CN114038256A - Teaching interactive system based on artificial intelligence - Google Patents
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- CN114038256A CN114038256A CN202111434776.0A CN202111434776A CN114038256A CN 114038256 A CN114038256 A CN 114038256A CN 202111434776 A CN202111434776 A CN 202111434776A CN 114038256 A CN114038256 A CN 114038256A
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Abstract
The invention discloses a teaching interaction system based on artificial intelligence, which relates to the technical field of network teaching and comprises a learning analysis module, a teaching evaluation module and a training analysis module; the learning analysis module is used for inputting the characteristic data acquired by the online monitoring module into the learning state evaluation model to acquire a learning state label; the teaching analysis module is used for acquiring learning state labels of all students in real time and analyzing teaching values, if the teaching values are smaller than a preset teaching threshold value, reminding information is generated to a teacher end to remind the teacher to change the teaching content or teaching mode of the current teaching course so as to improve the teaching quality and efficiency; the teaching evaluation module is used for comprehensively evaluating the teaching level of the teacher according to the teaching value with the timestamp stored in the database; the training analysis module is used for acquiring training information of students and analyzing progress coefficients, and advising the students to change learning modes in time so as to achieve better learning effect.
Description
Technical Field
The invention relates to the technical field of network teaching, in particular to a teaching interaction system based on artificial intelligence.
Background
On-line Learning, also called network education, E-Learning, is a network Learning mode, i.e. a brand new Learning mode in which students log on an on-line Learning platform through a PC or a terminal, and complete lessons selection, listening, homework and examination through a network to realize the Learning process.
At present, most mainstream on-demand and video recording teaching of online teaching is that a teacher records and uploads videos in advance, students download videos or see videos online for learning, all students can not be guaranteed to learn on time, and teachers can not observe whether all students are listening carefully or not in class, so that the teaching quality and efficiency can not be guaranteed, students can not listen carefully, what is learned is deficient, and full-process and all-around support and service can not be provided for autonomous learning of the students well; meanwhile, a complete evaluation system aiming at the teaching quality of teachers of the education platform is not formed at present; the teaching interactive system based on artificial intelligence is provided for the reason that teachers cannot be reasonably distributed for students to achieve effective recommendation of learning resources.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a teaching interaction system based on artificial intelligence.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an artificial intelligence-based teaching interaction system, which includes an online monitoring module, a teaching analysis module, a teaching evaluation module, an autonomous training module, and a training analysis module;
the online monitoring module is used for collecting characteristic data of a student in the learning process through a camera and an audio collection unit of the network control student end after the authentication is successful, and sending the collected characteristic data to the learning analysis module; the feature data comprises sound information and face image information;
the learning analysis module is used for inputting the characteristic data into the learning state evaluation model to obtain a learning state label and sending the learning state label to the teacher end, and the teacher end is used for confirming/modifying the received learning state label and feeding the learning state label back to the controller;
in a complete teaching course, the teaching analysis module is connected with the learning analysis module and is used for acquiring learning state labels of all students in real time and analyzing teaching values, if the teaching value GX is smaller than a preset teaching threshold value, reminding information is generated to a teacher end to remind the teacher to change the teaching content or teaching mode of the current teaching course so as to improve the teaching quality and efficiency;
the self-training module is used for training students to simulate questions after classes and recording training information, the training analysis module is used for acquiring the training information of the students and analyzing progress coefficients, if the progress coefficients G7 are smaller than a progress coefficient threshold value, the effect of the current self-training of the students is poor, the students are advised to change learning modes, and the interactive teaching module is used for communicating with teachers.
Further, the specific analysis steps of the teaching analysis module are as follows:
acquiring learning state labels of all students in real time to obtain the occupation ratio of each label; calculating the teaching attractiveness Qs of the corresponding teachers according to the influence factors of the labels stored in the database on the evaluation of the teaching attractiveness; wherein the learning status labels include concentration, interaction, resistance, and loss;
establishing a curve graph of teaching attraction degree Qs along with time change, and if Qs is larger than or equal to a preset attraction degree threshold value, intercepting a corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as an attraction curve segment;
counting the number of the attraction curve segments as W1, integrating all the attraction curve segments with time to obtain attraction energy E1, and calculating by using a formula GX of W1 × a1+ E1 × a2 to obtain a teaching value GX corresponding to a teacher, wherein a1 and a2 are coefficient factors.
Furthermore, the teaching analysis module is also used for stamping a time stamp on the teaching value GX and storing the teaching value GX in a database; the teaching evaluation module is used for comprehensively evaluating the teaching level of the teacher according to the teaching value with the timestamp stored in the database, and the specific evaluation method comprises the following steps:
obtaining a teaching value of the same teacher thirty days before the current time of the system according to the timestamp; carrying out grade evaluation on the teaching value to obtain evaluation signals, wherein the evaluation signals comprise excellent, general and unqualified;
counting the number of occurrences of each of the excellent, general and fail signals, and respectively marking as C1, C2 and C3; calculating a teacher teaching evaluation value GP by using a formula GP ═ C1 × 3+ C2)/(C3 × a3, wherein a3 is a coefficient factor; the teaching evaluation module is used for transmitting a teaching evaluation value GP to the controller;
the controller is used for sequencing the teacher information according to the teaching evaluation value GP and transmitting the sequenced teacher information and teaching evaluation value GP to the display module for real-time display.
Further, the specific process of performing grade evaluation on the teaching value to obtain the evaluation signal is as follows:
comparing the teaching value GX with grade thresholds, wherein the grade thresholds comprise X2 and X3; when GX is larger than or equal to X2, the evaluation signal is excellent, when X3 is larger than GX and smaller than X2, the evaluation signal is general, and when GX is smaller than or equal to X3, the evaluation signal is unqualified; wherein X2 and X3 are both fixed values and X2 > X3.
Furthermore, the teacher end and the student end are respectively connected with an authentication module, and the authentication module is used for verifying login requests of the teacher end and the student end; the verification method is face identification or fingerprint identification.
And the evaluation and correction module is used for carrying out iterative correction on the learning state evaluation model according to the learning state label fed back by the teacher end and the corresponding characteristic data.
Further, the specific construction steps of the learning state evaluation model are as follows:
acquiring standard training data; the standard training data comprises historical characteristic data and corresponding learning state labels; constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
after data normalization is carried out on the training set, the test set and the check set, the deep convolution neural network model is trained, tested and checked; and marking the trained deep convolutional neural network model as a learning state evaluation model.
Further, the specific analysis steps of the training analysis module are as follows:
aiming at the same training subject, acquiring the training achievement of each training of the student, and sequentially marking as B1, B2, B3, … and Bn; counting the number of times that Bi is more than or equal to B (i-1) as G1;
when Bi is larger than or equal to B (i-1), calculating the difference between Bi and B (i-1) to obtain a second progress value G2, calculating the difference between Bi and the assessment threshold value of the corresponding training subject to obtain a first average value difference G3, and calculating the difference between B (i-1) and the assessment threshold value of the corresponding training subject to obtain a second average value difference G4; obtaining a single value G5 by using a formula G5-G2 Xg 1+ G3 Xg 2+ G4 Xg 3, wherein G1, G2 and G3 are all preset coefficients; summing all the single values to obtain a progress over-value and marking as G6; the student progress coefficient G7 is obtained by using a formula G7 ═ G1 × G4+ G6 × G5, wherein G4 and G5 are preset coefficients.
Compared with the prior art, the invention has the beneficial effects that:
1. the learning analysis module is used for receiving the characteristic data acquired by the online monitoring module and inputting the characteristic data into the learning state evaluation model to acquire a learning state label; the controller sends corresponding learning reminding information to the student end according to the learning state label after receiving the learning state label to remind the student to concentrate on learning; the students do not need to input complicated information through a traditional human-computer interaction tool, only need to perform learning feedback through voice, and the evaluation and correction module is used for performing iterative correction on the learning state evaluation model according to the learning state labels and corresponding characteristic data fed back by the teacher end, and continuously performing self-optimization to achieve a better learning effect;
2. in a complete teaching course, the teaching analysis module is used for acquiring learning state labels of all students in real time and analyzing the learning state labels; calculating the teaching attractiveness Qs of the corresponding teachers according to the occupation ratio of each label and the influence factors of each label stored in the database on the evaluation of the teaching attractiveness; establishing a curve graph of the teaching attractiveness Qs along with the change of time, comparing the teaching attractiveness Qs with a preset attractiveness threshold, calculating through related processing to obtain a teaching value GX corresponding to a teacher, and if the GX is smaller than the preset teaching threshold, generating reminding information to a teacher end to remind the teacher to change the teaching content or teaching mode of the current teaching course so as to improve the teaching quality and efficiency;
3. the teaching evaluation module is used for comprehensively evaluating the teaching level of a teacher according to the teaching value with the timestamp stored in the database; carrying out grade evaluation on the teaching value GX to obtain an evaluation signal, counting the times of occurrence of excellent, general and unqualified signals respectively, and calculating to obtain a teaching evaluation value GP of a teacher; the controller is used for sequencing teacher information according to the teaching evaluation value GP, so that students and parents can conveniently and visually know the teaching level of the teacher, and a proper teacher is selected for teaching according to the teaching level of the teacher;
4. the training analysis module is used for acquiring training information of students and analyzing progress coefficients, if the progress coefficient G7 is smaller than a progress coefficient threshold value, the current autonomous training effect of the students is poor, the students are advised to change the learning mode, and the interactive teaching module is communicated with a teacher to achieve a better learning effect.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic block diagram of an artificial intelligence-based teaching interaction system according to the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a teaching interactive system based on artificial intelligence comprises a teacher end, a student end, an authentication module, an online monitoring module, a learning analysis module, a controller, an evaluation and correction module, a teaching analysis module, a database, a teaching evaluation module, a display module, an autonomous training module, a training analysis module and an interactive teaching module;
the teacher end and the student end are respectively connected with the authentication module, and the authentication module is used for verifying login requests of the teacher end and the student end; the verification mode is face identification or fingerprint identification;
the online monitoring module is used for controlling a camera and an audio acquisition unit of a student end to acquire feature data in the learning process of the student through a network after the authentication is successful, wherein the feature data comprises sound information and face image information; the collected characteristic data are sent to a learning analysis module;
the learning analysis module is used for receiving the characteristic data acquired by the online monitoring module, inputting the characteristic data into the learning state evaluation model to acquire a learning state label, and sending the learning state label to the teacher end, and the teacher end is used for confirming/modifying the received learning state label and feeding the learning state label back to the controller; after receiving the learning state label, the controller sends corresponding learning reminding information to the student end according to the learning state label to remind the student to study intensively; wherein the learning state labels include a focus label, an interactive label, an anti-reject label, and a disorderly label;
in this embodiment, the learning state evaluation model is constructed by an RBF neural network or a deep convolutional neural network; the concrete construction steps are as follows:
acquiring standard training data; the standard training data comprise historical characteristic data and corresponding learning state labels, wherein the learning state labels in the standard training data are obtained through manual labeling;
constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
after data normalization is carried out on the training set, the test set and the check set, training, testing and checking are carried out on the deep convolutional neural network model, and the deep convolutional neural network model which is trained is marked as a learning state evaluation model; in this embodiment, in the learning state process, when the learning state label is focused or interacted, it indicates that there is no abnormality; when the learning state label is resistant and lost, the abnormality is represented, and the student needs to be reminded to adjust the learning state and improve the learning efficiency;
the evaluation and correction module is connected with the teacher end and used for carrying out iterative correction on the learning state evaluation model according to the learning state label fed back by the teacher end and the corresponding characteristic data;
in a complete teaching course, the teaching analysis module is connected with the learning analysis module and is used for acquiring and analyzing the learning state labels of all students in real time; the specific analysis steps are as follows:
s1: acquiring learning state labels of all students in real time to obtain the occupation ratio of each label; calculating the teaching attractiveness Qs of the corresponding teachers according to the influence factors of the labels stored in the database on the evaluation of the teaching attractiveness;
s2: establishing a curve graph of the teaching attraction degree Qs along with the change of time, comparing the teaching attraction degree Qs with a preset attraction degree threshold, and if Qs is larger than or equal to the preset attraction degree threshold, intercepting a corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as an attraction curve segment;
s3: counting the number of the attraction curve segments as W1, integrating all the attraction curve segments with time to obtain attraction energy E1, and calculating by using a formula GX of W1 × a1+ E1 × a2 to obtain a teaching value GX corresponding to a teacher, wherein a1 and a2 are coefficient factors;
s4: comparing the teaching value GX with a preset teaching threshold value, if the GX is smaller than the preset teaching threshold value, indicating that the current teaching course has low attraction degree to students, generating reminding information to a teacher end, and reminding the teacher to change the teaching content or teaching mode of the current teaching course so as to improve the teaching quality and efficiency;
the teaching analysis module is also used for stamping a time stamp on the teaching value GX and storing the teaching value GX into a database;
the teaching evaluation module is connected with the database and used for comprehensively evaluating the teaching level of the teacher according to the teaching value with the timestamp stored in the database, and the specific evaluation method comprises the following steps:
v1: acquiring a teaching value GX of the same teacher thirty days before the current time of the system according to the timestamp;
v2: comparing the teaching value with a grade threshold value, and carrying out grade judgment on the teaching value GX to obtain an evaluation signal, wherein the evaluation signal comprises excellence, general and unqualified; the method specifically comprises the following steps:
the rank thresholds include X2, X3; x2 and X3 are both fixed values, and X2 is more than X3; when GX is larger than or equal to X2, the evaluation signal is excellent, when X3 is larger than GX and smaller than X2, the evaluation signal is general, and when GX is smaller than or equal to X3, the evaluation signal is unqualified;
v3: counting the number of occurrences of each of the excellent, general and fail signals, and respectively marking as C1, C2 and C3; calculating a teacher teaching evaluation value GP by using a formula GP ═ C1 × 3+ C2)/(C3 × a3, wherein a3 is a coefficient factor; the teaching evaluation value GP is used for reflecting the teaching level of the teacher;
the teaching evaluation module is used for transmitting the teaching evaluation value GP to the controller, the controller is used for sequencing teacher information according to the teaching evaluation value GP and transmitting the sequenced teacher information and the teaching evaluation value GP to the display module for real-time display, the teacher information is expressed as a personal resume and related teaching experiences of a teacher, students and parents can conveniently and visually know the teaching level of the teacher, and a proper teacher is selected for teaching according to the teaching level of the teacher;
the autonomous training module is used for performing simulated exercise training after a student class and recording training information, wherein the training information comprises training subjects and corresponding training scores;
the training analysis module is connected with the autonomous training module and used for acquiring training information of students and performing progress coefficient analysis, and the specific analysis steps are as follows:
aiming at the same training subject, acquiring the training achievement of each training of the student, and sequentially marking as B1, B2, B3, … and Bn; wherein n represents the nth training; when Bi is more than or equal to B (i-1), marking Bi as a first progress value, counting the occurrence times of the first progress value and marking as G1;
calculating the difference value between the first progress value Bi and B (i-1) to obtain a second progress value and marking the second progress value as G2; obtaining an assessment threshold corresponding to the training subjects, calculating a difference value between Bi and the assessment threshold corresponding to the training subjects to obtain a first average value difference, and marking the first average value difference as G3; calculating the difference value between the B (i-1) and the assessment threshold value of the corresponding training subject to obtain a second average value difference, and marking the second average value difference as G4; obtaining a single value G5 by using a formula G5-G2 Xg 1+ G3 Xg 2+ G4 Xg 3, wherein G1, G2 and G3 are all preset coefficients;
summing all the single values to obtain a progress over-value and marking as G6; obtaining a progress coefficient G7 of the student by using a formula G7-G1 Xg 4+ G6 Xg 5, wherein G4 and G5 are preset coefficients;
comparing the progress factor G7 to a progress factor threshold; if the progress coefficient G7 is not less than the progress coefficient threshold, the current autonomous training effect of the student is better; if the progress coefficient G7 is less than the progress coefficient threshold value, the current autonomous training effect of the students is poor, the students are advised to change the learning mode, and the students are communicated with the teacher through the interactive teaching module; and the interactive teaching module is used for teachers and students to log in the teaching platform and perform online interactive teaching.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows:
when the teaching interaction system works, an online monitoring module is used for collecting characteristic data of a student in the learning process through a camera and an audio collection unit of a network control student end after authentication is successful, and a learning analysis module is used for receiving the characteristic data collected by the online monitoring module and inputting the characteristic data into a learning state evaluation model to obtain a learning state label; the teacher end is used for confirming/modifying the received learning state label and feeding back the learning state label to the controller; the evaluation and correction module is used for carrying out iterative correction on the learning state evaluation model according to the learning state label fed back by the teacher end and the corresponding characteristic data; after receiving the learning state label, the controller sends corresponding learning reminding information to the student end according to the learning state label to remind the student to study intensively;
in a complete teaching course, the teaching analysis module is used for acquiring learning state labels of all students in real time and analyzing the learning state labels; acquiring learning state labels of all students in real time to obtain the proportion of each label, and calculating the teaching attractiveness Qs of corresponding teachers according to the influence factors of each label stored in the database on the evaluation of the teaching attractiveness; establishing a curve graph of the teaching attractiveness Qs along with the change of time, comparing the teaching attractiveness Qs with a preset attractiveness threshold, calculating through related processing to obtain a teaching value GX corresponding to a teacher, and if the GX is smaller than the preset teaching threshold, generating reminding information to a teacher end to remind the teacher to change the teaching content or teaching mode of the current teaching course so as to improve the teaching quality and efficiency;
the teaching evaluation module is used for comprehensively evaluating the teaching level of the teacher according to the teaching value with the timestamp stored in the database, comparing the teaching value with a grade threshold, carrying out grade evaluation on the teaching value GX to obtain an evaluation signal, counting the occurrence times of excellent, general and unqualified signals respectively, and calculating to obtain a teaching evaluation value GP of the teacher; the controller is used for sequencing the teacher information according to the teaching evaluation value GP, transmitting the sequenced teacher information and the teaching evaluation value GP to the display module for real-time display, facilitating students and parents to have a visual understanding of the teaching level of the teacher, and selecting a proper teacher for teaching according to the teaching level of the teacher; the training analysis module is used for acquiring training information of students and analyzing progress coefficients, if the progress coefficients G7 are smaller than a progress coefficient threshold value, the current autonomous training effect of the students is poor, the students are advised to change the learning mode, and the students are communicated with teachers through the interactive teaching module.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A teaching interaction system based on artificial intelligence is characterized by comprising an online monitoring module, a teaching analysis module, a teaching evaluation module, an autonomous training module and a training analysis module;
the online monitoring module is used for collecting characteristic data of a student in the learning process through a camera and an audio collection unit of the network control student end after the authentication is successful, and sending the collected characteristic data to the learning analysis module; the feature data comprises sound information and face image information;
the learning analysis module is used for inputting the characteristic data into the learning state evaluation model to obtain a learning state label and sending the learning state label to the teacher end, and the teacher end is used for confirming/modifying the received learning state label and feeding the learning state label back to the controller; the controller sends corresponding learning reminding information to the student end according to the learning state label after receiving the learning state label;
the teaching analysis module is connected with the learning analysis module and used for acquiring learning state labels of all students in real time and analyzing teaching values, if the teaching value GX is smaller than a preset teaching threshold value, reminding information is generated to a teacher end to remind the teacher to change the teaching content or teaching mode of the current teaching course;
the self-training module is used for training students to simulate questions after classes and recording training information, the training analysis module is used for acquiring the training information of the students and analyzing progress coefficients, if the progress coefficients G7 are smaller than a progress coefficient threshold value, the effect of the current self-training of the students is poor, the students are advised to change learning modes, and the interactive teaching module is used for communicating with teachers.
2. The artificial intelligence based teaching interaction system of claim 1, wherein the specific analysis steps of the teaching analysis module are as follows:
acquiring learning state labels of all students in real time to obtain the occupation ratio of each label; calculating the teaching attractiveness Qs of the corresponding teachers according to the influence factors of the labels stored in the database on the evaluation of the teaching attractiveness; wherein the learning status labels include concentration, interaction, resistance, and loss;
establishing a curve graph of teaching attraction degree Qs along with time change, and if Qs is larger than or equal to a preset attraction degree threshold value, intercepting a corresponding curve segment in the corresponding curve graph, marking the curve segment as red and marking the curve segment as an attraction curve segment;
counting the number of the attraction curve segments as W1, integrating all the attraction curve segments with time to obtain attraction energy E1, and calculating by using a formula GX of W1 × a1+ E1 × a2 to obtain a teaching value GX corresponding to a teacher, wherein a1 and a2 are coefficient factors.
3. The artificial intelligence based teaching interaction system of claim 2, wherein the teaching analysis module is further configured to timestamp the teaching value GX and store the timestamp in the database; the teaching evaluation module is used for comprehensively evaluating the teaching level of the teacher according to the teaching value with the timestamp stored in the database, and the specific evaluation method comprises the following steps:
obtaining a teaching value of the same teacher thirty days before the current time of the system according to the timestamp; carrying out grade evaluation on the teaching value to obtain evaluation signals, wherein the evaluation signals comprise excellent, general and unqualified;
counting the number of occurrences of each of the excellent, general and fail signals, and respectively marking as C1, C2 and C3; calculating a teacher teaching evaluation value GP by using a formula GP ═ C1 × 3+ C2)/(C3 × a3, wherein a3 is a coefficient factor; the teaching evaluation module is used for transmitting a teaching evaluation value GP to the controller;
the controller is used for sequencing the teacher information according to the teaching evaluation value GP and transmitting the sequenced teacher information and teaching evaluation value GP to the display module for real-time display.
4. The artificial intelligence based teaching interaction system of claim 3, wherein the specific process of obtaining the evaluation signal by rating the teaching value is as follows:
comparing the teaching value GX with grade thresholds, wherein the grade thresholds comprise X2 and X3; when GX is larger than or equal to X2, the evaluation signal is excellent, when X3 is larger than GX and smaller than X2, the evaluation signal is general, and when GX is smaller than or equal to X3, the evaluation signal is unqualified; wherein X2 and X3 are both fixed values and X2 > X3.
5. The artificial intelligence based teaching interaction system of claim 1, wherein the teacher end and the student end are respectively connected with an authentication module, and the authentication module is used for verifying login requests of the teacher end and the student end; the verification method is face identification or fingerprint identification.
6. The artificial intelligence based teaching interaction system of claim 1, further comprising an evaluation modification module, wherein the evaluation modification module is configured to iteratively modify the learning state evaluation model according to the learning state label and the corresponding feature data fed back by the teacher end.
7. The artificial intelligence based teaching interaction system of claim 1, wherein the learning state evaluation model is constructed by the following steps:
acquiring standard training data; the standard training data comprises historical characteristic data and corresponding learning state labels; constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
after data normalization is carried out on the training set, the test set and the check set, the deep convolution neural network model is trained, tested and checked; and marking the trained deep convolutional neural network model as a learning state evaluation model.
8. The artificial intelligence based teaching interaction system of claim 1, wherein the specific analysis steps of the training analysis module are as follows:
aiming at the same training subject, acquiring the training achievement of each training of the student, and sequentially marking as B1, B2, B3, … and Bn; counting the number of times that Bi is more than or equal to B (i-1) as G1;
when Bi is larger than or equal to B (i-1), calculating the difference between Bi and B (i-1) to obtain a second progress value G2, and calculating the difference between Bi and B (i-1) and the assessment threshold of the corresponding training subjects to obtain a first average value difference G3 and a second average value difference G4; obtaining a single value G5 by using a formula G5-G2 Xg 1+ G3 Xg 2+ G4 Xg 3, wherein G1, G2 and G3 are all preset coefficients;
summing all the single values to obtain a progress over-value and marking as G6; the student progress coefficient G7 is obtained by using a formula G7 ═ G1 × G4+ G6 × G5, wherein G4 and G5 are preset coefficients.
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