CN111477052A - Teaching auxiliary system based on AI analysis - Google Patents
Teaching auxiliary system based on AI analysis Download PDFInfo
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- CN111477052A CN111477052A CN201910064361.5A CN201910064361A CN111477052A CN 111477052 A CN111477052 A CN 111477052A CN 201910064361 A CN201910064361 A CN 201910064361A CN 111477052 A CN111477052 A CN 111477052A
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Abstract
The invention relates to the technical field of auxiliary teaching, in particular to a teaching auxiliary system based on AI analysis, which comprises an examination system and a background system, wherein a terminal control system is arranged in the examination system, the terminal control system is electrically connected with a display screen and input equipment, an information processing module is arranged in the background system, the information processing module is electrically connected with a core scheduling system, and the core scheduling system is respectively connected with an AI module, a question bank/label bank and student files/historical records. The invention utilizes the modern technical means and AI data analysis to electronize, label and digitize the traditional examination questions and utilizes the advantage of big data analysis as much as possible to eliminate the problems of difficult fluctuation of manual questions, examination point coverage deviation and the like. Meanwhile, aiming at different characteristics of each student, targeted study effect assessment is generated, and results are stored and recorded for a long time, so that the teacher can conveniently track and guide in a targeted manner.
Description
Technical Field
The invention relates to the technical field of auxiliary teaching, in particular to a teaching auxiliary system based on AI analysis.
Background
Along with the higher and higher requirements of parents on education quality, education workers are required to be capable of providing better teaching quality and targeted personalized tutoring requirements. Therefore, education workers are required to be capable of rapidly and accurately evaluating teaching effects and analyzing advantages and disadvantages of each child in a targeted manner.
For such a problem, most of the teachers master the learning effect of the students mainly through the traditional tests and the scoring mode after the tests. There are several major problems with this approach: firstly, data is not conducive to long-term storage and tracking; secondly, problems of variable difficulty, non-uniform standard, incomplete knowledge point coverage and the like exist because the questions are set by teachers.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a teaching auxiliary system based on AI analysis.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides a teaching auxiliary system based on AI analysis, is including examination system and backstage system, the inside terminal control system that is provided with of examination system, terminal control system electric connection has display screen and input device, the inside of backstage system is provided with information processing module, information processing module electric connection has core dispatch system, AI module, question bank/label storehouse, student's archives/historical record are inserted respectively to core dispatch system.
Preferably, the terminal control system is electrically connected with the information processing module.
Preferably, the examination system and the background system work processing step:
the method comprises the following steps: starting the device;
step two: inputting knowledge points, examination points and other requirements to generate examination papers;
step three: in the examination system, students answer questions and submit answers;
step four: automatically marking the paper by the system;
step five: calling an AI module to perform AI analysis;
step five: displaying and archiving the result;
step six: and generating the study effect evaluation of the student.
Preferably, the examination system and the background system electronize all standardized examination questions, tag the examination questions according to information such as knowledge points and examination points, form a massive examination library, and a teacher automatically generates examination papers according to requirements and completes the examination papers in the examination system by students.
Preferably, the AI module performs the processing steps of analyzing:
the method comprises the following steps: calling an AI module;
step two: carrying out data processing on the examination paper result of the examinee;
step three: starting AI analysis;
step four: inquiring a question bank, and acquiring a difficulty coefficient and covered knowledge points of the current test question;
step five: inquiring historical records to obtain the completion condition of the students under the condition that the knowledge points have the same difficulty;
step six: inquiring historical records to obtain the past mastering conditions of the students on the knowledge points;
step seven: inquiring a database to obtain the conditions of other student examinations in the examination;
step eight: calculating the comprehensive ranking condition of the examinee in the test and the mastering condition of the examinee on the knowledge point;
step nine: evaluating and displaying;
step ten: the results are stored in a database and the sample library is modified.
Preferably, in the eighth step, the calculation method is a CART classification regression tree algorithm or a K-Means clustering algorithm.
Compared with the prior art, the invention provides a teaching auxiliary system based on AI analysis, which has the following beneficial effects:
by means of a modern technical means and analysis of AI data, traditional examination questions are electronized, labeled and digitized, and the problems that manual questions are difficult to fluctuate, examination point coverage deviation and the like are eliminated by using the advantages of big data analysis as much as possible. Meanwhile, aiming at different characteristics of each student, targeted study effect assessment is generated, and results are stored and recorded for a long time, so that the teacher can conveniently track and guide in a targeted manner.
Drawings
Fig. 1 is a schematic structural diagram of an overall teaching assistance system based on AI analysis according to the present invention;
FIG. 2 is a schematic diagram of the working process steps of an AI analysis-based teaching assistance system according to the present invention;
fig. 3 is a schematic diagram of the processing steps of the AI module of the teaching assistance system based on AI analysis according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1-3, an AI analysis-based teaching assistance system includes an examination system and a background system, the examination system is internally provided with a terminal control system, the terminal control system is electrically connected to a display screen and an input device, the background system is internally provided with an information processing module, the information processing module is electrically connected to a core scheduling system, and the core scheduling system is respectively connected to an AI module, an item bank/label bank, and a student archive/history.
The terminal control system is electrically connected with the information processing module.
The examination system and the background system work processing steps are as follows:
the method comprises the following steps: starting the device;
step two: inputting knowledge points, examination points and other requirements to generate examination papers;
step three: in the examination system, students answer questions and submit answers;
step four: automatically marking the paper by the system;
step five: calling an AI module to perform AI analysis;
step five: displaying and archiving the result;
step six: and generating the study effect evaluation of the student.
The examination system and the background system electronize all standardized examination questions, tag the examination questions according to information such as knowledge points, examination points and the like to form a massive question bank, and teachers automatically generate examination papers according to requirements and complete the examination papers in the examination system by students.
The AI module performs analysis processing steps:
the method comprises the following steps: calling an AI module;
step two: carrying out data processing on the examination paper result of the examinee;
step three: starting AI analysis;
step four: inquiring a question bank, and acquiring a difficulty coefficient and covered knowledge points of the current test question;
step five: inquiring historical records to obtain the completion condition of the students under the condition that the knowledge points have the same difficulty;
step six: inquiring historical records to obtain the past mastering conditions of the students on the knowledge points;
step seven: inquiring a database to obtain the conditions of other student examinations in the examination;
step eight: calculating the comprehensive ranking condition of the examinee in the test and the mastering condition of the examinee on the knowledge point;
step nine: evaluating and displaying;
step ten: the results are stored in a database and the sample library is modified.
In the eighth step, the calculation method is a CART classification regression tree algorithm and a K-Means clustering algorithm.
When the electronic examination paper is used, a teacher combines the current teaching task and examination requirements, an examination paper is generated in the system according to information such as knowledge points, difficulty, question types and the like, and students perform electronic examinations by using the examination system and then submit the examination paper; the system automatically reads the paper and calls an AI module for analysis, different learning effect evaluation reports are generated for each student according to the coverage condition of the examination points and the completion condition of the students, the reports contain the knowledge point mastering conditions of the students, the aspects needing to be enhanced and other information, a teacher can conveniently give targeted guidance and teaching according to the condition of each different student, the information can also be sent to parents as the reference of the teaching effect, and the parents can conveniently and clearly know the learning condition of the children; the learning effect of the students is objectively and fairly evaluated as much as possible by combining AI artificial intelligence with big data analysis. Before evaluation, all test questions need to be electronized, then data processing such as label classification and difficulty identification is carried out, original data are formed, after students answer questions, the system can automatically call the historical records of the examination questions with the same knowledge points and the same difficulty as reference values, and the comprehensive ranking condition of the examinee in the examination, including the examinee in the same period and all historical examinees in the examination, the situation of the examinee's knowledge points in the corresponding knowledge points with different difficulties, the curve of the examinee's own knowledge points in the examination and the like are comprehensively calculated by combining the previous knowledge point mastering conditions of the students and the learning conditions of other students in the same batch and by using algorithms such as CART classification regression trees, K-Means clustering algorithms and the like.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. The utility model provides a teaching auxiliary system based on AI analysis, is including examination system and backstage system, its characterized in that, the inside terminal control system that is provided with of examination system, terminal control system electric connection has display screen and input device, the inside of backstage system is provided with information processing module, information processing module electric connection has core dispatch system, AI module, question bank/label storehouse, student's archives/historical record are inserted respectively to core dispatch system.
2. The AI analysis-based teaching assistance system of claim 1, wherein the terminal control system is electrically connected to the information processing module.
3. The AI analysis-based teaching assistance system of claim 1, wherein the examination system and the background system work processing steps are:
the method comprises the following steps: starting the device;
step two: inputting knowledge points, examination points and other requirements to generate examination papers;
step three: in the examination system, students answer questions and submit answers;
step four: automatically marking the paper by the system;
step five: calling an AI module to perform AI analysis;
step five: displaying and archiving the result;
step six: and generating the study effect evaluation of the student.
4. The AI analysis-based teaching assistance system of claim 1, wherein the examination system and the background system electronize all standardized test questions and tag the questions according to information such as knowledge points and examination points to form a massive question bank, and teachers automatically generate examination papers according to needs and the examination papers are completed by students in the examination system.
5. The AI-analysis-based teaching assistance system of claim 1, wherein the AI module performs the analysis processing steps of:
the method comprises the following steps: calling an AI module;
step two: carrying out data processing on the examination paper result of the examinee;
step three: starting AI analysis;
step four: inquiring a question bank, and acquiring a difficulty coefficient and covered knowledge points of the current test question;
step five: inquiring historical records to obtain the completion condition of the students under the condition that the knowledge points have the same difficulty;
step six: inquiring historical records to obtain the past mastering conditions of the students on the knowledge points;
step seven: inquiring a database to obtain the conditions of other student examinations in the examination;
step eight: calculating the comprehensive ranking condition of the examinee in the test and the mastering condition of the examinee on the knowledge point;
step nine: evaluating and displaying;
step ten: the results are stored in a database and the sample library is modified.
6. The AI-analysis-based teaching assistance system of claim 5, wherein in said step eight, the calculation method is CART classification regression tree algorithm, K-Means clustering algorithm.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910064361.5A CN111477052A (en) | 2019-01-23 | 2019-01-23 | Teaching auxiliary system based on AI analysis |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910064361.5A CN111477052A (en) | 2019-01-23 | 2019-01-23 | Teaching auxiliary system based on AI analysis |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113256252A (en) * | 2021-05-25 | 2021-08-13 | 上海金程教育培训有限公司 | B/S architecture-based test system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794947A (en) * | 2015-04-01 | 2015-07-22 | 广东小天才科技有限公司 | Teaching condition feedback method and device |
| CN105006181A (en) * | 2015-08-12 | 2015-10-28 | 李南方 | Customized learning device and method |
| CN105723437A (en) * | 2016-01-07 | 2016-06-29 | 汤美 | An intelligent teaching system |
| CN106446488A (en) * | 2015-08-07 | 2017-02-22 | 纬创资通股份有限公司 | Risk assessment system and data processing method |
| CN106991858A (en) * | 2017-05-24 | 2017-07-28 | 联阅科技（北京）有限公司 | A kind of automatic problem building and read and make comments system |
-
2019
- 2019-01-23 CN CN201910064361.5A patent/CN111477052A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794947A (en) * | 2015-04-01 | 2015-07-22 | 广东小天才科技有限公司 | Teaching condition feedback method and device |
| CN106446488A (en) * | 2015-08-07 | 2017-02-22 | 纬创资通股份有限公司 | Risk assessment system and data processing method |
| CN105006181A (en) * | 2015-08-12 | 2015-10-28 | 李南方 | Customized learning device and method |
| CN105723437A (en) * | 2016-01-07 | 2016-06-29 | 汤美 | An intelligent teaching system |
| CN106991858A (en) * | 2017-05-24 | 2017-07-28 | 联阅科技（北京）有限公司 | A kind of automatic problem building and read and make comments system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113256252A (en) * | 2021-05-25 | 2021-08-13 | 上海金程教育培训有限公司 | B/S architecture-based test system |
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