CN114255623A - Teaching method and system based on robot - Google Patents
Teaching method and system based on robot Download PDFInfo
- Publication number
- CN114255623A CN114255623A CN202111402122.XA CN202111402122A CN114255623A CN 114255623 A CN114255623 A CN 114255623A CN 202111402122 A CN202111402122 A CN 202111402122A CN 114255623 A CN114255623 A CN 114255623A
- Authority
- CN
- China
- Prior art keywords
- teaching
- course
- data
- robot
- instruction
- 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
Images
Classifications
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/08—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations
- G09B5/14—Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations with provision for individual teacher-student communication
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Electrically Operated Instructional Devices (AREA)
Abstract
The application discloses a robot-based teaching method and system, which are used for solving the technical problems of single function mode and poor interactivity of the existing teaching robot. The robot receives a teaching instruction sent by a teacher end, and selects a corresponding execution strategy according to a teaching stage where the teaching instruction is located; determining corresponding teaching equipment based on the execution strategy; controlling the teaching equipment to acquire course data aiming at the teaching instruction from the data center so as to perform teaching tutoring through the course data; receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the reduction degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction. By the aid of the method, teaching feedback can be acquired in time through teaching interaction, and diversity output of teaching contents is achieved.
Description
Technical Field
The application relates to the technical field of intelligent robots, in particular to a teaching method and system based on a robot.
Background
With the continuous development of internet communication, big data and artificial intelligence technology, educational robots have been applied to daily teaching. However, the educational robots provided in the current market can only participate in the teaching process based on simple voice interaction, the interaction mode is single, the teaching effect is not easy to know in the teaching process, and the participation degree is poor.
Disclosure of Invention
The embodiment of the application provides a teaching method and a teaching system based on a robot, which are used for solving the technical problems of single function mode and poor interactivity of the existing teaching robot.
In one aspect, an embodiment of the present application provides a teaching method based on a robot, including: the robot receives a teaching instruction sent by a teacher end, and selects a corresponding execution strategy according to a teaching stage where the teaching instruction is located; determining corresponding teaching equipment based on the execution strategy; controlling the teaching equipment to acquire course data aiming at the teaching instruction from the data center so as to perform teaching tutoring through the course data; receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the reduction degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction.
In an implementation of this application, control teaching equipment obtains the course data to the instruction of imparting knowledge to students from data center to give tutoring through the course data, specifically include: the multimedia equipment is controlled, and courseware data and/or question and answer data aiming at the teaching instruction are obtained from the data center; and controlling the multimedia equipment to display the courseware data and/or question and answer data.
In an implementation manner of the present application, feedback information that each student terminal sends respectively according to the course data is received, and according to the feedback information, the reduction degree of the course data to the teaching instruction is obtained, which specifically includes: determining a corresponding teaching target according to the teaching instruction; based on edge calculation, obtaining the mastery degree of the student end on the course data according to the feedback information respectively sent by each student end; and determining the reduction degree of the course data to the teaching instruction according to the mastery degree and the teaching target.
In an implementation manner of the present application, based on edge calculation, the degree of mastering of the student end on the course data is obtained according to the feedback information sent by each student end, which specifically includes: the feedback information comprises answer data and answering time of each student end; determining the number of student ends which finish answering the questions according to the answer data of each student end so as to calculate the answering rate of the questions; if the answer rate is higher than a first preset threshold value, calculating the correct rate of the question according to the answer data of each student end and the standard answer in the corresponding question-answering data, and calculating the average answer efficiency of the question according to the answer time of each student end; and determining the mastering degree of the student end on the course data according to the average answering efficiency and the accuracy.
In one implementation of the present application, after calculating the answer rate of the question, the method further includes: if the response rate is lower than a first preset threshold value, acquiring face image information of each student end through a built-in camera or a classroom environment sensor; the face image information comprises a plurality of face recognition key points; inputting the face image information into a preset face feature recognition model, and determining the posture features corresponding to the face recognition key points; inputting the attitude characteristics into a preset concentration degree estimation model to obtain a corresponding concentration degree estimation value; and if the concentration degree estimation value is lower than a second preset threshold value, a warning is sent to the student end.
In an implementation manner of the present application, before the robot receives the teaching instruction sent by the teacher, the method further includes: extracting examples of teaching notes uploaded by a teacher end to obtain corresponding teaching examples; determining teaching chapters corresponding to courses and knowledge points corresponding to the teaching chapters according to teaching examples; and extracting knowledge points and adding the knowledge points to a pre-constructed course graph.
In one implementation of the present application, the method further comprises: under the condition that the mastery degree of the student end to any knowledge point does not reach a preset condition, determining other knowledge points corresponding to the side with the maximum weight as knowledge points to be consolidated from sides connected with the knowledge points according to a pre-constructed knowledge point association network; and controlling the multimedia equipment to display the course data corresponding to the knowledge points to be consolidated so that each student end consolidates the knowledge points and other knowledge points associated with the knowledge points.
In an implementation manner of the present application, after obtaining the reduction degree of the course data to the teaching instruction, the method further includes: dividing each student end into a plurality of distribution areas according to the position of each student end, and determining the area reduction degree of course data in each distribution area to the teaching instruction according to each distribution area; and comparing the regional reduction degree with the reduction degree, determining a target distribution region with the corresponding regional reduction degree lower than the reduction degree, and performing auxiliary teaching on the student terminals in the target distribution region.
In an implementation manner of the present application, the teaching device is controlled to obtain course data for teaching instructions from the data center, and the method specifically includes: determining a first course resource keyword, a second course resource keyword and a third course resource keyword corresponding to the course data to be acquired according to the teaching instruction; the first course resource key word, the second course resource key word and the third course resource key word respectively correspond to the course resource nodes where the courses, the teaching sections and the knowledge points are located in the pre-constructed course map; determining a course resource node where a course is located from a course map according to the first course resource key word; and sequentially determining the course resource nodes where the teaching sections and the knowledge points corresponding to the courses are located from the course resource nodes where the courses are located through the second course resource key words and the third course resource key words so as to obtain corresponding course data.
On the other hand, the embodiment of the application also provides a teaching system based on a robot, and the system comprises: the robot is used for receiving the teaching instruction sent by the teacher end and selecting a corresponding execution strategy according to the teaching stage of the teaching instruction; determining corresponding teaching equipment based on the execution strategy; controlling the teaching equipment to acquire course data aiming at the teaching instruction from the data center so as to perform teaching tutoring through the course data; receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the reduction degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction; the teacher end is used for sending teaching instructions to the robot; the multimedia equipment is used for acquiring courseware data and/or question and answer data aiming at the teaching instruction from the data center; displaying the courseware data and/or question and answer data; the student terminal is used for generating corresponding feedback information according to the course data and sending the feedback information to the robot; and the cloud management platform stores course data and is used for coordinating and controlling the robot, the teacher end, the multimedia equipment and the student end.
The teaching method and system based on the robot provided by the embodiment of the application at least have the following beneficial effects: the robot can receive the teaching instruction sent by the teacher end through the interaction between the robot and the teacher end, the student end and the teaching equipment, thereby controlling the teaching equipment to acquire course data to teach and guide students, calculating the reduction degree of the teaching instruction through the feedback information of the student end, judging the current teaching achievement, realizing the diversity output of teaching contents and enriching the interaction mode. In addition, the teacher end can intervene in the current teaching through the teaching instruction, and compared with the traditional teaching guidance mode which is used as a teaching main body, the method is more convenient.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a robot-based teaching method according to an embodiment of the present disclosure;
fig. 2 is an overall architecture diagram of a robot-based teaching system according to an embodiment of the present disclosure;
fig. 3 is an overall architecture diagram of another robot-based teaching system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, 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 application.
The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a robot-based teaching method according to an embodiment of the present disclosure. As shown in fig. 1, the robot-based teaching method provided in the embodiment of the present application may mainly include the following steps:
s101: the robot receives a teaching instruction sent by a teacher end, and selects a corresponding execution strategy according to a teaching stage where the teaching instruction is located.
