CN109902128B - Learning path planning method, device, equipment and storage medium based on big data - Google Patents
Learning path planning method, device, equipment and storage medium based on big data Download PDFInfo
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Abstract
The application relates to a learning path planning method, a device, equipment and a storage medium based on big data, wherein the method comprises the following steps: acquiring characteristic information of a plurality of learners; classifying each learner according to the characteristic information to obtain a plurality of learner groups; counting the number of times each course in the course library is learned by the first learner group; sequencing courses in the course library according to the sequence of the learned times from more to less to obtain a course learning path corresponding to the first learner group; and obtaining a course learning path corresponding to each learner group. The learner group is established through the characteristic information of the learner, and a course learning path corresponding to the learner group is established according to the learning times of the learner group to each course in the course library, so that proper learning paths are planned for various learners, and the learning pertinence and the learning efficiency of the learners are improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a learning path planning method, apparatus, device, and storage medium based on big data.
Background
With the popularization of the internet and the widespread use of computer technology, conventional education is gradually shifting to an online education direction. Many existing online education can automatically provide learning path planning services for learners, provide references for the learners to reasonably arrange learning courses, and improve learning efficiency of the learners.
However, the current learning path planning method mainly plans a popular course learning path according to the knowledge points mastered by the existing learner, but does not consider individual differences among learners, does not classify the learners, and does not make proper learning path planning for different learners. Thus, the learner has low learning efficiency and lacks learning pertinence.
Disclosure of Invention
Aiming at the problem that the individual difference among learners is not considered in the current learning path planning method, so that the planned learning path cannot meet the demands of various learners, the application provides a learning path planning method, which establishes a learner group through characteristic information of the learners, establishes course learning paths corresponding to the learner group according to the learning times of all learners in the learner group to each course in a course library, plans proper learning paths for various learners, realizes the teaching of the learners according to the material, and improves the learning pertinence and the learning efficiency of the learners.
A learning path planning method based on big data comprises the following steps: acquiring characteristic information of a plurality of learners; the characteristic information of each learner at least comprises one of gender, age and course test result; inputting the characteristic information of each learner into a clustering model, and classifying each learner according to the characteristic information of the learner to obtain a plurality of learner groups; counting the number of times each course in the course library is learned by the first learner group; the first learner group is any one of the plurality of learner groups; sequencing courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group; and finally obtaining a course learning path corresponding to each learner group.
Optionally, the plurality of learners includes a first learner. After the course learning path corresponding to each learner group is obtained, the method further includes: matching the first learner with the belonging learner group according to the characteristic information of the first learner; acquiring a target course learning path corresponding to a learner group to which the first learner belongs; recommending the target course learning path to the first learner.
Optionally, classifying each learner according to the characteristic information of the learner to obtain a plurality of learner groups, including: classifying each learner according to any two of the gender, the age and the course test result, and establishing a father learner group and a son learner group.
Optionally, the classifying each learner according to any two of the gender, age and course test result, and establishing a parent learner group and a child learner group includes: dividing a plurality of age groups and a plurality of course test score groups; classifying each learner according to the age group of the learner, and establishing a plurality of father learner groups; classifying each learner in each father learner group according to a course test score segment to which the course test score of the learner belongs, and establishing a plurality of child learner groups of the father learner group.
Optionally, the classifying each learner according to any two of the gender, age and course test result, and establishing a parent learner group and a child learner group includes: dividing a plurality of age groups; classifying each learner according to the gender of the learner, and establishing two father learner groups; classifying each learner in each parent learner group according to the age group of the learner, and establishing a plurality of child learner groups of the parent learner group.
Optionally, after the course learning path corresponding to each learner group is obtained, the method further includes: obtaining a plurality of marking values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through a course learning interface of the terminal; respectively calculating the average value of the plurality of marking values corresponding to each course; obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value; sequencing courses in the course library according to the sequence of the weight values from large to small to obtain a course learning optimization path corresponding to the first learner group; and finally obtaining a course learning optimization path corresponding to each learner group.
Optionally, the expression of the weight value is:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i The average value corresponding to the i-th course is obtained; l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
Based on the same technical conception, the invention also provides a learning path planning device based on big data, which comprises the following steps:
the receiving and transmitting module is used for acquiring characteristic information of a plurality of learners; the characteristic information of each learner includes at least one of gender, age, and course test result.
The processing module is used for inputting the characteristic information of each learner into the clustering model, classifying each learner according to the characteristic information of the learner, and obtaining a plurality of learner groups; counting the number of times each course in the course library is learned by the first learner group; the first learner group is any one of the plurality of learner groups; sequencing courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group; and finally obtaining a course learning path corresponding to each learner group.
Optionally, the plurality of learners includes a first learner. The processing module is also used for matching the learner group to which the first learner belongs according to the characteristic information of the first learner; acquiring a target course learning path corresponding to a learner group to which the first learner belongs; recommending the target course learning path to the first learner.
Optionally, the processing module is specifically configured to classify each learner according to any two of the gender, age and course test performance, and establish a parent learner group and a child learner group.
Optionally, the processing module is specifically configured to divide a plurality of age groups and a plurality of course test score groups; classifying each learner according to the age group of the learner, and establishing a plurality of father learner groups; classifying each learner in each father learner group according to a course test score segment to which the course test score of the learner belongs, and establishing a plurality of child learner groups of the father learner group.
Optionally, the processing module is specifically configured to divide a plurality of age groups; classifying each learner according to the gender of the learner, and establishing two father learner groups; classifying each learner in each parent learner group according to the age group of the learner, and establishing a plurality of child learner groups of the parent learner group.
Optionally, the processing module is further configured to obtain a plurality of tag values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through a course learning interface of the terminal; respectively calculating the average value of the plurality of marking values corresponding to each course; obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value; sequencing courses in the course library according to the sequence of the weight values from large to small to obtain a course learning optimization path corresponding to the first learner group; and finally obtaining a course learning optimization path corresponding to each learner group.
Optionally, the expression of the weight value is:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i The average value corresponding to the i-th course is obtained; l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
Based on the same technical concept, the invention also provides a computer device, which comprises a transceiver, a memory and a processor, wherein the memory stores computer readable instructions, and the computer readable instructions, when executed by the processor, cause the processor to execute the steps in the learning path planning method based on big data.
Based on the same technical idea, the present invention also provides a storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps in the big data based learning path planning method as described above.
The beneficial effects of this application: the learner group is established through the characteristic information of the learner, and a course learning path corresponding to the learner group is established according to the learning times of all learners in the learner group to each course in the course library, so that proper learning paths are planned for various learners, the teaching of the learners according to the material is realized, and the learning pertinence and the learning efficiency of the learners are improved.
Drawings
Fig. 1 is a flow chart of a learning path planning method based on big data in an embodiment of the present application.
Fig. 2 is a flowchart illustrating a procedure of recommending course learning paths to a learner in an embodiment of the present application.
FIG. 3 is a flowchart illustrating a process for optimizing a learning path of a target course according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a learning path planning device based on big data in an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a computer device in an embodiment of the present application.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, procedures, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, procedures, steps, operations, elements, components, and/or groups thereof.
Fig. 1 is a flowchart of a learning path planning method based on big data in some embodiments of the present application, as shown in fig. 1, may include the following steps S1-S3:
s1, acquiring characteristic information of a plurality of learners.
In some embodiments, the characteristic information of each learner includes at least one of gender, age, and course test performance.
When a learner registers an account on a login interface of the terminal, information such as the sex and the age of the person is input, and a background server obtains the sex and the age information of the learner transmitted by the terminal. The terminal can be intelligent equipment such as a mobile phone or a computer. In addition, the background server recommends a problem answer interface from the problem of the course to the terminal, and is used for testing the mastering condition of the learner on the course; and, the background server counts the courses that each learner has learned and course test achievements.
It will be appreciated that the more learners that use the learned path planning system, the more feature information the backend server will obtain.
S2, inputting the characteristic information of each learner into a clustering model, and classifying each learner according to the characteristic information of the learner to obtain a plurality of learner groups.
In reality, learners of different sexes or ages learn different courses in different orders. In general, in colleges and universities, the proportion of students in the academic discipline is high, and the proportion of students in the academic discipline is high, so that the learning habits of men and women are different in a high probability. In the examination of a public officer, the grasping ability of an examinee on the literary and physical knowledge is often comprehensively examined; in the course related to learning and the examination of the public officer, the learning sequence of the course is different due to the difference of learning habits.
Similarly, in the process of learning the same course library, learners of different ages have different learning orders of courses because of different interests or knowledge point grasping speeds.
In addition, the learner has different knowledge points for each course, and the learner can arrange the learning sequence of each course to better master the knowledge points of each course. Course test achievements may reflect how well a learner holds knowledge points for each course.
Classifying the learners according to the gender, age and course test performance differences among the learners, classifying the learners with similar learning habits into one category, and further providing learning paths suitable for various learners.
In one embodiment, the clustering model classifies each learner according to the characteristic information of each learner to obtain multiple levels of learner groups.
In one embodiment, step S2 comprises the steps of: and classifying any two of the gender, age and course test results through the clustering model, and establishing a father learner group and a son learner group.
In one embodiment, the clustering model classifies each learner according to the age of each learner and course test performance to establish a parent learner group and a child learner group.
Optionally, step S2 specifically includes the following steps S211 to S213:
s211, dividing the clustering model into a plurality of age groups and a plurality of course test score groups.
S212, classifying each learner by the clustering model according to the age bracket of the learner, and establishing a plurality of father learner groups.
S213, classifying each learner in each father learner group according to the course test score segment of the learner, and establishing a plurality of child learner groups of the father learner group.
In one embodiment, the cluster model classifies each learner according to its gender and age, creating a parent learner group and a child learner group.
Optionally, step S2 specifically includes the following steps S221 to S223:
s221, dividing the clustering model into a plurality of age groups.
S222, classifying each learner by the clustering model according to the gender of the learner, and establishing two father learner groups.
S223, classifying each learner in each father learner group according to the age group of the learner by the clustering model, and establishing a plurality of child learner groups of the father learner group.
In one embodiment, the clustering model classifies each learner according to its gender and course test performance to establish a parent learner group and a child learner group.
Optionally, step S2 specifically includes the following steps S231-S233:
s231, the clustering model divides a plurality of course test score segments.
S232, classifying each learner by the clustering model according to the gender of the learner, and establishing two father learner groups.
S233, classifying each learner in each father learner group according to a course test score segment of the learner, and establishing a plurality of child learner groups of the father learner group.
The learners are divided into multiple levels of learner groups according to the multiple feature information, and the learners are finely divided, so that the similarity of the feature information of the learners in the same learner group is improved.
S3, counting the times that each course in the course library is learned by the first learner group; and sequencing the courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group. And finally obtaining a course learning path corresponding to each learner group.
The first learner group is any one of the plurality of learner groups.
The course learning path takes courses as basic units.
When the learner performs course learning through the course learning interface of the terminal, the learner selects the next course b to be learned after learning the current course a, so that a course selection path from the course a to the course b is formed. The background server obtains the learned courses of each learner in any learner group, and the times that each course in the course library is learned by all learners in the learner group are also obtained. And sequencing each course according to the sequence of the learned times of the courses from large to small, thereby obtaining a course learning path corresponding to the learner group.
As shown in fig. 2, in some embodiments, after step S3, the method further includes steps S411-S413:
s411, matching the learner group of the first learner according to the characteristic information of the first learner. The first learner is any one of the plurality of learners.
S412, obtaining a target course learning path corresponding to the learner group to which the first learner belongs.
S413, recommending the target course learning path to the first learner.
In this embodiment, when a learner enters a course learning interface at a terminal to perform course learning, the background server determines a learner group to which the learner belongs according to characteristic information of the learner, and recommends a course for the learner according to a course learning path corresponding to the learner group to which the learner belongs.
As shown in fig. 3, in some embodiments, after step S3, the method further includes steps S421-S424:
s421, obtaining a plurality of marking values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through the course learning interface of the terminal.
The learner may learn the selected course through the course learning interface of the terminal, and may mark the importance of the learned course at the course learning interface. For example, the course learning interface is provided with a marker field, in which a marker is provided, and the marker may be a five-pointed star image. The learner evaluates the importance of the course by selecting the number of five-pointed star images in the marker bar. The background server calculates the number of the five-pointed star images selected by the learner into corresponding score values or score segments, and the marking values are obtained.
S422, calculating the average value of the plurality of marking values corresponding to each course.
And calculating the sum of the plurality of marking values corresponding to each course, and then carrying out division operation on the sum of the plurality of marking values and the number of the plurality of marking values to obtain the average value.
S423, obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value.
The weight value is used to measure importance of a course relative to the first learner class.
S424, sorting courses in the course library according to the order of the weight values from large to small, and obtaining a course learning optimization path corresponding to the first learner group. And finally obtaining a course learning optimization path corresponding to each learner group.
According to the method and the device, the course learning path of the learner group is optimized according to the feedback information of the importance degree of the learner to the course, so that the course learning optimization path which is more suitable for the learner group is obtained, more reliable course recommendation is provided for the learner, and the learning efficiency of the learner is improved.
In some embodiments, the expression of the weight value is:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i The average value corresponding to the i-th course is obtained; l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
The weight value w i M times of course learned by the first learner group i Said average value n i And shows positive correlation.
According to the embodiment, the learner group is built through the characteristic information of the learners, and the course learning path corresponding to the learner group is built according to the times that all learners in the learner group learn each course in the course library, so that proper learning paths are planned for various learners, the teaching of the learners according to the material is achieved, and the learning pertinence and the learning efficiency of the learners are improved.
Based on the same technical conception, the invention also provides a learning path planning device based on big data, as shown in fig. 4, which comprises a receiving and transmitting module 1 and a processing module 2. The processing module 2 is configured to control the transceiving operation of the transceiving module 1.
The transceiver module 1 is used for acquiring characteristic information of a plurality of learners; the characteristic information of each learner includes at least one of gender, age, and course test result.
The processing module 2 is configured to input feature information of each learner into a clustering model, and classify each learner according to the feature information of the learner, so as to obtain a plurality of learner groups; counting the number of times each course in the course library is learned by the first learner group; the first learner group is any one of the plurality of learner groups; sequencing courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group; and finally obtaining a course learning path corresponding to each learner group.
In some embodiments, the plurality of learners includes a first learner. The processing module 2 is further configured to match the learner group to which the first learner belongs according to the feature information of the first learner; acquiring a target course learning path corresponding to a learner group to which the first learner belongs; recommending the target course learning path to the first learner.
In some embodiments, the processing module 2 is specifically configured to classify each learner according to any two of the gender, age and course test result, and establish a parent learner group and a child learner group.
In some embodiments, the processing module 2 is specifically configured to divide a plurality of age groups and a plurality of course test score groups; classifying each learner according to the age group of the learner, and establishing a plurality of father learner groups; classifying each learner in each father learner group according to a course test score segment to which the course test score of the learner belongs, and establishing a plurality of child learner groups of the father learner group.
In some embodiments, the processing module 2 is specifically configured to divide a plurality of age groups; classifying each learner according to the gender of the learner, and establishing two father learner groups; classifying each learner in each parent learner group according to the age group of the learner, and establishing a plurality of child learner groups of the parent learner group.
In some embodiments, the processing module 2 is further configured to obtain a plurality of flag values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through a course learning interface of the terminal; respectively calculating the average value of the plurality of marking values corresponding to each course; obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value; sequencing courses in the course library according to the sequence of the weight values from large to small to obtain a course learning optimization path corresponding to the first learner group; and finally obtaining a course learning optimization path corresponding to each learner group.
In some embodiments, the expression of the weight value is:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i The average value corresponding to the i-th course is obtained; l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
The weight value w i M times of course learned by the first learner group i Said average value n i And shows positive correlation.
According to the embodiment, the learner group is built through the characteristic information of the learners, and the course learning path corresponding to the learner group is built according to the times that all learners in the learner group learn each course in the course library, so that proper learning paths are planned for various learners, the teaching of the learners according to the material is achieved, and the learning pertinence and the learning efficiency of the learners are improved.
Based on the same technical concept, the present invention further provides a computer device, as shown in fig. 5, where the computer device includes a transceiver 901, a processor 902, and a memory 903, where the memory 903 stores computer readable instructions, where the computer readable instructions are executed by the processor 902, cause the processor to execute the steps of the learning path planning method based on big data in the foregoing embodiments.
The corresponding physical device of the transceiver module 1 shown in fig. 4 is the transceiver 901 shown in fig. 5, and the transceiver 901 can implement part or all of the functions of the transceiver module 1, or implement the same or similar functions as the transceiver module 1.
The corresponding entity device of the processing module 2 shown in fig. 4 is a processor 902 shown in fig. 5, where the processor 902 can implement part or all of the functions of the processing module 2, or implement the same or similar functions as the transceiver module 1.
Based on the same technical concept, the present invention also provides a storage medium storing computer readable instructions, which when executed by one or more processors, cause the one or more processors to perform the steps of the big data based learning path planning method in the above embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server or a network device, etc.) to perform the method described in the embodiments of the present application.
The embodiments of the present application have been described in connection with the accompanying drawings, but the present application is not limited to the specific embodiments described above, which are intended to be exemplary only, and not to be limiting, and many modifications may be made by one of ordinary skill in the art without departing from the spirit and scope of the application and the appended claims, which are to be accorded the full scope of the present application, using the equivalent structures or equivalent flow transformations of the present application and the contents of the accompanying drawings, or using them directly or indirectly in other related technical fields.
Claims (10)
1. The learning path planning method based on big data is characterized by comprising the following steps:
acquiring characteristic information of a plurality of learners; the characteristic information of each learner at least comprises one of gender, age and course test result;
inputting the characteristic information of each learner into a clustering model, and classifying each learner according to the characteristic information of the learner to obtain a plurality of learner groups;
counting the number of times each course in the course library is learned by the first learner group; the first learner group is any one of the plurality of learner groups; sequencing courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group; finally, course learning paths corresponding to each learner group are obtained;
the plurality of learners includes a first learner;
after the course learning path corresponding to each learner group is obtained, the method further includes:
matching the first learner with the belonging learner group according to the characteristic information of the first learner;
acquiring a target course learning path corresponding to a learner group to which the first learner belongs;
recommending the target course learning path to the first learner;
after the course learning path corresponding to each learner group is obtained, the method further includes:
obtaining a plurality of marking values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through a course learning interface of the terminal;
respectively calculating the average value of the plurality of marking values corresponding to each course;
obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value;
sequencing courses in the course library according to the sequence of the weight values from large to small to obtain a course learning optimization path corresponding to the first learner group; finally, course learning optimization paths corresponding to each learner group are obtained;
the expression of the weight value is as follows:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i Corresponds to the ith courseIs a mean value of (2); l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
2. The method for learning path planning based on big data as claimed in claim 1, wherein,
classifying each learner according to the characteristic information of the learner to obtain a plurality of learner groups, including:
classifying each learner according to any two of the gender, the age and the course test result, and establishing a father learner group and a son learner group.
3. The method for big data based path planning of claim 2, wherein,
classifying each learner according to any two of the gender, age and course test results, and establishing a father learner group and a son learner group, including:
dividing a plurality of age groups and a plurality of course test score groups;
classifying each learner according to the age group of the learner, and establishing a plurality of father learner groups;
classifying each learner in each father learner group according to a course test score segment to which the course test score of the learner belongs, and establishing a plurality of child learner groups of the father learner group.
4. The method for big data based path planning of claim 2, wherein,
classifying each learner according to any two of the gender, age and course test results, and establishing a father learner group and a son learner group, including:
dividing a plurality of age groups;
classifying each learner according to the gender of the learner, and establishing two father learner groups;
classifying each learner in each parent learner group according to the age group of the learner, and establishing a plurality of child learner groups of the parent learner group.
5. A learning path planning apparatus based on big data, comprising:
the receiving and transmitting module is used for acquiring characteristic information of a plurality of learners; the characteristic information of each learner at least comprises one of gender, age and course test result;
the processing module is used for inputting the characteristic information of each learner into the clustering model, classifying each learner according to the characteristic information of the learner, and obtaining a plurality of learner groups; counting the number of times each course in the course library is learned by the first learner group; the first learner group is any one of the plurality of learner groups; sequencing courses in the course library according to the sequence of the times that the courses are learned by the first learner group from more to less, so as to obtain a course learning path corresponding to the first learner group; finally, course learning paths corresponding to each learner group are obtained;
the plurality of learners includes a first learner;
the processing module is further configured to:
matching the first learner with the belonging learner group according to the characteristic information of the first learner;
acquiring a target course learning path corresponding to a learner group to which the first learner belongs; recommending the target course learning path to the first learner;
the processing module is further configured to:
obtaining a plurality of marking values corresponding to each course in the course library; the marking value is a score value evaluated by a learner on the importance degree of any course through a course learning interface of the terminal;
respectively calculating the average value of the plurality of marking values corresponding to each course;
obtaining a weight value of each course relative to the first learner group according to the number of times each course is learned by the first learner group and the average value;
sequencing courses in the course library according to the sequence of the weight values from large to small to obtain a course learning optimization path corresponding to the first learner group; finally, course learning optimization paths corresponding to each learner group are obtained;
the expression of the weight value is as follows:
w i ＝a·m i ·exp(b·n i )
wherein w is i The weight value corresponding to the ith course in the course library; m is m i For the number of times the i-th course was learned by said first learner class; n is n i The average value corresponding to the i-th course is obtained; l (L) ij The j mark value corresponding to the i course; j is an integer greater than or equal to 1, representing the number of learners; a. b is a constant greater than 0, respectively; a represents m i Weights at the expression; b represents n i Weights at the expression.
6. The big data based learned route planning apparatus according to claim 5, wherein:
the processing module is specifically configured to classify each learner according to any two of the gender, the age and the course test result, and establish a parent learner group and a child learner group.
7. The big data based learned route planning apparatus according to claim 6, wherein:
the processing module is specifically used for dividing a plurality of age groups and a plurality of course test score segments;
classifying each learner according to the age group of the learner, and establishing a plurality of father learner groups;
classifying each learner in each father learner group according to a course test score segment to which the course test score of the learner belongs, and establishing a plurality of child learner groups of the father learner group.
8. The big data based learned route planning apparatus according to claim 6, wherein:
the processing module is specifically used for dividing a plurality of age groups;
classifying each learner according to the gender of the learner, and establishing two father learner groups;
classifying each learner in each parent learner group according to the age group of the learner, and establishing a plurality of child learner groups of the parent learner group.
9. A computer device comprising a transceiver, a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps in the big data based path planning method of any of claims 1 to 4.
10. A storage medium storing computer readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps in the big data based learning path planning method of any of claims 1 to 4.
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| CN111831919A (en) * | 2020-07-27 | 2020-10-27 | 上海掌学教育科技有限公司 | Course planning method, device, storage medium and system |
| CN111814060B (en) * | 2020-09-02 | 2021-01-15 | 平安国际智慧城市科技股份有限公司 | Medical knowledge learning recommendation method and system |
| CN112784044A (en) * | 2021-01-18 | 2021-05-11 | 辽宁向日葵教育科技有限公司 | Knowledge base recommendation system based on content tags |
| CN112734142B (en) * | 2021-04-02 | 2021-07-02 | 平安科技（深圳）有限公司 | Resource learning path planning method and device based on deep learning |
| CN113743645B (en) * | 2021-07-16 | 2024-02-02 | 广东财经大学 | Online education course recommendation method based on path factor fusion |
| CN113918812A (en) * | 2021-10-11 | 2022-01-11 | 北京量子之歌科技有限公司 | Course learning path determination method, device, equipment and storage medium |
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