress statistics object.

[0116] For target lessons that are part of offline practical courses, in order to verify the learning outcomes of the target learners on the content of the target lessons, that is, to verify whether the target learners have mastered the content of the target lessons, learning behavior data related to the second learning progress statistics object can also be collected.

[0117] It's easy to understand that for offline practical courses, verifying the learner's learning outcomes for the target lesson usually requires offline operations. Therefore, optionally, after completing the offline operations, the learner can upload the corresponding learning test results (i.e., operation evaluation results) via a client device. The server device can then obtain the second learning test results corresponding to the learner's offline operations. Similar to online video courses, the instructor can also evaluate the learner's learning outcomes, such as the standardization of the learner's offline operations, to determine the corresponding second learning evaluation result. The instructor can upload the second learning evaluation result via a terminal device.

[0118] Optionally, in response to the target learner's upload of the learning test results corresponding to the offline operational content, the system can obtain the second learning test results corresponding to the offline operational content, as well as the second learning evaluation results corresponding to the target learner uploaded by the instructor through the terminal device. Of course, the method of obtaining the second learning test results and the second learning evaluation results is not limited to this. For example, in response to the instructor's upload of the second learning evaluation results corresponding to the target learner through the terminal device, the system can also obtain the second learning test results corresponding to the offline operational content, and the second learning evaluation results.

[0119] In the specific implementation process, optionally, the learning behaviors generated by the target learner during the learning of the target lesson can be captured by pre-added code or SDK in the learning platform. For example, multiple check-in operations triggered by the target learner, uploading of learning test results corresponding to offline operation content, etc., thereby triggering the collection of the third and fourth time values ​​related to the first learning progress statistics object, as well as the second learning test results and second learning evaluation results related to the second learning progress statistics object.

[0120] Finally, the third time value, the fourth time value, the second learning test result, and the second learning evaluation result are used as the learning behavior data generated by the target learner in the target lesson of the offline practical course under the course period j.

[0121] In this embodiment of the application, for any target lesson in an offline practical course within a semester, the learning behaviors triggered by the target learning policy for that target lesson, such as attendance checks and uploading of learning test results, are identified, and the corresponding learning behavior data is collected, thereby achieving dynamic and real-time collection of learning behavior data corresponding to the target lesson in the offline practical course within that semester.

[0122] The above provides an exemplary illustration of the dynamic collection process of learning behavior data corresponding to target lessons in online video courses and offline practical courses.

[0123] In one optional embodiment, for the target lesson, the collected learning behavior data, in addition to including information corresponding to the first and second learning progress statistics objects, may also include at least one of the following types of information: the target learner's identity information, such as the target learner's learning platform account; the lesson information of the target lesson currently being collected (e.g., lesson name), the lesson period information of the target lesson (e.g., lesson name), and the course information of the target course (e.g., course name); the course type of the target lesson, etc. Optionally, the types of information included in the learning behavior data can be customized.

[0124] After collecting learning behavior data generated by the target learner during the target lesson, the collected learning behavior data is usually stored in a database. In the above embodiment, the learning behavior data includes the target learner's identity information, the lesson information of the target lesson, the course information of the course to which the target lesson belongs, and the course information of the target course to which the target lesson belongs. This data can be used to filter out the learning behavior data corresponding to the target learner and the target lesson from the database. Therefore, the learning progress of the target learner in the target lesson can be calculated based on the learning behavior data corresponding to the target learner and the target lesson obtained from the database.

[0125] In practical applications, at the same time, multiple different learners may be learning the same or different course content through the learning platform, which will generate a large amount of learning behavior data. Correspondingly, the amount of learning behavior data collected from these multiple different learners will also be large.

[0126] In one optional embodiment, to ensure that the learning behavior data corresponding to the target learner can be stored correctly and completely, after collecting new learning behavior data generated by the target learner in learning the target lesson, the collected new learning behavior data can first be sent to a first message queue, wherein the first message queue includes, but is not limited to, a Kafka message queue. Then, the collected new learning behavior data is read from the first message queue and added to the database, thereby realizing the storage of the collected new learning behavior data.

[0127] In this embodiment, a first message queue is used to temporarily cache the newly collected learning behavior data. Then, the newly collected learning behavior data is read from the first message queue asynchronously and persistently stored in the database. This not only effectively alleviates the database write pressure under high concurrency scenarios, avoiding performance bottlenecks or data loss caused by a sudden influx of large amounts of data, but also forms a buffer mechanism between data collection and storage, ensuring that all learning behavior data is correctly cached and ultimately written completely to the database. This prevents information from being squeezed, overwritten, or omitted due to excessive server load, network fluctuations, or processing delays. Therefore, by introducing a first message queue as an intermediate cache, the integrity and reliability of learning behavior data storage are guaranteed, providing a solid data foundation for calculating the learning progress of subsequent target lessons.

[0128] As mentioned earlier, to ensure the timeliness of the calculation of the learning progress of the target lesson, the collection of new learning behavior data can be used as the trigger condition for calculating the learning progress of the lesson.

[0129] In one optional embodiment, to ensure that the learning progress of the target lesson is calculated in a timely and accurate manner, during the process of collecting learning behavior data generated by the target learner in learning the target lesson, in response to the collection of new learning behavior data, a task for calculating the learning progress of the target lesson can be generated first, and the task can be sent to a second message queue. The task for calculating the learning progress of the target lesson is used to instruct the server to retrieve the learning behavior data corresponding to the target learner and the target lesson from the database, and to calculate the learning progress of the target lesson using the retrieved learning behavior data. The second message queue includes, but is not limited to, a Kafka message queue, and in addition to the data corresponding to the target lesson, also includes the data for calculating the learning progress of the target lesson.

[0130] Next, the lesson learning progress calculation task is read from the second message queue at preset time intervals, wherein the time length corresponding to the preset time interval can be customized. Optionally, multiple computing nodes can be pre-configured for executing the lesson learning progress calculation task. A lesson learning progress calculation task can be executed by a single computing node or by multiple computing nodes working together. This embodiment does not impose any restrictions on this.

[0131] Then, in response to reading the lesson learning progress calculation task corresponding to the target lesson from the second message queue at preset time intervals, a target computing node for executing the lesson learning progress calculation task corresponding to the target lesson is determined from multiple computing nodes. Optionally, the number of target computing nodes can be one or more.

[0132] Then, the target computing node calculates the task based on the learning progress of the target lesson, and retrieves the collected learning behavior data of the target lesson from the database to calculate the learning progress of the target lesson.

[0133] Finally, in response to the completion of the learning progress calculation for the target lesson, the learning progress calculation task for the target lesson is deleted from the second message queue, and the learning progress for the target lesson is stored in the database.

[0134] In this embodiment, in response to the collection of new learning behavior data, a lesson learning progress calculation task corresponding to the target lesson is automatically generated and stored in a second message queue as the basis for task scheduling and asynchronous processing. By setting a preset time interval, the lesson learning progress calculation tasks to be processed are periodically read from the second message queue and executed, ensuring the timeliness of lesson learning progress calculation. After completing the lesson learning progress calculation task, it is deleted from the second message queue, ensuring that each lesson learning progress calculation task in the second message queue is processed only once, avoiding duplicate execution or omissions.

[0135] In one optional embodiment, to ensure the accuracy of the calculated learning progress corresponding to the target lesson, during the process of collecting learning behavior data generated by the target learner in learning the target lesson, a timed compensation task for the learning progress of the target lesson can be generated in response to the collection of new learning behavior data. This timed compensation task for the learning progress of the target lesson is used to trigger a recalculation of the learning progress of the target lesson at a target time, which is also the task triggering time corresponding to the timed compensation task for the learning progress of the target lesson.

[0136] Optionally, the target time can be customized. For example, the target time can be a specific time on the day new learning behavior data is collected, such as a time between 11:00 PM and 12:00 AM. At this time, there are fewer learners taking courses through the learning platform, which is not a peak learning period, and there is sufficient computing power to recalculate the learning progress of the target lesson.

[0137] Next, in response to the task triggering time corresponding to the timed compensation task for the learning progress of the target lesson, the system retrieves the collected learning behavior data and the learning progress of the target lesson from the database. The learning progress of the target lesson retrieved from the database is the same as the learning progress calculated in real-time upon receiving new learning behavior data, as described in the previous embodiment.

[0138] Then, based on the collected learning behavior data corresponding to the target lesson, the learning progress of the target lesson is recalculated. The calculation method for recalculating the learning progress of the target lesson is the same as the method used previously.

[0139] Finally, in response to a discrepancy between the recalculated learning progress of the target lesson and the learning progress retrieved from the database, the recalculated learning progress is updated in the database, effectively replacing the recalculated learning progress in the database. Conversely, if the recalculated learning progress matches the learning progress retrieved from the database, the learning progress in the database remains unchanged.

[0140] Understandably, the learning progress of the target course is calculated based on the learning progress of each lesson within the target course. Therefore, if the recalculated learning progress of the target lesson differs from the learning progress retrieved from the database, alternatively, a recalculation of the learning progress of the target course can be triggered to ensure its accuracy.

[0141] In this embodiment, in response to the collection of new learning behavior data, a timed compensation task for the learning progress of the target lesson is automatically generated. By periodically triggering this timed compensation task to verify the calculated learning progress of the target lesson, it is possible to promptly detect whether the learning progress of the target lesson is calculated incorrectly, and to promptly compensate and correct it if an error is found. Thus, the accuracy of the learning progress of the target lesson output to the target learner can be guaranteed.

[0142] The above explains the collection of learning behavior data corresponding to the target learner. The following explains the process of determining the first and second statistical values.

[0143] Figure 6 A flowchart illustrating the process of determining a first statistical value and a second statistical value, as provided in this application embodiment, is shown below. Figure 6 As shown, based on the collected learning behavior data, the content of the target lesson repeated by the target learner in different course periods under various course types is merged to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object. This includes the following steps:

[0144] 601. Map the different learning duration data corresponding to different course types from the collected learning behavior data to the same time axis to determine the first statistical value corresponding to the first learning progress statistical object; wherein, the different learning duration data corresponding to the same content in the target lesson are mapped to the same position on the time axis.

[0145] 602. Merge the different learning outcome data corresponding to different course types from the collected learning behavior data according to the category of the corresponding second learning progress statistical object to determine the second statistical value corresponding to the second learning progress statistical object.

[0146] To facilitate understanding, the embodiments of this application illustrate the process of determining the first statistical value and the second statistical value through examples.

[0147] Continuing with the previous example, suppose the target course includes course period 1 and course period 2. Course period 1 includes lesson A, lesson B, and lesson C, and course period 2 includes lesson A, lesson B, lesson C, and lesson D. The target lesson is lesson A. The course types corresponding to lesson A include online video courses and offline practical courses. Online video courses include online live video courses and online recorded video courses.

[0148] Assuming the first learning progress statistics object includes the learning start time and learning end time, and the second learning progress statistics object includes learning test results and learning evaluation results, the collected learning behavior data includes:

[0149] 1) In course period 1, the learning duration data 1 corresponding to lesson A, which belongs to online video live course, is: {the start time of the lesson video t1, the stop time of the lesson video t2}, and the learning outcome data 1 is: {online test score 1, teacher evaluation 1}.

[0150] 2) In course period 1, the learning time data 2 corresponding to lesson A, which belongs to online video recording course, is: {the start time of the lesson video t2, the stop time of the lesson video t3}, and the learning outcome data 2 is: {online test score 2, teacher evaluation 2}.

[0151] 3) In course period 2, the learning time data 3 corresponding to the offline practical course A is: {first sign-in operation time t2, last sign-in operation time t4}, and the learning outcome data 3 is: {offline operation test score 3, teacher evaluation 3}.

[0152] Here, t1, t2, t3, and t4 are all relative to lesson A. For example, t1 corresponds to the 10th second of the lesson video in lesson A, t2 corresponds to the 30th second of the lesson video, and so on. They are ordered from earliest to latest as t1, t2, t3, and t4.

[0153] It is worth noting that t1 and t2 in learning duration data 1 represent the target learner's learning of the content between t1 and t2 corresponding to lesson A; t2 and t3 in learning duration data 2 represent the target learner's learning of the content between t2 and t3 corresponding to lesson A; and t2 and t4 in learning duration data 3 represent the target learner's learning of the content between t2 and t4 corresponding to lesson A. The content between t2 and t4 includes the content between t2 and t3.

[0154] Based on this, it can be observed that the target learner may repeatedly study the same content while learning Lesson A through different course types. Therefore, merging the content of Lesson A that the target learner has repeatedly studied is helpful in determining the target learner's actual learning situation regarding Lesson A.

[0155] In the specific implementation process, the learning duration data 1, learning duration data 2, and learning duration data 3 from the collected learning behavior data can be mapped to the same timeline to achieve the merging of the same learning content. Different moments on the timeline correspond to different lesson content in lesson A.

[0156] Figure 7 A schematic diagram of a timeline provided for an embodiment of this application, such as... Figure 7As shown, learning duration data 1, learning duration data 2, and learning duration data 3 are mapped to the same time axis. On one hand, t2 and t3 corresponding to learning duration data 2 can be merged into t2 and t4 corresponding to learning duration data 3. On the other hand, t1 and t2 corresponding to learning duration data 1 can be combined with t2 and t4 corresponding to learning duration data 3. Thus, it can be determined that the target learner has studied the content of lesson A between t1 and t4. Furthermore, the learning durations between t1 and t4 are used as the first statistical value corresponding to the first learning progress statistical object related to the learning duration.

[0157] In practical applications, during the learning process of Lesson A, learners may undergo separate assessments for different types of Lesson A, such as answering online tests or performing offline tasks, generating corresponding learning outcome data. Additionally, instructors can also evaluate learners' performance on different types of Lesson A, thus generating further learning outcome data.

[0158] Based on this, by merging different learning outcome data, we can determine the actual learning situation of the target learner in lesson A.

[0159] In the specific implementation process, for the learning outcome data 1, learning outcome data 2 and learning outcome data 3 in the collected learning behavior data, they can be classified and merged according to the category of the second learning progress statistical object corresponding to these learning outcome data respectively.

[0160] For example, if online test score 1, online test score 2, and offline operation test score 3 all correspond to the learning test results in the second learning progress statistics object, then online test score 1, online test score 2, and offline operation test score 3 can be merged to determine the statistical value corresponding to the learning test results in the second learning progress statistics object.

[0161] For example, if Teacher Evaluation 1, Teacher Evaluation 2, and Teacher Evaluation 3 all correspond to the learning evaluation results in the second learning progress statistics object, then Teacher Evaluation 1, Teacher Evaluation 2, and Teacher Evaluation 3 can be merged to determine the statistical value corresponding to the learning evaluation results in the second learning progress statistics object.

[0162] Finally, the statistical values ​​corresponding to the learning test results and the learning evaluation results are used as the second statistical values ​​corresponding to the second learning progress statistical object.

[0163] Optionally, the above merging process includes, but is not limited to: summation, summation averaging, weighted averaging, determining the maximum value, determining the minimum value, etc.

[0164] In this embodiment of the application, according to the course type corresponding to the collected learning behavior data, the learning duration data and learning outcome data corresponding to the target lesson belonging to different course types are statistically processed separately. This realizes the merging of the content of the target lesson repeatedly learned by the target learner under multiple course types in different course periods, thereby more accurately describing the target learner's learning situation of the target lesson, so as to more accurately calculate the lesson learning progress corresponding to the target lesson in the future.

[0165] exist Figure 6 In the illustrated embodiment, in response to the collection of new learning behavior data, a full statistical merging process is directly performed on the already collected learning behavior data. In practical applications, to improve the calculation efficiency of the first and second statistical values ​​corresponding to the target lesson, a single lesson period can be used as a statistical merging unit. For the target lesson period, the first and second sub-statistical values ​​corresponding to each lesson period are calculated separately. Then, the multiple first sub-statistical values ​​corresponding to multiple lesson periods are merged to obtain the first statistical value; the multiple second sub-statistical values ​​corresponding to multiple lesson periods are merged to obtain the second statistical value. Thus, when new learning behavior data is collected, only the first and second sub-statistical values ​​of the lesson period corresponding to the new learning behavior data need to be recalculated. The first and second sub-statistical values ​​of other lesson periods do not need to be recalculated. By reducing the amount of data to be statistically merged, the calculation efficiency of the first and second statistical values ​​can be effectively improved. The following is combined with... Figure 8 Please provide a detailed explanation.

[0166] Figure 8 A flowchart illustrating another process for determining the first and second statistical values ​​provided in this application embodiment is shown below. Figure 8 As shown, based on the collected learning behavior data, the content of the target lesson repeated by the target learner in different course periods under various course types is merged to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object. This includes the following steps:

[0167] 801. Determine the first lesson period corresponding to the new learning behavior data collected in different lessons of the target course.

[0168] 802. Determine the learning behavior data corresponding to the first lesson in the collected learning behavior data.

[0169] 803. Map the different learning duration data corresponding to different course types in the learning behavior data of the first lesson period to the same time axis to determine the first sub-statistical value corresponding to the first learning progress statistical object under the first lesson period; wherein, the different learning duration data corresponding to the same content in the target lesson are mapped to the same position on the time axis.

[0170] 804. Merge the different learning outcome data corresponding to different course types in the learning behavior data of the first lesson period according to the category of the corresponding second learning progress statistical object to determine the second sub-statistic value corresponding to the second learning progress statistical object under the first lesson period.

[0171] 805. Obtain the first and second sub-statistics for the second lesson period (excluding the first lesson period) of the target course. The first and second sub-statistics for the second lesson period are determined before new learning behavior data is collected.

[0172] 806. Merge the first sub-statistical value corresponding to the first lesson period and the first sub-statistical value corresponding to the second lesson period to determine the first statistical value corresponding to the first learning progress statistical object; merge the second sub-statistical value corresponding to the first lesson period and the second sub-statistical value corresponding to the second lesson period to determine the second statistical value corresponding to the second learning progress statistical object.

[0173] To facilitate understanding, the embodiments of this application illustrate the process of determining the first statistical value and the second statistical value through examples.

[0174] Continuing with the example above, suppose the newly collected learning behavior data corresponds to lesson 1 within the target course's lesson 1 and lesson 2. In other words, the new learning behavior data was collected when the target learner was studying lesson A within lesson 1. Based on this, the first lesson is lesson 1, and the second lesson is lesson 2.

[0175] Based on the above assumptions about the collected learning behavior data, the learning behavior data corresponding to Lesson 1 can be further determined from the collected learning behavior data:

[0176] 1) In course period 1, the learning duration data 1 corresponding to lesson A, which belongs to online video live course, is: {the start time of the lesson video t1, the stop time of the lesson video t2}, and the learning outcome data 1 is: {online test score 1, teacher evaluation 1}.

[0177] 2) In course period 1, the learning time data 2 corresponding to lesson A, which belongs to online video recording course, is: {the start time of the lesson video t2, the stop time of the lesson video t3}, and the learning outcome data 2 is: {online test score 2, teacher evaluation 2}.

[0178] Then, learning duration data 1 and learning duration data 2 corresponding to lesson 1 are mapped to the same timeline to merge identical learning content. For example... Figure 7As shown, t1 and t2 corresponding to learning duration data 1 are concatenated with t2 and t3 corresponding to learning duration data 2 to determine that the target learner has studied the content of lesson A in lesson period 1, from t1 to t3. Furthermore, the learning durations corresponding to t1 to t3 are used as the first sub-statistical value corresponding to the first learning progress statistical object under lesson period 1.

[0179] For Learning Outcome Data 1 and Learning Outcome Data 2 in the collected learning behavior data, they can be categorized and merged according to the categories of the second learning progress statistics objects they correspond to. For example, if Online Test Score 1 and Online Test Score 2 correspond to the learning test results in the second learning progress statistics objects, then Online Test Score 1 and Online Test Score 2 can be merged to determine the sub-statistical value corresponding to the learning test results in the second learning progress statistics objects for Lesson 1. Similarly, if Teacher Evaluation 1 and Teacher Evaluation 2 both correspond to the learning evaluation results in the second learning progress statistics objects, then Teacher Evaluation 1 and Teacher Evaluation 2 can be merged to determine the sub-statistical value corresponding to the learning evaluation results in the second learning progress statistics objects for Lesson 1.

[0180] Optionally, the above merging process includes, but is not limited to: summation, summation averaging, weighted averaging, determining the maximum value, determining the minimum value, etc.

[0181] Then, the sub-statistics corresponding to the learning test results and the sub-statistics corresponding to the learning evaluation results are used as the second sub-statistics corresponding to the second learning progress statistics object in the first semester of course 1.

[0182] For Lesson 2, the process of determining the first and second sub-statistics corresponding to Lesson A is similar to that of Lesson 1, and will not be repeated here. It is worth emphasizing that the first and second sub-statistics corresponding to Lesson A in Lesson 2 are determined before new learning behavior data is collected.

[0183] Finally, the first sub-statistical value corresponding to lesson 1 and the first sub-statistical value corresponding to lesson 2 are merged to determine the first statistical value corresponding to the first learning progress statistical object; the second sub-statistical value corresponding to lesson 1 and the second sub-statistical value corresponding to lesson 2 are merged to determine the second statistical value corresponding to the second learning progress statistical object.

[0184] The specific merging process is similar to the process of determining the first and second sub-statistics corresponding to lesson 1. For example, the first sub-statistics corresponding to lesson 1 and the first sub-statistics corresponding to lesson 2 can be mapped to the same time axis to achieve their merging. Another example is that the second sub-statistics corresponding to lesson 1 and the second sub-statistics corresponding to lesson 2 can be merged according to the categories of the second learning progress statistics objects they correspond to, which will not be elaborated here.

[0185] In this embodiment, for a target lesson, a single lesson period is used as a statistical merging unit, and the first and second sub-statistical values ​​corresponding to each lesson period are calculated separately. Therefore, when new learning behavior data is collected, it is only necessary to recalculate the first and second sub-statistical values ​​for the first lesson period corresponding to the new learning behavior data, and then merge the recalculated first and second sub-statistical values ​​for the first lesson period with the previously calculated first and second sub-statistical values ​​for the second lesson period to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object. In this process, by reducing the amount of data requiring statistical merging, the calculation efficiency of the first and second statistical values ​​can be effectively improved.

[0186] The calculation process of the first and second statistical values ​​has been explained above. Next, the process of determining the learning progress of the target lesson based on the first and second statistical values ​​will be explained.

[0187] It is understandable that the learning progress corresponding to the target lesson refers to the learner's completion of learning the content contained in the target lesson. This learning progress can be expressed numerically, such as as a percentage, which can intuitively reflect the learner's actual mastery of the target lesson's content.

[0188] In this application, optionally, the learning progress corresponding to the target lesson can be expressed as: the ratio between the actual learning amount of the target learner for the target lesson (e.g., the first statistical value and the second statistical value calculated above) and the reference learning amount corresponding to the target lesson. The reference learning amount can be customized. For example, the reference learning amount can include a first statistical reference value corresponding to the first learning progress statistical object, which can specifically be the total lesson duration or the required lesson duration corresponding to the target lesson; or, for another example, the reference learning amount can also include a second statistical reference value corresponding to the second learning progress statistical object, which can specifically be the total test score, total evaluation score, expected test score, or expected evaluation score corresponding to the target lesson.

[0189] To facilitate understanding, let's take an example. Suppose the total duration of the target lesson is 60 minutes, and the learning requirement for that lesson is at least 50 minutes. Then, the first statistical reference value can be either the total lesson duration of 60 minutes or the required lesson duration of 50 minutes. If the first statistical value for the target learner's study time for the target lesson is 40 minutes, then when the first statistical value is the total lesson duration of 60 minutes, the ratio of the first statistical value to the first statistical reference value is 40 / 60 ≈ 66.7%, indicating that the target learner has studied 66.7% of the total lesson duration. When the first statistical value is the required lesson duration of 50 minutes, the ratio of the first statistical value to the first statistical reference value is 40 / 50 = 80%, indicating that the target learner has studied 80% of the required lesson duration.

[0190] Regarding the second statistical reference value, assuming the target lesson is an online video course with a maximum score of 100 points for its corresponding online test, requiring the learner to achieve at least 85 points, the second statistical reference value can be either the total test score of 100 points or the expected test score of 85 points. Assuming the learner's second statistical value for the target lesson, matching the test result, is 85 points, then when the second statistical value is the total test score of 100 points, the ratio of the second statistical value to the second statistical reference value is 85 / 100 = 85%, indicating that the learner has mastered 85% of the online test content. When the second statistical value is the expected test score of 85 points, the ratio of the second statistical value to the second statistical reference value is 85 / 85 = 100%, indicating that the learner has 100% met the learning requirements for the online test content. The specific configuration of the second statistical reference value corresponding to the learning evaluation result is similar to the aforementioned process and will not be repeated here.

[0191] Based on the above introduction to the first and second statistical reference values, it is clear that the ratios calculated for different first and second statistical reference values ​​have different meanings and can be used to reflect the learning progress of the target learners in different ways. Therefore, in practical applications, course providers can customize the corresponding first and second statistical reference values ​​based on the actual learning progress of each lesson.

[0192] As an optional method for determining the first statistical reference value and the second statistical reference value, the first statistical reference value corresponding to the first learning progress statistical object and the second statistical reference value corresponding to the second learning progress statistical object can be determined in response to the configuration operation of the course provider on the learning progress statistics information configuration interface for the target lessons included in the target course period of the target course.

[0193] It is worth noting that in this embodiment of the application, in calculating the learning progress of the target lesson, in addition to considering the first learning progress statistical object related to the learning duration, a second learning progress statistical object related to the learning outcome is also incorporated, such as learning test results and learning evaluation results, thereby forming a more comprehensive assessment of learning completion. For example, in practical applications, a learner is considered to have completed all the learning tasks of the lesson only when they have completed the specified learning duration and reached a certain test score or evaluation score.

[0194] In the specific implementation process, the calculation weights of the first and second learning progress statistical objects can be customized when calculating the learning progress of the target lesson.

[0195] As an optional method for determining the calculation weights corresponding to the first and second learning progress statistical objects, the calculation weights corresponding to the first and second learning progress statistical objects can be determined in response to the configuration operation of the course provider on the learning progress statistics information configuration interface for the target lessons included in the target course period of the target course.

[0196] Having determined the first statistical reference value, the second statistical reference value, and the calculation weights corresponding to the first and second learning progress statistical objects, the learning progress of the target lesson is determined based on the first and second statistical values, including:

[0197] Based on the first statistical value and the first statistical reference value, determine the learning time schedule corresponding to the target lesson;

[0198] Based on the second statistical value and the second statistical reference value, determine the learning outcome progress corresponding to the target lesson;

[0199] Based on the calculated weights, the learning time progress and learning outcome progress are weighted and summed to determine the learning progress of the corresponding lesson for the target lesson.

[0200] Specifically, the ratio of the first statistical value to the first statistical reference value is determined as the learning time progress corresponding to the target lesson, and the ratio of the second statistical value to the second statistical reference value is determined as the learning outcome progress corresponding to the target lesson. The first calculation weight corresponding to the first learning progress statistical object is used as the calculation weight corresponding to the learning time progress, and the second calculation weight corresponding to the second learning progress statistical object is used as the calculation weight corresponding to the learning outcome progress. The learning time progress and the learning outcome progress are weighted and summed to determine the lesson learning progress corresponding to the target lesson.

[0201] In this embodiment, when calculating the learning progress of the target lesson, not only are the first statistical value and first reference statistical value corresponding to the first learning progress statistical object related to learning duration, and the second statistical value and second reference statistical value corresponding to the second learning progress statistical object related to learning outcomes considered, but a calculation weight for different statistical objects is also introduced. This allows for a more reasonable and flexible integration of data from both the learning duration and learning outcomes dimensions. For example, for theoretical courses, a higher calculation weight can be assigned to the first learning progress statistical object, while for practical courses, a higher calculation weight can be assigned to the second learning progress statistical object. Therefore, the learning progress of the target lesson calculated using this scheme not only reflects the learner's completion of the target lesson but also more accurately reflects their true learning quality, thus avoiding bias caused by single-dimensional evaluation and improving the accuracy and guiding significance of learning progress feedback.

[0202] The above describes the course learning data processing method provided in the embodiments of this application, taking a single learner (i.e., the target learner) as an example.

[0203] It is understandable that the learning progress of a single learner's target lesson reflects their individual learning situation. However, the learning progress of multiple learners' target lessons can reflect the rationality of the target course design from a higher perspective. This is significant for course providers to adjust the target course, for example, by guiding them to adjust the aforementioned first and second statistical reference values, and to add or delete course periods or lessons included in the target course. The following examples illustrate this in detail.

[0204] Figure 9 A flowchart of another course learning data processing method provided in the embodiments of this application is shown below. Figure 9 As shown, it may include the following steps:

[0205] 901. For the target lesson, obtain the learning progress of the lesson corresponding to multiple learning methods.

[0206] The learning progress of each learning objective lesson can be calculated using the course learning data processing method described in the aforementioned embodiments, and will not be repeated in this application.

[0207] 902. Determine the completion rate of the target lesson based on the learning progress of each lesson corresponding to multiple learning methods.

[0208] 903. In response to the learning completion rate of the target lesson being lower than the set threshold, a first prompt message is output on the course provider's terminal interface to prompt the course provider to adjust the first statistical reference value and the second statistical reference value.

[0209] 904. In response to the course provider's adjustment of statistical reference values ​​triggered on the learning progress statistics configuration interface based on the first prompt information for the target lessons contained in the target course period of the target course, determine the adjusted first statistical reference value and the adjusted second statistical reference value.

[0210] 905. Based on the adjusted first statistical reference value and the adjusted second statistical reference value, recalculate the learning progress of the corresponding lesson for the target lesson.

[0211] In some application scenarios, when a learner completes the learning of a target lesson, they are considered to have mastered the content of that lesson. When a learner has mastered the content of all lessons included in the target course, a corresponding skills certificate can be issued to prove that they possess the relevant skills. For example, in the context of domestic service training, when a postpartum caregiver completes the corresponding postpartum caregiver course, they can be issued a postpartum caregiver certificate.

[0212] In one optional embodiment, for any learner, whether the learner has completed learning the target lesson can be determined based on the relationship between the learner's progress in learning the target lesson and a set value corresponding to the target lesson. For example, if the learner's progress in learning the target lesson is greater than or equal to the set value, the learner is considered to have completed learning the target lesson; if the learner's progress in learning the target lesson is less than the set value, the learner is considered to have not completed learning the target lesson.

[0213] The above-mentioned settings can be customized. For example, for a target lesson, the setting value can be 90%. Thus, if a learner's progress in a lesson is 91%, it is determined that the learner has completed the target lesson. If a learner's progress in a lesson is 89%, it is determined that the learner has not completed the target lesson.

[0214] Optionally, for different lessons within the target course (taking a target lesson as an example), customized numerical values ​​can be set to judge the completion status of the target lesson based on its actual difficulty level. For instance, when the target lesson is difficult, a lower value (e.g., 85%) can be set to appropriately lower the learning threshold for learners; conversely, when the target lesson is easy, a higher value (e.g., 100%) can be set to increase the requirements for learning quality. This approach ensures learners' effective mastery of the target lesson content while reasonably controlling the entry standards for skills certification or certificate acquisition.

[0215] Optionally, for different lessons within the target course (taking the target lesson as an example), the first and second statistical reference values ​​used to calculate the learning progress of the lesson can be dynamically adjusted based on the actual difficulty of the lesson content. This allows for personalized adjustments to the learning requirements for each learner, thereby influencing the final calculated learning progress. For instance, when the content of the target lesson is difficult, the required lesson duration corresponding to the first statistical reference value can be appropriately reduced (e.g., from 50 minutes to 40 minutes) to decrease the rigid requirement for learning time. Simultaneously, the required test score corresponding to the second statistical reference value can be reduced (e.g., from 85 points to 70 points). This improves the calculated learning progress without sacrificing learning quality, encouraging learners to continue learning. Conversely, when the content of the target lesson is relatively easy, the required lesson duration corresponding to the first statistical reference value can be appropriately increased (e.g., from 50 minutes to 60 minutes, which is close to or equal to the total lesson duration). At the same time, the required test score corresponding to the second statistical reference value can be increased (e.g., from 85 points to 100 points, which is to reach the total test score). This raises the standard for completing the target lesson, prevents the learning progress of the target lesson from being artificially inflated, and ensures the authenticity and effectiveness of the lesson learning progress.

[0216] As an optional method for determining the difficulty level of a target lesson, the completion rate of the target lesson can be determined first by assessing the learning progress of multiple learners. In simple terms, the completion rate equals the ratio of the number of learners who completed the target lesson to the total number of learners across all learners. Then, based on a pre-defined threshold and the completion rate, the difficulty level of the target lesson is determined. For example, if the completion rate is below the threshold, the lesson is considered more difficult; if the completion rate equals the threshold, the lesson is considered moderately difficult; and if the completion rate is above the threshold, the lesson is considered less difficult.

[0217] In the specific implementation process, to facilitate timely adjustments to the first and second statistical reference values ​​by course providers, a corresponding prompting mechanism is provided. For example, if the target lesson's content is particularly difficult, and the completion rate of the target lesson falls below a set threshold, a prompt message can be displayed on the course provider's terminal interface to prompt them to adjust the first and second statistical reference values. For instance, it could be: "The current completion rate for the target lesson is low; it is recommended to reduce the required lesson duration and / or the expected test score."

[0218] Subsequently, the learner can adjust the first and second statistical reference values ​​according to the first prompt. Specifically, for the target lessons included in the target course period, the learner triggers a statistical reference value adjustment operation in the learning progress statistics configuration interface according to the first prompt. The server responds to the statistical reference value adjustment operation triggered by the learner, determines the adjusted first and second statistical reference values, and recalculates the lesson learning progress corresponding to the target lesson based on these values. The specific process of recalculating the lesson learning progress corresponding to the target lesson can be found in the aforementioned embodiment and will not be repeated here.

[0219] In this embodiment, the learning progress of multiple learners corresponding to the target lesson is used to determine the completion rate of the target lesson, thereby assessing the difficulty of the target lesson content (i.e., learning difficulty). When the learning difficulty of the target lesson is high or low, personalized learning requirements are configured by dynamically adjusting the first and second statistical reference values, effectively solving the fairness and applicability issues caused by the "one-size-fits-all" assessment in traditional learning platforms. This not only ensures the learning quality of learners and improves the objectivity of calculating the learning progress of the target lesson, but also enhances the learners' enthusiasm for learning the target lesson. Furthermore, the intelligent prompting mechanism in this solution automatically sends a first prompt message to the course provider when the completion rate of the target lesson falls below a set threshold, suggesting that they adjust the first and second statistical reference values. This not only improves the course management efficiency of the course provider but also provides data support for continuous improvement of teaching quality.

[0220] Based on the above introduction to the difficulty of the target lesson content, if the learning completion rate determined based on the learning progress of multiple learners is lower than the set threshold, it is considered that the target lesson content may be too difficult, which is not conducive to the learners' learning.

[0221] For lessons where the content is particularly challenging, alternatively, adding new lessons within new semesters can provide learners with more content, thereby reducing the difficulty and increasing their motivation to learn the lesson, ultimately leading to better mastery of the content. The following will combine... Figure 10 Please provide a detailed explanation.

[0222] Figure 10 A flowchart of another course learning data processing method provided in the embodiments of this application is shown below. Figure 10 As shown, it may include the following steps:

[0223] 1001. For the target lesson, obtain the learning progress of each lesson corresponding to multiple learning methods.

[0224] 1002. Based on the learning progress of each lesson corresponding to multiple learning methods, determine the learning completion rate of the target lesson.

[0225] The specific implementation process of steps 1001 and 1002 can be referred to the aforementioned embodiments, and will not be repeated in this embodiment.

[0226] 1003. In response to the learning completion rate of the target lesson being lower than the set threshold, a second prompt message is output on the terminal interface of the course provider to prompt the course provider to adjust the lesson configuration of the target course.

[0227] The second prompt is used to guide the course provider to optimize and adjust the course arrangement of the target course to improve the completion rate of the target lesson. For example, the second prompt could suggest adding a pre-lesson introductory lesson before the target lesson to improve the learner's grasp of basic knowledge.

[0228] 1004. In response to the course provider's operation of adding a lesson segment for the target course on the course provider's terminal interface based on the second prompt information, determine the new course period corresponding to the target course. The new course period includes the lesson segments contained in other course periods of the target course, as well as the newly added lesson segment corresponding to the operation of adding a lesson segment.

[0229] 1005. Synchronize the learning progress of the lessons in other semesters of the target course to the new semester; display the lessons in the new semester of the target course, as well as the synchronized learning progress, on the terminal interface of the target learner.

[0230] In practice, learners can trigger a new lesson segment on the course provider's terminal interface based on the second prompt. The server responds to this new lesson segment addition operation by determining the new lesson segment and the new course period containing it. The new course period includes not only the new lesson segment but also the lesson segments from other courses of the target course.

[0231] To facilitate understanding, let's take an example. Suppose the target course includes Course 1 and Course 2. Course 1 contains Lesson A, Lesson B, and Lesson C, and Course 2 contains Lesson A, Lesson B, Lesson C, and Lesson D. The target lesson is Lesson A. The second prompt suggests adding a prerequisite knowledge lesson before Lesson A. Based on the second prompt, the learner adds Lesson A' to the target course on the course provider's terminal interface. In response to the learner's operation of adding Lesson A', a new course—Course 3—is determined, which includes Lesson A', Lesson A, Lesson B, Lesson C, and Lesson D.

[0232] Understandably, for the target lesson, learners can study lessons from any lesson period. That is, learners can study lessons included in the new lesson period—lesson period 3—as well as lessons included in the historical lesson periods—lesson period 1 and lesson period 2.

[0233] To facilitate any learner (i.e., the target learner) in tracking their own learning progress, after a new course period is determined, the learning progress of the lessons in other courses of the target course can be synchronized to the new course period. Thus, the lessons in the new course period corresponding to the target course, as well as the synchronized learning progress, can be displayed on the target learner's terminal interface.

[0234] Continuing with the example above, the learning progress of each lesson A, lesson B, lesson C, and lesson D contained in Lesson 1 and Lesson 2 will be synchronized to Lesson 3. This will allow the target learner's terminal interface to display not only Lesson A', Lesson A, Lesson B, Lesson C, and Lesson D contained in Lesson 3, but also the learning progress of each lesson A, Lesson B, Lesson C, and Lesson D that the target learner has previously studied.

[0235] In an optional embodiment, in response to the determination of a new course period, a third prompt message can be further output on the course provider's terminal interface to prompt the course provider to configure the learning progress statistics object for the newly added course. Subsequently, in response to the course provider's configuration operation for the newly added course on the learning progress statistics configuration interface, a third learning progress statistics object related to learning duration and a fourth learning progress statistics object related to learning outcomes, corresponding to the newly added course under various course types, are determined. The third and fourth learning progress statistics objects are used to instruct the collection of learning behavior data generated by the target learner during the learning of the newly added course.

[0236] The configuration process for the third and fourth learning progress statistics objects corresponding to the newly added lesson sections, as well as the collection process for the learning behavior data corresponding to the newly added lesson sections, can be found in the description of the target lesson sections in the aforementioned embodiments, and will not be repeated here.

[0237] In this embodiment, for learning scenarios where the content is difficult and the completion rate is low, a course learning data processing method is proposed that dynamically adjusts the course structure of the target course based on the completion rate and optimizes the learning experience by adding new lessons. Specifically, when the completion rate of the target lesson is lower than a set threshold, a second prompt message is output on the course provider's terminal interface, suggesting the addition of pre-lesson guidance lessons before the target lesson to optimize the lesson configuration of the target course. For the lesson addition operation performed by the course provider based on the second prompt message, a new course period for the target course is automatically generated. Simultaneously, the existing learning progress data from the original course period is synchronized to the new course period, ensuring that learners retain their historical learning outcomes after switching course periods. A complete list of lessons and learning progress information is displayed on the terminal interface. In summary, this solution, through dynamic course structure adjustment, synchronization of lesson learning progress data, and an intelligent prompt mechanism, achieves adaptive optimization of course content difficulty, effectively lowering the learning threshold and improving learners' learning enthusiasm and knowledge mastery.

[0238] The following will describe in detail one or more embodiments of the course learning data processing apparatus of this application. Those skilled in the art will understand that these apparatuses can all be configured using commercially available hardware components through the steps taught in this solution.

[0239] Figure 11 This is a schematic diagram of the structure of a course learning data processing device provided in an embodiment of this application, as shown below. Figure 11 As shown, the device includes: a determination module 11, a data acquisition module 12, and a processing module 13.

[0240] The determination module 11 is used to respond to the configuration operation of the course provider on the learning progress statistics information configuration interface for the target lesson included in the target course period of the target course, and to determine the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcome corresponding to the target lesson under various course types; the target course period is any course period of the target course that contains the target lesson.

[0241] The data collection module 12 is used to collect learning behavior data generated by the target learner when learning the target lesson in different periods of the target course, based on the first learning progress statistics object and the second learning progress statistics object.

[0242] The processing module 13 is used to respond to the collection of new learning behavior data, and according to the collected learning behavior data, merge the content of the target lesson in different course periods that is repeatedly learned by the target learner under the multiple course types, so as to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object; and determine the lesson learning progress corresponding to the target lesson based on the first statistical value and the second statistical value.

[0243] In an optional embodiment, the processing module 13 is further configured to: summarize the lessons included in different periods of the target course to determine multiple lessons corresponding to the target course, wherein the target lesson is any one of the multiple lessons; for the target learner, determine the course learning progress corresponding to the target course based on the lesson learning progress corresponding to the multiple lessons; and in response to the learning progress viewing operation triggered by the target learner for the target lesson, output the lesson learning progress corresponding to the target lesson and the course learning progress corresponding to the target course.

[0244] In an optional embodiment, when the processing module 13 merges the content of the target lesson repeated by the target learner in different course periods under multiple course types based on the collected learning behavior data to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object, it is specifically used to: map different learning duration data corresponding to different course types in the collected learning behavior data to the same time axis to determine the first statistical value corresponding to the first learning progress statistical object; wherein, different learning duration data corresponding to the same content in the target lesson are mapped to the same position on the time axis; merge different learning outcome data corresponding to different course types in the collected learning behavior data according to the category of the corresponding second learning progress statistical object to determine the second statistical value corresponding to the second learning progress statistical object.

[0245] In an optional embodiment, when the processing module 13 merges the content of the target lesson repeated by the target learner in different course periods under multiple course types based on the collected learning behavior data to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object, it is further specifically used to: determine the first course period corresponding to the newly collected learning behavior data in different course periods of the target course; determine the learning behavior data corresponding to the first course period in the collected learning behavior data; map the different learning duration data corresponding to different course types in the learning behavior data corresponding to the first course period to the same time axis to determine the first sub-statistical value corresponding to the first learning progress statistical object under the first course period; wherein, the different learning duration data corresponding to the same content in the target lesson are mapped to the same position on the time axis; The learning behavior data corresponding to different course types in the first course period are merged according to the category of the corresponding second learning progress statistical object to determine the second sub-statistical value corresponding to the second learning progress statistical object in the first course period; the first sub-statistical value and the second sub-statistical value corresponding to the second course period other than the first course period in different course periods of the target course are obtained, and the first sub-statistical value and the second sub-statistical value corresponding to the second course period are determined before new learning behavior data are collected; the first sub-statistical value corresponding to the first course period and the first sub-statistical value corresponding to the second course period are merged to determine the first statistical value corresponding to the first learning progress statistical object; the second sub-statistical value corresponding to the first course period and the second sub-statistical value corresponding to the second course period are merged to determine the second statistical value corresponding to the second learning progress statistical object.

[0246] In one optional embodiment, the multiple course types include: online video courses and offline practical courses. The online video courses include: online live video courses and online recorded video courses. The first learning progress statistics object includes: learning start time and learning end time. The second learning progress statistics object includes at least one of the following: learning test results and learning evaluation results.

[0247] In an optional embodiment, when the acquisition module 12 collects learning behavior data generated by the target learner learning the target lesson in different periods of the target course according to the first learning progress statistics object and the second learning progress statistics object, it is specifically used for: for the target lesson of the online video course in any period of the target course, in response to the target learner's viewing operation of the lesson video corresponding to the target lesson, obtaining a first time value corresponding to the learning start time and a second time value corresponding to the learning end time; in response to the target learner's online answering operation of the online test content corresponding to the target lesson, obtaining a first learning test result corresponding to the online test content and a first learning evaluation result corresponding to the target learner uploaded by the instructor through the terminal device; and using the first time value, the second time value, the first learning test result and the first learning evaluation result as the learning behavior data generated by the target learner learning the target lesson of the online video course in any period.

[0248] In an optional embodiment, when the acquisition module 12 collects learning behavior data generated by the target learner learning the target lesson in different periods of the target course according to the first learning progress statistics object and the second learning progress statistics object, it is further specifically used for: for the target lesson of the offline practical course in any period of the target course, in response to multiple check-in operations triggered by the target learner, determining the third time value corresponding to the learning start time and the fourth time value corresponding to the learning end time according to the operation time corresponding to the multiple check-in operations; in response to the target learner's upload operation of the learning test results corresponding to the offline operation content, obtaining the second learning test result corresponding to the offline operation content, and the second learning evaluation result corresponding to the target learner uploaded by the instructor through the terminal device; and using the third time value, the fourth time value, the second learning test result and the second learning evaluation result as the learning behavior data generated by the target learner learning the target lesson of the offline practical course in any period.

[0249] In an optional embodiment, the determining module 11 is further configured to: in response to the course provider's configuration operation on the learning progress statistics information configuration interface for the target lessons included in the target course period of the target course, determine the first statistical reference value corresponding to the first learning progress statistics object, the second statistical reference value corresponding to the second learning progress statistics object, and the calculation weights corresponding to the first learning progress statistics object and the second learning progress statistics object, respectively.

[0250] Accordingly, when determining the learning progress of the target lesson based on the first statistical value and the second statistical value, the processing module 13 is specifically used to: determine the learning time progress of the target lesson based on the first statistical value and the first statistical reference value; determine the learning outcome progress of the target lesson based on the second statistical value and the second statistical reference value; and perform a weighted summation of the learning time progress and the learning outcome progress based on the calculation weight to determine the learning progress of the target lesson.

[0251] In an optional embodiment, the determining module 11 is further configured to: obtain the learning progress of the target lesson corresponding to multiple learners respectively; determine the learning completion rate of the target lesson based on the learning progress of the multiple learners respectively; in response to the learning completion rate being lower than a set threshold, output a first prompt message on the terminal interface of the course provider to prompt the course provider to adjust the first statistical reference value and the second statistical reference value; and in response to the course provider's statistical reference value adjustment operation triggered in the learning progress statistics configuration interface based on the first prompt message for the target lesson included in the target course period of the target course, determine the adjusted first statistical reference value and the adjusted second statistical reference value.

[0252] Accordingly, the processing module 13 is further configured to: recalculate the learning progress of the target lesson based on the adjusted first statistical reference value and the adjusted second statistical reference value.

[0253] In an optional embodiment, the determining module 11 is further configured to: in response to the learning completion rate being lower than the set threshold, output a second prompt message on the terminal interface of the course provider to prompt the course provider to adjust the lesson configuration of the target course; in response to the course provider determining a new course period corresponding to the target course based on the second prompt message and a lesson addition operation triggered for the target course on the terminal interface of the course provider, the new course period includes the lessons included in other course periods of the target course, and the newly added lessons corresponding to the lesson addition operation; synchronize the lesson learning progress corresponding to the lessons included in other course periods of the target course to the new course period; and display the lessons included in the new course period corresponding to the target course, and the synchronized lesson learning progress, on the terminal interface of the target learner.

[0254] In an optional embodiment, the determining module 11 is further configured to: in response to the determination of the new course period, output a third prompt message on the terminal interface of the course provider to prompt the course provider to configure the learning progress statistics object for the newly added course; in response to the configuration operation of the course provider on the learning progress statistics information configuration interface for the newly added course, determine the third learning progress statistics object related to learning duration and the fourth learning progress statistics object related to learning outcome corresponding to the newly added course under various course types; the third learning progress statistics object and the fourth learning progress statistics object are used to indicate the collection of learning behavior data generated by the target learner in learning the newly added course.

[0255] In an optional embodiment, after acquiring new learning behavior data, the acquisition module 12 is further configured to send the acquired new learning behavior data to a first message queue; read the acquired new learning behavior data from the first message queue; and add the read learning behavior data to a database.

[0256] In an optional embodiment, the processing module 13 is further configured to: generate a lesson learning progress calculation task corresponding to the target lesson in response to the collection of new learning behavior data; send the lesson learning progress calculation task to a second message queue; determine a target computing node from multiple computing nodes to execute the lesson learning progress calculation task in response to reading the lesson learning progress calculation task from the second message queue at a preset time interval; obtain the collected learning behavior data corresponding to the target lesson from the database through the target computing node according to the lesson learning progress calculation task, so as to calculate the lesson learning progress corresponding to the target lesson; and delete the lesson learning progress calculation task from the second message queue and store the lesson learning progress corresponding to the target lesson in the database in response to the completion of the lesson learning progress calculation.

[0257] In an optional embodiment, the processing module 13 is further configured to: generate a timed compensation task for the learning progress of the target lesson in response to the collection of new learning behavior data; obtain the collected learning behavior data corresponding to the target lesson and the learning progress of the target lesson from the database in response to the current time being the task triggering time corresponding to the learning progress compensation task; recalculate the learning progress of the target lesson based on the collected learning behavior data; and update the recalculated learning progress of the target lesson to the database in response to the inconsistency between the recalculated learning progress of the target lesson and the learning progress of the target lesson obtained from the database.

[0258] Figure 11 The device shown can perform the steps described in the foregoing embodiments. For detailed execution process and technical effects, please refer to the description in the foregoing embodiments, which will not be repeated here.

[0259] In one possible design, the above Figure 11 The structure of the course learning data processing device shown can be implemented as an electronic device, such as... Figure 12 As shown, the electronic device may include: a memory 21 and a processor 22. The processor may include one or more units, and the memory may also include one or more units.

[0260] Memory 21 is used to store computer programs and can be configured to store various other data to support operation on the computing platform. Examples of this data include instructions for any application or method operating on the computing platform, contact data, phone book data, messages, pictures, videos, etc.

[0261] The aforementioned memory 21 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0262] The processor 22, coupled to the memory 21, is used to execute a computer program stored in the memory 21 to implement the course learning data processing method as described in the foregoing embodiments.

[0263] Furthermore, the electronic device also includes other components such as a communication component 23, a display 24, a power supply component 25, and an audio component 26. Figure 12 The diagram only shows some components and does not mean that the device includes only these components. Figure 12 The components shown. Additionally... Figure 12 The components within the dashed box are optional, not mandatory, and their specific requirements depend on the product form of the video content generation device. The course learning data processing device in this embodiment can be a terminal device such as a desktop computer, laptop computer, smartphone, or IoT device, or a server-side device such as a conventional server, cloud server, or server array. If the course learning data processing device in this embodiment is implemented as a terminal device such as a desktop computer, laptop computer, or smartphone, it may include... Figure 12 The components within the dashed box; if the video content generation device in this embodiment is implemented as a server-side device such as a conventional server, cloud server, or server array, then it may not include... Figure 12 The component within the dashed box.

[0264] The aforementioned communication component 23 is configured to facilitate wired or wireless communication between the device containing the communication component and other devices. The device containing the communication component can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G / LTE, 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), Bluetooth (BT), and other technologies.

[0265] The aforementioned display 24 includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a Touch Panel, the screen can be implemented as a touchscreen to receive input signals from the user. The Touch Panel includes one or more touch sensors to sense touches, swipes, and gestures on the Touch Panel. The touch sensors can sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation.

[0266] The aforementioned power supply component 25 provides power to various components of the device in which it resides. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which it resides.

[0267] The aforementioned audio component 26 can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC) configured to receive external audio signals when the device containing the audio component is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals can be further stored in memory or transmitted via a communication component. In some embodiments, the audio component also includes a speaker for outputting audio signals.

[0268] The detailed implementation methods and beneficial effects of each step in this embodiment have been described in detail in the foregoing embodiments, and will not be elaborated here.

[0269] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the processor to implement the steps in the above-described method embodiments. The computer-readable storage medium includes volatile or non-volatile components, or a combination thereof, and can be removable or non-removable. Examples of computer-readable storage media include, but are not limited to, phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), flash memory or other memory technologies, CD-ROM, Digital Video Disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium.

[0270] Accordingly, this application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are executed by a processor, the processor is able to implement the steps in the above method embodiments. It should be understood that each step or combination of steps in the above method flow can be implemented by the computer program or instructions. In addition, these computer programs or instructions can be applied to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable learning data processing device, so that the processor of the general-purpose computer, special-purpose computer, embedded processor, or other programmable learning data processing device can be implemented as a means to implement the corresponding functions in the above method embodiments.

[0271] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0272] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. This application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0273] Finally, it should be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0274] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing course learning data, characterized in that, The method includes: In response to the course provider's configuration operation on the learning progress statistics configuration interface for the target lessons included in the target course period, the first learning progress statistics object related to learning duration and the second learning progress statistics object related to learning outcome are determined for the target lessons under various course types; the target course period is any course period in the target course that contains the target lessons. Based on the first learning progress statistics object and the second learning progress statistics object, collect learning behavior data generated by the target learner when learning the target lesson in different periods of the target course; In response to the collection of new learning behavior data, based on the collected learning behavior data, the content of the target lesson in different course periods that is repeatedly learned by the target learner under the multiple course types is merged to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object; Based on the first statistical value and the second statistical value, the learning progress of the corresponding lesson is determined.

2. The method according to claim 1, characterized in that, The method further includes: The lesson sessions contained in different semesters of the target course are summarized to determine multiple lesson sessions corresponding to the target course, and the target lesson session is any one of the multiple lesson sessions; For the target learner, the course learning progress corresponding to the target course is determined based on the learning progress of each of the multiple lesson sections; In response to the learning progress viewing operation triggered by the target learner for the target lesson, the learning progress of the lesson corresponding to the target lesson and the learning progress of the course corresponding to the target course are output.

3. The method according to claim 1, characterized in that, The step of merging the content of the target lesson that the target learner repeatedly learns under the multiple course types in different course periods based on the collected learning behavior data, to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object, includes: The collected learning behavior data corresponding to different course types are mapped to the same timeline to determine the first statistical value corresponding to the first learning progress statistical object; wherein, different learning duration data corresponding to the same content in the target lesson are mapped to the same position on the timeline; The different learning outcome data corresponding to different course types in the collected learning behavior data are merged according to the category of the corresponding second learning progress statistical object to determine the second statistical value corresponding to the second learning progress statistical object.

4. The method according to claim 1, characterized in that, The step of merging the content of the target lesson that the target learner repeatedly learns under the multiple course types in different course periods based on the collected learning behavior data, to determine the first statistical value corresponding to the first learning progress statistical object and the second statistical value corresponding to the second learning progress statistical object, includes: Determine the first lesson period corresponding to the newly collected learning behavior data in different lesson periods of the target course; Identify the learning behavior data corresponding to the first lesson period from the collected learning behavior data; The learning behavior data corresponding to different course types in the first course period are mapped to the same time axis to determine the first sub-statistical value corresponding to the first learning progress statistical object in the first course period; wherein, the different learning duration data corresponding to the same content in the target course are mapped to the same position on the time axis. The learning behavior data corresponding to different course types in the first course period are merged according to the category of the corresponding second learning progress statistical object to determine the second sub-statistical value corresponding to the second learning progress statistical object in the first course period. Obtain the first sub-statistic and the second sub-statistic for the second course period (excluding the first course period) of the target course. The first sub-statistic and the second sub-statistic for the second course period are determined before new learning behavior data is collected. Merge the first sub-statistical value corresponding to the first lesson period and the first sub-statistical value corresponding to the second lesson period to determine the first statistical value corresponding to the first learning progress statistical object; The second sub-statistical value corresponding to the first lesson period and the second sub-statistical value corresponding to the second lesson period are merged to determine the second statistical value corresponding to the second learning progress statistical object.

5. The method according to any one of claims 1 to 4, characterized in that, The various course types include: online video courses and offline practical courses. The online video courses include: online live video courses and online recorded video courses. The first learning progress statistics object includes: learning start time and learning end time. The second learning progress statistics object includes at least one of the following: learning test results and learning evaluation results.

6. The method according to claim 5, characterized in that, The step of collecting learning behavior data of the target learner during different periods of the target course, based on the first learning progress statistics object and the second learning progress statistics object, includes: For any course period of the target course, if the course type is the online video course, in response to the target learner's viewing operation of the course video corresponding to the target course, the first time value corresponding to the learning start time and the second time value corresponding to the learning end time are obtained. In response to the target learner's online answering operation on the online test content corresponding to the target lesson, the first learning test result corresponding to the online test content is obtained, as well as the first learning evaluation result corresponding to the target learner uploaded by the instructor through the terminal device; The first time value, the second time value, the first learning test result, and the first learning evaluation result are used as the learning behavior data generated by the target learner when learning the target lesson of the online video course in any course period.

7. The method according to claim 5, characterized in that, The step of collecting learning behavior data of the target learner during different periods of the target course, based on the first learning progress statistics object and the second learning progress statistics object, includes: For the target lesson of the offline practical course under any course period of the target course, in response to multiple check-in operations triggered by the target learner, the third time value corresponding to the learning start time and the fourth time value corresponding to the learning end time are determined according to the operation time corresponding to the multiple check-in operations. In response to the target learner's upload of the learning test results corresponding to the offline operation content, the second learning test results corresponding to the offline operation content are obtained, as well as the second learning evaluation results corresponding to the target learner uploaded by the instructor through the terminal device. The third time value, the fourth time value, the second learning test result, and the second learning evaluation result are used as the learning behavior data generated by the target learner when learning the target lesson of the offline practical course in any course period.

8. The method according to claim 1, characterized in that, The method further includes: In response to the course provider's configuration operation on the learning progress statistics information configuration interface for the target lessons included in the target course period, the first statistical reference value corresponding to the first learning progress statistics object, the second statistical reference value corresponding to the second learning progress statistics object, and the calculation weights corresponding to the first learning progress statistics object and the second learning progress statistics object are determined respectively. The step of determining the learning progress of the target lesson based on the first statistical value and the second statistical value includes: Based on the first statistical value and the first statistical reference value, determine the learning time schedule corresponding to the target lesson; Based on the second statistical value and the second statistical reference value, determine the learning outcome progress corresponding to the target lesson; Based on the calculated weights, the learning time progress and the learning outcome progress are weighted and summed to determine the learning progress of the lesson corresponding to the target lesson.

9. The method according to claim 8, characterized in that, The method further includes: For the target lesson, obtain the learning progress of the lesson corresponding to each of the multiple learners; Based on the learning progress of each lesson corresponding to the multiple learning methods, the learning completion rate of the target lesson is determined; In response to the learning completion rate being lower than a set threshold, a first prompt message is output on the terminal interface of the course provider to prompt the course provider to adjust the first statistical reference value and the second statistical reference value; In response to the course provider's statistical reference value adjustment operation triggered in the learning progress statistics configuration interface based on the first prompt information and for the target lessons contained in the target course period of the target course, the adjusted first statistical reference value and the adjusted second statistical reference value are determined. Based on the adjusted first statistical reference value and the adjusted second statistical reference value, the learning progress of the target lesson is recalculated.

10. The method according to claim 9, characterized in that, The method further includes: In response to the learning completion rate being lower than the set threshold, a second prompt message is output on the terminal interface of the course provider to prompt the course provider to adjust the lesson configuration of the target course; In response to the course provider's operation of adding a lesson segment on the target course's terminal interface based on the second prompt information, a new course period corresponding to the target course is determined. The new course period includes the lesson segments contained in other course periods of the target course, as well as the newly added lesson segment corresponding to the operation of adding a lesson segment. The learning progress of the lessons corresponding to the lessons in other periods of the target course will be synchronized to the new period; The target learner's terminal interface displays the lessons included in the new course period corresponding to the target course, as well as the synchronized lesson learning progress.

11. The method according to claim 10, characterized in that, The method further includes: In response to the determination of the new course period, a third prompt message is output on the terminal interface of the course provider to prompt the course provider to configure the learning progress statistics object for the newly added course period; In response to the configuration operation of the course provider on the learning progress statistics configuration interface for the newly added lesson, the third learning progress statistics object related to learning duration and the fourth learning progress statistics object related to learning outcome are determined for the newly added lesson under various course types; the third learning progress statistics object and the fourth learning progress statistics object are used to indicate the collection of learning behavior data generated by the target learner in learning the newly added lesson.

12. The method according to claim 1, characterized in that, After acquiring the new learning behavior data, the method further includes: The newly collected learning behavior data is sent to the first message pair; Read the newly collected learning behavior data from the first message queue; The retrieved learning behavior data is added to the database.

13. The method according to claim 12, characterized in that, The method further includes: In response to the collection of new learning behavior data, a lesson learning progress calculation task corresponding to the target lesson is generated; Send the task of calculating the learning progress of the lesson to the second message queue; In response to reading the lesson learning progress calculation task from the second message queue at a preset time interval, a target computing node for executing the lesson learning progress calculation task is determined from multiple computing nodes; The target computing node calculates the task based on the learning progress of the lesson, and retrieves the collected learning behavior data corresponding to the target lesson from the database to calculate the learning progress of the lesson corresponding to the target lesson. In response to the completion of the lesson learning progress calculation corresponding to the target lesson, the lesson learning progress calculation task is deleted from the second message queue, and the lesson learning progress corresponding to the target lesson is stored in the database.

14. The method according to claim 13, characterized in that, The method further includes: In response to the collection of new learning behavior data, a timed compensation task for the learning progress of the target lesson is generated. In response to the task triggering time corresponding to the timed compensation task for the learning progress of the current lesson, the system retrieves the collected learning behavior data corresponding to the target lesson from the database, and retrieves the lesson learning progress corresponding to the target lesson from the database. Based on the collected learning behavior data corresponding to the target lesson, the lesson learning progress corresponding to the target lesson is recalculated; In response to a discrepancy between the recalculated learning progress of the target lesson and the learning progress of the target lesson obtained from the database, the recalculated learning progress of the target lesson is updated in the database.

15. An electronic device, characterized in that, include: The system includes a memory, a processor, and a communication interface; wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the course learning data processing method as described in any one of claims 1 to 14.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor of an electronic device, causes the processor to perform the course learning data processing method as described in any one of claims 1 to 14.

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