CN112131483A - Personalized course content recommendation method for online vocational education - Google Patents
Personalized course content recommendation method for online vocational education Download PDFInfo
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Abstract
The invention discloses a personalized course content recommendation method for online education, which comprises the steps of fragmenting courses, marking difficulty, combining with real-time feedback data of learners to perform dynamic course content recommendation and optimization, and simultaneously performing further optimization information collection on the current courses. The method has the advantages that a certain chapter or knowledge point can be directly selected for learning, the feedback information of the user for listening in the course in real time can be obtained, the follow-up recommended content can be adjusted according to the feedback information, and the course can be optimized.
Description
Technical Field
The invention relates to the technical field of online education, in particular to a personalized course content recommendation method for online education.
Background
Online education is an education method which is quite popular in recent years. In the prior art, online education is mainly performed in the form of online video courses, and generally, a learner registers a special account, fills in relevant education background information and course information to be learned, and recommends a corresponding course according to the course information input by the learner. Users can study directly through the network without going to school, and most of the users are recorded video courses which can be played repeatedly, so the users are increasingly popular with people who cannot go to school to study intensively.
The inventor of the patent application finds that the current online education course recommendation method has the following defects: firstly, the method comprises the following steps: course recommendation is very general, learners generally only can select a large course name first, and after entering a course, the learners select the course sections to be learned according to the course catalog, namely, the learners cannot directly select a certain section or knowledge point to learn. Secondly, the method comprises the following steps: the academic levels and comprehensive abilities of the taught objects are different and have large differences. For the situation, users with good bases can skip some basic courses in the learning process, users with poor bases cannot skip the basic courses, the existing recommendation method has no automatic filtering and screening functions, and the users can only skip manually one by one in the course of listening to the courses to screen the basic courses, namely, the existing recommendation method is not intelligent enough. Thirdly, the method comprises the following steps: the current teaching method is short of real-time course effect feedback, namely, in the course of a user listening to a course, a course system does not know the situation of the user listening to the course at that time, and can only obtain the feedback of the user through post-class survey or quiz at most, and the feedback sometimes can not reflect the real opinion of the user on the course, so that the course cannot be optimized and improved better.
In summary, there is a lack of a personalized course content recommendation method that can directly select a certain chapter or knowledge point for learning, can obtain feedback information of a user listening to a course in real time, and can adjust subsequent recommendation content and optimize the online education of the course according to the feedback information.
Disclosure of Invention
The invention aims to solve the technical problem of providing a personalized course content recommendation method which can directly select a certain chapter or knowledge point for learning, can obtain feedback information of a user listening to a course in real time, and can adjust subsequent recommendation content and optimize the online education of the course according to the feedback information.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: a personalized course content recommendation method for online education comprises the following steps:
the method comprises the steps that firstly, the curriculum to be used online is further fragmented, and index tags are established by taking single chapters or knowledge points as units;
secondly, combining the teacher opinions and the investigation results of the students in the early stage, and grading the difficulty degree of each chapter or knowledge point;
thirdly, storing the information in the steps into a database of the online vocational education system, and automatically selecting corresponding courses to be matched with the users according to education background information filled when each user enters the courses and the keywords of the courses to be learned;
fourthly, opening a camera of each online learning user to open the computer through a background system, and for users without the camera or with the camera not meeting the requirements, requiring the users to replace qualified computer equipment and then learning; capturing facial pictures of a user by controlling a camera through course background software according to a preset time interval, and then carrying out eye sight analysis on the pictures through an intelligent algorithm to obtain attention data feedback of the user during learning;
and fifthly, dynamically adjusting the difficulty of the subsequently recommended course content according to the attention data feedback of the user, and simultaneously storing the attention data feedback information of each user corresponding to the current course into a background database to be used as reference data for adjusting the current course.
Preferably, in the fourth step, the online education platform further comprises intelligent bracelet equipment capable of collecting movement information and pulse information of the hands of the user, each user for online learning is provided with the intelligent bracelet equipment, the bracelet equipment is connected to a computer for learning or a smart phone of the user, and then relevant data collected by the mobile phone equipment is transmitted to a background database of the online education platform through the computer or the mobile phone to serve as a part of an attention data source when the user learns.
Preferably, in the fifth step, if the user's attention is monitored to be inattentive, the background system automatically pauses the lesson and inserts a short joke or news to help the user relax for a period of time and then switch back to the lesson content.
After adopting the structure, the invention has the following beneficial effects: the course content is further fragmented, a user can directly and selectively learn about a single knowledge point, meanwhile, real-time feedback is added, the current state of the learner is collected in real time through devices such as a camera and a bracelet, attention degree data of the user can be calculated by combining related data processing algorithms, then, recommended courses can be optimized in real time by combining the data, and meanwhile, data support of content optimization can be provided for the current courses. The preference also adds an intelligent adjusting process to help the learner relax and then better enter a learning state.
In summary, the invention provides a personalized course content recommendation method which can directly select a certain chapter or knowledge point for learning, can obtain feedback information of the user listening to the course in real time, and can adjust subsequent recommendation content and optimize the online education of the course according to the feedback information.
Drawings
Fig. 1 is a flowchart of a personalized course content recommendation method for online education in accordance with the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
With reference to fig. 1, a method for recommending personalized course content for online education includes the following steps:
the method comprises the steps that firstly, the curriculum to be used online is further fragmented, and index tags are established by taking single chapters or knowledge points as units;
secondly, combining the teacher opinions and the investigation results of the students in the early stage, and grading the difficulty degree of each chapter or knowledge point;
thirdly, storing the information in the steps into a database of the online vocational education system, and automatically selecting corresponding courses to be matched with the users according to education background information filled when each user enters the courses and the keywords of the courses to be learned;
fourthly, opening a camera of each online learning user to open the computer through a background system, and for users without the camera or with the camera not meeting the requirements, requiring the users to replace qualified computer equipment and then learning; capturing facial pictures of a user by controlling a camera through course background software according to a preset time interval, and then carrying out eye sight analysis on the pictures through an intelligent algorithm to obtain attention data feedback of the user during learning;
and fifthly, dynamically adjusting the difficulty of the subsequently recommended course content according to the attention data feedback of the user, and simultaneously storing the attention data feedback information of each user corresponding to the current course into a background database to be used as reference data for adjusting the current course.
Preferably, in the fourth step, the online education platform further comprises intelligent bracelet equipment capable of collecting movement information and pulse information of the hands of the user, each user for online learning is provided with the intelligent bracelet equipment, the bracelet equipment is connected to a computer for learning or a smart phone of the user, and then relevant data collected by the mobile phone equipment is transmitted to a background database of the online education platform through the computer or the mobile phone to serve as a part of an attention data source when the user learns.
Preferably, in the fifth step, if the user's attention is monitored to be inattentive, the background system automatically pauses the lesson and inserts a short joke or news to help the user relax for a period of time and then switch back to the lesson content.
It should be noted that, regarding the data processing and algorithm, at present, well-developed programs and modules may implement corresponding functions, and when the application is implemented, only corresponding functional modules and algorithms need to be transferred and then simply integrated, and this part is not the key content of the application and also belongs to the conventional knowledge of those skilled in the art, and therefore, details are not described here.
The present invention and its embodiments have been described above, and the description is not intended to be limiting, and the drawings are only one embodiment of the present invention, and the actual configuration is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. A personalized course content recommendation method for online education is characterized by comprising the following steps: it comprises the following steps:
the method comprises the steps that firstly, the curriculum to be used online is further fragmented, and index tags are established by taking single chapters or knowledge points as units;
secondly, combining the teacher opinions and the investigation results of the students in the early stage, and grading the difficulty degree of each chapter or knowledge point;
thirdly, storing the information in the steps into a database of the online vocational education system, and automatically selecting corresponding courses to be matched with the users according to education background information filled when each user enters the courses and the keywords of the courses to be learned;
fourthly, opening a camera of each online learning user to open the computer through a background system, and for users without the camera or with the camera not meeting the requirements, requiring the users to replace qualified computer equipment and then learning; capturing facial pictures of a user by controlling a camera through course background software according to a preset time interval, and then carrying out eye sight analysis on the pictures through an intelligent algorithm to obtain attention data feedback of the user during learning;
and fifthly, dynamically adjusting the difficulty of the subsequently recommended course content according to the attention data feedback of the user, and simultaneously storing the attention data feedback information of each user corresponding to the current course into a background database to be used as reference data for adjusting the current course.
2. The personalized course content recommendation method for online education as claimed in claim 1, wherein: in the fourth step, the intelligent bracelet device capable of collecting the mobile information and the pulse information of the hands of the user is further included, the intelligent bracelet device is equipped for each user for online learning, the bracelet devices are connected to a computer for learning or an intelligent mobile phone of the user and then the relevant data collected by the mobile phone device are transmitted to a background database of an online education platform through the computer or the mobile phone and serve as a part of an attention data source when the user learns.
3. The personalized course content recommendation method for online education as claimed in claim 1, wherein: in the fifth step, if the attention of the user is monitored to be not focused, the background system automatically pauses the course, inserts short jokes or news to help the user relax for a period of time, and then switches back to the course content again.
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| CN202011114748.6A CN112131483A (en) | 2020-10-19 | 2020-10-19 | Personalized course content recommendation method for online vocational education |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112906633A (en) * | 2021-03-18 | 2021-06-04 | 南通师范高等专科学校 | Teaching optimization method based on student attention |
| CN113256464A (en) * | 2021-05-28 | 2021-08-13 | 智慧校园(广东)教育科技有限公司 | Online education course automatic distribution system and method based on cloud computing |
| CN113792248A (en) * | 2021-11-16 | 2021-12-14 | 深圳华埔之星科技有限公司 | Online education course sharing and distributing system based on Internet and mobile terminal |
| CN117372219A (en) * | 2023-12-05 | 2024-01-09 | 青岛理工大学 | Student course recommendation method and device and electronic equipment |
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| CN209625270U (en) * | 2019-03-08 | 2019-11-12 | 福建省软众数字科技股份有限公司 | A kind of device based on user preferences recommendation information |
| CN110916631A (en) * | 2019-12-13 | 2020-03-27 | 东南大学 | Student classroom learning state evaluation system based on wearable physiological signal monitoring |
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| CN106023693A (en) * | 2016-05-25 | 2016-10-12 | 北京九天翱翔科技有限公司 | Education system and method based on virtual reality technology and pattern recognition technology |
| CN109919810A (en) * | 2019-01-22 | 2019-06-21 | 山东科技大学 | Student's modeling and personalized course recommended method in on-line study system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112906633A (en) * | 2021-03-18 | 2021-06-04 | 南通师范高等专科学校 | Teaching optimization method based on student attention |
| CN113256464A (en) * | 2021-05-28 | 2021-08-13 | 智慧校园(广东)教育科技有限公司 | Online education course automatic distribution system and method based on cloud computing |
| CN113256464B (en) * | 2021-05-28 | 2022-03-04 | 智慧校园(广东)教育科技有限公司 | Online education course automatic distribution system and method based on cloud computing |
| CN113792248A (en) * | 2021-11-16 | 2021-12-14 | 深圳华埔之星科技有限公司 | Online education course sharing and distributing system based on Internet and mobile terminal |
| CN117372219A (en) * | 2023-12-05 | 2024-01-09 | 青岛理工大学 | Student course recommendation method and device and electronic equipment |
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Application publication date: 20201225 |