A method for advancing a learning progression based on an online higher education course

Abstract

The present application relates to the technical field of online education, in particular to a learning progress advancing method based on online higher education courses, comprising: monitoring the learning progress of users in real time, capturing users with learning progress lag according to the learning progress of users in online courses; monitoring the learning activity of users in real time, determining the online course learning supervision group according to the learning activity of users in online courses and the capture result of users with learning progress lag, the present application sets the online course learning supervision group and supervision queue through monitoring the learning progress and activity of users, thereby determining the service target of the method, further adopting the mode of mutual binding of users with leading learning progress in online courses and the service target of the method to bring certain incentive effect to the service target of the method, and configuring the online course learning prompt period to continuously prompt users to carry out online course learning, effectively advancing the learning progress of each user in online courses.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of online education, in particular to a learning progress advancing method based on online higher education courses. BACKGROUND

[0002] Online education is a new type of education mode that uses the Internet, artificial intelligence and other modern information technologies for teaching and learning interaction, and is an important part of education services.

[0003] The invention patent with the application number 201610655837.9 discloses an online education course system, which is characterized in that the system comprises: a login module for user login to the online education course system; a learning module for user to select specific content in the corresponding course and learn; and a push module for regularly pushing learning courses to the user. The learning module comprises 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 one by one. The course content learning unit is used for displaying the specific content of the course corresponding to the label and for user learning.

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

[0005] However, online education can greatly reduce the pressure of education resources, but the supervision of online education users is poor. For users with poor autonomy, it is easy to have poor learning activity and poor learning enthusiasm of online education courses, and the supervision of online courses is low at present, so the online education courses are virtual.

[0006] Therefore, the present application provides a learning progress advancing method based on online higher education courses. SUMMARY

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

[0008] To achieve the above-mentioned purposes, the present application is realized by the following technical solutions:

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

[0010] Real-time monitoring of the progress of online courses, capturing users with progress lag according to the progress of online courses; Real-time monitoring of the learning activity of online courses, determining the online course learning supervision group according to the learning activity of online courses and the capture result of users with learning progress lag, synchronously combining the capture result of users with learning activity and learning progress lag of online courses, sorting users in the online course learning supervision group to obtain a supervision queue; Traversing the supervision queue, binding a group of follow-up users for each user in the supervision queue; Set the initial prompt period of online course learning, configure the online course learning prompt period for each user in the supervision queue, monitor the learning state parameters of the users in the supervision queue after configuring the online course learning prompt period, set the online course learning prompt period decay logic, modify the next online course learning prompt period based on the online course learning prompt period decay logic and the user learning state parameters; When the online course learning prompt period of any user decays to half of the initial online course learning prompt period, refresh the step execution.

[0011] Further, the user online course learning progress monitoring stage identifies all course total time and current total course time length on the application program for learning online courses, and the ratio of the current total course time length to the total course time length is recorded as the user online course learning progress. The determination logic of the progress lag user is that any one group of values in the current total course time length of each user is not less than twice the value of another group, and the value of another group is determined as a user with learning progress lag. The learning progress lag user is captured based on the determination logic of the progress lag user.

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

[0013]

[0014] In the formula: f(a) is the online course learning activity of user a; k is the online course learning progress of the user; is the current daily average number of times the user opens the application program for learning online courses; is the current daily average online time of the user for learning online courses; n is the set of time stamps for the user to open the application program for learning online courses for the first time every day; t i is the ith set of time stamps; λ is a correction factor;

[0015] Wherein, the greater the online course learning activity f of the user is, the more active the user is in online course learning, and vice versa. The correction factor λ is 1 or 1.1. When the calculation target of the online course learning activity f falls in the capture result of the user with learning progress lag, the correction factor λ is 1. When the calculation target of the online course learning activity f does not fall in the capture result of the user with learning progress lag, the correction factor λ is 1.1.

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

[0017] The captured users with learning progress lag are denoted as set A, and the 10% of users with the minimum online course learning activity are denoted as set B.

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

[0019] Wherein, after set B is determined, further capture the users with online course learning progress belonging to the 10% of users with the maximum online course learning progress in set B, discard the captured users in set B, and then perform the determination of the online course learning supervision group.

[0020] Further, the logic for sorting the users in the online course learning supervision group to obtain the supervision queue is represented as:

[0021]

[0022] In the formula, Q(a) is the queue sorting score of user a.

[0023] Wherein, the user with the greater queue sorting score Q is arranged in the front, and the user with the smaller queue sorting score Q is arranged in the back, to obtain the supervision queue.

[0024] Further, the follow-up user is the user not belonging to the online course learning supervision group in the application program for learning online courses. Each user in the supervision queue binds a group of follow-up users. The user in the supervision queue autonomously selects a group of users not belonging to the online course learning supervision group in the application program for learning online courses as follow-up users.

[0025] Wherein, each group of users not belonging to the online course learning supervision group in the application program for learning online courses can be used as the follow-up user of one or more users in the online course learning supervision group. If the user in the online course learning supervision group does not select, a group of users not belonging to the online course learning supervision group in the application program for learning online courses is randomly configured as the follow-up user of the user in the online course learning supervision group.

[0026] Furthermore, the initial prompting period 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 prompting period is configured, the average online time of the application for learning online courses per day after the online course learning prompting period is configured, and the user's learning progress value after the online course learning prompting period is configured.

[0027] Furthermore, the online course learning prompt period decay logic is expressed as follows:

[0028]

[0029] In the formula: y is the decay value of the online course learning reminder cycle; t new The cumulative learning time in the online course application, based on the timestamp applied during the initial prompt period of online course learning, when the online course application was last opened; t new-1 Compared to t new The cumulative learning time in the online course application, based on the timestamp applied during the initial learning period after the last time the online course application was opened; k new The learning progress value in the online course application based on the timestamp applied during the initial prompt period of the online course learning period, when the online course application was last opened; k new-1 Compared to t new The learning progress value in the online course application based on the timestamp of the initial prompt period when the online course application was last opened; ω1 and ω2 are weights.

[0030] The next online course learning prompt period is the product of the difference between 1 and y and the previous online course learning prompt period. The learning progress advancement value is the difference between the current learning progress and the learning progress corresponding to the timestamp of the initial prompt period of the online course learning. The values ​​of weights ω1 and ω2 are both within the range of (1, 2), and ω1 < ω2.

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

[0032] Furthermore, the online course learning prompts provided to the user during the online course learning prompt period include: user-defined text information and corresponding tracking of the user's current online course learning progress.

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

[0034] The application provides a learning progress promoting method based on online higher education courses. In the execution process, the learning progress and activity of users are monitored, an online course learning supervision group and a supervision queue are set, the service target of the method is determined, the users with high learning progress of the online courses are bound to the service target of the method, the users are continuously prompted to carry out online course learning by setting an online course learning prompt period, and the learning progress of the users in the online courses is effectively promoted. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

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

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0038] The present application will be further described below in combination with the embodiments.

[0039] Embodiment 1:

[0040] A learning progress promoting method based on online higher education courses in this embodiment, as shown in Figure 1 , includes:

[0041] S1: Real-time monitoring of the learning progress of users in online courses, capturing users with progress lag according to the learning progress of users in online courses;

[0042] S2: Real-time monitoring of the user's online course learning activity, determining the online course learning supervision group according to the user's online course learning activity and the capture result of the learning progress lag, synchronously combining the user's online course learning activity and the capture result of the learning progress lag, sorting the users in the online course learning supervision group to obtain a supervision queue;

[0043] The monitoring target of the user's online course learning activity is all users in the application program for learning online courses, and the monitoring logic of the user's online course learning activity is represented as:

[0044]

[0045] In the formula: f(a) is the online course learning activity of user a; k is the online course learning progress of the user; is the number of times the user opens the application program for learning online courses per day; is the online time of the user for learning online courses per day; n is a set of time stamps for the user to open the application program for learning online courses for the first time every day; t i is the i-th set of time stamps; λ is a correction factor;

[0046] Wherein, the greater the user's online course learning activity f, the more active the user's online course learning, and vice versa, the less active the user's online course learning, the correction factor λ is 1 or 1.1, the calculation target of the user's online course learning activity f falls in the capture result of the learning progress lag, the correction factor λ is 1, and the calculation target does not fall in the capture result of the learning progress lag, the correction factor λ is 1.1;

[0047] The logic representation for sorting the users in the online course learning supervision group to obtain a supervision queue is:

[0048]

[0049] In the formula: Q(a) is the queue sorting score of user a;

[0050] Wherein, the user corresponding to the greater queue sorting score Q is arranged in front, and the user corresponding to the smaller queue sorting score Q is arranged in back to obtain the supervision queue;

[0051] S3: Traversing the supervision queue, binding a group of follow-up users for each user in the supervision queue;

[0052] S4: set the initial online course learning prompt period, configure the online course learning prompt period for each user in the supervision queue, monitor the learning state parameters of the users in the supervision queue after the online course learning prompt period is configured, set the online course learning prompt period decay logic, modify the next online course learning prompt period based on the online course learning prompt period decay logic and the user learning state parameters;

[0053] The online course learning prompt period decay logic is represented as:

[0054]

[0055] In the formula, y is the online course learning prompt period decay value; t new is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; t new-1 is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; k new is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; k new is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; k new-1 is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; k new is the cumulative learning time in the online course application based on the online course learning initial prompt period application timestamp when the online course application is opened last time; ω1, ω2 are weights;

[0056] The next online course learning prompt period is the difference between 1-y and the product of the last online course learning prompt period, the learning progress promotion value is the difference between the current learning progress and the learning progress corresponding to the online course learning initial prompt period application timestamp, and the weights ω1, ω2 are both in the range of (1, 2) and ω1< ω2.

[0057] S5: refresh the step execution when the online course learning prompt period of any user decays to half of the online course learning initial prompt period.

[0058] In this embodiment, the steps of the method in the above embodiment can bring learning progress promotion effect to the users of online learning courses, reduce the problem of lagging learning progress of users with poor autonomy, and promote the enthusiasm of online learning users, thereby improving the actual benefits of online education courses.

[0059] Embodiment 2:

[0060] In the specific implementation level, on the basis of embodiment 1, this embodiment refers toFigure 1 Further specific description is made to the learning progress promoting method based on online higher education courses in Embodiment 1:

[0061] In the user online course learning progress monitoring stage, all course total time and current learned course time total value in the application program for learning online courses are identified, and the ratio of the current learned course time total value to the course total time is recorded as the user online course learning progress. The determination logic of the progress lagged user is that, in the current learned course time total value of each user, any one group of values is not less than twice the value of another group, and the value of another group is determined as the learning progress lagged user. The learning progress lagged user is captured based on the determination logic of the progress lagged user;

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

[0063] The captured learning progress lagged user is recorded as set A, and the 10% of users with the minimum 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 set B, the online course learning progress of the user belongs to the 10% of users with the maximum online course learning progress of all users, and the captured user is discarded in set B, and the determination of the online course learning supervision group is further executed.

[0066] In this embodiment, the above setting further limits the logic of the method for monitoring the user online course learning progress in Embodiment 1.

[0067] Embodiment 3:

[0068] In the specific implementation level, on the basis of Embodiment 1, this embodiment refers to Figure 1 Further specific description is made to the learning progress promoting method based on online higher education courses in Embodiment 1:

[0069] The follow-up user is the user in the application program for learning online courses which does 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 user in the supervision queue selects a group of users in the application program for learning online courses which do not belong to the online course learning supervision group as the follow-up users;

[0070] The user in the application program for learning the online course and not belonging to the user in the online course learning supervision group can be a follow-up user of one or more online course learning supervision groups.

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

[0072] As shown in Figure 1 The online course learning initial prompt period is defined by the user end, and the user learning state parameters include: the number of times of opening the application program for learning the online course per day after the online course learning prompt period is configured, the online course learning prompt period online time of the user per day after the online course learning prompt period is configured, and the user learning progress promotion value after the online course learning prompt period is configured.

[0073] As shown in Figure 1 Based on the online course learning prompt period decay logic and the user learning state parameters, the operation of modifying the online course learning prompt period is performed once in each online course learning prompt period, and the next online course learning prompt period is calculated and reconfigured.

[0074] The online course learning prompt content fed back to the user in the online course learning prompt period includes: the user end defined text information and the current online course learning progress of the corresponding bound follow-up user.

[0075] In summary, in the execution process of the method in the above embodiments, the learning progress and activity of the user are monitored, the online course learning supervision group and the supervision queue are set, and the service target of the method is determined. Further, the user with the leading online course learning progress is bound to the service target of the method, which brings a certain incentive effect to the service target of the method. At the same time, the online course learning prompt period is configured to continuously prompt the user to carry out online course learning, and the learning progress of each user in the online course is effectively promoted.

[0076] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

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 identification of users lagging behind in the progress of users' online course learning; S2: Monitor users' online course learning activity in real time. Based on the capture results of users' online course learning activity and users with lagging learning progress, determine the online course learning supervision group. Simultaneously combine the capture results of users' online course learning activity and users with lagging learning progress to sort the users in the online course learning supervision group to obtain the supervision queue. The target of the online course learning activity monitoring is all users in the application for learning online courses. The logic for monitoring learning activity during classes is expressed as follows: ; In the formula: f(a) represents the online course learning activity level of user a; k represents the user's online course learning progress; The average number of times a user opens the application for learning online courses per day; This represents the average daily online time a user spends learning online courses through the application. n is the set of timestamps for the first time a user opens the application for learning online courses each day. t i For the i-th group of timestamps; λ is a correction factor; where, the larger the user's online course learning activity f, the more active the user is in online course learning, and vice versa, the less active the user is. The correction factor λ takes the value of 1 or 1.

1. If the user's online course learning activity f is calculated in the capture results of users whose learning progress is lagging behind, the correction factor λ takes the value of 1. If the calculation target is not in the capture results of users whose learning progress is lagging behind, the correction factor λ takes the value of 1.

1. S3: Traverse the supervisor queue and bind a set of follow-up users to each user in the supervisor queue; S4: Set the initial reminder period for online course learning, configure the online course learning reminder period for each user in the monitoring queue, monitor the learning status parameters of users in the monitoring queue after configuring the online course learning reminder period, set the decay logic of the online course learning reminder period, and modify the next online course learning reminder period based on the decay logic of the online course learning reminder period and the user's learning status parameters. The online course learning prompt period decay logic is expressed as follows: ; In the formula: y is the decay value of the online course learning reminder cycle; t new The cumulative learning time in the online course application based on the timestamp applied during the initial prompt period of online course learning when the online course application was last opened. t new-1 Compared to t new The cumulative learning time in the online course application, based on the timestamp applied during the initial learning period after the last time the online course application was opened; k new The learning progress progress value in the online course application based on the timestamp of the initial prompt period for online course learning when the online course application is last opened; k new-1 Compared to t new The learning progress advancement value in the online course application based on the timestamp of the initial prompt period of the online course learning when the application was last opened; ω1 and ω2 are weights; where, the next online course learning prompt period is: the product of the difference between 1-y and the previous online course learning prompt period, the learning progress advancement value is: the difference between the current learning progress and the learning progress corresponding to the timestamp of the initial prompt period of the online course learning, the weights ω1 and ω2 are both in the range of (1, 2), and ω1 < ω2; S5: When the online course learning prompt period for any user decays to half of the initial online course learning prompt period, the refresh step is executed.

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

3. The method for advancing learning progress based on online higher education courses according to claim 1, characterized in that, The logic for determining the online course learning supervision group is as follows: users whose learning progress is lagging behind are denoted as set A, and the 10% of users with the lowest online course learning activity are denoted as set B; the union of set A and set B is denoted as the online course learning supervision group; after set B is determined, users whose online course learning progress belongs to the 10% of all users with the highest online course learning progress are further captured in set B, and the captured users are discarded from set B before the determination of the online course learning supervision group is performed again.

4. The method for advancing learning progress based on online higher education courses according to claim 1, characterized in that, The logical representation of sorting users in the online course learning supervision group to obtain the supervision queue is as follows: In the formula: Q(a) is the queue sorting score of user a; where users with larger queue sorting scores Q are arranged first, and users with smaller queue sorting scores Q are arranged last, so as to obtain the supervision queue.

5. The 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 are not in the online course learning supervision group. A set of follow-up users is bound to each user in the supervision queue. Users in the supervision queue can choose a set of users in the online course learning application who are not in the online course learning supervision group as follow-up users. Each set of users in the online course learning application who are not in the online course learning supervision group can be used as follow-up users for one or more online course learning supervision groups. If a user in the online course learning supervision group does not select a follow-up user, a set of users in the online course learning supervision group who are not in the online course learning supervision group are randomly assigned as follow-up users.

6. The method for advancing learning progress based on online higher education courses according to claim 1, characterized in that, Based on the online course learning prompt cycle decay logic combined with user learning status parameters, the operation of modifying the online course learning prompt cycle is executed once in each online course learning prompt cycle, and the calculation and reconfiguration are performed for the next online course learning prompt cycle.

7. The method for advancing learning progress based on online higher education courses according to claim 1, characterized in that, The online course learning reminders provided to users during the online course learning reminder period include: user-defined text information and corresponding tracking of the user's current online course learning progress.

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