CN111353098A - Course pushing method and device based on Internet of things - Google Patents
Course pushing method and device based on Internet of things Download PDFInfo
- Publication number
- CN111353098A CN111353098A CN202010108532.2A CN202010108532A CN111353098A CN 111353098 A CN111353098 A CN 111353098A CN 202010108532 A CN202010108532 A CN 202010108532A CN 111353098 A CN111353098 A CN 111353098A
- Authority
- CN
- China
- Prior art keywords
- student
- data
- current
- historical
- internet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000006399 behavior Effects 0.000 claims abstract description 79
- 238000010606 normalization Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 6
- 230000006855 networking Effects 0.000 claims 1
- 238000011156 evaluation Methods 0.000 abstract description 10
- 238000004891 communication Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Educational Technology (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The embodiment of the invention provides a course pushing method and device based on the Internet of things, wherein the method collects learning behavior data, current performance data and historical performance data of a plurality of students through the Internet of things; for any student, judging the passing prediction result of the student examination according to the learning behavior data, the current performance data and the historical performance data of the student; and determining the courses pushed to the trainees according to the prediction result. The embodiment of the invention has the capability of remotely analyzing the learning quality of the student, and can intelligently judge the learning short board and recommend the learning content. Compared with the traditional means such as subjective evaluation and examination result evaluation, the method has the advantages of real-time performance and normalization.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a course pushing method and device based on the Internet of things.
Background
Traditional student study quality analysis relies on means such as teacher's on-site observation, examination score, and the attitude evaluation subjectivity is strong, and examination score is a knife and a knife, and how effectively to analyze study attitude and study quality, and then pertinence promotion teaching level and study quality become the difficult problem in industry.
Disclosure of Invention
Embodiments of the present invention provide a course pushing method and apparatus based on the internet of things, which overcome the above problems or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a course pushing method based on the internet of things, including:
the method comprises the steps that learning behavior data, current achievement data and historical achievement data of a plurality of students are collected through the Internet of things;
for any student, judging the passing prediction result of the student examination according to the learning behavior data, the current performance data and the historical performance data of the student;
and determining the courses pushed to the trainees according to the prediction result.
Further, the method for collecting learning behavior data, current performance data and historical performance data of a plurality of students through the internet of things specifically comprises the following steps:
establishing student image data, and acquiring the behavior of the student in the class learning period through a camera for identification to obtain learning behavior data of the student;
acquiring current score data and historical score data of a student through a preset examination application program;
and the learning behavior data, the current result data and the historical result data of the trainees are sent to a preset database through the Internet of things.
Further, the step of judging the prediction result of the student passing the examination according to the learning behavior data, the current performance data and the historical performance data of the student specifically comprises the following steps:
acquiring the times of behaviors irrelevant to learning in the learning behavior data of each student in the plurality of students, and carrying out normalization processing on the times of the behaviors irrelevant to learning of each student to obtain a learning behavior index of each student;
acquiring the current result data of each student in the plurality of students to carry out normalization processing on the current result data of each student so as to obtain the current result index of each student;
acquiring historical result data of each student in the plurality of students to perform normalization processing on the historical result data of each student to obtain a historical result index of each student;
and obtaining the prediction result of the student passing the examination according to the learning behavior index, the current result index and the historical result index of each student.
Further, the historical result data is the historical result data of a plurality of preset subjects; the current achievement data is preset current achievement data of a plurality of disciplines;
correspondingly, the current achievement data of each student is normalized to obtain the learning behavior index of each student, which specifically comprises the following steps:
for the current score of each subject, obtaining a normalized value of the current score of each subject according to the best score and the worst score of the subject in the plurality of members; carrying out weighted summation according to the normalized values of the current achievements of all the disciplines of each student to obtain the learning behavior index of each student;
the normalization processing is performed on the historical result data of each student to obtain the learning behavior index of each student, and the normalization processing specifically comprises the following steps:
for the historical score of each subject, obtaining a normalized value of the historical score of each subject according to the best score and the worst score of the subject in the plurality of members; and carrying out weighted summation according to the normalized values of the historical achievements of all the disciplines of each student to obtain the learning behavior index of each student.
Further, the method for obtaining the prediction result of the student passing the examination according to the learning behavior index, the current result index and the historical result index of each student specifically comprises the following steps:
calculating the probability of passing the student assessment according to a formula K which is V + T/D;
wherein K represents the prediction result of the student passing the assessment, V represents the learning behavior index of the student, and D represents the historical achievement index of the student; t represents the current performance index of the trainee.
Further, determining the course pushed to the student according to the prediction result, specifically:
if the prediction result is higher than a preset threshold value, judging that the student cannot pass the examination;
calculating and predicting the average value of the current score of each subject of the examined student, and if the current score of one subject of the ineligible student is lower than the average value of the current scores of the same subject of the examined student, taking the subject as a target subject;
and acquiring the courses of the target subject through the Internet of things, and pushing the courses to the students which cannot pass the examination.
Further, the course of the target subject is obtained through the internet of things and pushed to the students who can not pass the examination, and the method specifically comprises the following steps:
and obtaining the courses of the target subject of the subject with the highest current score of the target subject from the subjects passing the examination through the Internet of things, and pushing the courses to the subjects failing to pass the examination.
In a second aspect, an embodiment of the present invention provides a course pushing device based on an internet of things, including:
the acquisition module is used for acquiring learning behavior data, current score data and historical score data of a plurality of students through the Internet of things;
the prediction module is used for judging the prediction result of the examination of any student according to the learning behavior data, the current performance data and the historical performance data of the student;
and the pushing module is used for determining the courses pushed to the student according to the prediction result.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
The course pushing method and device based on the Internet of things have the capability of remotely analyzing the learning quality of students, and can intelligently judge the learning short board and recommend the learning content. Compared with the traditional means such as subjective evaluation and examination result evaluation, the method has the advantages of real-time performance and normalization.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a course pushing method based on the internet of things according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a course pushing device based on the internet of things according to an embodiment of the present invention;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow diagram of a course pushing method based on the internet of things according to an embodiment of the present invention, as shown in fig. 1, including S101, S102, and S103, specifically:
s101, collecting learning behavior data, current achievement data and historical achievement data of a plurality of students through the Internet of things.
It should be noted that, in the embodiment of the present invention, the internet of Things (IOT) is used to collect data required for course pushing, and the internet of Things (IOT) is used to collect any object or process that needs to be monitored, connected, and interacted in real time by various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors, laser scanners, and the like, collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, location, and the like, and implement ubiquitous connection between objects and objects, and between objects and people by various possible network accesses, thereby implementing intelligent sensing, identification, and management on the objects and processes. The internet of things is an information bearer based on the internet, a traditional telecommunication network and the like, and all common physical objects which can be independently addressed form an interconnected network. The embodiment of the invention solves the problem of low efficiency caused by the fact that the prior art needs to use manpower, and particularly, related data can only be manually collected by teachers.
The learning behavior data refers to the behavior of the student in the learning process, and the data obtained by technical means such as quantification and the like, and the current achievement data and the historical achievement data refer to the achievement data obtained by the student at the current moment and the historical moment respectively, it can be understood that the achievement data is a numerical value, the subject of the achievement data of the embodiment of the invention can be Chinese, mathematics and the like, the embodiment of the invention is not limited specifically,
and S102, for any student, judging the passing prediction result of the student according to the learning behavior data, the current result data and the historical result data of the student.
It should be noted that the examination in the embodiment of the present invention refers to an examination performed by a student after a period of time after a current result, and the examination is an evaluation result made based on an examination result after a period of time.
S103, determining courses pushed to the trainees according to the prediction results.
After the prediction result is obtained, the embodiment of the invention needs to further determine the courses for pushing the student, so that the student can prepare for subsequent examination through learning the relevant courses.
The course pushing method based on the Internet of things has the ability of remotely analyzing the learning quality of students, and can intelligently judge learning short boards and recommend learning contents. Compared with the traditional means such as subjective evaluation and examination result evaluation, the method has the advantages of real-time performance and normalization.
On the basis of the above embodiments, the collecting of learning behavior data, current performance data and historical performance data of a plurality of students through the internet of things specifically includes:
and establishing image data of the student, and acquiring the behavior of the student during the class learning period through the camera for identification to obtain the learning behavior data of the student.
Specifically, the behavior during the class learning period may include behaviors of connecting the head and the ear, using a mobile phone, sleeping, answering questions, and the like, and the specific behavior may be identified and determined by an image identification technology.
And acquiring current achievement data and historical achievement data of the student through a preset examination application program.
It should be noted that, in the embodiment of the present invention, the achievement data of the trainee is obtained through a preset examination application program, that is, all the examination achievements of the trainee are pre-recorded in the examination application program, and even the examination is completed through the examination application program. It can be understood that, in the prior art, a scheme of taking an examination and recording examination results by using an application program has been implemented, and the embodiment of the present invention does not specifically limit the manner of acquiring current result data and historical result data of a trainee through a preset examination application program.
And the learning behavior data, the current result data and the historical result data of the trainees are sent to a preset database through the Internet of things.
On the basis of the above embodiments, the determining, according to the learning behavior data, the current performance data, and the historical performance data of the trainee, a prediction result that the trainee passes the examination specifically includes:
s201, obtaining the times of behaviors irrelevant to learning in the learning behavior data of each student of the plurality of students, and carrying out normalization processing on the times of the behaviors irrelevant to learning of each student to obtain the learning behavior index of each student.
The embodiment of the invention acquires the behaviors irrelevant to learning in the learning behavior data because the behaviors irrelevant to learning are easier to identify, for example, obvious behavior differences exist between the joint of the head and the ear and between the sleep and the serious listening and speaking. After the times of the behaviors irrelevant to learning are obtained, normalization processing is carried out, the purpose of normalization is to quantify the behaviors and simultaneously consider the behaviors of all students in a comprehensive mode, so that the learning behavior index of each student is fairly and objectively obtained.
S202, acquiring the current result data of each student of the plurality of students, and normalizing the current result data of each student to obtain the current result index of each student.
It should be noted that, by acquiring the current result data of each student of a plurality of students, the best current result data and the worst current result data can be known, and the current result index of each student can be acquired through normalization processing.
S203, acquiring historical result data of each student of the plurality of students, and normalizing the historical result data of each student to obtain a historical result index of each student.
By acquiring the historical result data of each student of a plurality of students, the best historical result data and the worst historical result data can be known, and the historical result index of each student can be acquired through normalization processing.
And S204, obtaining a prediction result of the student passing the examination according to the learning behavior index, the current result index and the historical result index of each student.
On the basis of the above embodiments, the historical result data is the historical result data of a plurality of preset disciplines; the current achievement data is preset current achievement data of a plurality of disciplines.
Correspondingly, the current achievement data of each student is normalized to obtain the learning behavior index of each student, which specifically comprises the following steps:
for the current score of each subject, obtaining a normalized value of the current score of each subject according to the best score and the worst score of the subject in the plurality of members; and carrying out weighted summation according to the normalized values of the current achievements of all the disciplines of each student to obtain the learning behavior index of each student.
The normalization processing is performed on the historical result data of each student to obtain the learning behavior index of each student, and the normalization processing specifically comprises the following steps:
for the historical score of each subject, obtaining a normalized value of the historical score of each subject according to the best score and the worst score of the subject in the plurality of members; and carrying out weighted summation according to the normalized values of the historical achievements of all the disciplines of each student to obtain the learning behavior index of each student.
On the basis of the above embodiments, the obtaining of the prediction result of the student's examination according to the learning behavior index, the current performance index and the historical performance index of each student specifically includes:
calculating the probability of passing the student assessment according to a formula K which is V + T/D;
wherein K represents the prediction result of the student passing the assessment, V represents the learning behavior index of the student, and D represents the historical achievement index of the student; t represents the current performance index of the trainee.
On the basis of the above embodiments, determining the course to be pushed to the student according to the prediction result specifically includes:
if the prediction result is higher than a preset threshold value, judging that the student cannot pass the examination;
calculating and predicting the average value of the current score of each subject of the examined student, and if the current score of one subject of the ineligible student is lower than the average value of the current scores of the same subject of the examined student, taking the subject as a target subject;
and acquiring the courses of the target subject through the Internet of things, and pushing the courses to the students which cannot pass the examination.
On the basis of the above embodiments, the course of the target subject is obtained through the internet of things and pushed to the student who can not pass the examination, which specifically includes:
and obtaining the courses of the target subject of the subject with the highest current score of the target subject from the subjects passing the examination through the Internet of things, and pushing the courses to the subjects failing to pass the examination.
Fig. 2 is a schematic structural diagram of a course pushing device based on the internet of things according to an embodiment of the present invention, and as shown in fig. 2, the course pushing device includes: the system comprises an acquisition module 201, a prediction module 202 and a push module 203, and specifically:
the acquisition module 201 is used for acquiring learning behavior data, current score data and historical score data of a plurality of students through the Internet of things;
the prediction module 202 is used for judging a prediction result of the examination of any student according to the learning behavior data, the current result data and the historical result data of the student;
and the pushing module 203 is configured to determine the courses to be pushed to the trainees according to the prediction result.
The course pushing device based on the Internet of things has the ability of remotely analyzing the learning quality of students, and can intelligently judge learning short boards and recommend learning contents. Compared with the traditional means such as subjective evaluation and examination result evaluation, the method has the advantages of real-time performance and normalization.
Fig. 3 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device may include: a processor (processor)310, a communication Interface (communication Interface)320, a memory (memory)330 and a communication bus 340, wherein the processor 310, the communication Interface 320 and the memory 330 communicate with each other via the communication bus 340. The processor 310 may invoke a computer program stored on the memory 330 and executable on the processor 310 to perform the method for pushing lessons based on internet of things provided by the above embodiments, for example, including: the method comprises the steps that learning behavior data, current achievement data and historical achievement data of a plurality of students are collected through the Internet of things; for any student, judging the passing prediction result of the student examination according to the learning behavior data, the current performance data and the historical performance data of the student; and determining the courses pushed to the trainees according to the prediction result.
In addition, the logic instructions in the memory 330 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the course pushing method based on the internet of things provided in the foregoing embodiments, for example, the method includes: the method comprises the steps that learning behavior data, current achievement data and historical achievement data of a plurality of students are collected through the Internet of things; for any student, judging the passing prediction result of the student examination according to the learning behavior data, the current performance data and the historical performance data of the student; and determining the courses pushed to the trainees according to the prediction result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A course pushing method based on the Internet of things is characterized by comprising the following steps:
the method comprises the steps that learning behavior data, current achievement data and historical achievement data of a plurality of students are collected through the Internet of things;
for any student, judging the passing prediction result of the student examination according to the learning behavior data, the current performance data and the historical performance data of the student;
and determining the courses pushed to the trainees according to the prediction result.
2. The course pushing method based on the internet of things as claimed in claim 1, wherein the learning behavior data, current performance data and historical performance data of a plurality of students are collected through the internet of things, and specifically the course pushing method comprises the following steps:
establishing student image data, and acquiring the behavior of the student in the class learning period through a camera for identification to obtain learning behavior data of the student;
acquiring current score data and historical score data of a student through a preset examination application program;
and the learning behavior data, the current result data and the historical result data of the trainees are sent to a preset database through the Internet of things.
3. The course pushing method based on the internet of things as claimed in claim 1, wherein the step of judging the prediction result of the student passing the examination according to the learning behavior data, the current performance data and the historical performance data of the student is specifically as follows:
acquiring the times of behaviors irrelevant to learning in the learning behavior data of each student in the plurality of students, and carrying out normalization processing on the times of the behaviors irrelevant to learning of each student to obtain a learning behavior index of each student;
acquiring the current result data of each student in the plurality of students to carry out normalization processing on the current result data of each student so as to obtain the current result index of each student;
acquiring historical result data of each student in the plurality of students to perform normalization processing on the historical result data of each student to obtain a historical result index of each student;
and obtaining the prediction result of the student passing the examination according to the learning behavior index, the current result index and the historical result index of each student.
4. The course pushing method based on the internet of things as claimed in claim 3, wherein the historical performance data is historical performance data of a plurality of preset subjects; the current achievement data is preset current achievement data of a plurality of disciplines;
correspondingly, the current achievement data of each student is normalized to obtain the learning behavior index of each student, which specifically comprises the following steps:
for the current score of each subject, obtaining a normalized value of the current score of each subject according to the best score and the worst score of the subject in the plurality of members; carrying out weighted summation according to the normalized values of the current achievements of all the disciplines of each student to obtain the learning behavior index of each student;
the normalization processing is performed on the historical result data of each student to obtain the learning behavior index of each student, and the normalization processing specifically comprises the following steps:
for the historical score of each subject, obtaining a normalized value of the historical score of each subject according to the best score and the worst score of the subject in the plurality of members; and carrying out weighted summation according to the normalized values of the historical achievements of all the disciplines of each student to obtain the learning behavior index of each student.
5. The course pushing method based on the internet of things as claimed in claim 3 or 4, wherein the prediction result of the student passing examination is obtained according to the learning behavior index, the current performance index and the historical performance index of each student, and specifically comprises the following steps:
calculating the probability of passing the student assessment according to a formula K which is V + T/D;
wherein K represents the prediction result of the student passing the assessment, V represents the learning behavior index of the student, and D represents the historical achievement index of the student; t represents the current performance index of the trainee.
6. The course pushing method based on the internet of things as claimed in claim 4, wherein the course pushed to the student is determined according to the prediction result, specifically:
if the prediction result is higher than a preset threshold value, judging that the student cannot pass the examination;
calculating and predicting the average value of the current score of each subject of the examined student, and if the current score of one subject of the ineligible student is lower than the average value of the current scores of the same subject of the examined student, taking the subject as a target subject;
and acquiring the courses of the target subject through the Internet of things, and pushing the courses to the students which cannot pass the examination.
7. The course pushing method based on the internet of things as claimed in claim 6, wherein the course of the target subject is obtained through the internet of things and pushed to the student who cannot pass the examination, specifically:
and obtaining the courses of the target subject of the subject with the highest current score of the target subject from the subjects passing the examination through the Internet of things, and pushing the courses to the subjects failing to pass the examination.
8. The utility model provides a course pusher based on thing networking which characterized in that includes:
the acquisition module is used for acquiring learning behavior data, current score data and historical score data of a plurality of students through the Internet of things;
the prediction module is used for judging the prediction result of the examination of any student according to the learning behavior data, the current performance data and the historical performance data of the student;
and the pushing module is used for determining the courses pushed to the student according to the prediction result.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the internet of things based course pushing method as claimed in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the internet of things based course pushing method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010108532.2A CN111353098A (en) | 2020-02-21 | 2020-02-21 | Course pushing method and device based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010108532.2A CN111353098A (en) | 2020-02-21 | 2020-02-21 | Course pushing method and device based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111353098A true CN111353098A (en) | 2020-06-30 |
Family
ID=71197037
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010108532.2A Pending CN111353098A (en) | 2020-02-21 | 2020-02-21 | Course pushing method and device based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111353098A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114912027A (en) * | 2022-05-31 | 2022-08-16 | 济南大学 | Learning scheme recommendation method and system based on learning outcome prediction |
| CN116957870A (en) * | 2023-09-18 | 2023-10-27 | 山西美分钟信息科技有限公司 | Control method, device, equipment and medium for clinical skill assessment management system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130011821A1 (en) * | 2011-04-07 | 2013-01-10 | Tristan Denley | Course recommendation system and method |
| CN104834705A (en) * | 2015-04-30 | 2015-08-12 | 周佳盛 | Method and system for recommending courses |
| CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
| CN108961879A (en) * | 2018-07-18 | 2018-12-07 | 夏璐 | A kind of online education man-machine interaction method and system based on artificial intelligence |
| CN109146174A (en) * | 2018-08-21 | 2019-01-04 | 广东恒电信息科技股份有限公司 | A kind of elective course accurate recommendation method based on result prediction |
-
2020
- 2020-02-21 CN CN202010108532.2A patent/CN111353098A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130011821A1 (en) * | 2011-04-07 | 2013-01-10 | Tristan Denley | Course recommendation system and method |
| CN104834705A (en) * | 2015-04-30 | 2015-08-12 | 周佳盛 | Method and system for recommending courses |
| CN108182489A (en) * | 2017-12-25 | 2018-06-19 | 浙江工业大学 | Method is recommended in a kind of individualized learning based on on-line study behavioural analysis |
| CN108961879A (en) * | 2018-07-18 | 2018-12-07 | 夏璐 | A kind of online education man-machine interaction method and system based on artificial intelligence |
| CN109146174A (en) * | 2018-08-21 | 2019-01-04 | 广东恒电信息科技股份有限公司 | A kind of elective course accurate recommendation method based on result prediction |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114912027A (en) * | 2022-05-31 | 2022-08-16 | 济南大学 | Learning scheme recommendation method and system based on learning outcome prediction |
| CN116957870A (en) * | 2023-09-18 | 2023-10-27 | 山西美分钟信息科技有限公司 | Control method, device, equipment and medium for clinical skill assessment management system |
| CN116957870B (en) * | 2023-09-18 | 2023-12-22 | 山西美分钟信息科技有限公司 | Control method, device, equipment and medium for clinical skill assessment management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107609478A (en) | A kind of real-time analysis of the students system and method for matching classroom knowledge content | |
| CN111353098A (en) | Course pushing method and device based on Internet of things | |
| CN115358897B (en) | Student management method, system, terminal and storage medium based on electronic student identity card | |
| JP2018205354A (en) | Learning support device, learning support system, and program | |
| CN105224920A (en) | A kind ofly will proceed to the learning test diagnostic system on line under line | |
| CN113889271A (en) | Mental health test method and device for students | |
| CN112101231A (en) | Learning behavior monitoring method, terminal, small program and server | |
| CN105260963A (en) | Subject accomplishment evaluation system | |
| CN115050457A (en) | Method, device, equipment, medium and product for evaluating quality of on-line inquiry service | |
| Saucerman et al. | Automating the Detection of Reflection-on-Action. | |
| CN106846198A (en) | A kind of computer application examination system | |
| CN109919099A (en) | A kind of user experience evaluation method and system based on Expression Recognition | |
| CN111724050A (en) | Teaching risk early warning method based on teaching process data | |
| TWI524294B (en) | Online learning style automated diagnostic system, online learning style automated diagnostic method and computer readable recording medium | |
| CN108039081B (en) | Robot teaching evaluation method and device | |
| CN110059117A (en) | A kind of analysis and processing method and device of big data of imparting knowledge to students | |
| CN110710983A (en) | Method for self-help evaluation of child psychological behavior development | |
| CN105469116A (en) | Picture recognition and data extension method for infants based on man-machine interaction | |
| CN115776446A (en) | Service quality monitoring system based on big data | |
| CN110750620B (en) | Group decision capability evaluation method and device | |
| CN114971240A (en) | Reading behavior risk assessment processing method and device | |
| CN113822589A (en) | Intelligent interviewing method, device, equipment and storage medium | |
| CN112101643A (en) | Student score prediction method and system based on online education | |
| CN116416456B (en) | Self-distillation-based image classification method, system, storage medium and electronic device | |
| KR20140105112A (en) | Language evaluation system through network and its method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |