CN115222564A - Intelligent course recommendation method for online learning platform - Google Patents

Intelligent course recommendation method for online learning platform Download PDF

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CN115222564A
CN115222564A CN202210858062.0A CN202210858062A CN115222564A CN 115222564 A CN115222564 A CN 115222564A CN 202210858062 A CN202210858062 A CN 202210858062A CN 115222564 A CN115222564 A CN 115222564A
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姚伟伟
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Shenzhen Renrenshi Network Technology Co ltd
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Abstract

The invention discloses an intelligent course recommendation method for an online learning platform, which relates to the technical field of course recommendation and solves the technical problem that corresponding teachers and adaptive courses are not arranged for different students according to the learning conditions of the different students; the students with different learning situations recommend different teachers and different teaching materials to improve the overall learning effect, so that different students can obtain academic improvement effect.

Description

Intelligent course recommendation method for online learning platform
Technical Field
The invention belongs to the technical field of course recommendation, and particularly relates to an intelligent course recommendation method for an online learning platform.
Background
The online learning platform mainly records the conditions of course training, examination competition, examination exercise, questionnaire investigation, training exchange and the like of online participation of students, and realizes the whole-course tracking management of the learning conditions of the students and the comprehensive grasp of the learning and training requirements of the employees.
The embodiment of the invention with patent publication number CN112132480A discloses a teacher and resource matching method and system of a big data online education platform, and the method comprises the following steps: music data which students like to listen to everyday are imported through a music data import interface; matching the learning subjects, the learning level, the rhythm, the tone and the character information with the teaching subjects, the teaching levels, the teaching rhythm, the teaching tone and the teaching style of teachers in a teacher database to obtain the matching index of the teachers; and taking the teacher corresponding to the maximum value of the matching index as a target recommendation teacher. The students are interested in and like teachers predicted by songs which the students like to listen to in daily life, the subjects, the class levels, the class rhythm, the class tone and the class style of the teachers are liked by the students, the students can choose proper teachers without listening to many courses, and the online education platform can intelligently match the students with the teachers liked by the students, and is efficient and reliable.
In the course is recommended to online learning platform carries out the course, and learning platform is with multiunit course propelling movement in the homepage, and the student selects the multiunit course of homepage propelling movement, selects the course that is fit for individual preference, and this kind of recommendation mode still exists following not enough in the concrete recommendation process and need improve:
corresponding teachers and adaptive courses are not arranged for different students according to the learning conditions of the different students, so that the overall training and learning effect is poor in the learning process.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art; therefore, the invention provides an intelligent course recommendation method for an online learning platform, which is used for solving the technical problem that corresponding teachers and adaptive courses are not arranged for different students according to the learning conditions of the different students.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides an intelligent course recommendation method for an online learning platform, including the following steps:
s1, enabling corresponding class teachers to set trial listening classes in the same time period, sending lesson setting instructions to student terminals by a platform before the trial listening classes set forth, calling students to listen to the trial listening classes, generating listening parameters according to listening effects, carrying out heat rating treatment on different class teachers, and dividing different class teachers into class A, class B and class C teachers;
s2, acquiring the overall performance parameters of students needing course selection, comprehensively evaluating the students needing course selection according to the acquired performance parameters, dividing different course selection students into different comprehensive evaluation levels, wherein the comprehensive evaluation levels comprise senior students, middle students and errand students, acquiring the partial conditions of the three classes of students through the overall performance parameters, and generating partial signals;
s3, preparing cloud platform subject data teaching materials, classifying different cloud platform subject data teaching materials, and dividing the cloud platform subject data teaching materials into primary teaching materials, lifting teaching materials and tip-pulling teaching materials;
s4, in the course selection process of the students, acquiring comprehensive evaluation levels of the corresponding students, acquiring the partial conditions of the corresponding students, matching lessee-substituting teachers of the corresponding subjects according to the partial conditions, matching different teaching materials according to the heat ratings of the lessee-substituting teachers, binding the corresponding teachers and the corresponding teaching materials to generate recommended courses, and pushing the recommended courses to corresponding student terminals.
Preferably, in step S1, the step of performing the popularity rating process on different lessee representatives is as follows:
s11, acquiring the number of watching persons and rating points of a teacher trying to listen to the class, wherein the rating points are overall rating points given by students after listening, averaging a plurality of overall rating points to obtain the rating points, and marking the number of watching persons as GK i Scoring the score as PJ i Wherein i represents different classes, and i is a positive integer;
s12, adopt
Figure 475933DEST_PATH_IMAGE001
Obtaining the heat value RD of teachers in different classes i Wherein C1 and C2 are both preset fixed coefficient factors, and the heat value RD of teachers in different classes is calculated i Comparing with preset values X1 and X2, andx1 is more than X2, and the comparison mode is as follows:
s121, when heat value RD i When the number is less than X1, generating a low-heat signal, and evaluating a corresponding class-substitute teacher as a class-C teacher;
s122, when the heat value X1 is less than or equal to RD i When the number is less than X2, generating a secondary heat signal, and evaluating a corresponding class-substitute teacher as a class-B teacher;
s123, when the heat value X2 is less than or equal to RD i And generating a high-heat signal, and evaluating the corresponding class-substituting teacher as a class-A teacher.
Preferably, in the step S2, the step of comprehensively evaluating the students performing the course selection according to the acquired result parameters includes:
s21, acquiring the total score parameter value of the course selection students and marking as ZS k Where k represents different course-choosing students, will ZS k Comparing with preset values X3 and X4, wherein X3 is less than X4, when ZS k When the number is less than X3, the corresponding course selection student is marked as a student who is poor or equal to ZS when the number is more than or equal to X3 k If the number is less than X4, the corresponding course selection student is marked as a Zhongsheng, and if X4 is not more than ZS k Then, marking the corresponding course selection student as a superior student;
s22, sequentially acquiring each group of subject performance parameters from the overall performance parameters, and marking the subject performance parameters as XC yk Wherein y represents different disciplines, y =1, 2, \8230;, 7;
s23, averaging the subject performance parameters of different subjects to obtain a to-be-processed average value JZ for the subject performance parameters of different groups of different students yk ；
S24, calculating the academic achievement parameter XC yk With the mean value to be processed JZ yk Comparison is carried out when XC yk ＜JZ yk Generating a partial signal, marking y and k to determine that partial score of a certain subject of a designated student exists, and when XC yk ≥JZ yk No signal is generated.
Preferably, in step S3, the step of classifying the different cloud platform subject data textbooks is as follows:
s31, different cloud platform subject data teaching materials and preset primary template teaching materials are addedComparing the lines to obtain a comparison parameter BD y ；
S32, comparing the parameter value BD y Comparing with preset values T1 and T2 when BD y When the number is less than T1, marking the corresponding cloud platform subject data teaching materials as tip-drawing teaching materials, and when the number is more than or equal to T1 and BD y When the number is less than T2, marking the corresponding cloud platform subject data teaching materials as promotion teaching materials, and when the number is less than T2, marking the corresponding cloud platform subject data teaching materials as BD y And marking the corresponding cloud platform subject data teaching materials as primary teaching materials.
Preferably, in the step S4, the specific processing step of pushing the recommended course to the corresponding student terminal is as follows:
s41, when the obtained comprehensive evaluation grade of the corresponding student is a high class student, matching a class substitute teacher of the corresponding subject according to the partial condition, extracting a tip drawing teaching material of the corresponding subject from a cloud platform subject data teaching material, extracting a C class teacher with the corresponding subject heat rating as C class, binding different C class teachers and tip drawing teaching materials into recommended courses, and pushing the recommended courses to corresponding student terminals for the corresponding student to select courses;
s42, when the obtained comprehensive evaluation grade of the corresponding student is a midlife class, matching a class substitute teacher of the corresponding subject according to the partial condition, extracting a promotion teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting a class B teacher with the corresponding subject heat rating as class B, binding different class B teachers and the promotion teaching material into a recommended course, and pushing the recommended course to the corresponding student terminal for the corresponding student to select the course;
s43, when the obtained comprehensive evaluation level of the corresponding student is a student, matching a class-substituting teacher of the corresponding subject according to the partial situation, extracting a primary teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting an A class teacher with the corresponding subject heat rating as class A, binding different class-A teachers and the primary teaching material into a recommended course, and pushing the recommended course to the corresponding student terminal for the corresponding student to select the course.
Compared with the prior art, the invention has the beneficial effects that: different teachers are classified into A, B and C teachers through the parameter data of the trial listening lessons, different students are respectively evaluated into senior students, middle students and students according to the overall performance parameters, corresponding partial conditions are obtained through the performance parameters, different cloud platform subject data teaching materials are classified into primary teaching materials, promotion teaching materials and tip-drawing teaching materials, the C teachers and the tip-drawing teaching materials are directly recommended when the senior students select lessons, the B teachers and the promotion teaching materials are directly recommended when the middle students select lessons, the A teachers and the primary teaching materials are directly recommended when the students select lessons, the senior students recommend the low-heat teachers and the tip-drawing teaching materials when listening and speaking, the learning attitude of the senior students is better, the learning effect can be improved, the students can select the corresponding primary teaching materials to carry out hot compensation on the individual conditions when listening and speaking, the learning effect of the A teachers can be improved, and the different learning effects can be improved.
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FIG. 1 is a schematic flow chart of the method 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.
Example one
Referring to fig. 1, the present application provides an intelligent course recommendation method for an online learning platform, including the following steps:
s1, make the teacher of riding instead of lessons that corresponds set up the examination and listen to the class in same time quantum to before the examination is listened to the class and is spoken, the platform sends the instruction of opening lessons to student terminal, calls for the student to listen to the class to the examination, through listening to the effect of speaking, carries out the hotness rating to the teacher of riding instead of lessons of difference and handles, and the teacher of riding instead of lessons of difference divides into A, B and three class teachers of C, and concrete rating processing step is:
s11, obtaining the number of watching people and the rating (specifically, the rating is the overall rating given by students after listening), and marking the number of watching people as GK i (specifically, the value of the number of viewers is the number of viewers at the end of the class trial by the teacher), and the rating is marked as PJ i Wherein i represents different classes, and i is a positive integer;
s12, adopt
Figure 658653DEST_PATH_IMAGE001
Obtaining the heat value RD of the teachers in different classes i Wherein C1 and C2 are both preset fixed coefficient factors, and the heat value RD of teachers in different classes i Comparing with preset values X1 and X2, wherein X1 is less than X2, and the comparison mode is as follows:
s121, current heat value RD i When the number is less than X1, generating a low-heat signal, and evaluating a corresponding class-substitute teacher as a class-C teacher;
s122, when the heat value X1 is less than or equal to RD i When the number is less than X2, generating a secondary heat signal, and evaluating a corresponding class-B teacher as a class-B teacher;
s123, when the heat value X2 is less than or equal to RD i Generating a high-heat signal, and evaluating a corresponding class-substitute teacher as a class-A teacher;
s2, acquiring the overall result parameters of students needing course selection, comprehensively evaluating the students needing course selection according to the acquired result parameters, dividing different course selection students into different comprehensive evaluation levels, wherein the comprehensive evaluation levels comprise senior students, middle students and poor students, acquiring the partial conditions of the three types of students through the result parameters, generating partial signals, and performing comprehensive evaluation in the following main modes:
s21, acquiring the total score parameter value of the course selection students and marking as ZS k (specifically, the overall achievement parameter is the sum of the end-of-period achievements of the corresponding period), wherein k represents different course-choosing students, ZS k Comparing with preset values X3 and X4, wherein X3 is less than X4, when ZS k When the number is less than X3, the corresponding course selection student is marked as a student who is poor or equal to ZS when the number is more than or equal to X3 k When the number is less than X4, the corresponding course selection student is marked as a midlife student, and when X4 is less than or equal to ZS k Then, marking the corresponding course selection student as a superior student;
s22, sequentially acquiring each group of subject performance parameters from the overall performance parameters, and marking the subject performance parameters as XC yk Wherein y represents different disciplines, y =1, 2, \8230;, 7;
s23, averaging subject performance parameters of different subjects to obtain average JZ to be processed for the subject performance parameters of a plurality of groups of different students yk ；
S24, calculating the academic achievement parameter XC yk With the to-be-processed mean value JZ yk Comparison is carried out when XC yk ＜JZ yk Generating a partial signal, marking y and k to determine that partial score of a certain subject of a designated student exists, and when XC yk ≥JZ yk No signal is generated;
s3, preparing cloud platform subject data teaching materials, classifying different cloud platform subject data teaching materials, dividing the cloud platform subject data teaching materials into primary teaching materials, promoting the teaching materials and pointing the teaching materials, wherein the specific classification form is as follows:
s31, comparing different cloud platform subject data teaching materials with preset primary template teaching materials to obtain comparison parameter values BD y (specifically, the comparison parameter value is a contact ratio parameter between the cloud platform subject data teaching material and a preset primary template teaching material);
s32, comparing the parameter values BD y Comparing with preset values T1 and T2, wherein the value of T1 is 90%, and the value of T2 is 70%;
s33 when BD y When the number is less than T1, marking the corresponding cloud platform subject data teaching materials as tip-drawing teaching materials, and when the number is more than or equal to T1 and BD y When the number is less than T2, the corresponding cloud platform subject data teaching materials are marked as promotion teaching materials, and when the number is less than T2, BD y Then, marking the corresponding cloud platform subject data teaching materials as primary teaching materials;
s4, in the course of course selection of students, acquiring comprehensive evaluation levels of corresponding students, acquiring the partial situation of the corresponding students, matching course teachers in generation of corresponding subjects according to the partial situation, matching different teaching materials according to the heat rating of the course teachers in generation, binding the corresponding teachers and the corresponding teaching materials to generate recommended courses, pushing the recommended courses to corresponding student terminals, wherein the specific processing steps are as follows:
s41, when the obtained comprehensive evaluation grade of the corresponding student is a high-class student, matching a class-substitute teacher of the corresponding subject according to the partial condition, extracting a tip-drawing teaching material of the corresponding subject from a cloud platform subject data teaching material, extracting a class-C teacher with the corresponding subject heat rating as class-C, binding different class-C teachers and tip-drawing teaching materials into recommended courses, and pushing the recommended courses to corresponding student terminals for the corresponding student to select courses (the number of the classes of the corresponding teacher does not exceed 15, and if the number of the classes of the corresponding student is 15, the classes of the corresponding student are not recommended);
s42, when the obtained comprehensive evaluation grade of the corresponding student is a midlife class, matching a class substitute teacher of the corresponding subject according to the partial condition, extracting a promotion teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting a class B teacher with the corresponding subject heat rating as class B, binding different class B teachers and the promotion teaching material into a recommended course, and pushing the recommended course to the corresponding student terminal for the corresponding student to select the course;
s43, when the obtained comprehensive evaluation level of the corresponding student is a student, matching a class-substituting teacher of the corresponding subject according to the partial situation, extracting a primary teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting an A class teacher with the corresponding subject heat rating as class A, binding different class-A teachers and the primary teaching material into a recommended course, and pushing the recommended course to the corresponding student terminal for the corresponding student to select the course.
Example two
In the specific implementation process of this embodiment, compared with the first embodiment, the specific difference is that in step S32, T1 takes a value of 95%, and T2 takes a value of 65%;
experiment of the invention
The parameter data of the first embodiment and the second embodiment are scattered in an experiment for experience, corresponding experience scores are obtained, students experience the recommended courses and then give the corresponding experience scores, and the experience score data are shown in the following table:
Figure DEST_PATH_IMAGE002
as can be seen from the data in the table, the overall experience of the second embodiment is better than that of the first embodiment, and an external operator can select the corresponding embodiment according to personal requirements.
Part of data in the formula is obtained by removing dimensions and calculating the numerical value of the data, and the formula is a formula which is closest to the real condition and obtained by simulating a large amount of collected data through software; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or obtained through simulation of a large amount of data.
The working principle of the invention is as follows: firstly, enabling different classes of teachers to set up corresponding trial listening lessons, extracting corresponding parameter data after the trial listening lessons are finished, carrying out heat rating processing on different teachers according to the parameter data, and dividing different teachers into class A, class B and class C teachers;
according to the overall result parameters of the course selection students, comprehensively evaluating different students according to the overall result parameters, respectively evaluating the different students as senior students, middle students and errand students, and acquiring corresponding partial conditions through the result parameters;
the method comprises the steps of classifying different cloud platform subject data teaching materials, dividing the cloud platform subject data teaching materials into primary teaching materials, promoting the teaching materials and pointing out the teaching materials, directly recommending C class teachers and pointing out the teaching materials when superior students select courses, directly recommending B class teachers and promoting the teaching materials when middle students select courses, directly recommending A class teachers and primary teaching materials when inferiority students select courses, recommending teachers and pointing out the teaching materials with lower enthusiasm to the superior students when the superior students listen to the teaching materials, improving learning effect due to better learning attitude of the superior students, recommending different teachers and different teaching materials according to different learning conditions of different students so as to improve integral learning effect and enable different students to obtain academic promotion effect.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.

Claims (5)

1. An intelligent course recommendation method for an online learning platform is characterized by comprising the following steps:
s1, enabling corresponding class teachers to set trial listening classes in the same time period, sending lesson setting instructions to student terminals by a platform before the trial listening classes set forth, calling students to listen to the trial listening classes, generating listening parameters according to listening effects, carrying out heat rating treatment on different class teachers, and dividing different class teachers into class A, class B and class C teachers;
s2, acquiring overall score parameters of students needing course selection, comprehensively evaluating the students needing course selection according to the acquired score parameters, dividing different course selection students into different comprehensive evaluation levels, wherein the comprehensive evaluation levels comprise senior students, middle students and poor students, acquiring the partial conditions of the three classes of students through the overall score parameters, and generating partial signals;
s3, preparing cloud platform subject data teaching materials, classifying different cloud platform subject data teaching materials, and dividing the cloud platform subject data teaching materials into primary teaching materials, lifting teaching materials and tip-pulling teaching materials;
s4, in the course selection process of the students, acquiring comprehensive evaluation levels of the corresponding students, acquiring the partial conditions of the corresponding students, matching lessee-substituting teachers of the corresponding subjects according to the partial conditions, matching different teaching materials according to the heat ratings of the lessee-substituting teachers, binding the corresponding teachers and the corresponding teaching materials to generate recommended courses, and pushing the recommended courses to corresponding student terminals.
2. The intelligent course recommendation method for the online learning platform as claimed in claim 1, wherein in step S1, the step of performing popularity rating processing on different classes teachers is as follows:
s11, acquiring the number of watching persons and rating points of a teacher trying to listen to the class, wherein the rating points are overall rating points given by students after listening, averaging a plurality of overall rating points to obtain the rating points, and marking the number of watching persons as GK i Scoring the score as PJ i Wherein i represents different classes, and i is a positive integer;
s12, adopt
Figure DEST_PATH_IMAGE001
Obtaining the heat value RD of teachers in different classes i Wherein C1 and C2 are both preset fixed coefficient factors, and the heat value RD of teachers in different classes i Comparing with preset values X1 and X2, wherein X1 is less than X2, and the comparison mode is as follows:
s121, when heat value RD i When the number is less than X1, generating a low-heat signal, and evaluating a corresponding class-substitute teacher as a class-C teacher;
s122, when the heat value X1 is less than or equal to RD i When the number is less than X2, generating a secondary heat signal, and evaluating a corresponding class-B teacher as a class-B teacher;
s123, when the heat value X2 is less than or equal to RD i And generating a high-heat signal, and evaluating the corresponding class-substituting teacher as a class-A teacher.
3. The intelligent course recommendation method for the online learning platform as claimed in claim 2, wherein in the step S2, the step of comprehensively evaluating the students who choose courses according to the obtained performance parameters comprises:
s21, acquiring the total score parameter value of the course selection students and marking as ZS k Where k represents different course-choosing students, will ZS k Comparing with preset values X3 and X4, wherein X3 is less than X4, when ZS k When the number is less than X3, the corresponding course selection student is marked as a student who is poor or equal to ZS when the number is more than or equal to X3 k When the number is less than X4, the corresponding course selection student is marked as a midlife student, and when X4 is less than or equal to ZS k Then, marking the corresponding course selection student as a superior student;
s22, sequentially acquiring each group of subject performance parameters from the overall performance parameters, and marking the subject performance parameters as XC yk Wherein y represents different disciplines, y =1, 2, \8230;, 7;
s23, averaging subject performance parameters of different subjects to obtain average JZ to be processed for the subject performance parameters of a plurality of groups of different students yk ；
S24, calculating a subject score parameter XC yk With the to-be-processed mean value JZ yk Comparison is carried out when XC yk ＜JZ yk Generating a partial signal, marking y and k to determine the partial condition of the score of a certain subject of a designated student when XC yk ≥JZ yk No signal is generated.
4. The method as claimed in claim 3, wherein in the step S3, the step of classifying the different cloud platform subject data textbooks comprises:
s31, comparing different cloud platform subject data teaching materials with preset primary template teaching materials to obtain comparison parameter values BD y ；
S32, comparing the parameter value BD y Comparing with preset values T1 and T2 when BD y When the number is less than T1, marking the corresponding cloud platform subject data teaching material as a tip drawing teaching material, and when the number is more than or equal to T1 and BD y When the number is less than T2, the corresponding cloud platform subject data teaching materials are marked as promotion teaching materials, and when the number is less than T2, BD y And marking the corresponding cloud platform subject data teaching materials as primary teaching materials.
5. The intelligent course recommendation method for the online learning platform according to claim 4, wherein in the step S4, the specific processing steps of pushing the recommended course to the corresponding student terminal are as follows:
s41, when the obtained comprehensive evaluation grade of the corresponding student is a high class student, matching a class substitute teacher of the corresponding subject according to the partial condition, extracting a tip drawing teaching material of the corresponding subject from a cloud platform subject data teaching material, extracting a C class teacher with the corresponding subject heat rating as C class, binding different C class teachers and tip drawing teaching materials into recommended courses, and pushing the recommended courses to corresponding student terminals for the corresponding student to select courses;
s42, when the obtained comprehensive evaluation grade of the corresponding student is of a middle age, matching a class-substitute teacher of the corresponding subject according to the partial condition, extracting a promoted teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting a class-B teacher with the corresponding subject heat rating as class-B, binding different class-B teachers and the promoted teaching material into recommended courses, and pushing the recommended courses to corresponding student terminals for the corresponding student to select courses;
s43, when the obtained comprehensive evaluation grade of the corresponding student is poor class, matching a class-substitute teacher of the corresponding subject according to the partial condition, extracting a primary teaching material of the corresponding subject from the cloud platform subject data teaching material, extracting a class-A teacher with the corresponding subject heat rating as class A, binding different class-A teachers and the primary teaching material into recommended courses, and pushing the recommended courses to corresponding student terminals for the corresponding student to select courses.
CN202210858062.0A 2022-07-21 2022-07-21 Intelligent course recommendation method for online learning platform Pending CN115222564A (en)

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* Cited by examiner, † Cited by third party
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CN116384840A (en) * 2023-05-29 2023-07-04 湖南工商大学 Course recommendation method and related equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116384840A (en) * 2023-05-29 2023-07-04 湖南工商大学 Course recommendation method and related equipment
CN116384840B (en) * 2023-05-29 2023-08-22 湖南工商大学 Course recommendation method and related equipment

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