CN114926312B - Data acquisition and analysis method and system based on artificial intelligence - Google Patents
Data acquisition and analysis method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN114926312B CN114926312B CN202210704878.8A CN202210704878A CN114926312B CN 114926312 B CN114926312 B CN 114926312B CN 202210704878 A CN202210704878 A CN 202210704878A CN 114926312 B CN114926312 B CN 114926312B
- Authority
- CN
- China
- Prior art keywords
- information
- student
- label
- consumption
- representing
- 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.)
- Active
Links
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 14
- 238000004458 analytical method Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 14
- 230000006399 behavior Effects 0.000 claims description 31
- 238000013135 deep learning Methods 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 14
- 238000011176 pooling Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 230000005284 excitation Effects 0.000 claims description 5
- 238000013528 artificial neural network Methods 0.000 abstract description 2
- 239000000463 material Substances 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a data acquisition analysis method, system and device based on artificial intelligence, which is characterized in that: the method comprises the following steps: a certain number of student samples are obtained, wherein the student samples are provided with multi-dimensional data information and label information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student; training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model; and inputting student information to be analyzed into the trained student portrait model to obtain the student portrait. The method can intelligently analyze mass student data through the utilized deep neural network, and construct accurate student portraits for teaching management reference of schools.
Description
Technical Field
The invention relates to the technical field of artificial intelligence teaching, in particular to a data acquisition and analysis method and system based on artificial intelligence.
Background
Along with the rapid development of informatization of schools in China, education big data become research hotspots, and student basic data, behavior data, consumption data and achievement data are taken as important components of the education big data, so that multidimensional characteristics of students can be comprehensively and systematically reflected. How to extract hidden and valuable information from massive student campus data, change the traditional simple inquiry and statistics of the data, provide efficient and objective decision information for student management work, and is a problem to be solved urgently in each school. The student behavior portraits are formed by comprehensively analyzing the students by utilizing various campus data, the life and learning conditions of the students are pre-warned, the messages are timely pushed to coaches and relevant lessons teachers, the schools are helped to grasp the learning dynamics of the students, the active upward student behaviors are prompted to be encouraged, bad behavior habits are timely corrected, the students are guided to develop to correct and active healthy behavior activities, the student network ideological and political education methods are enriched, the work management level of the students is improved, and healthy growth of the students is further promoted.
The prior art fails to effectively mine and analyze various information of students, and scattered recorded information cannot obtain accurate images of the students.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides a data acquisition and analysis method based on artificial intelligence: comprises the following steps:
step 1, acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and label information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student;
step 2: training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
step 3: and inputting student information to be analyzed into the trained student portrait model to obtain the student portrait.
Optionally, the basic information in the step 1 includes school number, name, gender, date of birth, class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, and the book borrowing information comprises book borrowing frequency, borrowing number variety and borrowing duration in a library.
Optionally, the consumption label comprises ultrahigh consumption, high consumption, ordinary consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
Optionally, the student portrait model is a deep learning network model.
Correspondingly, the application also provides a data acquisition analysis system based on artificial intelligence, which is characterized in that: comprises the following modules:
the information acquisition module is used for acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and tag information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student;
the student portrait model constructing and training module is used for training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
and the student portrait generation module is used for inputting student information to be analyzed into the trained student portrait model to obtain a student portrait.
Optionally, the basic information includes school number, name, gender, date of birth, class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, and the book borrowing information comprises book borrowing frequency, borrowing number variety and borrowing duration in a library.
Optionally, the consumption label comprises ultrahigh consumption, high consumption, ordinary consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
Optionally, the student portrait model is a deep learning network model.
Correspondingly, the application also provides a data acquisition and analysis device based on artificial intelligence, which comprises a memory and a processor, wherein the memory contains a computer program, and the computer program is used for the method.
Correspondingly, the application also provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is used for executing the method.
The inventive contribution of this application is:
1. through multidimensional data information including basic information, consumption information, achievement information, behavior information and the like of students; the method has the advantages that label information comprising basic labels, consumption labels, achievement labels, behavior labels and the like of students is established, the dimension is rich, and the image of the students can be comprehensively reflected.
2. Different from the prior art, the student portrait model is constructed, is a deep neural network, and is designed with an excitation function and a loss function suitable for the model, so that the precision of the student portrait model is improved.
Drawings
Fig. 1 is a main logical structure diagram of the present invention.
Detailed Description
The application provides a data acquisition and analysis method based on artificial intelligence: comprises the following steps:
step 1, acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and label information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student;
step 2: training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
step 3: and inputting student information to be analyzed into the trained student portrait model to obtain the student portrait.
Optionally, the basic information in the step 1 includes school number, name, gender, date of birth, class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, and the book borrowing information comprises book borrowing frequency, borrowing number variety and borrowing duration in a library.
Optionally, the consumption label comprises ultrahigh consumption, high consumption, ordinary consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
Optionally, the student portrait model is a deep learning network model.
Optionally, the deep learning network model includes an input layer, a plurality of hidden layers, and an output layer, where the input layer receives student sample data during training and may also be used to receive student information to be analyzed; the plurality of hidden layers comprise a plurality of convolution layers, one or more pooling layers and a full connection layer; the output layer outputs a user representation of the student.
Optionally, the deep learning network model pooling layer adopts the following pooling method:
x e ＝f(u e )
wherein x is e Representing the output of the current layer, w e Representing the weight of the current layer,
representing a loss function, x e-1 Representing the output of the next layer, b e Representing bias, delta representing a preset constant;
n represents the size of a student sample data set, and i is 1-N; q (Q) yi Representing student sample x i In its tag y i Weight at M yi Representing student sample x i In its tag y i Deviation at, M j Representing the deviation at output node j; θ j,i For student sample x i Corresponding to the label y i Is included in the vector.
The excitation function RL is:
n represents the size of the student sample data set; w (W) yi Representing a student sample feature vector x i In its tag y i Weights at (a).
Optionally, the loss function adopted by the deep learning network model is:
wherein, psi (θ) j,i )＝(-1) k cos(mθ j,i ) -2k, m is a preset parameter, m is more than or equal to 1 and less than or equal to 4;
k＝abs(sign(cosθ j,i ))-(abs(sign(cos2θ j,i ))-sign(cosθ j,i ))/2。
correspondingly, the application also provides a data acquisition analysis system based on artificial intelligence, which is characterized in that: comprises the following modules:
the information acquisition module is used for acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and tag information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student;
the student portrait model constructing and training module is used for training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
and the student portrait generation module is used for inputting student information to be analyzed into the trained student portrait model to obtain a student portrait.
Optionally, the basic information includes school number, name, gender, date of birth, class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, and the book borrowing information comprises book borrowing frequency, borrowing number variety and borrowing duration in a library.
Optionally, the consumption label comprises ultrahigh consumption, high consumption, ordinary consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
Optionally, the student portrait model is a deep learning network model.
Optionally, the deep learning network model includes an input layer, a plurality of hidden layers, and an output layer, where the input layer receives student sample data during training and may also be used to receive student information to be analyzed; the plurality of hidden layers comprise a plurality of convolution layers, one or more pooling layers and a full connection layer; the output layer outputs a user representation of the student.
Optionally, the deep learning network model pooling layer adopts the following pooling method:
x e ＝f(u e )
wherein x is e Representing the output of the current layer, w e Representing the current layerThe weight of the material to be weighed,
representing a loss function, x e-1 Representing the output of the next layer, b e Representing bias, delta representing a preset constant;
n represents the size of a student sample data set, and i is 1-N; q (Q) yi Representing student sample x i In its tag y i Weight at M yi Representing student sample x i In its tag y i Deviation at, M j Representing the deviation at output node j; θ j,i For student sample x i Corresponding to the label y i Is included in the vector.
The excitation function RL is:
n represents the size of the student sample data set; w (W) yi Representing a student sample feature vector x i In its tag y i Weights at (a).
Optionally, the loss function adopted by the deep learning network model is:
wherein, psi (θ) j,i )＝(-1) k cos(mθ j,i ) -2k, m is a preset parameter, m is more than or equal to 1 and less than or equal to 4;
k＝abs(sign(cosθ j,i ))-(abs(sign(cos2θ j,i ))-sign(cosθ j,i ))/2。
correspondingly, the application also provides a data acquisition and analysis device based on artificial intelligence, which comprises a memory and a processor, wherein the memory contains a computer program, and the computer program is used for the method.
Correspondingly, the application also provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is used for executing the method.
It should be noted that the above embodiments or further limitations may be combined and used by themselves without conflict, and all forms part of the present disclosure, not explicitly stated, but all combinations fall within the scope of the present application.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
Claims (4)
1. The data acquisition and analysis method based on artificial intelligence is characterized by comprising the following steps of: comprises the following steps:
step 1, acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and label information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student; the basic information in the step 1 comprises school number, name, gender, birth date and class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, wherein the book borrowing information comprises book borrowing frequency, number type and time of borrowing in a library;
step 2: training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
step 3: inputting student information to be analyzed into a trained student portrait model to obtain a student portrait;
the student portrait model is a deep learning network model;
the deep learning network model comprises an input layer, a plurality of hidden layers and an output layer, wherein the input layer is used for receiving student sample data during training or is used for receiving student information to be analyzed; the plurality of hidden layers comprise a plurality of convolution layers, one or more pooling layers and a full connection layer; the output layer outputs the user portrait of the student;
the deep learning network model pooling layer adopts the following pooling method:
x e ＝f(u e )
wherein x is e Representing the output of the current layer, w e Representing the weight of the current layer,
representing a loss function, x e-1 Representing the output of the next layer, b e Representing bias, delta representing a preset constant;
n represents the size of a student sample data set, and i is 1-N; q (Q) yi Representing student sample x i In its tag y i Weight at M yi Representing student sample x i In its tag y i Deviation at, M j Representing the deviation at output node j; θ j,i For student sample x i Corresponding to the label y i Vector included angle of (2);
the excitation function RL is:
n represents the size of the student sample data set; w (W) yi Representing a student sample feature vector x i In its tag y i Weights at;
the loss function adopted by the deep learning network model is as follows:
wherein, psi (θ) j,i )＝(-1) k cos(mθ j,i ) -2k, m is a preset parameter, m is more than or equal to 1 and less than or equal to 4;
k＝abs(sign(cosθ j,i ))-(abs(sign(cos2θ j,i ))-sign(cosθ j,i ))/2；
the consumption label comprises ultrahigh consumption, high consumption, common consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
2. The utility model provides a data acquisition analysis system based on artificial intelligence which characterized in that: comprises the following modules:
the information acquisition module is used for acquiring a certain number of student samples, wherein the student samples are provided with multi-dimensional data information and tag information, and the multi-dimensional data information comprises basic information, consumption information, achievement information and behavior information of the students; the label information comprises a basic label, a consumption label, a score label and a behavior label of a student; the basic information comprises school number, name, gender, birth date and class information; the consumption information comprises the total monthly consumption amount of the campus card, the single maximum consumption amount and the monthly consumption times; the achievement information comprises attended course information, attended competition information, obtained rewarding information, classroom performance information and score or grade information of each course; the behavior information comprises book borrowing information, wherein the book borrowing information comprises book borrowing frequency, number type and time of borrowing in a library;
the student portrait model constructing and training module is used for training the constructed student portrait model based on the multidimensional data information and the label information of the student sample to obtain a trained student portrait model;
the student portrait generation module is used for inputting student information to be analyzed into the trained student portrait model to obtain a student portrait;
the student portrait model is a deep learning network model;
the deep learning network model comprises an input layer, a plurality of hidden layers and an output layer, wherein the input layer is used for receiving student sample data during training or is used for receiving student information to be analyzed; the plurality of hidden layers comprise a plurality of convolution layers, one or more pooling layers and a full connection layer; the output layer outputs the user portrait of the student;
the deep learning network model pooling layer adopts the following pooling method:
x e ＝f(u e )
wherein x is e Representing the output of the current layer, w e Representing the weight of the current layer,
representing a loss function, x e-1 Representing the output of the next layer, b e Representing bias, delta representing a preset constant;
n represents the size of a student sample data set, and i is 1-N; q (Q) yi Representing student sample x i In its tag y i Weight at M yi Representing student sample x i In its tag y i Deviation at, M j Representing the deviation at output node j; θ j,i For student sample x i Corresponding to the label y i Vector included angle of (2);
the excitation function RL is:
n represents the size of the student sample data set; w (W) yi Representing student sample characteristicsVector x i In its tag y i Weights at;
the loss function adopted by the deep learning network model is as follows:
wherein, psi (θ) j,i )＝(-1) k cos(mθ j,i ) -2k, m is a preset parameter, m is more than or equal to 1 and less than or equal to 4;
k＝abs(sign(cosθ j,i ))-(abs(sign(cos2θ j,i ))-sign(cosθ j,i ))/2；
the consumption label comprises ultrahigh consumption, high consumption, common consumption and low consumption; the achievement label comprises excellent achievement, good achievement, qualified achievement and unqualified achievement; the behavior labels comprise book borrowing labels, and the book borrowing labels comprise academic deep ploughing, extensive hunting, basic borrowing and low-frequency borrowing.
3. An artificial intelligence based data acquisition and analysis device comprising a memory and a processor, the memory containing a computer program for performing the method of claim 1.
4. A computer storage medium storing a computer program for executing the method of claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210704878.8A CN114926312B (en) | 2022-06-21 | 2022-06-21 | Data acquisition and analysis method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210704878.8A CN114926312B (en) | 2022-06-21 | 2022-06-21 | Data acquisition and analysis method and system based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114926312A CN114926312A (en) | 2022-08-19 |
| CN114926312B true CN114926312B (en) | 2023-06-30 |
Family
ID=82815415
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210704878.8A Active CN114926312B (en) | 2022-06-21 | 2022-06-21 | Data acquisition and analysis method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114926312B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117669896A (en) * | 2024-01-31 | 2024-03-08 | 长春财经学院 | Student information data acquisition and analysis system and method based on big data |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652291A (en) * | 2020-05-18 | 2020-09-11 | 温州医科大学 | Method for establishing student growth portrait based on group sparse fusion hospital big data |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110110939A (en) * | 2019-05-15 | 2019-08-09 | 杭州华网信息技术有限公司 | The academic record prediction and warning method of behavior is serialized based on deep learning student |
| CN112256755A (en) * | 2020-10-20 | 2021-01-22 | 中电科新型智慧城市研究院有限公司福州分公司 | Student abnormal behavior analysis method based on deep learning |
| CN113626499B (en) * | 2021-08-09 | 2024-05-10 | 青软创新科技集团股份有限公司 | Student portrait mining implementation method based on big data number bin technology |
-
2022
- 2022-06-21 CN CN202210704878.8A patent/CN114926312B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111652291A (en) * | 2020-05-18 | 2020-09-11 | 温州医科大学 | Method for establishing student growth portrait based on group sparse fusion hospital big data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114926312A (en) | 2022-08-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Recker | Scientific research in information systems: a beginner's guide | |
| Mertler et al. | Advanced and multivariate statistical methods: Practical application and interpretation | |
| CN107230174B (en) | Online interactive learning system and method based on network | |
| Anoopkumar et al. | A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration | |
| Pal et al. | Analysis and mining of educational data for predicting the performance of students | |
| Nehm et al. | Human vs. computer diagnosis of students’ natural selection knowledge: testing the efficacy of text analytic software | |
| Kurniawan et al. | Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits) | |
| Oyedeji et al. | Analysis and prediction of student academic performance using machine learning | |
| Oslington et al. | Third-graders’ predictive reasoning strategies | |
| CN114926312B (en) | Data acquisition and analysis method and system based on artificial intelligence | |
| CN110993102A (en) | Campus big data-based student behavior and psychological detection result accurate analysis method and system | |
| Trandafili et al. | Discovery and evaluation of student's profiles with machine learning | |
| Alnemer | Determinants of entrepreneurial intention among students of management stream in the Kingdom of Saudi Arabia | |
| Anıl et al. | The effect of computer-supported education on student attitudes: A meta-analytical comparison for the period 2005-2015 | |
| Kiss et al. | A network analysis of L2 motivational factors: Structure, connectivity, and central relational links | |
| Nuankaew et al. | Implementation of Data Analytics and Machine Learning in Thailand Education Sector. | |
| Jayakody et al. | Enhancing competencies of less-able students to achieve learning outcomes: Learner aware tool support through Business intelligence | |
| Uvidia Fassler et al. | Application of knowledge discovery in data bases analysis to predict the academic performance of university students based on their admissions test | |
| Šarić et al. | Data-driven student clusters based on online learning behavior in a flipped classroom with an intelligent tutoring system | |
| Razak et al. | Prediction of Secondary Students Performance: A Case Study | |
| Bhagya et al. | Predictive Analysis in Academic: An Insight to Challenges and Techniques | |
| Rojas et al. | A proposal for predicting academic achievement through a vectorial model of lexical availability | |
| Koti et al. | Analysis of students performance using learning analytics—a case study | |
| Katz et al. | Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching | |
| Jianyun | Big data assisted online teaching platform for ideological and political theory course in universities |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |