CN112687138A - Interactive teaching platform based on Internet of things - Google Patents
Interactive teaching platform based on Internet of things Download PDFInfo
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Abstract
The invention discloses an interactive teaching platform based on the Internet of things, which comprises a cloud platform, a teacher evaluation unit, a course pushing unit, a course management unit, a difficulty marking module, an analysis processing module, a reading and amending unit, a registration and login unit and a database, wherein the cloud platform is used for storing a plurality of classes of teaching data; the reviewing unit is used for a teacher to review post-lesson work, electronic work can be automatically generated after the lesson is finished, the user answers the electronic work through a mobile phone or a computer terminal, the electronic work is submitted after the answer is finished, then the teacher reviews the answered electronic work, and the electronic work is represented as an electronic document of the post-lesson work; through arranging and reading the homework in batches on the network, the time of teachers and students is reduced, meanwhile, the use of paper is saved, the consumption of resources is reduced, the best course is pushed for the users through the course pushing unit, the teaching quality of the teaching platform is improved, and the time for the users to select the courses is reduced.
Description
Technical Field
The invention relates to the technical field of teaching platforms based on the Internet of things, in particular to an interactive teaching platform based on the Internet of things.
Background
The interactive teaching is a teaching mode, namely, the teaching activities are regarded as the interaction and communication between life and life of teachers and students, and the teaching process is regarded as a dynamically developed interactive influence and interactive activity process for teaching and learning. In the process, through optimizing the teaching interaction mode, namely through adjusting the relation and the interaction between teachers and students, harmonious interaction between teachers and students, student interaction, interaction between learning individuals and teaching intermediaries are formed, the interaction influence between people and the environment is strengthened, so that teaching resonance is generated, and the teaching structure mode for improving the teaching effect is achieved.
Patent publication No. CN103854526A discloses a student operating system for an interactive teaching platform, which has a learning method guidance module: providing learning methods of teaching stages of each department for students to refer to; and (3) a test preparation strategy: providing a test preparation strategy and method for students taking an end-of-term test and an upcoming-rise test; the health plaster module: providing nutrition meal collocation suggestions and other nutrition items needing attention in the growth process for students; campus literature show module: providing a campus literature special area, and collecting the well literature for students to read and appreciate. Due to the adoption of the technical scheme, the student operation system for the interactive teaching platform can effectively enable students to actively participate in daily teaching activities by adding simple equipment, and sets the related database and module to help the students to obtain related teaching resources more quickly and better. And the technology is mature, the cost is low, and the method is very suitable for large-scale popularization and use.
However, in this patent, the ability of the professional teacher in the teaching platform cannot be improved, and at the same time, the consumption of paper resources by the job cannot be saved.
Disclosure of Invention
The invention aims to provide an interactive teaching platform based on the Internet of things, a teacher makes an approval for post-lesson work through an approval unit, electronic work can be automatically generated after a course is finished, a user answers the electronic work through a mobile phone or a computer terminal, the electronic work is submitted after the answer is finished, then the teacher makes an approval for the electronic work which is answered, and the electronic work is represented as an electronic document of the post-lesson work; through the homework of arranging and reading in batches on the net, the time of teachers and students is reduced, meanwhile, the use of paper is saved, and the consumption of resources is reduced.
The purpose of the invention can be realized by the following technical scheme:
an interactive teaching platform based on the Internet of things comprises a cloud platform, a teacher evaluation unit, a course pushing unit, a course management unit, a difficulty marking module, an analysis processing module, an approval unit, a registration unit and a database;
course propelling movement unit is used for through course data to user propelling movement course, and the course data includes ten days, and the commenting quantity of this course, the average number of people who increases concern the number of people and watch on line mark the course as i, i 1, 2,.
The method comprises the following steps: acquiring the number of good comments of the course within ten days, and marking the number of good comments of the course as Ki;
step two: acquiring the number of the increased attention people of the curriculum within ten days, and marking the number of the increased attention people of the curriculum as Ri;
step three: acquiring the average number of people who watch the course online within ten days, and marking the average number of people who watch the course online as Pi;
step four: by the formula
Acquiring a preference coefficient Xi of the courses, wherein c1, c2 and c3 are all preset proportional coefficients, c1+ c2+ c3 is 2.36541, c1 is larger than c2 is larger than c3 is larger than 0, and beta is a correction factor and is taken as 2.3012546;
step five: comparing the preference coefficient Xi of the course with a preference coefficient threshold:
if the preference coefficient Xi of the course is smaller than the preference coefficient threshold, judging that the preference of the course is low, marking the course as a low preference course, and then sending the name of the low preference course and the name of the teacher to the cloud platform;
if the preference coefficient Xi of the course is larger than or equal to the preference coefficient threshold, judging that the preference of the course is high, marking the course as a pushing course, and then pushing the pushing course to a login interface of a user.
Further, the registration login module is used for the user, the teacher and the manager to send user data, teacher data and manager data through the mobile phone terminal to register and send the user data, the teacher data and the manager data which are successfully registered to the database to be stored, the user data comprise the name, the age, the sex, the grade and the mobile phone number of the user or the child of the user for real name authentication, the manager comprises the name, the age, the time of enrollment and the mobile phone number of the real name authentication of the manager, and the teacher data comprise the name, the age, the time of enrollment, the name of the teacher and the mobile phone number of the real name authentication of the manager.
Further, the cloud platform receives the name of the low-preference course and the name of the teacher, and sends the name of the low-preference course and the name of the teacher to the course management unit, the course management unit analyzes the network data of the low-preference course, the network data comprises signal intensity, signal fluctuation frequency and picture definition when the low-preference course is live broadcast, the low-preference course is marked as o, o is 1, 2, and k is a natural number, and the specific analysis process is as follows:
s1: acquiring the signal intensity of the low-preference course during live broadcasting, and marking the signal intensity during live broadcasting as Qo;
s2: acquiring the signal fluctuation frequency of the low-preference course during live broadcasting, and marking the signal fluctuation frequency during live broadcasting as Po;
s3: acquiring the definition of the picture of the low-preference course during live broadcasting, and marking the definition of the picture during live broadcasting as Do;
s4: by the formula
Acquiring a live broadcast quality coefficient Wo, wherein b1, b2 and b3 are all preset proportional coefficients, b1+ b2+ b3 is 2.36501, and b1 is greater than b2 and greater than b3 is greater than 0;
s5: comparing the live broadcast quality coefficient Wo with a quality coefficient threshold:
if the live broadcast quality coefficient Wo is less than the quality coefficient threshold value, judging that the live broadcast quality is poor, generating a debugging signal, sending the debugging signal to a mobile phone terminal of a manager, simultaneously sending the name of the low-preference course to a mobile phone terminal of a teacher, and planning live broadcast time again for the course teacher;
and if the live broadcast quality coefficient Wo is not less than the quality coefficient threshold, judging that the live broadcast quality is good, generating a warning signal, transmitting the warning signal to the mobile phone terminal of the lesson teacher, and setting live broadcast time for the lesson teacher.
Furthermore, the difficulty marking module is used for marking difficulty of the course content by the user, and the user can mark the difficulty of the course content in a mode of setting a bookmark and send the marked difficulty to the analysis processing module when watching the course;
the analysis processing module analyzes the difficult data after receiving the marked difficult, the difficult data comprises the number of people marked by the difficult, the frequency marked by the difficult and the time length marked by the difficult, the difficult is marked as j, j is 1, 2, and f is a natural number, and the specific analysis process comprises the following steps:
t1: acquiring the number of people marked with difficulty, and marking the number of people marked with difficulty as Nj;
t2: acquiring the frequency of the difficulty mark, and marking the frequency of the difficulty mark as Pj;
t3: acquiring the time length of the difficulty mark, and marking the time length of the difficulty mark as Sj;
t4: by the formula
The difficulty flag coefficient Lj is obtained, wherein,
the correction factor is 2.03214, r1, r2 and r3 are preset proportionality coefficients, r1+ r2+ r3 is 2.321452, and r1 is greater than r2 and is greater than r 3;
t5: comparing the difficulty marking coefficient Lj to a marking coefficient threshold:
if the difficulty marking coefficient Lj is smaller than the marking coefficient threshold value, judging that the difficulty is low, generating a difficulty low signal, and sending the difficulty low signal to the cloud platform;
if the difficulty marking coefficient Lj is larger than or equal to the marking coefficient threshold value, judging that the difficulty is high, generating a difficulty signal, and sending the difficulty signal to the cloud platform;
the cloud platform receives the low-difficulty signal and the high-difficulty signal and sends the low-difficulty signal and the high-difficulty signal to the mobile phone terminal of the lesson teacher, when the mobile phone terminal of the teacher receives the low-difficulty signal, the lesson content of the difficulty is sent to the mobile phone terminal of the difficulty marking user in a key mode, when the mobile phone terminal of the teacher receives the high-difficulty signal, the lesson content is formulated again, live broadcast time is set again, and then the live broadcast time is sent to the mobile phone terminal of the difficulty marking user.
Further, the teacher comparison unit is used for comparing any teacher in the teaching platform through analysis of teaching data, the teaching data comprise the speed of speech, the decibel value of sound of the teacher in the course and the number of interactions of the teacher, and the teacher is marked as g, g 1, 2,.
L1: acquiring the speed of a teacher in a course, and marking the speed of the teacher in the course as Yg;
l2: acquiring the sound decibel value of the teacher in the course, and marking the sound decibel value of the teacher in the course as Bg;
l3: acquiring the interaction times of teachers in the courses, and marking the interaction times of the teachers in the courses as Hg;
l4: by the formula
Acquiring a rating coefficient Tg of a teacher, wherein x1, x2 and x3 are all preset proportionality coefficients, x1+ x2+ x3 is 2.032514, and x1 is greater than x2 and is greater than x 3;
l5: comparing the teacher's scale coefficient Tg with a scale coefficient threshold:
if the evaluation coefficient Tg of the teacher is larger than or equal to the evaluation coefficient threshold, judging that the teacher is qualified, marking the teacher as a qualified teacher, generating a qualified signal and sending the name of the qualified teacher to the cloud platform for storage;
if the comparison coefficient Tg of the teacher is smaller than the comparison coefficient threshold, judging that the teacher is unqualified, marking the teacher as an unqualified teacher, generating an unqualified signal and sending the name of the unqualified teacher to the cloud platform for storage;
and after receiving the unqualified signal, the cloud platform generates an auditing signal and marks the unqualified teacher as an auditing teacher, sets an auditing time threshold, compares the auditing teacher after the auditing time threshold, generates a removal instruction if the auditing teacher is still judged to be unqualified, marks the teacher as a leaving teacher, and deletes the data of the leaving teacher in the database.
Furthermore, the reviewing unit is used for a teacher to review post-lesson homework, electronic homework can be automatically generated after the lesson is finished, the user answers the electronic homework through a mobile phone or a computer terminal, the electronic homework is submitted after the answering is finished, then the teacher reviews the answered electronic homework, and the electronic homework is represented as an electronic document of the post-lesson homework.
Compared with the prior art, the invention has the beneficial effects that:
1. in the invention, a course pushing unit pushes courses to a user through course data, obtains the commenting quantity of the courses in ten days, the number of the increasing concerned people of the courses and the average number of people who the courses watch online in ten days, obtains the preference coefficient Xi of the courses through a formula, and compares the preference coefficient Xi of the courses with the preference coefficient threshold: if the preference coefficient Xi of the course is smaller than the preference coefficient threshold, judging that the preference of the course is low, marking the course as a low preference course, and then sending the name of the low preference course and the name of the teacher to the cloud platform; if the preference coefficient Xi of the course is more than or equal to the preference coefficient threshold, judging that the preference of the course is high, marking the course as a pushed course, then pushing the pushed course to a login interface of a user, pushing the best course for the user, improving the teaching quality of the teaching platform and reducing the time for the user to select the course;
2. in the invention, a teacher evaluation unit evaluates any teacher in a teaching platform by analyzing teaching data, obtains the speech speed and the sound decibel value of the teacher in a course and the interaction times of the teacher in the course, obtains a evaluation coefficient Tg of the teacher through a formula, and compares the evaluation coefficient Tg of the teacher with a threshold value of the evaluation coefficient: if the evaluation coefficient Tg of the teacher is larger than or equal to the evaluation coefficient threshold, judging that the teacher is qualified, marking the teacher as a qualified teacher, generating a qualified signal and sending the name of the qualified teacher to the cloud platform for storage; if the comparison coefficient Tg of the teacher is smaller than the comparison coefficient threshold, judging that the teacher is unqualified, marking the teacher as an unqualified teacher, generating an unqualified signal and sending the name of the unqualified teacher to the cloud platform for storage; after receiving the unqualified signal, the cloud platform generates an auditing signal and marks the unqualified teacher as an auditing teacher, sets an auditing time threshold, compares the auditing teacher after the auditing time threshold, generates a removal instruction if the auditing teacher is still judged to be unqualified, marks the teacher as a leaving teacher, and deletes the data of the leaving teacher in the database; by comparing the staffs of teachers in the teaching platform, the comprehensive quality of the staffs of teachers is improved, and the teaching quality in the teaching platform is improved;
3. according to the invention, the reviewing unit is used for a teacher to review post-lesson work, electronic work can be automatically generated after the lesson is finished, a user answers the electronic work through a mobile phone or a computer terminal, the electronic work is submitted after the answer is finished, then the teacher reviews the answered electronic work, and the electronic work is represented as an electronic document of the post-lesson work; through the homework of arranging and reading in batches on the net, the time of teachers and students is reduced, meanwhile, the use of paper is saved, and the consumption of resources is reduced.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of 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.
Referring to fig. 1, an interactive teaching platform based on the internet of things includes a cloud platform, a teacher evaluation unit, a course pushing unit, a course management unit, a difficulty marking module, an analysis processing module, a reviewing unit, a registration unit and a database;
the registration login module is used for a user, a teacher and a manager to send user data, teacher data and manager data through a mobile phone terminal for registration and send the user data, the teacher data and the manager data which are successfully registered to a database for storage, the user data comprises the name, the age, the sex, the grade and the mobile phone number of real name authentication of the user or children of the user, the manager comprises the name, the age, the time of enrollment and the mobile phone number of real name authentication of the manager, and the teacher data comprises the name, the age, the time of enrollment, an instructional subject and the mobile phone number of real name authentication of the manager;
course propelling movement unit is used for through course data to user propelling movement course, and the course data includes ten days, and the commenting quantity of this course, the average number of people who increases concern the number of people and watch on line mark the course as i, i 1, 2,.
The method comprises the following steps: acquiring the number of good comments of the course within ten days, and marking the number of good comments of the course as Ki;
step two: acquiring the number of the increased attention people of the curriculum within ten days, and marking the number of the increased attention people of the curriculum as Ri;
step three: acquiring the average number of people who watch the course online within ten days, and marking the average number of people who watch the course online as Pi;
step four: by the formula
Acquiring a preference coefficient Xi of the courses, wherein c1, c2 and c3 are all preset proportional coefficients, c1+ c2+ c3 is 2.36541, c1 is larger than c2 is larger than c3 is larger than 0, and beta is a correction factor and is taken as 2.3012546;
step five: comparing the preference coefficient Xi of the course with a preference coefficient threshold:
if the preference coefficient Xi of the course is smaller than the preference coefficient threshold, judging that the preference of the course is low, marking the course as a low preference course, and then sending the name of the low preference course and the name of the teacher to the cloud platform;
if the preference coefficient Xi of the course is more than or equal to the preference coefficient threshold, judging that the preference of the course is high, marking the course as a pushed course, and then pushing the pushed course to a login interface of a user;
the cloud platform receives the name of low preference degree course and teacher's name and sends the name of low preference degree course and teacher's name to course administrative unit, course administrative unit carries out the analysis to the network data of low preference degree course, signal intensity, signal fluctuation frequency and picture definition when network data includes that low preference degree course carries out the live broadcast, mark low preference degree course as o, o is 1, 2, the.
S1: acquiring the signal intensity of the low-preference course during live broadcasting, and marking the signal intensity during live broadcasting as Qo;
s2: acquiring the signal fluctuation frequency of the low-preference course during live broadcasting, and marking the signal fluctuation frequency during live broadcasting as Po;
s3: acquiring the definition of the picture of the low-preference course during live broadcasting, and marking the definition of the picture during live broadcasting as Do;
s4: by the formula
Acquiring a live broadcast quality coefficient Wo, wherein b1, b2 and b3 are all preset proportional coefficients, b1+ b2+ b3 is 2.36501, and b1 is greater than b2 and greater than b3 is greater than 0;
s5: comparing the live broadcast quality coefficient Wo with a quality coefficient threshold:
if the live broadcast quality coefficient Wo is less than the quality coefficient threshold value, judging that the live broadcast quality is poor, generating a debugging signal, sending the debugging signal to a mobile phone terminal of a manager, simultaneously sending the name of the low-preference course to a mobile phone terminal of a teacher, and planning live broadcast time again for the course teacher;
if the live broadcast quality coefficient Wo is larger than or equal to the quality coefficient threshold value, judging that the live broadcast quality is good, generating a warning signal, transmitting the warning signal to a mobile phone terminal of the course teacher, and defining live broadcast time for the course teacher;
the difficulty marking module is used for marking difficulty of the course content by a user, and the user can mark the difficulty of the course content in a bookmark setting mode and send the marked difficulty to the analysis processing module when watching the course;
the analysis processing module analyzes the difficult data after receiving the marked difficult, the difficult data comprises the number of people marked by the difficult, the frequency marked by the difficult and the time length marked by the difficult, the difficult is marked as j, j is 1, 2, and f is a natural number, and the specific analysis process comprises the following steps:
t1: acquiring the number of people marked with difficulty, and marking the number of people marked with difficulty as Nj;
t2: acquiring the frequency of the difficulty mark, and marking the frequency of the difficulty mark as Pj;
t3: acquiring the time length of the difficulty mark, and marking the time length of the difficulty mark as Sj;
t4: by the formula
The difficulty flag coefficient Lj is obtained, wherein,
the correction factor is 2.03214, r1, r2 and r3 are preset proportionality coefficients, r1+ r2+ r3 is 2.321452, and r1 is greater than r2 and is greater than r 3;
t5: comparing the difficulty marking coefficient Lj to a marking coefficient threshold:
if the difficulty marking coefficient Lj is smaller than the marking coefficient threshold value, judging that the difficulty is low, generating a difficulty low signal, and sending the difficulty low signal to the cloud platform;
if the difficulty marking coefficient Lj is larger than or equal to the marking coefficient threshold value, judging that the difficulty is high, generating a difficulty signal, and sending the difficulty signal to the cloud platform;
the cloud platform receives the difficulty low signal and the difficulty high signal and sends the difficulty low signal and the difficulty high signal to the mobile phone terminal of the lesson teacher, when the mobile phone terminal of the teacher receives the difficulty low signal, the lesson content of the difficulty is sent to the mobile phone terminal of the difficulty mark user in a key mode, when the mobile phone terminal of the teacher receives the difficulty high signal, the lesson content is re-formulated and the live broadcast time is set, and then the live broadcast time is sent to the mobile phone terminal of the difficulty mark user;
the teacher unit of comparing is used for comparing through the analysis to the teaching data, to the teacher of wanting to teach in the teaching platform, and the teaching data includes teacher's speech rate, sound decibel value and teacher's interactive number of times in the course, marks the teacher g, and g 1, 2, is.
L1: acquiring the speed of a teacher in a course, and marking the speed of the teacher in the course as Yg;
l2: acquiring the sound decibel value of the teacher in the course, and marking the sound decibel value of the teacher in the course as Bg;
l3: acquiring the interaction times of teachers in the courses, and marking the interaction times of the teachers in the courses as Hg;
l4: by the formula
Acquiring a rating coefficient Tg of a teacher, wherein x1, x2 and x3 are all preset proportionality coefficients, x1+ x2+ x3 is 2.032514, and x1 is greater than x2 and is greater than x 3;
l5: comparing the teacher's scale coefficient Tg with a scale coefficient threshold:
if the evaluation coefficient Tg of the teacher is larger than or equal to the evaluation coefficient threshold, judging that the teacher is qualified, marking the teacher as a qualified teacher, generating a qualified signal and sending the name of the qualified teacher to the cloud platform for storage;
if the comparison coefficient Tg of the teacher is smaller than the comparison coefficient threshold, judging that the teacher is unqualified, marking the teacher as an unqualified teacher, generating an unqualified signal and sending the name of the unqualified teacher to the cloud platform for storage;
after receiving the unqualified signal, the cloud platform generates an auditing signal and marks the unqualified teacher as an auditing teacher, sets an auditing time threshold, compares the auditing teacher after the auditing time threshold, generates a removal instruction if the auditing teacher is still judged to be unqualified, marks the teacher as a leaving teacher, and deletes the data of the leaving teacher in the database;
the reviewing unit is used for a teacher to review post-lesson work, electronic work can be automatically generated after the lesson is finished, the user answers the electronic work through a mobile phone or a computer terminal, the electronic work is submitted after the answer is finished, then the teacher reviews the answered electronic work, and the electronic work is represented as an electronic document of the post-lesson work;
the classification management unit is used for classifying and storing the course videos in the cloud platform, the contents of the live course can be recorded after the live course is finished, the recorded videos are sent to the cloud platform to be stored, the recorded videos are marked as storage videos, the classification management unit acquires the calling times of the storage videos, the calling times are sequenced from high to low, meanwhile, the date of each calling is recorded, if the time of the storage videos to the last calling exceeds a time threshold value L1, the storage videos are marked as useless videos, and the useless videos in the cloud platform are deleted through the classification management unit.
The working principle of the invention is as follows: course propelling movement unit passes through the course data and pushes away the course to the user, acquires the commented quantity of course in ten days, the number of concern of the increase of course and the average number of people that the course was watched on line in ten days, acquires the preference coefficient Xi of course through the formula, compares the preference coefficient Xi and the preference coefficient threshold value of course: if the preference coefficient Xi of the course is smaller than the preference coefficient threshold, judging that the preference of the course is low, marking the course as a low preference course, and then sending the name of the low preference course and the name of the teacher to the cloud platform; if the preference coefficient Xi of the course is more than or equal to the preference coefficient threshold, judging that the preference of the course is high, marking the course as a pushed course, and then pushing the pushed course to a login interface of a user;
the teacher evaluation unit is used for evaluating any teacher in the teaching platform through analysis of teaching data, obtaining the speech speed and the sound decibel value of the teacher in the course and the interaction times of the teacher in the course, obtaining the evaluation coefficient Tg of the teacher through a formula, and comparing the evaluation coefficient Tg of the teacher with the evaluation coefficient threshold value: if the evaluation coefficient Tg of the teacher is larger than or equal to the evaluation coefficient threshold, judging that the teacher is qualified, marking the teacher as a qualified teacher, generating a qualified signal and sending the name of the qualified teacher to the cloud platform for storage; if the comparison coefficient Tg of the teacher is smaller than the comparison coefficient threshold, judging that the teacher is unqualified, marking the teacher as an unqualified teacher, generating an unqualified signal and sending the name of the unqualified teacher to the cloud platform for storage; after receiving the unqualified signal, the cloud platform generates an auditing signal and marks the unqualified teacher as an auditing teacher, sets an auditing time threshold, compares the auditing teacher after the auditing time threshold, generates a removal instruction if the auditing teacher is still judged to be unqualified, marks the teacher as a leaving teacher, and deletes the data of the leaving teacher in the database; by comparing the staffs teachers in the teaching platform, the comprehensive quality of the staffs teachers is improved, and the teaching quality in the teaching platform is improved.
The above formulas are all quantitative calculation, the formula is a formula obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (6)
1. An interactive teaching platform based on the Internet of things is characterized by comprising a cloud platform, a teacher evaluation unit, a course pushing unit, a course management unit, a difficulty marking module, an analysis processing module, a reviewing unit, a registration unit and a database;
course propelling movement unit is used for through course data to user propelling movement course, and the course data includes ten days, and the commenting quantity of this course, the average number of people who increases concern the number of people and watch on line mark the course as i, i 1, 2,.
The method comprises the following steps: acquiring the number of good comments of the course within ten days, and marking the number of good comments of the course as Ki;
step two: acquiring the number of the increased attention people of the curriculum within ten days, and marking the number of the increased attention people of the curriculum as Ri;
step three: acquiring the average number of people who watch the course online within ten days, and marking the average number of people who watch the course online as Pi;
step four: by the formula
Acquiring a preference coefficient Xi of the courses, wherein c1, c2 and c3 are all preset proportional coefficients, c1+ c2+ c3 is 2.36541, c1 is larger than c2 is larger than c3 is larger than 0, and beta is a correction factor and is taken as 2.3012546;
step five: comparing the preference coefficient Xi of the course with a preference coefficient threshold:
if the preference coefficient Xi of the course is smaller than the preference coefficient threshold, judging that the preference of the course is low, marking the course as a low preference course, and then sending the name of the low preference course and the name of the teacher to the cloud platform;
if the preference coefficient Xi of the course is larger than or equal to the preference coefficient threshold, judging that the preference of the course is high, marking the course as a pushing course, and then pushing the pushing course to a login interface of a user.
2. The interactive teaching platform based on the internet of things of claim 1, wherein the registration login module is used for users, teachers and managers to send user data, teacher data and manager data through mobile phone terminals for registration and send the user data, teacher data and manager data which are successfully registered to the database for storage, the user data comprises mobile phone numbers for authenticating names, ages, sexes, grades and real names of users or children of the users, the managers comprise mobile phone numbers for authenticating names, ages, enrollment times and real names of the managers, and the teacher data comprises mobile phone numbers for authenticating names, ages, enrollment times, academic subjects and real names of the teachers.
3. The interactive teaching platform based on the internet of things as claimed in claim 1, wherein the cloud platform receives names of low-preference courses and names of teachers and sends the names of the low-preference courses and the names of the teachers to the course management unit, the course management unit analyzes network data of the low-preference courses, the network data includes signal intensity, signal fluctuation frequency and picture definition when the low-preference courses are live broadcast, the low-preference courses are marked as o, o 1, 2,.
S1: acquiring the signal intensity of the low-preference course during live broadcasting, and marking the signal intensity during live broadcasting as Qo;
s2: acquiring the signal fluctuation frequency of the low-preference course during live broadcasting, and marking the signal fluctuation frequency during live broadcasting as Po;
s3: acquiring the definition of the picture of the low-preference course during live broadcasting, and marking the definition of the picture during live broadcasting as Do;
s4: by the formula
Acquiring a live broadcast quality coefficient Wo, wherein b1, b2 and b3 are all preset proportional coefficients, b1+ b2+ b3 is 2.36501, and b1 is greater than b2 and greater than b3 is greater than 0;
s5: comparing the live broadcast quality coefficient Wo with a quality coefficient threshold:
if the live broadcast quality coefficient Wo is less than the quality coefficient threshold value, judging that the live broadcast quality is poor, generating a debugging signal, sending the debugging signal to a mobile phone terminal of a manager, simultaneously sending the name of the low-preference course to a mobile phone terminal of a teacher, and planning live broadcast time again for the course teacher;
and if the live broadcast quality coefficient Wo is not less than the quality coefficient threshold, judging that the live broadcast quality is good, generating a warning signal, transmitting the warning signal to the mobile phone terminal of the lesson teacher, and setting live broadcast time for the lesson teacher.
4. The interactive teaching platform based on the internet of things as claimed in claim 1, wherein the difficulty marking module is used for marking difficulty points of course contents by a user, and the user can mark difficulty points of the course contents in a bookmark setting manner and send the marked difficulty points to the analysis processing module when watching the course;
the analysis processing module analyzes the difficult data after receiving the marked difficult, the difficult data comprises the number of people marked by the difficult, the frequency marked by the difficult and the time length marked by the difficult, the difficult is marked as j, j is 1, 2, and f is a natural number, and the specific analysis process comprises the following steps:
t1: acquiring the number of people marked with difficulty, and marking the number of people marked with difficulty as Nj;
t2: acquiring the frequency of the difficulty mark, and marking the frequency of the difficulty mark as Pj;
t3: acquiring the time length of the difficulty mark, and marking the time length of the difficulty mark as Sj;
t4: by the formula
The difficulty flag coefficient Lj is obtained, wherein,
the correction factor is 2.03214, r1, r2 and r3 are preset proportionality coefficients, r1+ r2+ r3 is 2.321452, and r1 is greater than r2 and is greater than r 3;
t5: comparing the difficulty marking coefficient Lj to a marking coefficient threshold:
if the difficulty marking coefficient Lj is smaller than the marking coefficient threshold value, judging that the difficulty is low, generating a difficulty low signal, and sending the difficulty low signal to the cloud platform;
if the difficulty marking coefficient Lj is larger than or equal to the marking coefficient threshold value, judging that the difficulty is high, generating a difficulty signal, and sending the difficulty signal to the cloud platform;
the cloud platform receives the low-difficulty signal and the high-difficulty signal and sends the low-difficulty signal and the high-difficulty signal to the mobile phone terminal of the lesson teacher, when the mobile phone terminal of the teacher receives the low-difficulty signal, the lesson content of the difficulty is sent to the mobile phone terminal of the difficulty marking user in a key mode, when the mobile phone terminal of the teacher receives the high-difficulty signal, the lesson content is formulated again, live broadcast time is set again, and then the live broadcast time is sent to the mobile phone terminal of the difficulty marking user.
5. The interactive teaching platform based on the internet of things as claimed in claim 1, wherein the teacher evaluation unit is configured to evaluate any teacher in the teaching platform through analysis of teaching data, the teaching data includes the speed of speech, decibel value of sound of the teacher and the number of interactions of the teacher in the course, and the teacher is marked as g, g 1, 2,.
L1: acquiring the speed of a teacher in a course, and marking the speed of the teacher in the course as Yg;
l2: acquiring the sound decibel value of the teacher in the course, and marking the sound decibel value of the teacher in the course as Bg;
l3: acquiring the interaction times of teachers in the courses, and marking the interaction times of the teachers in the courses as Hg;
l4: by the formula
Acquiring a rating coefficient Tg of a teacher, wherein x1, x2 and x3 are all preset proportionality coefficients, x1+ x2+ x3 is 2.032514, and x1 is greater than x2 and is greater than x 3;
l5: comparing the teacher's scale coefficient Tg with a scale coefficient threshold:
if the evaluation coefficient Tg of the teacher is larger than or equal to the evaluation coefficient threshold, judging that the teacher is qualified, marking the teacher as a qualified teacher, generating a qualified signal and sending the name of the qualified teacher to the cloud platform for storage;
if the comparison coefficient Tg of the teacher is smaller than the comparison coefficient threshold, judging that the teacher is unqualified, marking the teacher as an unqualified teacher, generating an unqualified signal and sending the name of the unqualified teacher to the cloud platform for storage;
and after receiving the unqualified signal, the cloud platform generates an auditing signal and marks the unqualified teacher as an auditing teacher, sets an auditing time threshold, compares the auditing teacher after the auditing time threshold, generates a removal instruction if the auditing teacher is still judged to be unqualified, marks the teacher as a leaving teacher, and deletes the data of the leaving teacher in the database.
6. The interactive teaching platform based on the internet of things as claimed in claim 1, wherein the reviewing unit is used for a teacher to review post-lesson homework, an electronic homework is automatically generated after the lesson is finished, the user answers the electronic homework through a mobile phone or a computer terminal, the electronic homework is submitted after the answer is finished, and then the teacher reviews the electronic homework finished with the answer, wherein the electronic homework is represented as an electronic document of the post-lesson homework.
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