CN113052737A - Learning method and system based on forced supervision and intelligent recommendation - Google Patents
Learning method and system based on forced supervision and intelligent recommendation Download PDFInfo
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- CN113052737A CN113052737A CN202110480783.8A CN202110480783A CN113052737A CN 113052737 A CN113052737 A CN 113052737A CN 202110480783 A CN202110480783 A CN 202110480783A CN 113052737 A CN113052737 A CN 113052737A
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Abstract
The invention discloses a learning method and a system based on forced supervision and intelligent recommendation, wherein the method comprises the following steps: s1, a learning task is issued to a user by a learning platform, and after the user logs in the learning platform, the learning platform learns courses corresponding to the learning task on the basis of the learning task; s2, in the learning process of the user, the learning platform randomly pops up a question and answer page related to the course, and the course can be played continuously after the user answers correctly; s3, after course learning is completed, the learning platform generates post-class detection test questions for the user, evaluates the answer results of the user to obtain detection scores, and carries out wrong question statistics; and S4, combining the course learning condition of the user by the learning platform, and intelligently recommending courses to the user. The invention can effectively realize supervision in the learning process, improve the learning efficiency and the learning effect, and can improve the effectiveness and the accuracy of course pushing.
Description
Technical Field
The invention relates to intelligent education, in particular to a learning method and a system based on forced supervision and intelligent recommendation.
Background
With the development of technologies such as cloud computing, education work which can only be performed on line in the past has gradually started to be performed on line, and particularly when the work is inconvenient to go out and gather, online education and online learning become a research hotspot gradually.
However, at present, online education lacks an effective supervision mechanism, and compared with offline education, students are more likely to have the situations of being not concentrated in energy, poor in size and the like, so that the online learning efficiency is greatly reduced; meanwhile, the existing online learning system is difficult to effectively recommend courses according to the self condition of the user.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a learning method and a system based on forced supervision and intelligent recommendation, which can effectively realize supervision in the learning process, improve the learning efficiency and the learning effect, and improve the effectiveness and the accuracy of course pushing.
The purpose of the invention is realized by the following technical scheme: a learning method based on forced supervision and intelligent recommendation comprises the following steps:
s1, a learning task is issued to a user by a learning platform, and after the user logs in the learning platform, the learning platform learns courses corresponding to the learning task on the basis of the learning task;
s2, in the learning process of the user, the learning platform randomly pops up a question and answer page related to the course, and the course can be played continuously after the user answers correctly;
s3, after course learning is completed, the learning platform generates post-class detection test questions for the user, evaluates the answer results of the user to obtain detection scores, and carries out wrong question statistics;
and S4, combining the course learning condition of the user by the learning platform, and intelligently recommending courses to the user.
Preferably, in step S1, the lesson on the learning platform is a video lesson which does not allow fast forwarding and dragging. In step S1, the user logs in the learning platform through the wechat applet or the WEB on the PC computer.
The step S4 includes:
for any target user, counting the times that the target user has performed course learning in the platform;
judging whether the learning times are larger than a preset threshold value:
if not, recommending courses based on the popularity and the basic information of the target user;
and if so, recommending the course based on the target user behavior, the target user basic information and the course relevance.
Wherein the course recommendation process based on popularity and basic information of the target user comprises the following steps:
A01. traversing courses on the learning platform, and counting the number of browsing people and the number of learning people for each course; the number of browsing people refers to the number of people who view the basic information of the course, and the number of learning people refers to the number of people who learn the course;
A02. and carrying out weighted fusion on the number of learners and the number of browsers of each course to obtain the popularity of each course:
for any course, if the number of browsing people is x and the number of learning people is y, the popularity Z of the course is calculated as follows:
Z=a*x+b*y；
wherein a and b are preset weighting coefficients, b > a >0, and a + b = 1;
A03. sorting according to the popularity of the courses from big to small, taking the top M courses as an initial set recommended by the courses, wherein M is a preset constant;
a04, based on the basic information of the target user, obtaining a final course recommendation set from the initial set of course recommendations:
and screening the courses in the initial set according to the preference condition of other users consistent with the basic information of the target user on the courses in the initial set to obtain a final recommended set recommended to the user:
screening other users with the age range, the post, the region, the subject and the gender of the target user from all the users of the platform as consistent users;
screening out courses collected, browsed or learned by the consistent user from the initial set, and adding the courses into the final course recommendation set;
A05. and pushing the courses in the final course recommendation set to the target user.
The course recommendation process based on the target user behavior, the target user basic information and the course relevance comprises the following steps:
b01, based on platform association rules, selecting other courses related to the courses which have been learned by the target user from the platform courses, and adding the selected courses into the set A as association rule selection results; the platform association rule is used for indicating whether the courses are related or not, is preset by the platform, and for any course, other courses related to the course are known under the platform association rule;
b02, based on the collaborative filtering rule, performing circle-level division on the target user, screening the platform courses according to the course preference condition of other users in the same circle with the target user, and recording the screened courses into a set B as the selection result of the collaborative filtering rule:
screening other users with the age range, the post, the region, the subject and the gender consistent with those of the target user from all users of the platform as users in the same circle layer;
screening out courses collected, browsed or learned by users in the same circle layer from the platform courses, and adding the courses into the set B;
b03, adding the courses in the set A and the set B into the same set, and removing repeated courses to obtain an alternative set T1;
b04, acquiring a course set interested by the user based on the target user behavior:
the learning platform sets at least one keyword for each course on the platform; for each course browsed or learned by a user, the learning platform adds the corresponding keyword into the keyword set as an interesting keyword, and for repeated keywords, counts the repeated times;
for each interested keyword, the learning platform screens all courses containing the keyword from the course platform and adds the courses into the same set to form a course set which is interested by the user;
b05, the learning platform calculates the average learning duration according to the learning duration of each learning process of the user, then selects courses with the difference between the duration and the average learning duration not exceeding a set threshold from the course set in which the user is interested, and adds the courses into the alternative set T2;
b06, the learning platform acquires an intersection of the alternative set T1 and the alternative set T2 to obtain a final course recommendation set and recommend the final course recommendation set to the user.
A learning system based on forced governor and intelligent recommendation, comprising:
the task issuing module is used for the learning platform to issue the learning tasks to the user;
the platform learning module is used for learning courses corresponding to the learning tasks on the learning platform based on the learning tasks after the user logs in the platform;
the forced supervising module is used for enabling the learning platform to randomly pop up a question and answer page related to the curriculum in the learning process of the user, and the curriculum can be played continuously after the user answers the page correctly;
the post-lesson detection module is used for generating post-lesson detection test questions for the user by the learning platform after the course learning is finished, evaluating the answer results of the user to obtain detection scores and carrying out wrong question statistics;
the intelligent recommendation module is used for the learning platform to perform intelligent course recommendation to the user by combining the course learning condition of the user
The invention has the beneficial effects that: the invention can effectively realize supervision in the learning process, improve the learning efficiency and the learning effect, and can improve the effectiveness and the accuracy of course pushing.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a learning method based on forced governor and intelligent recommendation is characterized in that: the method comprises the following steps:
s1, a learning task is issued to a user by a learning platform, and after the user logs in the learning platform, the learning platform learns courses corresponding to the learning task on the basis of the learning task;
s2, in the learning process of the user, the learning platform randomly pops up a question and answer page related to the course, and the course can be played continuously after the user answers correctly;
s3, after course learning is completed, the learning platform generates post-class detection test questions for the user, evaluates the answer results of the user to obtain detection scores, and carries out wrong question statistics;
and S4, combining the course learning condition of the user by the learning platform, and intelligently recommending courses to the user.
In an embodiment of the present application, in the step S1, the lesson on the learning platform is a video lesson which does not allow fast forwarding and dragging; in step S1, the user logs in the learning platform through the wechat applet or the WEB on the PC computer.
After the detection in step S3 is completed, the method further includes a learning report generation step:
the learning platform analyzes the learning situation according to the specific situation of the learning process: firstly, counting the delay time of each question and answer page answered by a user; then, counting the correct rate of the user to answer the question and answer page, and analyzing the learning condition of the user to obtain the learning habit of the user; the learning habits comprise the time length for which a user can concentrate on learning in course learning;
the learning platform analyzes knowledge point mastering conditions of the user based on detection scores and wrong statistics of the user;
and then generating a learning report of the user according to the delay time of the user for answering the question and answer page in the course learning process, the answer accuracy, the learning habit of the user, the detection score of the user in the detection process, the wrong question statistical result and the knowledge point mastering condition obtained by analysis, and viewing the learning report by the user and a superior login platform of the user.
In embodiments of the present application, the learning platform may also exceed a threshold for the delay time of the answer; then, counting users with answer accuracy lower than a threshold value on the question and answer page, and increasing the pop-up times of the question and answer page when the users are on the bus to urge the users to concentrate on learning;
further, the step S4 includes:
for any target user, counting the times that the target user has performed course learning in the platform;
judging whether the learning times are larger than a preset threshold value:
if not, recommending courses based on the popularity and the basic information of the target user;
and if so, recommending the course based on the target user behavior, the target user basic information and the course relevance.
Wherein the course recommendation process based on popularity and basic information of the target user comprises the following steps:
A01. traversing courses on the learning platform, and counting the number of browsing people and the number of learning people for each course; the number of browsing people refers to the number of people who view the basic information of the course, and the number of learning people refers to the number of people who learn the course;
A02. and carrying out weighted fusion on the number of learners and the number of browsers of each course to obtain the popularity of each course:
for any course, if the number of browsing people is x and the number of learning people is y, the popularity Z of the course is calculated as follows:
Z=a*x+b*y；
wherein a and b are preset weighting coefficients, b > a >0, and a + b = 1;
A03. sorting according to the popularity of the courses from big to small, taking the top M courses as an initial set recommended by the courses, wherein M is a preset constant;
a04, based on the basic information of the target user, obtaining a final course recommendation set from the initial set of course recommendations:
and screening the courses in the initial set according to the preference condition of other users consistent with the basic information of the target user on the courses in the initial set to obtain a final recommended set recommended to the user:
screening other users with the age range, the post, the region, the subject and the gender of the target user from all the users of the platform as consistent users;
screening out courses collected, browsed or learned by the consistent user from the initial set, and adding the courses into the final course recommendation set;
A05. and pushing the courses in the final course recommendation set to the target user.
The course recommendation process based on the target user behavior, the target user basic information and the course relevance comprises the following steps:
b01, based on platform association rules, selecting other courses related to the courses which have been learned by the target user from the platform courses, and adding the selected courses into the set A as association rule selection results; the platform association rule is used for indicating whether the courses are related or not, is preset by the platform, and for any course, other courses related to the course are known under the platform association rule;
b02, based on the collaborative filtering rule, performing circle-level division on the target user, screening the platform courses according to the course preference condition of other users in the same circle with the target user, and recording the screened courses into a set B as the selection result of the collaborative filtering rule:
screening other users with the age range, the post, the region, the subject and the gender consistent with those of the target user from all users of the platform as users in the same circle layer;
screening out courses collected, browsed or learned by users in the same circle layer from the platform courses, and adding the courses into the set B;
b03, adding the courses in the set A and the set B into the same set, and removing repeated courses to obtain an alternative set T1;
b04, acquiring a course set interested by the user based on the target user behavior:
the learning platform sets at least one keyword for each course on the platform; for each course browsed or learned by a user, the learning platform adds the corresponding keyword into the keyword set as an interesting keyword, and for repeated keywords, counts the repeated times;
for each interested keyword, the learning platform screens all courses containing the keyword from the course platform and adds the courses into the same set to form a course set which is interested by the user;
b05, the learning platform calculates the average learning duration according to the learning duration of each learning process of the user, then selects courses with the difference between the duration and the average learning duration not exceeding a set threshold from the course set in which the user is interested, and adds the courses into the alternative set T2;
b06, the learning platform acquires an intersection of the alternative set T1 and the alternative set T2 to obtain a final course recommendation set and recommend the final course recommendation set to the user.
In the step B06, the learning platform recommends the number of times of occurrence of the keyword of each course in the final recommendation set according to the statistics, and recommends the keyword to the user after arranging the keywords from large to small;
regarding the course containing a keyword, taking the occurrence frequency of the keyword in the keyword set as the occurrence frequency of the keyword of the course;
for a course containing a plurality of keywords, the times of occurrence of each keyword in the keyword set are added to serve as the times of occurrence of the keywords of the course.
As shown in fig. 2, a learning system based on forced governor and intelligent recommendation includes:
the task issuing module is used for the learning platform to issue the learning tasks to the user;
the platform learning module is used for learning courses corresponding to the learning tasks on the learning platform based on the learning tasks after the user logs in the platform;
the forced supervising module is used for enabling the learning platform to randomly pop up a question and answer page related to the curriculum in the learning process of the user, and the curriculum can be played continuously after the user answers the page correctly;
the post-lesson detection module is used for generating post-lesson detection test questions for the user by the learning platform after the course learning is finished, evaluating the answer results of the user to obtain detection scores and carrying out wrong question statistics;
and the intelligent recommendation module is used for combining the course learning condition of the user by the learning platform and intelligently recommending the courses to the user.
While the foregoing description shows and describes a preferred embodiment of the invention, it is to be understood, as noted above, that the invention is not limited to the form disclosed herein, but is not intended to be exhaustive or to exclude other embodiments and may be used in various other combinations, modifications, and environments and may be modified within the scope of the inventive concept described herein by the above teachings or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A learning method based on forced supervision and intelligent recommendation is characterized in that: the method comprises the following steps:
s1, a learning task is issued to a user by a learning platform, and after the user logs in the learning platform, the learning platform learns courses corresponding to the learning task on the basis of the learning task;
s2, in the learning process of the user, the learning platform randomly pops up a question and answer page related to the course, and the course can be played continuously after the user answers correctly;
s3, after course learning is completed, the learning platform generates post-class detection test questions for the user, evaluates the answer results of the user to obtain detection scores, and carries out wrong question statistics;
and S4, combining the course learning condition of the user by the learning platform, and intelligently recommending courses to the user.
2. The learning method based on forced governor and intelligent recommendation of claim 1, wherein: in step S1, the lesson on the learning platform is a video lesson that does not allow fast forwarding and dragging.
3. The learning method based on forced governor and intelligent recommendation of claim 1, wherein: in step S1, the user logs in the learning platform through the wechat applet or the WEB on the PC computer.
4. The learning method based on forced governor and intelligent recommendation of claim 1, wherein: after the detection in step S3 is completed, the method further includes a learning report generation step:
the learning platform analyzes the learning situation according to the specific situation of the learning process: firstly, counting the delay time of each question and answer page answered by a user; then, counting the correct rate of the user to answer the question and answer page, and analyzing the learning condition of the user to obtain the learning habit of the user; the learning habits comprise the time length for which a user can concentrate on learning in course learning;
the learning platform analyzes knowledge point mastering conditions of the user based on detection scores and wrong statistics of the user;
and then generating a learning report of the user according to the delay time of the user for answering the question and answer page in the course learning process, the answer accuracy, the learning habit of the user, the detection score of the user in the detection process, the wrong question statistical result and the knowledge point mastering condition obtained by analysis, and viewing the learning report by the user and a superior login platform of the user.
5. The learning method based on forced governor and intelligent recommendation of claim 1, wherein: the step S4 includes:
for any target user, counting the times that the target user has performed course learning in the platform;
judging whether the learning times are larger than a preset threshold value:
if not, recommending courses based on the popularity and the basic information of the target user;
and if so, recommending the course based on the target user behavior, the target user basic information and the course relevance.
6. The learning method based on forced governor and intelligent recommendation of claim 5, wherein: the course recommendation process based on the popularity and the basic information of the target user comprises the following steps:
A01. traversing courses on the learning platform, and counting the number of browsing people and the number of learning people for each course; the number of browsing people refers to the number of people who view the basic information of the course, and the number of learning people refers to the number of people who learn the course;
A02. and carrying out weighted fusion on the number of learners and the number of browsers of each course to obtain the popularity of each course:
for any course, if the number of browsing people is x and the number of learning people is y, the popularity Z of the course is calculated as follows:
Z=a*x+b*y；
wherein a and b are preset weighting coefficients, b > a >0, and a + b = 1;
A03. sorting according to the popularity of the courses from big to small, taking the top M courses as an initial set recommended by the courses, wherein M is a preset constant;
a04, based on the basic information of the target user, obtaining a final course recommendation set from the initial set of course recommendations:
and screening the courses in the initial set according to the preference condition of other users consistent with the basic information of the target user on the courses in the initial set to obtain a final recommended set recommended to the user:
screening other users with the age range, the post, the region, the subject and the gender of the target user from all the users of the platform as consistent users;
screening out courses collected, browsed or learned by the consistent user from the initial set, and adding the courses into the final course recommendation set;
A05. and pushing the courses in the final course recommendation set to the target user.
7. The learning method based on forced governor and intelligent recommendation of claim 5, wherein: the course recommendation process based on the target user behavior, the target user basic information and the course relevance comprises the following steps:
b01, based on platform association rules, selecting other courses related to the courses which have been learned by the target user from the platform courses, and adding the selected courses into the set A as association rule selection results; the platform association rule is used for indicating whether the courses are related or not, is preset by the platform, and for any course, other courses related to the course are known under the platform association rule;
b02, based on the collaborative filtering rule, performing circle-level division on the target user, screening the platform courses according to the course preference condition of other users in the same circle with the target user, and recording the screened courses into a set B as the selection result of the collaborative filtering rule:
screening other users with the age range, the post, the region, the subject and the gender consistent with those of the target user from all users of the platform as users in the same circle layer;
screening out courses collected, browsed or learned by users in the same circle layer from the platform courses, and adding the courses into the set B;
b03, adding the courses in the set A and the set B into the same set, and removing repeated courses to obtain an alternative set T1;
b04, acquiring a course set interested by the user based on the target user behavior:
the learning platform sets at least one keyword for each course on the platform; for each course browsed or learned by a user, the learning platform adds the corresponding keyword into the keyword set as an interesting keyword, and for repeated keywords, counts the repeated times;
for each interested keyword, the learning platform screens all courses containing the keyword from the course platform and adds the courses into the same set to form a course set which is interested by the user;
b05, the learning platform calculates the average learning duration according to the learning duration of each learning process of the user, then selects courses with the difference between the duration and the average learning duration not exceeding a set threshold from the course set in which the user is interested, and adds the courses into the alternative set T2;
b06, the learning platform acquires an intersection of the alternative set T1 and the alternative set T2 to obtain a final course recommendation set and recommend the final course recommendation set to the user.
8. The learning method based on forced governor and intelligent recommendation of claim 7, wherein: in the step B06, the learning platform recommends the number of times of occurrence of the keyword of each course in the final recommendation set according to the statistics, and recommends the keyword to the user after arranging the keywords from large to small;
regarding the course containing a keyword, taking the occurrence frequency of the keyword in the keyword set as the occurrence frequency of the keyword of the course;
for a course containing a plurality of keywords, the times of occurrence of each keyword in the keyword set are added to serve as the times of occurrence of the keywords of the course.
9. A learning system based on forced governor and intelligent recommendation, based on the method of any one of claims 1-8, characterized in that: the method comprises the following steps:
the task issuing module is used for the learning platform to issue the learning tasks to the user;
the platform learning module is used for learning courses corresponding to the learning tasks on the learning platform based on the learning tasks after the user logs in the platform;
the forced supervising module is used for enabling the learning platform to randomly pop up a question and answer page related to the curriculum in the learning process of the user, and the curriculum can be played continuously after the user answers the page correctly;
the post-lesson detection module is used for generating post-lesson detection test questions for the user by the learning platform after the course learning is finished, evaluating the answer results of the user to obtain detection scores and carrying out wrong question statistics;
and the intelligent recommendation module is used for combining the course learning condition of the user by the learning platform and intelligently recommending the courses to the user.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113269667A (en) * | 2021-07-20 | 2021-08-17 | 北京点趣教育科技有限公司 | Wrong question pushing method and device and electronic equipment |
| CN114418807A (en) * | 2022-03-30 | 2022-04-29 | 北京英华在线科技有限公司 | Course recommendation method and system of online education platform based on historical scores |
| CN114510600A (en) * | 2022-04-18 | 2022-05-17 | 光合新知(北京)科技有限公司 | Learning system and method based on human-computer interaction |
| CN115511677A (en) * | 2022-10-18 | 2022-12-23 | 天津景润网络科技有限公司 | Intelligent education resource platform allocation method and system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080294625A1 (en) * | 2007-05-23 | 2008-11-27 | Hitachi, Ltd. | Item recommendation system |
| WO2016029627A1 (en) * | 2014-08-29 | 2016-03-03 | 风腾科技（北京）有限公司 | Course teaching system and operation method for course teaching system |
| US20170061286A1 (en) * | 2015-08-27 | 2017-03-02 | Skytree, Inc. | Supervised Learning Based Recommendation System |
| CN108986574A (en) * | 2018-09-06 | 2018-12-11 | 北京春秋泰阁文化传播有限公司 | A kind of immediate interactive type and big data analysis online teaching platform and method |
| CN109740048A (en) * | 2018-12-12 | 2019-05-10 | 中国联合网络通信集团有限公司 | A kind of course recommended method and device |
| CN109740861A (en) * | 2018-12-12 | 2019-05-10 | 中国联合网络通信集团有限公司 | A kind of learning data analysis method and device |
| CN111191122A (en) * | 2019-12-20 | 2020-05-22 | 重庆邮电大学 | Learning resource recommendation system based on user portrait |
| CN111626901A (en) * | 2020-05-19 | 2020-09-04 | 南京汉天软件有限公司 | Household remote education system and method |
-
2021
- 2021-04-30 CN CN202110480783.8A patent/CN113052737A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080294625A1 (en) * | 2007-05-23 | 2008-11-27 | Hitachi, Ltd. | Item recommendation system |
| WO2016029627A1 (en) * | 2014-08-29 | 2016-03-03 | 风腾科技（北京）有限公司 | Course teaching system and operation method for course teaching system |
| US20170061286A1 (en) * | 2015-08-27 | 2017-03-02 | Skytree, Inc. | Supervised Learning Based Recommendation System |
| CN108986574A (en) * | 2018-09-06 | 2018-12-11 | 北京春秋泰阁文化传播有限公司 | A kind of immediate interactive type and big data analysis online teaching platform and method |
| CN109740048A (en) * | 2018-12-12 | 2019-05-10 | 中国联合网络通信集团有限公司 | A kind of course recommended method and device |
| CN109740861A (en) * | 2018-12-12 | 2019-05-10 | 中国联合网络通信集团有限公司 | A kind of learning data analysis method and device |
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