In the embodiment of the application, the teacher end can directly participate in interaction with the teaching equipment, and can also realize calling of the teaching equipment and feedback of teaching contents by issuing the teaching instruction. The teaching instruction comprises a triggering instruction and a voice instruction, and the teacher end can trigger different teaching instructions through the mobile terminal or send the voice instruction to the robot. After the robot receives the corresponding trigger instruction or recognizes the keyword in the voice instruction, the teaching stage corresponding to the teaching instruction can be determined, and therefore the corresponding execution strategy is selected. The teaching phase refers to each phase in a teaching flow set by a teaching plan or a teacher, and the execution strategy is a teaching operation to be executed by the robot according to the application requirement of the teaching phase. For example, the pre-class review stage may be a question or a pre-class test corresponding to the execution policy of the pre-class review stage. Or a new knowledge teaching stage, and the execution strategy corresponding to the stage can be a group discussion or a screen display courseware.
It should be noted that, when the teaching instruction sent by the teacher end is to start a class and the instruction is not issued subsequently, the robot can teach according to the teaching flow, and automatically select the corresponding execution strategy according to the teaching stage at present. The teaching process can be determined according to teaching plan information uploaded by a teacher terminal in the data center or determined by the robot.
S102: and determining the corresponding teaching equipment based on the execution strategy.
Teaching equipment refers to equipment directly used for teaching or indirectly assisting teaching. For example, the multimedia device (including screen and sound) can directly show pictures or play voice to students for direct teaching. Of course, the teaching equipment can also comprise auxiliary teaching equipment such as intelligent curtains, and the like, and the teaching equipment can control the opening and closing of the curtains when playing videos, reduce the interference of external light, create a good learning environment for students, and achieve the effect of auxiliary teaching.
S103: and controlling the teaching equipment to acquire course data aiming at the teaching instruction from the data center so as to perform teaching tutoring through the course data.
The data center stores course data of different courses, and the course data is expressed in a structured form through a course map. Course data and high in the clouds encyclopedia knowledge have been fused to the course map, and teaching equipment is when acquireing the course data, through the incidence relation between each node in the course map, can fix a position corresponding course data fast to give guidance to imparting knowledge to students' end.
In one embodiment, before the robot receives a teaching instruction sent by a teacher, the cloud management platform serves as a master control center for coordinating other components, and a course map facing a plurality of courses is constructed in advance according to historical teaching plan information uploaded by the teacher so as to obtain course data in the following process.
Specifically, the cloud management platform extracts examples of teaching plans uploaded by the teacher side, and obtains corresponding teaching examples. And then, according to the teaching example, determining the teaching section of the teaching example in the corresponding course and the knowledge point corresponding to the teaching section. And finally, extracting the knowledge points and adding the knowledge points to a pre-constructed course map.
In one embodiment, after the robot selects the teaching equipment, the robot can control the multimedia equipment, acquire courseware data and/or question and answer data for teaching instructions from the data center, and then control the multimedia equipment to display the courseware data and/or question and answer data.
Specifically, the course data in the data center is presented from multiple levels of courses, teaching chapters, knowledge points, and the like. When course data are acquired, the robot control teaching equipment determines a first course resource keyword, a second course resource keyword and a third course resource keyword corresponding to the course data to be acquired according to a teaching instruction; the first course resource key word, the second course resource key word and the third course resource key word respectively correspond to the course resource nodes where the courses, the teaching sections and the knowledge points are located in the pre-constructed course map; determining a course resource node where a course is located from a course map according to the first course resource key word; and determining the course resource nodes where the teaching sections and the knowledge points corresponding to the courses are located in sequence from the course resource nodes where the courses are located through the second course resource key words and the third course resource key words so as to obtain corresponding course data.
The process that the multimedia equipment acquires data from the data center is equivalent to traversing the course graph, and the course resource nodes where the courses, teaching chapters and knowledge points are located are sequentially determined according to the sequence of the node levels from high to low, so that the corresponding course data are found. For example, what this lesson will teach is the Pythagorean theorem, which belongs to the Pythagorean theorem knowledge point under the second chapter of the math course, and the corresponding first course resource keyword is math, the second course resource keyword is the second chapter, and the third course resource keyword is the Pythagorean theorem. It should be noted that, if the courseware data is linked to the course resource node where the teaching section is located, the corresponding course resource node can be located through the first course resource keyword and the second course resource keyword.
For example, the teacher end is provided with a mobile terminal, and the mobile terminal comprises keys such as "show courseware", "group discussion" and "question and answer". After the teacher clicks the question answering key, the robot receives a corresponding teaching instruction and determines that the current strategy to be executed is to carry out question answering. And then, controlling the multimedia equipment to acquire question and answer question data corresponding to the course from the data center, and displaying the question on a screen of the multimedia equipment. The student end can answer the relevant questions through the respective electronic white boards, and corresponding answer data can be fed back to the robot and the teacher end after the answers are finished.
S104: and the robot receives feedback information respectively sent by each student terminal according to the course data, and obtains the reduction degree of the course data to the teaching instruction according to the feedback information.
After the robot carries out corresponding teaching tutoring according to the teaching instruction sent by the teacher end, the feedback information made by the student end is received, and the reduction degree of the teaching instruction is calculated according to the feedback information so as to evaluate the current learning achievement.
In one embodiment, the robot first determines a corresponding teaching objective based on the teaching instruction. For example, when the teaching instruction sent by the teacher end is to show courseware, the teaching objective is to make the student end see the courseware data clearly. Or the teacher sends a voice command to test the questions related to the student, and the students need to master the questions, so that the teaching target is that the learning degree of the questions asked and answered under a certain knowledge point is 100% for the student.
Then, the robot can obtain the mastery degree of the student end on the course data according to the feedback information respectively sent by each student end based on the edge computing capability of the robot.
Specifically, the feedback information includes answer data and answering time of each student. The robot can set the answering time length, and when the answering time length is reached, the answering data of each student end is recovered. By judging whether the answer data of each student end is null or not, the number of the student ends which finish the question answering can be determined, and therefore the answering rate is calculated. It should be noted that the feedback information may also include voice feedback, for example, when the current application requirement of the teacher end is to show courseware data, the student end can perform clear feedback on the courseware data, and reply "see clearly" or "see not clearly" through voice. After the robot obtains the feedback information, the current mastering degree of the courseware degree of the student end can be obtained.
Further, if the answer rate is higher than a first preset threshold value, which indicates that the current classroom order is good, and the obtained answer data has referential property, the correct rate of the question is calculated according to the answer data of each student end and the standard answer in the corresponding question data. Meanwhile, the average answering efficiency of the problem is calculated according to the answering time of each student terminal.
Further, when the mastery degree of the student end is measured, the robot can evaluate the comprehensive answering efficiency and the accuracy. Namely, the mastering degree of the student end on the course data is determined according to the average answering efficiency and the accuracy. Generally, the mastery degree increases in a logarithmic curve trend, the increasing speed is high in the early stage, and the increasing speed gradually slows down and tends to be stable in the later stage along with the time.
And finally, after the mastery degree of the student end on the course data is obtained, the robot can determine the restoration degree of the course data to the teaching instruction according to the mastery degree and the teaching target corresponding to the teaching instruction.
For example, if the teaching instruction sent by the teacher end is a question and answer, the teaching target corresponding to the teaching instruction is set to be that the student end mastery degree exceeds 80%. Assuming that the class students are 60, the first preset threshold is set to 90%. When the answering time is up, the robot recovers feedback information of each student end, and if the number of the students who finish the question answering is 56, the answering rate exceeds a first preset threshold value. Then, the robot can calculate the accuracy and the average answering efficiency comprehensively to obtain the mastering degree of the student end to the course data. If the mastery degree exceeds 80%, the teaching target is already completed at this time, and the reduction degree of the teaching instruction is 100%.
In the embodiment of the application, the reduction degree is used for measuring the execution condition of the robot on the teaching instruction, when the reduction degree is higher than a preset threshold value, the feedback of the student end on the teaching instruction sent by the current teacher end is in accordance with the expected expectation, at the moment, the execution condition of the robot on the teaching instruction is good, and the teacher end or the robot can continue teaching according to the teaching flow. When the reduction degree is lower than the preset threshold value, the robot can intervene the classroom to a certain degree according to the reduction degree so as to adopt corresponding teaching measures to enable the learning achievement to meet the expectation of teachers as far as possible. The robot is through in time acquireing student's feedback information and making corresponding teaching adjustment, still is favorable to the propulsion of teaching flow when having improved the teaching effect, has promoted teaching efficiency.
In one embodiment, the robot can acquire the reduction degree of the teaching instruction in a partitioning manner, namely, the robot is divided into a plurality of distribution areas according to the positions of the student terminals, and the regional reduction degree of the course data in the distribution areas to the teaching instruction is determined for each distribution area. And comparing the reduction degree of each area with the overall reduction degree, determining a target distribution area with the reduction degree of the area lower than the overall reduction degree, and indicating that the reduction degree of the target distribution area does not reach an average level, wherein the robot needs to perform special auxiliary teaching on the target distribution area. For example, the student terminals can be uniformly divided according to the number of rows in the classroom, assuming that the students are divided into 6 rows in total, each two rows can be divided into different distribution areas, if the area reduction degrees corresponding to 1 row and 2 rows are 90%, the area reduction degrees corresponding to 3 rows and 4 rows are 95%, and the area reduction degrees corresponding to 5 rows and 6 rows are 80%, it is indicated that the mastery degree of the student terminals in the current 5 rows and 6 rows does not reach the average level of the class, and special auxiliary teaching needs to be performed.
The reason for the low degree of restoration of the area is various, for example, the area may be far away from the multimedia device, the screen may be difficult for the student to see, or the learning ability of the student in the area may be poor. The robot can correspondingly control the size of the word size of the multimedia equipment display data, or realize the auxiliary teaching in a mode of giving guidance after class to the student side in the target distribution area.
In one embodiment, in order to ensure the teaching effect when the mastery degree of the student end on any knowledge point does not reach the preset condition, the robot determines, through a pre-established knowledge point association network, other knowledge points corresponding to the side with the largest weight as knowledge points to be consolidated, that is, other knowledge points with the strongest association with the knowledge points, from the sides connected with the knowledge points. And then controlling the multimedia equipment to display the course data corresponding to the knowledge points to be consolidated, so that each student end consolidates the knowledge points and other knowledge points associated with the knowledge points.
It should be noted that the knowledge point association network is pre-constructed by the cloud management platform based on the cloud knowledge point set and the course map. The cloud management platform firstly determines the association degree of all knowledge points through the cloud knowledge point set and the course map. And then, taking the knowledge points as nodes, taking the association relation among the knowledge points as edges, and expressing the weight of the edges as the association degree among the knowledge points to construct a knowledge point association network. The greater the weight of an edge, the stronger the association between two knowledge points it connects.
The knowledge points are associated through the knowledge point association network, and the association degree of the knowledge points can be visually obtained, so that the knowledge points can be comprehensively explained through the relevant knowledge points of one knowledge point when teaching guidance is performed, and the teaching effect is better.
In one embodiment, when the question and answer is performed, if the answer rate is lower than a first preset threshold value, the number of the current student ends which do not complete answering is more. At the moment, the robot needs to detect the concentration degree of the students and judges whether the current answering rate of the problem caused by the fact that the students do not concentrate on the student end is low or not, so that corresponding intervention measures are taken, and the class concentration degree of the students is improved.
Specifically, the robot acquires face image information of each student through a built-in camera or a classroom environment sensor. The face image information comprises a plurality of face recognition key points, such as a nose, eyes and a mouth. And then inputting the face image information into a preset face feature recognition model, and determining the posture features corresponding to the face recognition key points. After the posture characteristics corresponding to the key points among the people are obtained, the posture characteristics are input into a preset concentration degree estimation model, and a corresponding concentration degree estimation value is obtained. When the concentration degree estimation value is lower than a second preset threshold value, the fact that the concentration degree of the current student end is poor is indicated, the robot needs to conduct extra teaching intervention, at the moment, the robot can send out a warning to the student end, and the warning mode comprises voice warning or action prompting.
The above is the method embodiment proposed by the present application. Based on the same inventive concept, the embodiment of the present application further provides a teaching system based on a robot, and the overall architecture of the teaching system is as shown in fig. 2.
The teaching system based on the robot comprises a teacher end, the robot, a student end, a multimedia device (digital environment) and a cloud management platform (not shown in the figure). The cloud management platform is used as a control center of the teaching system, and a course map for teaching resources and relevant information of classes, teachers, students and the like are stored in a corresponding data center. The teacher end is equipped with mobile terminal, and the student end is equipped with intelligent teaching equipment such as whiteboard.
The robot, the teacher end and the student end are automatically networked in the local area network, and the teacher end can send a teaching instruction to the robot through the mobile terminal or voice. The robot is used as a teaching main body, can receive a teaching instruction sent by a teacher end, performs corresponding data processing according to the teaching instruction, determines a corresponding execution strategy, and calls teaching equipment in a digital environment according to the execution strategy to perform teaching tutoring on a student end, so as to implement the strategy. The teaching equipment comprises an electronic desk, an electronic whiteboard, multimedia equipment, a classroom environment sensor, an intelligent mobile terminal and other equipment. Correspondingly, the student end can feed back course data acquired by the teaching equipment and return feedback information to the robot and the teacher end. And the robot responds to the request of the teacher end and determines the reduction degree of the course data to the teaching instruction according to the feedback information. Meanwhile, the teacher end can also interrupt or adjust the teaching flow according to the feedback information of the student end, and the student end is guided in teaching.
In one embodiment, the robot is used for receiving a teaching instruction sent by a teacher end and selecting a corresponding execution strategy according to a teaching stage where the teaching instruction is located; determining corresponding teaching equipment based on the execution strategy; controlling the teaching equipment to acquire course data aiming at the teaching instruction from the data center so as to perform teaching tutoring through the course data; receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the reduction degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction.
The teacher end is used for sending teaching instructions to the robot; the multimedia equipment is used for acquiring courseware data and/or question and answer data aiming at the teaching instruction from the data center; displaying the courseware data and/or question and answer data; the student terminal is used for generating corresponding feedback information according to the course data and sending the feedback information to the robot; and the cloud management platform stores course data and is used for coordinating and controlling the robot, the teacher end, the multimedia equipment and the student end.
Fig. 3 is an overall architecture diagram of another robot-based teaching system according to an embodiment of the present application. As shown in fig. 3, the robot may determine the application requirements of the teacher end according to the teaching instruction, and control the teaching device in the digital environment to perform teaching tutoring based on the application requirements of the teacher end and its own auxiliary requirements. The student end can make corresponding feedback to the teacher end and the robot after acquiring corresponding teaching resources, and can also feed back to the teaching equipment. And the teacher end can directly control the teaching tool to intervene in the current classroom in the teaching process. The teaching equipment is in a digital environment, can interact with other teaching equipment in the environment, and can also be dispatched by a robot and a teacher.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A robot-based teaching method, the method comprising:
the robot receives a teaching instruction sent by a teacher end, and selects a corresponding execution strategy according to a teaching stage where the teaching instruction is located;
determining corresponding teaching equipment based on the execution strategy;
controlling the teaching equipment to acquire course data aiming at the teaching instruction from a data center so as to perform teaching tutoring through the course data;
receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the restoration degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction.
2. The robot-based teaching method according to claim 1, wherein the controlling the teaching device obtains course data for the teaching instruction from a data center to perform teaching tutoring through the course data includes:
the multimedia equipment is controlled, and courseware data and/or question and answer data aiming at the teaching instruction are obtained from a data center;
and controlling the multimedia equipment to display the courseware data and/or the question and answer data.
3. The robot-based teaching method according to claim 1, wherein receiving feedback information respectively sent by each student end according to the course data, and obtaining the reduction degree of the course data to the teaching instruction according to the feedback information specifically comprises:
determining a corresponding teaching target according to the teaching instruction;
based on edge calculation, obtaining the mastery degree of the student end on the course data according to the feedback information respectively sent by each student end;
and determining the reduction degree of the course data to the teaching instruction according to the mastery degree and the teaching target.
4. The robot-based teaching method according to claim 3, wherein the obtaining of the mastery degree of the student side on the course data according to the feedback information sent by each student side based on the edge calculation specifically comprises:
determining the number of student ends which finish answering the questions according to the answer data of each student end so as to calculate the answering rate of the questions;
if the answer rate is higher than a first preset threshold value, calculating the correct rate of the question according to the answer data of each student end and the standard answer in the corresponding question-answer data, and calculating the average answer efficiency of the question according to the answer time of each student end;
and determining the mastering degree of the student end on the course data according to the average answering efficiency and the accuracy.
5. The robot-based tutoring method of claim 4, wherein after calculating the response rate of the question, the method further comprises:
if the response rate is lower than the first preset threshold value, acquiring face image information of each student end through a built-in camera or a classroom environment sensor; the face image information comprises a plurality of face identification key points;
inputting the face image information into a preset face feature recognition model, and determining the posture features corresponding to the face recognition key points;
inputting the attitude characteristics into a preset concentration degree estimation model to obtain a corresponding concentration degree estimation value;
and if the concentration degree estimation value is lower than a second preset threshold value, a warning is sent to the student end.
6. A robot-based teaching method according to claim 1, wherein before the robot receives the teaching instruction from the teacher's terminal, the method further comprises:
extracting examples of teaching notes uploaded by a teacher end to obtain corresponding teaching examples;
determining teaching chapters corresponding to courses and knowledge points corresponding to the teaching chapters according to the teaching examples;
and extracting the knowledge points and adding the knowledge points to a pre-constructed course map.
7. The robot-based tutorial method of claim 1, further comprising:
under the condition that the mastery degree of a student end to any knowledge point does not reach a preset condition, determining other knowledge points corresponding to the side with the maximum weight as knowledge points to be consolidated from sides connected with the knowledge points according to a pre-constructed knowledge point association network;
and controlling the multimedia equipment to display the course data corresponding to the knowledge points to be consolidated so that each student end consolidates the knowledge points and other knowledge points associated with the knowledge points.
8. The robot-based tutoring method of claim 1, wherein after obtaining the level of reduction of the tutorial instructions from the lesson data, the method further comprises:
dividing each student end into a plurality of distribution areas according to the position of each student end, and determining the area reduction degree of the course data in each distribution area to the teaching instruction according to each distribution area;
and comparing the regional reduction degree with the reduction degree, determining a target distribution region with the corresponding regional reduction degree lower than the reduction degree, and performing auxiliary teaching on the student terminals in the target distribution region.
9. The robot-based teaching method according to claim 6, wherein the controlling of the teaching device to obtain the course data for the teaching instruction from a data center specifically comprises:
determining a first course resource keyword, a second course resource keyword and a third course resource keyword corresponding to the course data to be acquired according to the teaching instruction; the first course resource key word, the second course resource key word and the third course resource key word respectively correspond to the course resource nodes where the courses, the teaching sections and the knowledge points are located in the pre-constructed course map;
determining a course resource node where a course is located from the course graph according to the first course resource keyword;
and determining the course resource nodes where the teaching sections and the knowledge points corresponding to the courses are located in sequence in the course resource nodes where the courses are located through the second course resource key words and the third course resource key words so as to acquire corresponding course data.
10. A robot-based tutorial system, in which the system comprises:
the robot is used for receiving a teaching instruction sent by a teacher end and selecting a corresponding execution strategy according to a teaching stage where the teaching instruction is located; determining corresponding teaching equipment based on the execution strategy; controlling the teaching equipment to acquire course data aiming at the teaching instruction from a data center so as to perform teaching tutoring through the course data; receiving feedback information respectively sent by each student terminal according to the course data, and obtaining the restoration degree of the course data to the teaching instruction according to the feedback information; the feedback information comprises answer data and answer time of each student end, and the reduction degree is used for evaluating the execution condition of the robot on the teaching instruction;
the teacher end is used for sending teaching instructions to the robot;
the multimedia equipment is used for acquiring courseware data and/or question and answer data aiming at the teaching instruction from a data center; displaying the courseware data and/or the question and answer data;
the student terminal is used for generating corresponding feedback information according to the course data and sending the feedback information to the robot;
and the cloud management platform is stored with course data and is used for coordinating and controlling the robot, the teacher end, the multimedia equipment and the student end.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111402122.XA CN114255623A (en) | 2021-11-19 | 2021-11-19 | Teaching method and system based on robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111402122.XA CN114255623A (en) | 2021-11-19 | 2021-11-19 | Teaching method and system based on robot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114255623A true CN114255623A (en) | 2022-03-29 |
Family
ID=80793145
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111402122.XA Pending CN114255623A (en) | 2021-11-19 | 2021-11-19 | Teaching method and system based on robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114255623A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116597461A (en) * | 2023-07-14 | 2023-08-15 | 广东信聚丰科技股份有限公司 | Topic knowledge point association method and system based on artificial intelligence |
| CN118365493A (en) * | 2024-06-20 | 2024-07-19 | 北京爱宾果科技有限公司 | Teaching quality assessment method and system for educational robot |
-
2021
- 2021-11-19 CN CN202111402122.XA patent/CN114255623A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116597461A (en) * | 2023-07-14 | 2023-08-15 | 广东信聚丰科技股份有限公司 | Topic knowledge point association method and system based on artificial intelligence |
| CN116597461B (en) * | 2023-07-14 | 2023-09-22 | 广东信聚丰科技股份有限公司 | Topic knowledge point association method and system based on artificial intelligence |
| CN118365493A (en) * | 2024-06-20 | 2024-07-19 | 北京爱宾果科技有限公司 | Teaching quality assessment method and system for educational robot |
| CN118365493B (en) * | 2024-06-20 | 2024-09-20 | 北京爱宾果科技有限公司 | Teaching quality assessment method and system for educational robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110334626B (en) | Online learning system based on emotional state | |
| Richards | Teachers' maxims in language teaching | |
| CN114255623A (en) | Teaching method and system based on robot | |
| CN107516445A (en) | Online programming teaching method and system | |
| CN109101879B (en) | Posture interaction system for VR virtual classroom teaching and implementation method | |
| CN112765419B (en) | Interactive live broadcast classroom system | |
| CN101218616A (en) | System and method for learning languages | |
| Ronstadt et al. | Validation of an innovative teaching approach for entrepreneurship courses | |
| CN110176163A (en) | A kind of tutoring system | |
| CN111459286A (en) | Web-based VR interactive learning education system and method | |
| CN108876225A (en) | On-line education system and method based on man-machine hybrid intelligent | |
| CN115630860A (en) | Teaching quality evaluation method, device, equipment and storage medium | |
| Ma et al. | Practice and thinking of online teaching during epidemic period | |
| CN116403445A (en) | Self-adaptive auxiliary teaching method and device, electronic equipment and storage medium | |
| CN114885216A (en) | Exercise pushing method and system, electronic equipment and storage medium | |
| CN112651860B (en) | Discussion type robot teaching system, method and device | |
| CN111667128B (en) | Teaching quality assessment method, device and system | |
| CN112133147A (en) | Online automatic teaching and interaction system based on teaching plan and preset question bank | |
| CN114299772A (en) | Geometric tutoring system and method based on semantic net | |
| Rosenski et al. | Secondary students' experience using 3D pen in spatial geometry: affective states while problem solving | |
| CN108154717A (en) | A kind of intelligent classroom system based on pattern-recognition | |
| CN111815489A (en) | Big data-based algorithm analysis design teaching method and device | |
| He et al. | Virtual Reality Technology in the Teaching of Sports Dance in Colleges and Universities | |
| CN110853428A (en) | Recording and broadcasting control method and system based on Internet of things | |
| US20020152187A1 (en) | Expert system for intelligent teaching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |