CN116453387A - AI intelligent teaching robot control system and method - Google Patents
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Abstract
The invention belongs to the technical field of teaching robots, and particularly relates to an AI intelligent teaching robot control system and method, wherein the system comprises the following steps of. According to the invention, the attention of students can be concentrated by setting the questioning information, meanwhile, the quality score of the current period of test teaching can be calculated according to the accuracy of the response information of the students, a corresponding review plan can be generated according to the quality score of the teaching, and the teaching content of the subsequent teaching video can be regulated and controlled in real time according to the execution time of the review plan, so that the playing of the teaching video can be completed within the teaching plan, and the intelligent teaching robot can be ensured to improve the learning effect of the students and not to influence the teaching plan after being put into use.
Description
Technical Field
The invention belongs to the technical field of teaching robots, and particularly relates to an AI intelligent teaching robot control system and method.
Background
Education is purposeful, planned activities of people are cultivated, the basic stone of China development, but along with the rapid development of artificial intelligence and computer technology, intelligent teaching robots gradually appear in the education field, in schools, the intelligent teaching robots can assist teachers to teach students 'knowledge, in families, the intelligent teaching robots can help parents to guide students' homework, review learned knowledge and the like, the intelligent teaching robots are provided with a display screen for playing teaching pictures, compared with teachers or parents to conduct teaching in a way of teaching, video teaching can fully embody teaching pictures according to teaching contents, the interestingness of the teaching contents is increased, and the attention of students is attracted, so that the acceptance of students is increased correspondingly, and the learning results of the students are improved.
In the prior art, most of intelligent teaching robots play preset teaching videos according to an inherent program, but students easily take place the situation of distraction during a lesson, the mode of playing the teaching videos for teaching cannot know the mastering condition of the students on the teaching contents, and the follow-up teaching videos continue to be played according to the established program, so that part of students are disjointed from the previous teaching contents, the expected teaching quality can be greatly reduced undoubtedly, and based on the scheme, the intelligent teaching robot control method capable of checking the teaching quality and regulating the follow-up teaching videos in real time according to the teaching quality is provided.
Disclosure of Invention
The invention aims to provide an AI intelligent teaching robot control system and method, which can be used for checking teaching quality and regulating subsequent teaching videos in real time according to the teaching quality, so that after the intelligent teaching robot is put into use, the learning effect of students can be improved, and the teaching plan cannot be influenced.
The technical scheme adopted by the invention is as follows:
an AI intelligent teaching robot control method comprises the following steps:
acquiring a student group of a teaching point, and matching a teaching video data set according to the student group;
acquiring student demands, and calling corresponding teaching videos from the teaching video data set according to the student demands;
basic information of each student in the student group is acquired and summarized into a basic information set;
acquiring questioning information in the teaching video, and extracting response students according to the basic information set;
acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;
inputting the accuracy of the response information into a data conversion model, and calibrating a conversion result as a teaching quality score;
acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
and acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan.
In a preferred scheme, the step of acquiring the student population of the teaching point and matching the teaching video data set according to the student population comprises the following steps:
obtaining the geographic position of the teaching point, and matching all teaching videos conforming to the local teaching characteristics;
the specific school segments of the student group are obtained, the teaching video is screened according to the school segments, and the screening result is summarized into a teaching video data set;
the teaching robot comprises an interaction screen, and teaching videos in the teaching video data set can be put in through the interaction screen.
In a preferred scheme, the step of acquiring the questioning information in the teaching video and extracting the answering students according to the basic information set comprises the following steps:
acquiring basic information of each student from the basic information set, wherein the basic information comprises student names and learning achievements;
the classification threshold value is obtained, the classification threshold value is compared with the student score, the student group is classified into a plurality of sampling intervals, and the sampling intervals are arranged according to the sequence of the student score from high to low;
and generating a sampling plan according to the arrangement sequence of the sampling intervals, and extracting response students in the sampling intervals according to the sampling plan.
In a preferred embodiment, the step of generating a sampling plan according to the arrangement order of the sampling intervals includes:
acquiring the duration of the teaching video and the time nodes of the questioning information distributed in the teaching video, and calibrating the time nodes as questioning nodes;
acquiring the number of the questioning contents in the questioning information, wherein the questioning information comprises a plurality of questioning contents, and the number of the questioning contents is not less than 3;
acquiring a central node of the teaching video and comparing the central node with the question node;
if the questioning nodes are distributed behind the central node, distributing the questioning contents according to the arrangement sequence of the sampling intervals;
if the questioning nodes are distributed in front of the central node, distributing the questioning contents according to the reverse arrangement sequence of the sampling intervals;
and if the number of the questioning contents is higher than the number of the sampling intervals, repeatedly distributing the questioning contents according to the distribution sequence of the questioning contents.
In a preferred embodiment, the step of obtaining response information corresponding to the question information and answered by all the answering students, and calculating accuracy of the response information in real time includes:
obtaining response information;
obtaining a standard answer corresponding to the questioning information, comparing the standard answer with the response information to obtain the repetition rate between the response information and the standard answer, and calibrating the repetition rate as a parameter to be evaluated;
obtaining standard parameters corresponding to standard answers, and comparing the standard parameters with the parameters to be evaluated, wherein each standard parameter corresponds to one standard parameter;
if the value of the parameter to be evaluated is smaller than the standard parameter, judging that the response information is wrong;
if the value of the parameter to be evaluated is larger than or equal to the standard parameter, judging that the response information is correct;
obtaining a measuring and calculating function, inputting the number of the response information and the number of the questioning information to the measuring and calculating function, and calibrating the measuring and calculating result as the accuracy of the response information.
In a preferred scheme, after the response information is judged to be correct, basic information of students corresponding to the response information is acquired and screened out from a sampling interval;
and after the answer information is judged to be wrong, acquiring basic information of the student corresponding to the answer information, and retaining the basic information in a sampling interval.
In a preferred embodiment, the step of inputting the accuracy of the response information into a data conversion model and calibrating the conversion result to be a teaching quality score includes:
obtaining an evaluation interval from the data conversion model, wherein the evaluation interval is (0, a ], (a, b ], (b, c) … …;
acquiring a teaching quality score corresponding to each evaluation interval;
and comparing the accuracy of the response information with an evaluation interval, and outputting a corresponding teaching quality score.
In a preferred embodiment, the step of obtaining the evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to the comparison result, and adjusting the teaching content of the next teaching video according to the review plan includes:
acquiring an evaluation interval corresponding to the evaluation difference and outputting a review plan, wherein the review plan comprises repeated playing of teaching videos, repeated setting of questioning information and brief discussion of videos;
acquiring the review time length of the review plan and the teaching time length of the next teaching lesson, and taking the difference value between the teaching time length and the review time length as the effective time length;
acquiring teaching contents of a next teaching video, wherein the teaching contents comprise necessary teaching contents and unnecessary teaching contents, and the unnecessary teaching contents can be screened out from the next teaching video;
locating the time mark occupied by the necessary teaching content to the necessary time, and comparing the necessary time with the effective time;
if the effective duration is smaller than the necessary duration, indicating that the necessary teaching content cannot be completely played, and smoothly extending the necessary teaching content to the next teaching video, and screening unnecessary teaching content in the next teaching video;
if the effective time length is longer than or equal to the necessary time length, the necessary teaching content can be completely played, and the necessary teaching content does not need to be forward to the next teaching video.
The invention also provides an AI intelligent teaching robot control system, which is applied to the AI intelligent teaching robot control method, and comprises the following steps:
the first acquisition module is used for acquiring a student group of the teaching point and matching the teaching video data set according to the student group;
the video calling module is used for obtaining the demands of students and calling corresponding teaching videos from the teaching video data set according to the demands of the students;
the second acquisition module is used for acquiring basic information of each student in the student group and summarizing the basic information into a basic information set;
the extraction module is used for acquiring the questioning information in the teaching video and extracting the answering students according to the basic information set;
the measuring and calculating module is used for acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;
the data conversion module is used for inputting the accuracy rate of the response information into the data conversion model and calibrating the conversion result as a teaching quality score;
the comparison module is used for acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
and the regulation and control module is used for acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and regulating and controlling the teaching content of the next teaching video according to the review plan.
And, an AI intelligent teaching robot control terminal includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the AI intelligent teaching robot control method.
The invention has the technical effects that:
according to the invention, the attention of students can be concentrated by setting the questioning information, meanwhile, the quality score of the current period of test teaching can be calculated according to the accuracy of the response information of the students, a corresponding review plan can be generated according to the quality score of the teaching, and the teaching content of the subsequent teaching video can be regulated and controlled in real time according to the execution time of the review plan, so that the playing of the teaching video can be completed within the teaching plan, and the intelligent teaching robot can be ensured to improve the learning effect of the students and not to influence the teaching plan after being put into use.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the present invention provides an AI intelligent teaching robot control method, which includes:
s1, acquiring a student group of a teaching point, and matching a teaching video data set according to the student group;
s2, acquiring student demands, and calling corresponding teaching videos from the teaching video data set according to the student demands;
s3, acquiring basic information of each student in the student group, and summarizing the basic information into a basic information set;
s4, acquiring questioning information in the teaching video, and extracting response students according to the basic information set;
s5, obtaining response information corresponding to the questioning information, which is answered by all answering students, and calculating the accuracy of the response information in real time;
s6, inputting the accuracy rate of the response information into a data conversion model, and calibrating a conversion result into a teaching quality score;
s7, acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
s8, acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan.
As described in the above steps S1 to S8, with the rapid development of artificial intelligence and computer technology, intelligent teaching robots gradually appear in the education field, in schools, it is possible to assist teachers to teach students knowledge, in families, it is possible to coach students ' homework, review learned knowledge, etc., to ensure the interest of teaching contents, and attract the attention of students, intelligent teaching robots are equipped with a display screen for playing teaching pictures, compared with teachers or parents to conduct teaching in a manner of lecturing, video teaching is able to fully embody teaching pictures according to teaching contents, this undoubtedly can make students ' acceptances correspondingly increased, improve students ' learning effects, based on this, after determining student groups of teaching points, this scheme can match teaching video data sets conforming to teaching plans of teaching points, then determine learning requirements according to students, it is enough to determine teaching videos to be played, it is ensured that learning effects of students are interleaved in teaching videos, these question information needs to be set based on the played teaching videos, and when the teaching contents are played, and compared with the teaching results are converted from the teacher groups by the mode of learning, and the quality is able to evaluate the corresponding to evaluate the quality of students by the method, and judge whether the quality is required to evaluate the quality of the students is required to be compared with the corresponding to the quality of the teaching plans, and the quality is evaluated by the corresponding to evaluate the quality of the teaching plans, and the quality is calculated by the answer is calculated by the comparison, and the quality is calculated and the answer is calculated by the question is calculated and the answer is calculated, the teaching content of the next teaching video can be adjusted, so that the playing of the teaching video can be completed within the teaching plan, and the intelligent teaching robot can achieve the teaching purpose and cannot influence the teaching plan after being put into use.
In a preferred embodiment, the step of obtaining a student population of teaching points and matching the teaching video data set according to the student population comprises:
s101, obtaining the geographic position of a teaching point, and matching all teaching videos conforming to local teaching characteristics;
s102, acquiring specific school segments of a student group, screening teaching videos according to the school segments, and summarizing screening results into a teaching video data set;
the teaching robot comprises an interaction screen, and teaching videos in the teaching video data set can be put in through the interaction screen.
As described in the above steps S101-S102, for different regions, the used teaching materials may be different, for example, the teaching materials implemented in China include a human teaching board, a threo teaching board, a north master board, a xiang teaching board, a jaw teaching board, a western teaching board, a chinese teaching board, a part editing board, etc., so that the teaching contents in the teaching materials are different, and before determining the teaching video to be put, the geographic position of the teaching point needs to be determined in advance, the used teaching materials are determined according to the geographic position, then the teaching video data set is determined based on the teaching materials, and then the corresponding teaching video is selected according to the specific learning stage and the learning progress of the student group, and finally the teaching video is put through the interactive screen.
In a preferred embodiment, the steps of obtaining the questioning information in the teaching video and extracting the answering students according to the basic information set include:
s401, basic information of each student is acquired from a basic information set, wherein the basic information comprises student names and learning achievements;
s402, acquiring a classification threshold value, comparing the classification threshold value with student achievements, classifying a student group into a plurality of sampling intervals, and arranging the plurality of sampling intervals according to the sequence of the student achievements from high to low;
s403, generating a sampling plan according to the arrangement sequence of the sampling intervals, and extracting response students in the sampling intervals according to the sampling plan.
As described in the above steps S401-S403, before the questioning information is sent, the basic information of the student group needs to be acquired in advance, and the present embodiment further groups the basic information by the learning score of the students, which aims to distinguish the learning ability of the students, and not evaluate the superiority of the students, and the extraction process is not doped with subjective factors, and can be performed on the premise of not violating the observation of the students carried out in China, and for different subjects, the learning score of each student is different, then the students may exist in any section, of course, the teaching video is set according to the preset requirement, then the matched sampling section is consistent with the subject, and after the sampling section is determined, the answering students are extracted by adopting a random sampling mode in the section, so as to avoid the occurrence of a partial phenomenon.
In a preferred embodiment, the step of generating the sampling plan according to the arrangement order of the sampling intervals includes:
stp1, acquiring duration of a teaching video and time nodes of questioning information distributed in the teaching video, and calibrating the time nodes as questioning nodes;
stp2, obtaining the number of questioning contents in questioning information, wherein the questioning information comprises a plurality of questioning contents, and the number of the questioning contents is not less than 3;
stp3, the central node of the teaching video is obtained, and compared with the question node;
if the questioning nodes are distributed behind the central node, distributing questioning contents according to the arrangement sequence of the sampling intervals;
if the questioning nodes are distributed in front of the central node, distributing questioning contents according to the reverse arrangement sequence of the sampling intervals;
if the number of the questioning contents is higher than the number of the sampling intervals, repeated allocation is performed according to the allocation sequence of the questioning contents.
As described in the above steps Stp1-Sto3, the duration of the teaching video is pre-recorded, so that the duration of the teaching video can be directly obtained, as for the setting of the question information, the teacher can prepare the question information in a video in a targeted manner, in general, in the process of teaching students, the teacher follows the order of easy to difficult to learn, so that students can gradually understand the teaching content, in this embodiment, the question content is distributed according to the distribution condition of the question nodes relative to the central node, in the early stage of the teaching content playing, even if students with poor receiving capability can rapidly understand the teaching content, at this time, the question content is distributed according to the reverse arrangement order of the sampling interval, the accuracy rate of the answer questions of students with poor receiving capability is mainly checked, and as the teaching goes deep, the students with poor receiving capability can not understand the teaching content in a short time, so that the students with poor receiving capability can obviously deviate from the assessment effect of the teaching quality, the sampling interval is adopted to sequentially understand the question content, as for finding out the poor receiving capability, the students can search for the distribution condition of the question content relative to the central node, the students with poor receiving capability is required to acquire the answer information in the corresponding to the class interval, the answer information is further, the answer information is ensured, the answer probability is further ensured, the answer is ensured to be correctly obtained, the answer information is further is ensured, the answer information is correctly is acquired from the students is required to be correctly, and the students are required to be correctly answered, and the students are required, and the students are correctly has the answer information is better, and the answer information is better by students, and has better answer the students, and has better answer information is required by students after the students, and has better answer. Of course, after the student answers the mistake, the teaching robot does not send out the instruction of answering the mistake directly, can set up some humanized sentences to calm its emotion correspondingly, for example: the method is very excellent, unfortunately and correct answers are somewhat in and out, the answer is very good, the situation that the correct answer is nearly the right answer is just about to be obtained, the answer is repeated again, the correct answer can be certainly obtained next time, and the like, and the method can also excite the learning desire of the students answering the wrong answer.
In a preferred embodiment, the step of acquiring response information corresponding to the question information and responding to the responses of the students, and calculating the accuracy of the response information in real time includes:
s501, obtaining response information;
s502, obtaining a standard answer corresponding to the questioning information, comparing the standard answer with the response information to obtain the repetition rate between the response information and the standard answer, and calibrating the repetition rate as a parameter to be evaluated;
s503, obtaining standard parameters corresponding to standard answers, and comparing the standard parameters with parameters to be evaluated, wherein each standard parameter corresponds to one standard parameter;
if the value of the parameter to be evaluated is smaller than the standard parameter, judging that the response information is wrong;
if the value of the parameter to be evaluated is larger than or equal to the standard parameter, judging that the response information is correct;
s504, acquiring a measuring and calculating function, inputting the correct number of the response information and the correct number of the questioning information into the measuring and calculating function, and calibrating the measuring and calculating result as the accuracy of the response information.
As described in the above steps S501-S504, when calculating the accuracy of the answer information, it is first required to determine the standard answer corresponding to the content of the question, and each standard answer corresponds to one standard parameter, for example, in chinese teaching, if any one of the questions is wrong, the corresponding standard parameter should be 100% at this time, and for the question of the sentence interpretation, even if a little inconsistent content appears, it can be determined that the answer is correct, the repetition rate between the answer information answered by the answering student and the standard answer is calibrated as the parameter to be evaluated, and then the magnitude between the parameter to be evaluated and the standard parameter is compared, so as to determine the accuracy of the answer information, where the measurement function of the accuracy is f=nz, where f represents the accuracy of the answer information, N represents the number of the answer information determined to be correct, and Z represents the total number of the content of the question, and the accuracy of the answer information can be clearly measured based on the above formula.
In a preferred embodiment, the step of inputting the accuracy of the response information into the data conversion model and calibrating the conversion result to be a teaching quality score includes:
s601, acquiring an evaluation interval from a data conversion model, wherein the evaluation interval is (0, a), (a, b), (b, c) … …;
s602, obtaining a teaching quality score corresponding to each evaluation interval;
s603, comparing the accuracy of the response information with the evaluation interval, and outputting a corresponding teaching quality score.
As described in the above steps S601-S603, when the accuracy of the response information is converted into the teaching quality score, the evaluation interval needs to be determined first, and the number of the evaluation intervals may be set according to specific requirements, for example, (0,0.6), (0.6,0.8) and (0.8,1) are set according to specific requirements, and the teaching quality scores corresponding to the three evaluation intervals are 1,2 and 3 respectively, which may, of course, be other scoring forms, and the specific scoring form may be set according to the actual requirements, which is not limited specifically in the present embodiment.
In a preferred embodiment, the steps of acquiring an evaluation interval, comparing the evaluation interval with an evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan include:
s801, acquiring an evaluation interval corresponding to the evaluation difference and outputting a review plan, wherein the review plan comprises repeated playing of teaching videos, repeated setting of questioning information and brief discussion videos;
s802, acquiring the review time length of the review plan and the teaching time length of the next teaching class, and taking the difference between the teaching time length and the review time length as the effective time length;
s803, acquiring teaching contents of a next teaching video, wherein the teaching contents comprise necessary teaching contents and unnecessary teaching contents, and the unnecessary teaching contents can be screened out from the next teaching video;
s804, locating the time mark occupied by the necessary teaching content to the necessary time, and comparing the necessary time with the effective time;
if the effective duration is smaller than the necessary duration, indicating that the necessary teaching content cannot be completely played, and smoothly extending the necessary teaching content to the next teaching video, and screening unnecessary teaching content in the next teaching video;
if the effective time length is longer than or equal to the necessary time length, the necessary teaching content can be completely played, and the necessary teaching content does not need to be forward to the next teaching video.
As described in the above steps S801 to S804, before each implementation of a new teaching content, it is necessary to review the previous learning content, which may be related to the new teaching content or not, but the learning node is not listed here one by one with respect to the review content of the node, before outputting the review plan, the learning effect of the student group needs to be determined in advance, mainly based on the comparison result of the evaluation threshold and the teaching quality score, when the teaching quality score is smaller than the evaluation threshold, the teaching video needs to be replayed, and when the teaching quality score is greater than or equal to the evaluation threshold, the evaluation difference is output in advance, for example, the learning effect of the student can be intuitively determined with respect to the evaluation difference, the teaching quality score adopts 10 minutes, the evaluation threshold value is set to be 7, the actual teaching quality score of the student group is 10, obviously, the student group has better grasp of the teaching content, so that when executing the review plan, the students only need to play brief discussion videos to help the students review the learned content, and if the actual teaching quality score of the student group is 7, most students can grasp the teaching content, but the students can not grasp the teaching content completely, so that in order to ensure the successful teaching of the next new teaching content, the execution of the review plan obviously needs to be increased by a certain time, particularly, the execution time of the review plan can be realized by combining repeated playing of the teaching videos and repeated setting of the question information, the execution time of the review plan should be participated in the planning by teachers without limitation, after the execution time length of the review plan is determined, the playing time length of the next teaching video needs to be correspondingly adjusted within the teaching plan, each teaching video necessarily contains necessary teaching contents and unnecessary teaching contents, for example, a sentence in Chinese teaching is still taken as an example, the unnecessary teaching contents are related to background introduction of an author and other supplementary knowledge which does not affect the learning teaching contents of students, and particularly, after the students are read for supplementing after class, based on the supplementary knowledge, after the execution time length of the review plan is determined, the time length of the teaching contents in the next teaching video can be adjusted, the unnecessary teaching contents are screened out under the condition that the effective time length is smaller than the necessary time length, and a teacher can selectively keep according to the importance degree of the unnecessary teaching contents under the condition that the effective time length is greater than or equal to the necessary time length.
The invention also provides an AI intelligent teaching robot control system, which is applied to the AI intelligent teaching robot control method, and comprises the following steps:
the first acquisition module is used for acquiring a student group of the teaching point and matching the teaching video data set according to the student group;
the video calling module is used for obtaining the demands of students and calling corresponding teaching videos from the teaching video data set according to the demands of the students;
the second acquisition module is used for acquiring basic information of each student in the student group and summarizing the basic information into a basic information set;
the extraction module is used for acquiring questioning information in the teaching video and extracting response students according to the basic information set;
the measuring and calculating module is used for acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;
the data conversion module is used for inputting the accuracy rate of the response information into the data conversion model and calibrating the conversion result into a teaching quality score;
the comparison module is used for acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
the regulation and control module is used for acquiring the evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to the comparison result, and regulating and controlling the teaching content of the next teaching video according to the review plan.
When the intelligent teaching robot works, the first acquisition module is used for acquiring the student group of the teaching point, the teaching video data set is matched according to the student group, the video calling module is used for calling the teaching video to be played according to the student demand, a plurality of question information is arranged in the teaching video, the extraction module is used for extracting the response students based on the basic information of the student group acquired by the second acquisition module, the response information of the response students is summarized, the measuring and calculating module is used for calculating the accuracy of the response information, the data conversion module is used for converting the accuracy of the response information into the teaching quality score, the comparison module is used for evaluating the effect of the teaching quality, a corresponding review plan is generated, finally, the regulation and control module is used for regulating the teaching content of the next teaching video according to the review plan, the required teaching content can be completed within the teaching plan, the judging process related to the above can be nested by adopting the if … … else function, and the algorithm capable of achieving the purpose is also applicable to the scheme without limitation.
And, an AI intelligent teaching robot control terminal includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the AI intelligent teaching robot control method.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method 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, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. An AI intelligent teaching robot control method is characterized in that: comprising the following steps:
acquiring a student group of a teaching point, and matching a teaching video data set according to the student group;
acquiring student demands, and calling corresponding teaching videos from the teaching video data set according to the student demands;
basic information of each student in the student group is acquired and summarized into a basic information set;
acquiring questioning information in the teaching video, and extracting response students according to the basic information set;
acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;
inputting the accuracy of the response information into a data conversion model, and calibrating a conversion result as a teaching quality score;
acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
and acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan.
2. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the student group of the teaching point and matching the teaching video data set according to the student group comprises the following steps:
obtaining the geographic position of the teaching point, and matching all teaching videos conforming to the local teaching characteristics;
the specific school segments of the student group are obtained, the teaching video is screened according to the school segments, and the screening result is summarized into a teaching video data set;
the teaching robot comprises an interaction screen, and teaching videos in the teaching video data set can be put in through the interaction screen.
3. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the questioning information in the teaching video and extracting the answering students according to the basic information set comprises the following steps:
acquiring basic information of each student from the basic information set, wherein the basic information comprises student names and learning achievements;
the classification threshold value is obtained, the classification threshold value is compared with the student score, the student group is classified into a plurality of sampling intervals, and the sampling intervals are arranged according to the sequence of the student score from high to low;
and generating a sampling plan according to the arrangement sequence of the sampling intervals, and extracting response students in the sampling intervals according to the sampling plan.
4. The AI intelligent teaching robot control method according to claim 3, characterized in that: the step of generating a sampling plan according to the arrangement sequence of the sampling intervals comprises the following steps:
acquiring the duration of the teaching video and the time nodes of the questioning information distributed in the teaching video, and calibrating the time nodes as questioning nodes;
acquiring the number of the questioning contents in the questioning information, wherein the questioning information comprises a plurality of questioning contents, and the number of the questioning contents is not less than 3;
acquiring a central node of the teaching video and comparing the central node with the question node;
if the questioning nodes are distributed behind the central node, distributing the questioning contents according to the arrangement sequence of the sampling intervals;
if the questioning nodes are distributed in front of the central node, distributing the questioning contents according to the reverse arrangement sequence of the sampling intervals;
and if the number of the questioning contents is higher than the number of the sampling intervals, repeatedly distributing the questioning contents according to the distribution sequence of the questioning contents.
5. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the response information corresponding to the question information and responding to the student responses and calculating the accuracy of the response information in real time comprises the following steps:
obtaining response information;
obtaining a standard answer corresponding to the questioning information, comparing the standard answer with the response information to obtain the repetition rate between the response information and the standard answer, and calibrating the repetition rate as a parameter to be evaluated;
obtaining standard parameters corresponding to standard answers, and comparing the standard parameters with the parameters to be evaluated, wherein each standard parameter corresponds to one standard parameter;
if the value of the parameter to be evaluated is smaller than the standard parameter, judging that the response information is wrong;
if the value of the parameter to be evaluated is larger than or equal to the standard parameter, judging that the response information is correct;
obtaining a measuring and calculating function, inputting the number of the response information and the number of the questioning information to the measuring and calculating function, and calibrating the measuring and calculating result as the accuracy of the response information.
6. The AI intelligent teaching robot control method according to claim 5, characterized in that: after the response information is judged to be correct, basic information of students corresponding to the response information is acquired, and screened out from the sampling interval;
and after the answer information is judged to be wrong, acquiring basic information of the student corresponding to the answer information, and retaining the basic information in a sampling interval.
7. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of inputting the accuracy of the response information into a data conversion model and calibrating the conversion result as a teaching quality score comprises the following steps:
obtaining an evaluation interval from the data conversion model, wherein the evaluation interval is (0, a ], (a, b ], (b, c) … …;
acquiring a teaching quality score corresponding to each evaluation interval;
and comparing the accuracy of the response information with an evaluation interval, and outputting a corresponding teaching quality score.
8. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan comprises the following steps:
acquiring an evaluation interval corresponding to the evaluation difference and outputting a review plan, wherein the review plan comprises repeated playing of teaching videos, repeated setting of questioning information and brief discussion of videos;
acquiring the review time length of the review plan and the teaching time length of the next teaching lesson, and taking the difference value between the teaching time length and the review time length as the effective time length;
acquiring teaching contents of a next teaching video, wherein the teaching contents comprise necessary teaching contents and unnecessary teaching contents, and the unnecessary teaching contents can be screened out from the next teaching video;
locating the time mark occupied by the necessary teaching content to the necessary time, and comparing the necessary time with the effective time;
if the effective duration is smaller than the necessary duration, indicating that the necessary teaching content cannot be completely played, and smoothly extending the necessary teaching content to the next teaching video, and screening unnecessary teaching content in the next teaching video;
if the effective time length is longer than or equal to the necessary time length, the necessary teaching content can be completely played, and the necessary teaching content does not need to be forward to the next teaching video.
9. An AI intelligent teaching robot control system, applied to the AI intelligent teaching robot control method of any one of claims 1 to 8, characterized in that: comprising the following steps:
the first acquisition module is used for acquiring a student group of the teaching point and matching the teaching video data set according to the student group;
the video calling module is used for obtaining the demands of students and calling corresponding teaching videos from the teaching video data set according to the demands of the students;
the second acquisition module is used for acquiring basic information of each student in the student group and summarizing the basic information into a basic information set;
the extraction module is used for acquiring the questioning information in the teaching video and extracting the answering students according to the basic information set;
the measuring and calculating module is used for acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;
the data conversion module is used for inputting the accuracy rate of the response information into the data conversion model and calibrating the conversion result as a teaching quality score;
the comparison module is used for acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;
if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;
if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;
and the regulation and control module is used for acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and regulating and controlling the teaching content of the next teaching video according to the review plan.
10. AI intelligent teaching robot control terminal, its characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the AI intelligent teaching robot control method of any of claims 1-8.
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