CN111489088A - Student ability evaluation system based on big data analysis - Google Patents
Student ability evaluation system based on big data analysis Download PDFInfo
- Publication number
- CN111489088A CN111489088A CN202010288644.0A CN202010288644A CN111489088A CN 111489088 A CN111489088 A CN 111489088A CN 202010288644 A CN202010288644 A CN 202010288644A CN 111489088 A CN111489088 A CN 111489088A
- Authority
- CN
- China
- Prior art keywords
- module
- signal
- signal output
- output end
- signal connection
- 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.)
- Withdrawn
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 25
- 238000007405 data analysis Methods 0.000 title claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 36
- 230000014509 gene expression Effects 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 18
- 230000005540 biological transmission Effects 0.000 claims abstract description 3
- 230000008054 signal transmission Effects 0.000 claims abstract description 3
- 238000000605 extraction Methods 0.000 claims description 34
- 230000009471 action Effects 0.000 claims description 30
- 230000010354 integration Effects 0.000 claims description 18
- 238000004458 analytical method Methods 0.000 claims description 14
- 230000006399 behavior Effects 0.000 claims description 14
- 238000010195 expression analysis Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 6
- 230000001815 facial effect Effects 0.000 abstract description 4
- 101100055496 Arabidopsis thaliana APP2 gene Proteins 0.000 description 7
- 101100016250 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) GYL1 gene Proteins 0.000 description 7
- 238000000034 method Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000012546 transfer Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000006467 substitution reaction Methods 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
- 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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/174—Facial expression recognition
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Educational Administration (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Educational Technology (AREA)
- Health & Medical Sciences (AREA)
- Development Economics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Multimedia (AREA)
- Primary Health Care (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention relates to the technical field of education, and discloses a student ability evaluation system based on big data analysis, which comprises a central processing unit, wherein the signal input end of the central processing unit is in signal connection with an APP, a scene selection module is arranged in the APP, the signal output end of the central processing unit is in signal connection with the signal output end of a monitoring module, the signal output end of the monitoring module is in signal connection with the signal output end of a facial recognition unit, the signal output end of the facial recognition unit is in signal connection with a signal input module of an information transmission module, and the signal output end of the signal transmission module is in signal connection with the signal input end of a behavior processing system. The invention selects different scenes by using the scene selection module in the APP, monitors the processing conditions of the student in different scenes by using the monitoring module, and detects the embodiment of micro expression and limb movement of the student in different scenes by using the behavior processing system, thereby evaluating different abilities of the student by using the evaluation module.
Description
Technical Field
The invention relates to the technical field of education, in particular to a student ability evaluation system based on big data analysis.
Background
The student age is an important stage of life and is an optimal stage for exercising personal ability. However, the ability of a teacher to evaluate students is to embody the learning ability of the students only through examinations, which is an unobtrusive embodiment, the students need to cultivate learning ability in schools alone, and also need to cultivate practical ability, adaptive ability, interpersonal interaction ability, expression ability, innovation development ability, organization management ability, autonomous learning ability, anti-frustration ability, time management ability, self-restraint ability, and the like, however, the existing ability evaluation system for the students lacks of various abilities.
Disclosure of Invention
The invention aims to provide a student ability evaluation system based on big data analysis, and the purpose of conveniently evaluating various abilities of students is achieved.
In order to achieve the purpose, the invention provides the following technical scheme: the utility model provides a student ability evaluation system based on big data analysis, includes central processing unit, central processing unit signal input part and APP signal connection, the inside sight selection module that is provided with of APP, central processing unit signal output part and monitoring module signal output part signal connection, monitoring module signal output part and facial recognition unit signal output part signal connection, facial recognition unit signal output part and information transfer module signal input module signal connection, signal transfer module signal output part and action processing system signal input part signal connection.
The behavior processing system comprises a micro expression extraction unit and a limb action extraction unit, wherein the micro expression extraction unit comprises a micro expression acquisition module and a micro expression analysis module, the signal output end of the micro expression acquisition module is in signal connection with the signal input end of the micro expression analysis module, the limb action extraction unit comprises a limb action acquisition module and a limb action analysis module, the signal output end of the limb action acquisition module is in signal connection with the signal input end of the limb action analysis module, the signal output ends of the micro expression extraction unit and the limb action extraction unit are in signal connection with the signal input end of an information integration module, the signal output end of the information integration module is in signal connection with the signal input end of the integration information analysis module, the signal output end of the integration information analysis module is in signal connection with the signal output end of an information screening module and the signal input end of an information integration module, and the signal output end of the information integration module II is in signal connection with the signal input end of the evaluation module.
Preferably, the face recognition unit comprises an image extraction module, an image recognition module and an image judgment module, wherein the signal output end of the image extraction module is in signal connection with the signal input end of the image recognition module, and the signal output end of the image recognition module is in signal connection with the signal input end of the image judgment module.
Preferably, the APP is internally provided with a learning number input module.
Preferably, the APP is in signal connection with the login module.
Preferably, the behavior processing system signal output end is in signal connection with the feedback unit signal input end, the feedback unit signal output end is in signal connection with the information input module signal input end, the information input module signal output end is in signal connection with the storage unit signal input end, the storage unit signal output end is in signal connection with the information extraction module signal input end, and the central processing unit signal output end is in signal connection with the information extraction module signal input end.
Preferably, the storage unit is a database.
Preferably, the monitoring module is a camera.
The invention provides a student ability evaluation system based on big data analysis. The method has the following beneficial effects:
(1) the invention selects different scenes by using the scene selection module in the APP, monitors the processing conditions of the student in different scenes by using the monitoring module, and detects the embodiment of micro expression and limb action of the student in different scenes by using the behavior processing system, thereby evaluating different abilities of the student by using the evaluation module.
(2) According to the invention, the storage unit, the face recognition unit and the number input module are arranged, each student is recognized by the face recognition unit, the abilities of the students are finally stored by the storage unit, and the number input module is convenient for teachers to check the abilities of each student.
Drawings
FIG. 1 is a block diagram of the present invention;
FIG. 2 is a block diagram of a behavior processing system of the present invention.
In the figure: the system comprises a login module 1, an APP2, a scene selection module 201, a study number input module 202, a central processing unit 3, a monitoring module 4, a face recognition unit 5, an image extraction module 501, an image recognition module 502, an image judgment module 503, an information transmission module 6, a behavior processing system 7, a micro-expression extraction unit 701, a micro-expression acquisition module 7011, a micro-expression analysis module 7012, a limb action extraction unit 702, a limb action acquisition module 7021, a limb action analysis module 7022, an information integration module I703, an information integration analysis module 704, an information screening module 705, an information integration module II 706, an evaluation module 707, a feedback unit 8, an information input module 9, a storage unit 10 and an information extraction module 11.
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 "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1-2, the present invention provides a technical solution: a student ability evaluation system based on big data analysis comprises a central processing unit 3, a signal input end of the central processing unit 3 is in signal connection with an APP2, an APP2 is in signal connection with a login module 1, a scene selection module 201 and a learning number input module 202 are arranged in the APP2, a signal output end of the central processing unit 3 is in signal connection with a signal output end of a monitoring module 4, the monitoring module 4 is a camera, a signal output end of the monitoring module 4 is in signal connection with a signal output end of a face recognition unit 5, the face recognition unit 5 comprises an image extraction module 501, an image recognition module 502 and an image judgment module 503, a signal output end of the image extraction module 501 is in signal connection with a signal input end of the image recognition module 502, a signal output end of the image recognition module 502 is in signal connection with a signal input end of the image judgment module 503, a signal output end of, the signal output end of the signal transmission module 6 is in signal connection with the signal input end of the behavior processing system 7, the signal output end of the behavior processing system 7 is in signal connection with the signal input end of the feedback unit 8, the signal output end of the feedback unit 8 is in signal connection with the signal input end of the information input module 9, the signal output end of the information input module 9 is in signal connection with the signal input end of the storage unit 10, the signal output end of the storage unit 10 is in signal connection with the signal input end of the information extraction module 11, the storage unit 10 is a database, the signal output end of the central processing unit 3 is in signal connection with, by providing the storage unit 10, the face recognition unit 5, and the school code input module 202, each student is recognized by the face recognition unit 5, the abilities of the students are finally stored through the storage unit 10, and the teacher can conveniently view the abilities of each student using the number input module 202.
The behavior processing system 7 comprises a micro expression extraction unit 701 and a limb action extraction unit 702, the micro expression extraction unit 701 comprises a micro expression acquisition module 7011 and a micro expression analysis module 7012, the signal output end of the micro expression acquisition module 7011 is in signal connection with the signal input end of the micro expression analysis module 7012, the limb action extraction unit 702 comprises a limb action acquisition module 7021 and a limb action analysis module 7022, the signal output end of the limb action acquisition module 7021 is in signal connection with the signal input end of the limb action analysis module 7022, the signal output ends of the micro expression extraction unit 701 and the limb action extraction unit 702 are in signal connection with the signal input end of an information integration module 703, the signal output end of the information integration module 703 is in signal connection with the signal input end of an integrated information analysis module 704, the signal output end of the integrated information analysis module 704 is in signal connection with the signal output end of an information screening module 705 and the signal input end of an information integration module 706, the signal output end of the information integration module II 706 is in signal connection with the signal input end of the evaluation module 707, different situations are selected by utilizing the situation selection module 201 in the APP2, the processing conditions of students under different situations are monitored by the monitoring module 4, the behavior processing system 7 is utilized to detect the embodiment of micro-expression and limb actions of the students under different situations, and therefore different abilities of the students are evaluated by the evaluation module 707.
When in use, the student is firstly placed in a laboratory, the property in the laboratory can be changed according to the judging capability of the teacher, the teacher can log in the APP2 through the log-in module 1, the scene selection module 201 in APP selects corresponding scenes, the central processor 3 controls the monitoring module 4 to identify students through the face identification unit 5, the students process and analyze the embodied micro expressions and limb movements under different scenes through the behavior processing module 7, the information screening module 705 is used for screening useless information, and finally the result is evaluated through the evaluation module 707, the information is input into the storage unit 10 through the feedback unit 8 and the information input module 9, the information stored in the storage unit 10 is extracted through the information extraction module 11, and the teacher can conveniently check the ability of each student through the school number input module 202.
In summary, by selecting different scenarios using the scenario selection module 201 in the APP2, monitoring the processing conditions of the student in different scenarios using the monitoring module 4, and detecting the expression of micro-expression and limb movement of the student in different scenarios using the behavior processing system 7, the different abilities of the student are evaluated by the evaluation module 707.
By providing the storage unit 10, the face recognition unit 5, and the school number input module 202, each student is recognized by the face recognition unit 5, the abilities of the students are finally stored by the storage unit 10, and the ability of each student is conveniently checked by the teacher by the school number input module 202.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
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 (7)
1. A student ability evaluation system based on big data analysis, comprising a central processor (3), characterized in that: the signal input end of the central processing unit (3) is in signal connection with the APP (2), a scene selection module (201) is arranged inside the APP (2), the signal output end of the central processing unit (3) is in signal connection with the signal output end of the monitoring module (4), the signal output end of the monitoring module (4) is in signal connection with the signal output end of the face recognition unit (5), the signal output end of the face recognition unit (5) is in signal connection with the signal input module of the information transmission module (6), and the signal output end of the signal transmission module (6) is in signal connection with the signal input end of the behavior processing system (7);
the behavior processing system (7) comprises a micro expression extraction unit (701) and a limb action extraction unit (702), the micro expression extraction unit (701) comprises a micro expression acquisition module (7011) and a micro expression analysis module (7012), the signal output end of the micro expression acquisition module (7011) is in signal connection with the signal input end of the micro expression analysis module (7012), the limb action extraction unit (702) comprises a limb action acquisition module (7021) and a limb action analysis module (7022), the signal output end of the limb action acquisition module (7021) is in signal connection with the signal input end of the limb action analysis module (7022), the signal output ends of the micro expression extraction unit (701) and the limb action extraction unit (702) are in signal connection with the signal input end of an information integration module I (703), and the signal output end of the information integration module I (703) is in signal connection with the signal input end of an integration information analysis module (704), the signal output end of the integrated information analysis module (704), the signal output end of the information screening module (705) and the signal input end of the information integration module II (706) are in signal connection, and the signal output end of the information integration module II (706) is in signal connection with the signal input end of the evaluation module (707).
2. The big data analysis-based student ability evaluation system according to claim 1, wherein: the face recognition unit (5) comprises an image extraction module (501), an image recognition module (502) and an image judgment module (503), wherein the signal output end of the image extraction module (501) is in signal connection with the signal input end of the image recognition module (502), and the signal output end of the image recognition module (502) is in signal connection with the signal input end of the image judgment module (503).
3. The big data analysis-based student ability evaluation system according to claim 1, wherein: APP (2) is internally provided with a study number input module (202).
4. The big data analysis-based student ability evaluation system according to claim 1, wherein: APP (2) and login module (1) signal connection.
5. The big data analysis-based student ability evaluation system according to claim 1, wherein: behavior processing system (7) signal output part and feedback unit (8) signal input part signal connection, feedback unit (8) signal output part and information input module (9) signal input part signal connection, information input module (9) signal output part and memory cell (10) signal input part signal connection, memory cell (10) signal output part and information extraction module (11) signal input part signal connection, central processing unit (3) signal output part and information extraction module (11) signal input part signal connection.
6. The big data analysis-based student ability evaluation system according to claim 5, wherein: the storage unit (10) is a database.
7. The big data analysis-based student ability evaluation system according to claim 1, wherein: the monitoring module (4) is a camera.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010288644.0A CN111489088A (en) | 2020-04-14 | 2020-04-14 | Student ability evaluation system based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010288644.0A CN111489088A (en) | 2020-04-14 | 2020-04-14 | Student ability evaluation system based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111489088A true CN111489088A (en) | 2020-08-04 |
Family
ID=71797979
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010288644.0A Withdrawn CN111489088A (en) | 2020-04-14 | 2020-04-14 | Student ability evaluation system based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111489088A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113093625A (en) * | 2021-04-12 | 2021-07-09 | 广州宏途教育网络科技有限公司 | Student behavior analysis system for intelligent classroom |
-
2020
- 2020-04-14 CN CN202010288644.0A patent/CN111489088A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113093625A (en) * | 2021-04-12 | 2021-07-09 | 广州宏途教育网络科技有限公司 | Student behavior analysis system for intelligent classroom |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hussain et al. | Student Engagement Predictions in an e‐Learning System and Their Impact on Student Course Assessment Scores | |
| US20200175264A1 (en) | Teaching assistance method and teaching assistance system using said method | |
| Bailin | Critical thinking and science education | |
| Lee et al. | Children’s use of metacognition in solving everyday problems: An initial study from an Asian context | |
| Hosp et al. | Reading assessment: Reading fluency, reading fluently, and comprehension—Commentary on the special topic | |
| Ha et al. | The impact of misspelled words on automated computer scoring: A case study of scientific explanations | |
| CN109685692A (en) | A kind of noninductive acquisition and analysis system of various dimensions student learning behavior | |
| Earle | Formative and summative assessment of science in English primary schools: evidence from the Primary Science Quality Mark | |
| CN108665976A (en) | A kind of children's concentration evaluation system and method | |
| CN112190262A (en) | Analysis system and analysis method for psychological measurement | |
| 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 | |
| CN115691797A (en) | University student mental health early warning system | |
| CN114121287A (en) | Student mental health detection method, system and readable storage medium | |
| Clarke et al. | Classification accuracy of easyCBM first-grade mathematics measures: Findings and implications for the field | |
| Cheng et al. | Does big-fish–little-pond effect always exist? Investigation of goal orientations as moderators in the Hong Kong context | |
| WO2022009875A1 (en) | Program for determining concentration level | |
| CN111489088A (en) | Student ability evaluation system based on big data analysis | |
| Nentwig et al. | Performance and levels of contextualization in a selection of OECD countries in PISA 2006 | |
| Arqoub et al. | Extending learning management system for learning analytics | |
| JP2021096836A (en) | Bullying indication determination program and system, and bullying determination program and system | |
| Morley | Claims about the reliability of student evaluations of instruction: The ecological fallacy rides again | |
| CN107862032B (en) | Intelligent learning type media interaction analysis method and device | |
| CN109616209A (en) | A kind of general children's health apparent motion ability intelligence evaluation system | |
| CN105528505A (en) | Online automatic learning style diagnosis system and 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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200804 |