Learning progress promoting method based on online higher education course

Abstract

The invention relates to the technical field of online education, in particular to a learning progress promoting method based on an online higher education curriculum, which comprises the following steps: monitoring an online curriculum learning progress of a user in real time, and capturing a user with a delayed progress according to the online curriculum learning progress of the user; the method comprises the following steps of: monitoring the online course learning activeness of a user in real time, and determining an online course learning supervision group according to the online course learning activeness of the user and a capture result of the user with a lagged learning progress, and setting the online course learning supervision group and a supervision queue through monitoring the learning progress and activeness of the user. The method comprises the following steps of: determining a service target of the method, further adopting a mode of mutually binding a user with a front online course learning progress with the service target of the method, bringing a certain incentive effect to the service target of the method, and simultaneously configuring an online course learning prompt period to continuously prompt the user to carry out online course learning. And the learning progress of each user on-line course is effectively promoted.

Description

Technical Field

[0001] The present invention relates to the field of online education technology, and in particular to a method for advancing learning progress based on online higher education courses. Background Art

[0002] Online education is a new educational method that uses modern information technologies such as the Internet and artificial intelligence to conduct teaching and learning interaction, and is an important part of educational services.

[0003] The invention patent with application number 201610655837.9 discloses an online education course system, which is characterized in that the system includes: a login module: this module is used for users to log in to the online education course system; a learning module: this module is used for users to select specific content in the corresponding course and study; a push module: this module is used to regularly push learning courses to users; the learning module includes a course content classification unit, a course content selection unit and a course content learning unit, the course content classification unit classifies the specific content of the course and determines the classification category, the course content selection unit is provided with a plurality of labels corresponding to the classification categories, and the course content learning unit is used to display the specific content of the course corresponding to the label for users to learn.

[0004] The application aims to solve the problem that "the existing online education courses are still in their infancy and there is no mature online education course system, so the functions of the existing online education course system are relatively simple."

[0005] However, although online education can alleviate the pressure on educational resources to a great extent, the supervision of online education users is relatively poor. For users with poor autonomy, it is easy for them to have low learning activity and enthusiasm in online education courses. In addition, the supervision of online courses is currently low, which makes online education courses ineffective.

[0006] To this end, we propose a learning progress advancement method based on online higher education courses. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for advancing learning progress based on online higher education courses, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] A method for advancing learning progress based on an online higher education course, comprising:

[0010] Monitor the user's online course learning progress in real time, and capture users who are lagging behind in progress based on the user's online course learning progress; monitor the user's online course learning activity in real time, determine the online course learning supervision group based on the user's online course learning activity and the capture results of users with lagging learning progress, and simultaneously sort the users in the online course learning supervision group in combination with the user's online course learning activity and the capture results of users with lagging learning progress to obtain a supervision queue; traverse the supervision queue, and bind a group of follow-up users to each user in the supervision queue; set the initial reminder period for online course learning, configure the online course learning reminder period for each user in the supervision queue, monitor the learning status parameters of the users in the supervision queue after the online course learning reminder period is configured, set the online course learning reminder period attenuation logic, and modify the next online course learning reminder period based on the online course learning reminder period attenuation logic and the user's learning status parameters; when the online course learning reminder period of any user decays to half of the initial reminder period of online course learning, the refresh step is executed.

[0011] Furthermore, in the user's online course learning progress monitoring stage, the total duration of all courses and the total duration of currently learned courses are identified on the user's online course learning application, and the ratio of the current total duration of learned courses to the total duration of all courses is recorded as the user's online course learning progress. The judgment logic for users with lagging progress is that, among the total duration of the current learned courses of each user, if any group of values ​​is not less than twice of another group of values, then the other group of values ​​is judged as users with lagging learning progress, and users with lagging learning progress are captured based on the judgment logic for users with lagging progress.

[0012] Furthermore, the user online course learning activity monitoring target is all users in the application program for learning online courses, and the monitoring logic of the user online course learning activity is expressed as:

[0013]

[0014] Where: f(a) is the online course learning activity of user a; k is the online course learning progress of the user; The average number of times a user opens an application for learning online courses per day; is the average daily online time of the user's online course application; n is the timestamp set of the user's first opening of the online course application every day; t i is the timestamp of the i-th group; λ is the correction factor;

[0015] Among them, the greater the user's online course learning activity f, the more active the user's online course learning is, and vice versa. The correction factor λ is 1 or 1.1. The calculation target of the user's online course learning activity f has fallen into the capture results of users with lagging learning progress, and the correction factor λ is 1. The calculation target does not fall into the capture results of users with lagging learning progress, and the correction factor λ is 1.1.

[0016] Furthermore, the determination logic of the online course learning supervision group is expressed as:

[0017] The captured users with lagging learning progress are recorded as set A, and the 10% of users with the lowest online course learning activity are recorded as set B;

[0018] The union of set A and set B is recorded as the online course learning supervision group;

[0019] Among them, after set B is determined, the users whose online course learning progress belongs to the 10% with the largest online course learning progress among all users are further captured in set B, the captured users are discarded in set B, and then the online course learning supervision group is determined.

[0020] Furthermore, the users in the online course learning supervision group are sorted to obtain the logical representation of the supervision queue as follows:

[0021]

[0022] Where: Q(a) is the queue ranking score of user a;

[0023] Among them, the corresponding users with larger queue ranking scores Q are arranged in front, and the corresponding users with smaller queue ranking scores Q are arranged in the back, so as to obtain a supervision queue.

[0024] Furthermore, the follow-up users are users in the online course learning application who do not belong to the online course learning supervision group. A group of follow-up users is bound to each user in the supervision queue. The users in the supervision queue independently select a group of users in the online course learning application who do not belong to the online course learning supervision group as follow-up users.

[0025] Among them, users in each group of online course learning applications who do not belong to the online course learning supervision group can serve as follow-up users of one or more online course learning supervision groups. When the users in the online course learning supervision group do not select the user according to the user, a group of users in the online course learning supervision group who do not belong to the online course learning supervision group in the online course learning application will be randomly configured as follow-up users.

[0026] Furthermore, the initial reminder cycle of online course learning is customized by the user, and the user learning status parameters include: the average number of times the user opens the application for learning online courses per day after the online course learning reminder cycle is configured, the average daily online time of the user's application for learning online courses after the online course learning reminder cycle is configured, and the user's learning progress advancement value after the online course learning reminder cycle is configured.

[0027] Furthermore, the online course learning reminder cycle decay logic is expressed as:

[0028]

[0029] Where: y is the attenuation value of the online course learning reminder cycle; t new The accumulated learning time in the online course application based on the timestamp of the initial prompt cycle of online course learning when the online course application was opened the most recently; new-1 Compared to t new The accumulated learning time in the online course application based on the timestamp of the initial reminder cycle of online course learning when the online course application was last opened; k new The learning progress advancement value in the online course application based on the timestamp of the initial prompt cycle of online course learning when the online course application is opened for the most recent time; k new-1 Compared to t new The learning progress advancement value in the online course application based on the application timestamp of the initial reminder cycle of online course learning when the online course application was last opened; ω1 and ω2 are weights;

[0030] Among them, the next online course learning reminder cycle is: the difference of 1-y, multiplied by the product of the previous online course learning reminder cycle, the learning progress advancement value is: the difference between the current learning progress and the learning progress corresponding to the application timestamp of the initial reminder cycle of the online course learning, the values ​​of the weights ω1 and ω2 are both in the range of (1, 2), and ω1<ω2.

[0031] Furthermore, based on the attenuation logic of the online course learning reminder cycle combined with the user learning status parameters, the operation of modifying the online course learning reminder cycle is executed once in each online course learning reminder cycle, and the next online course learning reminder cycle is calculated and reconfigured.

[0032] Furthermore, the online course learning reminder content fed back to the user in the online course learning reminder cycle includes: user-defined text information and the current online course learning progress of the corresponding bound follow-up user.

[0033] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0034] The present invention provides a method for promoting learning progress based on online higher education courses. During the execution of the method, the method sets an online course learning supervision group and a supervision queue by monitoring the learning progress and activity of users, thereby determining the service target of the method. Furthermore, a method of binding users with advanced online course learning progress with the method service target is adopted to bring a certain incentive effect to the method service target. At the same time, an online course learning reminder cycle is configured to continuously remind users to carry out online course learning, thereby effectively promoting the learning progress of each user's online course. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 A flowchart of a learning progress advancement method based on online higher education courses. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The present invention will be further described below in conjunction with the embodiments.

[0039] Embodiment 1:

[0040] A method for advancing learning progress based on an online higher education course in this embodiment, such as Figure 1 As shown, including:

[0041] S1: Real-time monitoring of users’ online course learning progress, and catching users who lag behind based on their online course learning progress;

[0042] S2: Real-time monitoring of users’ online course learning activity, determining the online course learning supervision group based on the users’ online course learning activity and the captured results of users who are lagging behind in learning progress, and simultaneously combining the users’ online course learning activity and the captured results of users who are lagging behind in learning progress to sort the users in the online course learning supervision group to obtain the supervision queue;

[0043] The target of monitoring the user's online course learning activity is all users in the application of learning online courses. The monitoring logic of the user's online course learning activity is expressed as follows:

[0044]

[0045] Where: f(a) is the online course learning activity of user a; k is the online course learning progress of the user; The average number of times a user opens an application for learning online courses per day; is the average daily online time of the user's online course application; n is the timestamp set of the user's first opening of the online course application every day; t i is the timestamp of the i-th group; λ is the correction factor;

[0046] Among them, the greater the user's online course learning activity f, the more active the user's online course learning is, and vice versa, the less active the user is. The correction factor λ takes a value of 1 or 1.1. The calculation target of the user's online course learning activity f has fallen into the capture results of users with lagging learning progress, and the correction factor λ takes a value of 1. The calculation target does not fall into the capture results of users with lagging learning progress, and the correction factor λ takes a value of 1.1;

[0047] Sorting the users in the online course learning supervision group to obtain the logical representation of the supervision queue is:

[0048]

[0049] Where: Q(a) is the queue ranking score of user a;

[0050] Among them, the corresponding users with larger queue ranking scores Q are arranged in front, and the corresponding users with smaller queue ranking scores Q are arranged in the back, so as to obtain a supervision queue;

[0051] S3: traverse the supervision queue and bind a group of follow-up users to each user in the supervision queue;

[0052] S4: Setting the initial reminder cycle of online course learning, configuring the online course learning reminder cycle for each user in the supervision queue, monitoring the learning state parameters of the users in the supervision queue after configuring the online course learning reminder cycle, setting the online course learning reminder cycle attenuation logic, and modifying the next online course learning reminder cycle based on the online course learning reminder cycle attenuation logic combined with the user learning state parameters;

[0053] The attenuation logic of the online course learning reminder cycle is expressed as:

[0054]

[0055] Where: y is the attenuation value of the online course learning reminder cycle; t new The accumulated learning time in the online course application based on the timestamp of the initial prompt cycle of online course learning when the online course application was opened the most recently; new-1 Compared to t new The accumulated learning time in the online course application based on the timestamp of the initial reminder cycle of online course learning when the online course application was last opened; k new The learning progress advancement value in the online course application based on the timestamp of the initial prompt cycle of online course learning when the online course application is opened for the most recent time; k new-1 Compared to t new The learning progress advancement value in the online course application based on the application timestamp of the initial reminder cycle of online course learning when the online course application was last opened; ω1 and ω2 are weights;

[0056] Among them, the next online course learning reminder cycle is: the difference of 1-y, multiplied by the previous online course learning reminder cycle, the learning progress advancement value is: the difference between the current learning progress and the learning progress corresponding to the application timestamp of the initial reminder cycle of the online course learning, the values ​​of weights ω1 and ω2 are both in the range of (1, 2), and ω1<ω2;

[0057] S5: When the online course learning reminder period of any user decays to half of the initial online course learning reminder period, the refresh step is executed.

[0058] In this embodiment, by executing the steps of the method in the above embodiment, it is possible to bring about an effect of advancing the learning progress for users of online learning courses, reduce the problem of lagging online course learning progress for users with poor autonomy, and help promote the enthusiasm of online learning users, thereby improving the actual benefits of online education courses.

[0059] Embodiment 2:

[0060] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A method for advancing learning progress based on an online higher education course in Example 1 is further described in detail:

[0061] During the user online course learning progress monitoring phase, the total duration of all courses and the total duration of the currently learned courses are identified on the user's online course learning application, and the ratio of the total duration of the currently learned courses to the total duration of all courses is recorded as the user's online course learning progress. The judgment logic for users with lagging progress is that if any set of values ​​in the total duration of the currently learned courses of each user is not less than twice the value of another set, then the other set of values ​​is judged as a user with lagging learning progress. Based on the judgment logic for users with lagging progress, users with lagging learning progress are captured;

[0062] The determination logic of the online course learning supervision group is expressed as:

[0063] The captured users with lagging learning progress are recorded as set A, and the 10% of users with the lowest online course learning activity are recorded as set B;

[0064] The union of set A and set B is recorded as the online course learning supervision group;

[0065] Among them, after set B is determined, the users whose online course learning progress belongs to the 10% with the largest online course learning progress among all users are further captured in set B, the captured users are discarded in set B, and then the online course learning supervision group is determined.

[0066] In this embodiment, through the above settings, the logic of the method in Example 1 for monitoring the learning progress of the user's online course is further limited.

[0067] Embodiment 3:

[0068] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A method for advancing learning progress based on an online higher education course in Example 1 is further described in detail:

[0069] The follow-up users are users in the online course learning application who do not belong to the online course learning supervision group. In the stage of binding a group of follow-up users for each user in the supervision queue, the users in the supervision queue independently select a group of users in the online course learning application who do not belong to the online course learning supervision group as follow-up users;

[0070] Among them, users in each group of online course learning applications who do not belong to the online course learning supervision group can serve as follow-up users of one or more online course learning supervision groups. When the users in the online course learning supervision group do not select the user according to the user, a group of users in the online course learning supervision group who do not belong to the online course learning supervision group in the online course learning application will be randomly configured as follow-up users.

[0071] Through the above settings, the logic of binding follow-up users to the online course learning supervision group in Example 1 is further limited.

[0072] like Figure 1 As shown, the initial reminder cycle for online course learning is customized by the user, and the user learning status parameters include: the average number of times the user opens the application for learning online courses per day after the online course learning reminder cycle is configured, the average daily online time of the user's application for learning online courses after the online course learning reminder cycle is configured, and the user's learning progress advancement value after the online course learning reminder cycle is configured.

[0073] like Figure 1 As shown, based on the online course learning reminder cycle attenuation logic combined with the user learning state parameters, the operation of modifying the online course learning reminder cycle is performed once in each online course learning reminder cycle, and the next online course learning reminder cycle is calculated and reconfigured;

[0074] The online course learning reminder content fed back to the user during the online course learning reminder cycle includes: user-customized text information and corresponding binding to follow up the user's current online course learning progress.

[0075] In summary, during the execution of the method in the above embodiment, the online course learning supervision group and the supervision queue are set by monitoring the user's learning progress and activity, so as to determine the service goal of the method, and further adopt the method of binding the users with the online course learning progress to the method service goal, so as to bring certain incentive effects to the method service goal, and at the same time configure an online course learning reminder cycle to continuously remind users to carry out online course learning, which effectively promotes the learning progress of each user's online course.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for advancing learning progress based on online higher education courses, characterized in that: include: S1: Real-time monitoring of users’ online course learning progress, and catching users who lag behind based on their online course learning progress; S2: Real-time monitoring of users’ online course learning activity, determining the online course learning supervision group based on the users’ online course learning activity and the captured results of users who are lagging behind in learning progress, and simultaneously combining the users’ online course learning activity and the captured results of users who are lagging behind in learning progress to sort the users in the online course learning supervision group to obtain the supervision queue; S3: traverse the supervision queue and bind a group of follow-up users to each user in the supervision queue; S4: Setting the initial reminder cycle of online course learning, configuring the online course learning reminder cycle for each user in the supervision queue, monitoring the learning state parameters of the users in the supervision queue after configuring the online course learning reminder cycle, setting the online course learning reminder cycle attenuation logic, and modifying the next online course learning reminder cycle based on the online course learning reminder cycle attenuation logic combined with the user learning state parameters; S5: When the online course learning reminder period of any user decays to half of the initial online course learning reminder period, the refresh step is executed.

2. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: During the user's online course learning progress monitoring stage, the total duration of all courses and the total duration of currently learned courses are identified on the user's online course learning application, and the ratio of the current total duration of learned courses to the total duration of all courses is recorded as the user's online course learning progress. The judgment logic for users with lagging progress is that, among the total duration of the current learned courses of each user, if any set of values ​​is not less than twice that of another set of values, then the other set of values ​​is judged as users with lagging learning progress, and users with lagging learning progress are captured based on the judgment logic for users with lagging progress.

3. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The monitoring target of the user's online course learning activity is all users in the application program for learning online courses. The monitoring logic of the user's online course learning activity is expressed as follows: Where: f(a) is the online course learning activity of user a; k is the online course learning progress of the user; The average number of times a user opens an application for learning online courses per day; The average daily online time of users studying online courses in the application; n is the timestamp set of the user opening the online course learning application for the first time every day; t i is the timestamp of the i-th group; λ is the correction factor; Among them, the greater the user's online course learning activity f, the more active the user's online course learning is, and vice versa. The correction factor λ is 1 or 1.

1. The calculation target of the user's online course learning activity f has fallen into the capture results of users with lagging learning progress, and the correction factor λ is 1. The calculation target does not fall into the capture results of users with lagging learning progress, and the correction factor λ is 1.

1.

4. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The determination logic of the online course learning supervision group is expressed as: The captured users with lagging learning progress are recorded as set A, and the 10% of users with the lowest online course learning activity are recorded as set B; The union of set A and set B is recorded as the online course learning supervision group; Among them, after set B is determined, the users whose online course learning progress belongs to the 10% with the largest online course learning progress among all users are further captured in set B, the captured users are discarded in set B, and then the online course learning supervision group is determined.

5. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: Sorting the users in the online course learning supervision group to obtain the logical representation of the supervision queue is: Where: Q(a) is the queue ranking score of user a; Among them, the corresponding users with larger queue ranking scores Q are arranged in front, and the corresponding users with smaller queue ranking scores Q are arranged in the back, so as to obtain a supervision queue.

6. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The follow-up users are users in the online course learning application who do not belong to the online course learning supervision group. In the stage of binding a group of follow-up users for each user in the supervision queue, the users in the supervision queue independently select a group of users in the online course learning application who do not belong to the online course learning supervision group as follow-up users; Among them, users in each group of online course learning applications who do not belong to the online course learning supervision group can serve as follow-up users of one or more online course learning supervision groups. When the users in the online course learning supervision group do not select the user according to the user, a group of users in the online course learning supervision group who do not belong to the online course learning supervision group in the online course learning application will be randomly configured as follow-up users.

7. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The initial reminder cycle for online course learning is customized by the user, and the user learning status parameters include: the average number of times the user opens the application for learning online courses per day after the online course learning reminder cycle is configured, the average daily online time of the user's application for learning online courses after the online course learning reminder cycle is configured, and the user's learning progress advancement value after the online course learning reminder cycle is configured.

8. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The online course learning reminder cycle decay logic is expressed as: Where: y is the attenuation value of the online course learning reminder cycle; t new The accumulated learning time in the online course application based on the timestamp of the initial reminder cycle of online course learning when the online course application was opened the most recently; t new-1 Compared to t new The accumulated learning time in the online course application based on the timestamp of the initial reminder cycle of online course learning when the online course application was last opened; k new The learning progress advancement value in the online course application based on the timestamp of the initial prompt cycle of online course learning when the online course application is opened for the most recent time; k new-1 Compared to t new The learning progress advancement value in the online course application based on the application timestamp of the initial reminder cycle of online course learning when the online course application was last opened; ω1 and ω2 are weights; Among them, the next online course learning reminder cycle is: the difference of 1-y, multiplied by the product of the previous online course learning reminder cycle, the learning progress advancement value is: the difference between the current learning progress and the learning progress corresponding to the application timestamp of the initial reminder cycle of the online course learning, the values ​​of the weights ω1 and ω2 are both in the range of (1, 2), and ω1<ω2.

9. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: Based on the attenuation logic of the online course learning reminder cycle combined with the user learning status parameters, the operation of modifying the online course learning reminder cycle is executed once in each online course learning reminder cycle, and the next online course learning reminder cycle is calculated and reconfigured.

10. A method for advancing learning progress based on online higher education courses according to claim 1, characterized in that: The online course learning reminder content fed back to the user in the online course learning reminder cycle includes: user-defined text information and the current online course learning progress of the corresponding bound follow-up user.

Images