CN113762801B - Network course management method, device, equipment and storage medium - Google Patents
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Abstract
Provided herein are a method, apparatus, device, and storage medium for managing a network course, including: classifying all network courses according to the course types to obtain various courses; the network courses with highest comprehensive scores in all network courses corresponding to each class of courses are determined to be lead courses of the class of courses, and various lead courses respectively corresponding to various classes are obtained; providing various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses; determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses; matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user; and ordering the network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for the user so that the user can learn the network courses from shallow to deep. Course recommendations can be made herein to more closely match the needs of the user.
Description
Technical Field
The present invention relates to the field of network courses, and in particular, to a method, apparatus, device, and storage medium for managing a network course.
Background
With the increasing maturity and popularization of multimedia technology in internet teaching application, network video courses for the financial institution education gradually enter the field of vision of people, most of the existing financial institution education network video courses are provided through a financial institution education platform, when the financial institution education platform provides courses, all the financial institution courses are generally placed on a platform of the financial institution education platform for users to watch, most of the financial institution education platform can conduct course recommendation of the same type according to the courses browsed by the users, for example, a certain user browses stock class courses, and the platform can recommend a plurality of other related stock class courses to the users to watch.
However, because the user has low knowledge of the education courses of the financial suppliers, the user may have random browsing when browsing the courses, so that the subsequent recommendation courses do not meet the needs of the user, and therefore, a management method for network courses is needed at present, the accuracy of recommendation can be improved when the user recommends learning the education courses of the financial suppliers, and the user needs are met to conduct course recommendation.
Disclosure of Invention
An objective of the embodiments herein is to provide a method, an apparatus, a device, and a storage medium for managing a network course, so as to improve accuracy of course recommendation, and make the course recommendation more fit to requirements of users.
To achieve the above object, in one aspect, an embodiment herein provides a method for managing a network course, including:
classifying all network courses according to the course types to obtain various courses;
determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
and ordering the network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for the user so that the user can learn the network courses from shallow to deep.
Preferably, the learning information of the user on various lead courses includes:
playing the duration of the pilot course;
and/or, collecting information of the lead course;
and/or, message information of the lead course.
Preferably, the determining the behavior label of the user according to the registration information of the user and various lead course learning information includes:
according to the registration academy, registration age and registration preference of the user, risk scoring and capability scoring are carried out on the user;
determining initial risk orientation and initial learning ability of the user according to the risk scoring result and the ability scoring result of the user;
and correcting the initial risk orientation and/or the initial learning capacity according to learning information of various pilot courses corresponding to the user.
Preferably, the scoring the risk and the capability of the user according to the registration academy, the registration age and the registration preference of the user includes:
quantifying the registration academy, the registration age and the registration preference of the user respectively to obtain a registration academy quantified value, a registration age quantified value and a registration preference quantified value of the user;
according to the quantized values of the registration academy, the quantized values of the registration age and the quantized values of the registration preference of the user, and weights corresponding to the registration academy, the registration age and the registration preference in the initial risk orientation, risk scoring is carried out on the user;
And scoring the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively.
Preferably, the scoring the risk of the user according to the quantified values of the registration academy, the quantified values of the registration age and the quantified values of the registration preference of the user, and weights corresponding to the registration academy, the registration age and the registration preference in the initial risk orientation respectively, includes:
determining a risk score result of the user by the following formula:
X＝a 1 ×A+b 1 ×B+c 1 ×C；
wherein X is risk scoring result of the user, a 1 B for registering weights of the academy in the initial risk orientation 1 To register the weight of age in the initial risk orientation, c 1 The method comprises the steps that A is the registration academic quantization value of a user, B is the registration age quantization value of the user, and C is the registration preference quantization value of the user;
the scoring the ability of the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively, including:
Determining a capability scoring result of the user by the following formula:
Y＝a 2 ×A+b 2 ×B+c 2 ×C；
y is the ability scoring result of the user, a 2 B for registering weights of the academy in the initial learning ability 2 C, for registering the weight of the age in the initial learning ability 2 Weights in the initial learning ability are selected for registration.
Preferably, the sorting the network courses corresponding to the required course category according to the relevance includes:
acquiring course content of all network courses in the required course category;
judging whether definition keywords exist in course contents of any network course in the class of courses, wherein the definition keywords are used for defining under calibration words and sentences;
if yes, determining a calibration word and sentence corresponding to the definition class keyword, and judging whether the calibration word and sentence is cited in course contents corresponding to other network courses in the class courses;
if so, then a correlation is set to exist between the network courses with the reference relationship, and the ordering of the referenced network courses is lower than the ordering of the referenced network courses.
Preferably, the method further comprises:
judging whether the current network course is completed in the network courses corresponding to the required course categories;
if so, unlocking the next network course related to the current network course;
If not, locking the next network course related to the current network course.
In another aspect, embodiments herein provide a device for managing network courses, the device comprising:
course classification module: classifying all network courses according to the course types to obtain various courses;
and a lead course determining module: determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
the learning information determining module: providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
the behavior label determining module: determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
the required course category determining module: matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
the course group determining module: and ordering the network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for the user so that the user can learn the network courses from shallow to deep.
In yet another aspect, embodiments herein also provide a computer device including a memory, a processor, and a computer program stored on the memory, which when executed by the processor, performs instructions of any of the methods described above.
In yet another aspect, embodiments herein also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor of a computer device, performs instructions of any of the methods described above.
According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention can determine various lead course learning information of the user through the lead courses corresponding to each type of courses after the user learns and classifies, can determine the behavior label of the user according to the learning information and the registration information of the user, further match the behavior label of the user with the course labels of various courses, and can determine the category of the user required courses after successful matching.
The foregoing and other objects, features and advantages will be apparent from the following more particular description of preferred embodiments, as illustrated in the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments herein or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments herein and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a flow chart illustrating a method of managing network courses provided by embodiments herein;
FIG. 2 illustrates a flow diagram provided by embodiments herein for determining behavior tags of a user;
FIG. 3 illustrates a flow diagram for risk scoring and capability scoring for users provided by embodiments herein;
FIG. 4 illustrates a flow diagram for ordering network courses by relevance provided by embodiments herein;
FIG. 5 is another flow diagram of a method of managing network courses provided by embodiments herein;
FIG. 6 is a schematic block diagram of a network course management device according to an embodiment of the present disclosure;
Fig. 7 shows a schematic structural diagram of a computer device provided in embodiments herein.
Description of the drawings:
100. a course classification module;
200. a lead course determining module;
300. a learning information determination module;
400. a behavior label determining module;
500. a required course category determination module;
600. a course group determining module;
702. a computer device;
704. a processor;
706. a memory;
708. a driving mechanism;
710. an input/output module;
712. an input device;
714. an output device;
716. a presentation device;
718. a graphical user interface;
720. a network interface;
722. a communication link;
724. a communication bus.
Detailed Description
The following description of the embodiments of the present disclosure will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the disclosure. All other embodiments, based on the embodiments herein, which a person of ordinary skill in the art would obtain without undue burden, are within the scope of protection herein.
With the increasing maturity and popularization of multimedia technology in internet teaching application, network video courses for the financial institution education gradually enter the field of vision of people, most of the existing financial institution education network video courses are provided through a financial institution education platform, when the financial institution education platform provides courses, all the financial institution courses are generally placed on a platform of the financial institution education platform for users to watch, most of the financial institution education platform can conduct course recommendation of the same type according to the courses browsed by the users, for example, a certain user browses stock class courses, and the platform can recommend a plurality of other related stock class courses to the users to watch.
However, the user does not know the education courses of the financial merchants to a high degree, so that the user can have random browsing conditions when browsing the courses, and a plurality of follow-up recommended courses do not meet the requirements of the user.
To solve the above-mentioned problems, embodiments herein provide a method for managing network courses. FIG. 1 is a schematic diagram of the steps of a method of managing network courses provided by embodiments herein, the present disclosure provides the method steps of operation as described in the examples or flowcharts, but may include more or less steps of operation based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When a system or apparatus product in practice is executed, it may be executed sequentially or in parallel according to the method shown in the embodiments or the drawings.
Referring to fig. 1, a method for managing network courses includes:
s101: classifying all network courses according to the course types to obtain various courses;
s102: determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
S103: providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
s104: determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
s105: matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
s106: and ordering the network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for the user so that the user can learn the network courses from shallow to deep.
For network courses of different domains, all network courses of each domain can be classified into several classes according to course types. For example, for education-teaching-type network video courses, the courses can be classified into: chinese, mathematics, foreign language, music, sports, art, etc.; for the network video courses of the finance and commerce education, the following courses can be roughly classified according to different user requirements: financial, insurance, securities, letters, futures, funds, stocks, etc. All financial education network courses can be classified according to the course types.
In this embodiment, the determining, as the lead course of the class course, the network course with the highest comprehensive score in all network courses corresponding to each class course includes:
acquiring the evaluation quantity of all network courses in each class of courses, wherein the evaluation quantity comprises a good evaluation quantity and a poor evaluation quantity;
and determining the proportion of the number of the likes of the network courses to the number of the evaluations as the comprehensive score of the network courses.
For each section of network course, after the course is heard, the user is required to evaluate, the evaluation comprises good evaluation and bad evaluation, the comprehensive score of the corresponding network course can be reflected according to the evaluation of the user, the higher the good evaluation ratio is, the more popular the course is, the better the teaching effect and the good user experience of the section Cheng Zeng can be reflected to a certain extent, and therefore the ratio can be used as the comprehensive score of the course.
In this embodiment, the learning information of the user on each kind of lead course includes:
playing the duration of the pilot course;
and/or, collecting information of the lead course;
and/or, message information of the lead course.
When the user watches the lead course, the total playing time is recorded, and if the user does not watch the lead course, the time of the lead course is 0. And recording whether the user collects the lead course and whether the user leaves a message on the lead course, and identifying the left message content of the user by the keyword identification technology, wherein the keyword corresponding to the left message content of the user is determined to be a positive message or a negative message by the keyword identification technology, and the keyword corresponding to the positive message comprises: good, benefited, inspired, etc.; the keywords corresponding to the negative messages comprise: poor, boring, inaudible, etc.
In embodiments herein, the registration information of the user includes a registration history of the user including junior middle and below, senior middle school, specialty, family, master study, doctor study and above, a registration age, and a registration preference; the registration preference may be a user selection preference course type of: financial, insurance, securities, letters, futures, funds, stocks, etc.
Referring to fig. 2, in the embodiment herein, determining a behavior tag of a user according to registration information of the user and various types of lead course learning information includes:
s201: according to the registration academy, registration age and registration preference of the user, risk scoring and capability scoring are carried out on the user;
s202: determining initial risk orientation and initial learning ability of the user according to the risk scoring result and the ability scoring result of the user;
s203: and correcting the initial risk orientation and/or the initial learning capacity according to learning information of various pilot courses corresponding to the user.
The purpose of risk scoring is to assess the financial risk that the user can bear, and the purpose of capability scoring is to assess the learning ability of the user. The risk scoring and the capability scoring are mainly carried out according to the basic information of the user, and because the basic information only represents the rough condition of the user, in order to improve the accuracy of evaluating the user, the learning information of various lead courses is corrected by the user to obtain more accurate evaluation results, and further the behavior label of the user is determined. Therefore, after the situation of the user is accurately known, courses with higher fitting performance can be recommended to the user.
In this embodiment, the behavior label of the user needs to be matched with the course labels corresponding to the various courses respectively. The course labels corresponding to various courses can be determined according to the risk degree and learning difficulty of the corresponding courses, and the courses labels of the two courses can be determined as low risk and easy due to lower risk of financial management and insurance courses and easier learning; because of medium risks of stocks, securities and fund courses, the learning difficulty is moderate, and the course labels of the three courses can be determined as medium risks and medium; because of higher risk of trust and futures courses, the learning difficulty is higher, and the course labels of the two courses can be determined as high risk and difficult.
Referring to fig. 3, in the embodiment herein, the risk scoring and the capability scoring for the user according to the registration history, the registration age and the registration preference of the user includes:
s301: quantifying the registration academy, the registration age and the registration preference of the user respectively to obtain a registration academy quantified value, a registration age quantified value and a registration preference quantified value of the user;
s302: according to the quantized values of the registration academy, the quantized values of the registration age and the quantized values of the registration preference of the user, and weights corresponding to the registration academy, the registration age and the registration preference in the initial risk orientation, risk scoring is carried out on the user;
S303: and scoring the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively.
Wherein, the registration calendar, registration age and registration preference of the user are quantified respectively, and for the registration calendar, the following can be adopted: the values of junior middle school, specialty, family, master study, doctor study and the above six are assigned respectively. Generally, the higher the learning history, the stronger the risk bearing ability, and thus the higher the specific assignment, for example, the higher the registered learning history is, the higher the value of the registered learning history is, and the following values are sequentially given from low to high: 2. 4, 6, 7, 8, 10, and thereby correspondingly determining the user's registration academic quantitative value.
For registration age, the following may apply: the four classes 18-25 years old, 25-35 years old, 35-55 years old and over 55 years old are assigned values respectively. Generally, the greater the age, the weaker the risk bearing ability and the weaker the learning ability, so the specific assignment manner is assigned lower according to the greater the age, for example, the registration age is quantified as 10 at the highest, and the assignment manner is sequentially from the small to the large according to the ages: 10. 8, 6 and 4, and further correspondingly determining the registration age quantization value of the user.
For registering preferences, the preferences may be divided into three categories, the first category may include: financial management and insurance; the second category may include: stock, securities, funds; the third class may include: trust and futures. The three types are respectively assigned. Generally, from class one to class three courses, the more risk bearing capability a user is required to have, the more learning capability is. The specific assignment is increased from low to high from the first class to the third class, for example, the registration preference quantization value is 10 at the highest, and the assignment is sequentially as follows: 1. 3, 5, and further correspondingly determining the registration preference quantized value of the user.
Of course, the specific quantization mode can be set according to different actual requirements.
In this embodiment, the performing risk scoring on the user according to the quantified values of the registration academy, the quantified values of the registration age, and the quantified values of the registration preference of the user, and weights corresponding to the registration academy, the registration age, and the registration preference in the initial risk orientation respectively includes:
determining a risk score result of the user by the following formula:
X＝a 1 ×A+b 1 ×B+c 1 ×C；
wherein X is risk scoring result of the user, a 1 B for registering weights of the academy in the initial risk orientation 1 To register the weight of age in the initial risk orientation, c 1 For the weight of the registration preference in the initial risk orientation, A is the registration academic quantitative value of the user, B is the registration age quantitative value of the user, and C is the registration preference quantitative value of the user.
According to the actual situation, the weights of the registration academy, the registration age and the registration preference in the initial risk orientation can be equal, and the weights of the three can be determined according to different proportions. In order to improve the accuracy of risk scoring for users, the registration history and registration age may be weighted higher than the registration preference in view of the greater impact of history and age on risk orientation. For example, the weights of the registration academy, the registration age, and the registration preference are respectively determined as: 40%, 40% and 20%, and determining a risk score result for the user by the above formula.
The scoring the ability of the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively, including:
determining a capability scoring result of the user by the following formula:
Y＝a 2 ×A+b 2 ×B+c 2 ×C；
y is the ability scoring result of the user, a 2 B for registering weights of the academy in the initial learning ability 2 C, for registering the weight of the age in the initial learning ability 2 Weights in the initial learning ability are selected for registration.
According to the actual situation, the weights of the registration academy, the registration age and the registration preference in the initial learning ability can be equal, and the weights of the three can be determined according to different proportions. In order to improve accuracy of the learning ability score for the user in consideration of the fact that the influence of the history on the learning ability is greater, the weight of the registration history may be made higher than the weight of the registration age and the registration preference. For example, the weights of the registration academy, the registration age, and the registration preference are respectively determined as: 60%, 20%, and the ability scoring results of the users are determined by the above formula.
After calculation, determining a risk score result and a capability score result of the user, wherein if the risk score result of the user is 3 points or less, the corresponding initial risk orientation is 'low risk', if the risk score result of the user is 3-6 points, the corresponding initial risk orientation is 'medium risk', and if the risk score result of the user is above 6 points, the corresponding initial risk orientation is 'high risk'.
If the ability score result of the user is 3 points or less, the corresponding initial learning ability is "poor", if the risk score result of the user is 3-6 points, the corresponding initial learning ability is "general", and if the risk score result of the user is 6 points or more, the corresponding initial learning ability is "strong".
In this embodiment, the correcting the initial risk orientation and/or the initial learning ability according to learning information of the user corresponding to each type of lead course includes:
determining the intention course of the user according to the learning information of the user corresponding to various lead courses;
judging whether the risk scoring result of the user can be corrected or not;
if yes, judging whether the risk correction direction corresponding to the user corresponds to the intention course or not;
if so, correcting the initial risk orientation of the user according to the risk correction direction;
judging whether the capability scoring result of the user can be corrected or not;
if yes, judging whether the capacity correction direction corresponding to the user corresponds to the intention course or not;
and if so, correcting the initial learning ability of the user according to the ability correction direction.
Specifically, according to the learning information of the user corresponding to various lead courses, the playing time length, the collection or the message leaving content of the user for various lead courses is determined. And determining the lead course with the longest playing time length and/or the collected lead course and/or the course with the left message content being a front message as the intention course of the user. If the playing time length of the two types of lead courses is equal, the course in which the collected or left message content is the front left message is determined as the intention course of the user, and of course, the determination of the intention course can be performed in any other practical mode.
Judging whether the risk scoring result of the user is modifiable may be: judging whether a risk scoring result of the user is in a set correction range or not; the determining whether the capability scoring result of the user is modifiable may be: and judging whether the capability scoring result of the user is in a set correction range. The correction range is set to 2.8-3 minutes at 3 minutes or less, the correction range is set to 3-3.2 minutes at 3-6 minutes at 5.8-6 minutes, and the correction range is set to 6-6.2 minutes at 6 minutes or more.
Exemplary: if the risk score result and the ability score result of the user are both 3 points and below (initial risk orientation: low risk, initial learning ability: bad), the specific risk score result is assumed to be 2.9 points, the ability score result is 2.6 points between 2.8 and 3 points, and the ability score result is not between 2.8 and 3 points, the risk score result of the representative user can be corrected, but the ability score result cannot be corrected. Since 2.8-3 points are close to the "middle risk" (3-6 points), the risk correction direction corresponding to the user is the "middle risk". Judging course labels corresponding to the intention courses of the users, if the course labels of the intention courses of the users are "risk in stroke", correcting the initial risk orientation of the users to be "risk in stroke", and obtaining the behavior labels of the users as the initial risk orientation: "middle risk", initial learning ability: "difference".
Further, the class of the courses required by the user can be determined by matching the behavior label of the user with the course label. For network courses, the course labels of financial and insurance courses are 'low risk', 'easy'; course labels of stock, securities and foundation courses are "apoplexy insurance", "moderate"; course labels of trusted and futures courses are "high risk", "difficult".
The behavior tags of the user include risk tags: "Low risk", "Stroke risk", "high risk"; capability label: "bad", "general", "strong", wherein the risk tag and the capability tag are combined to form a behavior tag of the user, generally the user corresponding capability tag of which risk tag is "low risk" is "bad"; user correspondence capability labels with risk labels of "medium risk" are "general"; a user correspondence capability label with a risk label of "high risk" is "strong". When the behavior label of the user is matched with the course label, the corresponding course label is low-risk and easy when the behavior label of the user is low-risk and poor; when the behavior labels of the users are "stroke risk", "general", the corresponding course labels are "stroke risk", "moderate"; when the behavior labels of the users are high risk and strong, the corresponding course labels are high risk and difficult.
If the behavior label of the user is not one of the three combinations, judging whether the user has initial risk orientation or the correction of the initial learning ability; if the correction exists, judging the correction as the correction of the initial risk orientation or the correction of the initial learning ability; if the initial risk orientation is corrected, matching the risk label of the user with the course label without considering the capability label of the user; and if the initial learning ability is corrected, matching the course labels according to the ability labels of the users, and not considering risk labels of the users. If no correction exists, matching is carried out according to the risk label or the capability label of the user, and the label can be determined according to the actual situation according to the specific label, and the other label is not considered when matching. The desired class of the user is ultimately determined.
For each class of network courses, all the network courses have the classification of basic courses, advanced courses and advanced courses, but because a certain course is not embodied in the course names, the basic or advanced courses cannot be seen, if the courses are not ordered according to the basic-advanced order, the user cannot learn the courses without laws, and the actual learning requirement of the user cannot be met. In this regard, after the class of the course required by the user is determined, the courses of the corresponding classes are ordered according to the relevance, so that the user can learn the network courses from shallow to deep, the actual learning requirement of the user is met, and the learning effect of the user is ensured.
Referring to fig. 4, in an embodiment herein, the ranking the network courses corresponding to the desired course category by relevance includes:
s401: acquiring course content of all network courses in the required course category;
s402: judging whether definition keywords exist in course contents of any network course in the class of courses, wherein the definition keywords are used for defining under calibration words and sentences;
s403: if yes, determining a calibration word and sentence corresponding to the definition class keyword, and judging whether the calibration word and sentence is cited in course contents corresponding to other network courses in the class courses;
s404: if the network courses are referenced, setting that the correlation exists between the network courses with the reference relation, and the sequence of the referenced network courses is lower than that of the referenced network courses;
s405: if the definition class correlation does not exist between the network course and other network courses;
s406: if not, it is determined that there is no defined class correlation in the network course of the class course.
In this embodiment, relevance refers to the existence of a front-to-back association between network courses in a class of courses, and defining class relevance is one possibility of relevance, namely that one network course of two network courses refers to a nominal word in the other network course, the referenced network course is in front, and the referenced network course is in back.
When course content of all network courses in the required course category is obtained, corresponding course content can be obtained by obtaining courseware of the network courses; the corresponding course content can also be obtained by obtaining the video and audio records of the network courses.
After courseware or audio-video records are obtained, keyword extraction can be directly carried out on the courseware or audio-video records, or keyword extraction can be carried out on the text information after the courseware or audio-video records are converted into the text information, so that whether definition keywords exist in the text information or not is judged. The definition class keywords may be: the term before/after the definition class keyword is generally defined as the calibration term. After determining the calibrated words and sentences in one network course in the required course category, judging whether the course content corresponding to other network courses in the class of courses drinks the calibrated words and sentences.
For example, mention of a heavy warehouse in course a refers to a high proportion of money from a financial account entering the financial market and a small proportion of the balance of the financial account at the time of a financial transaction. "refer to" as defining class keywords and "heavy bin" as calibration words and sentences. If the word "re-bin" is referenced in another network course B, then A, B courses have a correlation, and a course is ranked lower than B course, requiring learning a course first and then learning B course.
If the nominal word is not cited in the course content corresponding to other network courses in the class of courses, no definition class correlation exists between the network courses and other network courses, and the correlation between the network courses and other network courses can be determined in an auxiliary way through other feasible methods.
If the definition class keywords do not exist in the course content of all network courses in the class course, determining that the definition class correlation does not exist in the network courses of the class course, and determining the correlation among all network courses in the required course category in an assisted manner by other feasible methods.
Referring to fig. 5, embodiments herein further include:
s501: judging whether the current network course is completed in the network courses corresponding to the required course categories;
s502: if so, unlocking the next network course related to the current network course;
s503: if not, locking the next network course related to the current network course.
Specifically, the network courses of the required course category may include: pre-learning lessons, teaching video lessons, real-time operation lessons and operation lessons.
For the pre-learning lessons, there is a pre-learning operation after the pre-learning lessons, the user can upload the pre-learning operation by himself, only the pre-learning operation is uploaded, meaning that the corresponding pre-learning lessons are completed.
For teaching video lessons and real operation video lessons, only the video watching progress is above the setting progress, which means that the corresponding teaching food department or real operation video lessons are completed, and the setting progress can be set to 85% or 90% according to the requirements.
For the actual operation class, the user is required to finish the actual operation task according to the knowledge points in the actual operation video class and upload the completion condition, which can be pictures, characters or videos, only the completion condition of the actual operation task is uploaded, which means that the corresponding actual operation class is completed.
For the job lesson, the relevant questions in the background question library are associated according to the knowledge points in the actual operation video lesson to form a job, and only the job is completed, which means that the job lesson is completed.
In addition, the users can be rated according to the class listening time, the work completion amount, the work accuracy and the scoring of the actual operation class of the users, and a user learning report is formed for the users to refer to, so that the learning progress and the learning condition of the users can be tracked in time.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Based on the above-mentioned method for managing network courses, the embodiment of the present disclosure further provides a device for managing network courses. The described devices may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that employ the methods described in embodiments herein in combination with the necessary devices to implement the hardware. Based on the same innovative concepts, the embodiments herein provide for devices in one or more embodiments as described in the following examples. Since the implementation of the device for solving the problem is similar to the method, the implementation of the device in the embodiment herein may refer to the implementation of the foregoing method, and the repetition is not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Specifically, fig. 6 is a schematic block diagram of an embodiment of a network course management device provided in this embodiment, and referring to fig. 6, the network course management device provided in this embodiment includes: course classification module 100, lead course determination module 200, learning information determination module 300, behavior label determination module 400, required course category determination module 500, course group determination module 600.
Course classification module 100: classifying all network courses according to the course types to obtain various courses;
the lead course determination module 200: determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
learning information determination module 300: providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
behavior tag determination module 400: determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
the required course category determination module 500: matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
course group determination module 600: and ordering the network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for the user so that the user can learn the network courses from shallow to deep.
Referring to fig. 7, a computer device 702 is further provided in an embodiment of the present disclosure based on the above-described method for managing network courses, where the above-described method is executed on the computer device 702. The computer device 702 may include one or more processors 704, such as one or more Central Processing Units (CPUs) or Graphics Processors (GPUs), each of which may implement one or more hardware threads. The computer device 702 may also comprise any memory 706 for storing any kind of information, such as code, settings, data, etc., and in a particular embodiment, a computer program on the memory 706 and executable on the processor 704, which computer program, when executed by the processor 704, may execute instructions according to the methods described above. For example, and without limitation, the memory 706 may include any one or more of the following combinations: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may store information using any technique. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 702. In one case, the computer device 702 can perform any of the operations of the associated instructions when the processor 704 executes the associated instructions stored in any memory or combination of memories. The computer device 702 also includes one or more drive mechanisms 708, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like, for interacting with any memory.
The computer device 702 may also include an input/output module 710 (I/O) for receiving various inputs (via an input device 712) and for providing various outputs (via an output device 714). One particular output mechanism may include a presentation device 716 and an associated graphical user interface 718 (GUI). In other embodiments, input/output module 710 (I/O), input device 712, and output device 714 may not be included as just one computer device in a network. The computer device 702 can also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the above-described components together.
Communication link 722 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. Communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.
Corresponding to the method in fig. 1-5, embodiments herein also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the above method.
Embodiments herein also provide a computer readable instruction wherein the program therein causes the processor to perform the method as shown in fig. 1 to 5 when the processor executes the instruction.
It should be understood that, in the various embodiments herein, the sequence number of each process described above does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments herein.
It should also be understood that in embodiments herein, the term "and/or" is merely one relationship that describes an associated object, meaning that three relationships may exist. For example, a and/or B may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the elements may be selected according to actual needs to achieve the objectives of the embodiments herein.
In addition, each functional unit in the embodiments herein may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions herein are essentially or portions contributing to the prior art, or all or portions of the technical solutions may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments herein. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Specific examples are set forth herein to illustrate the principles and embodiments herein and are merely illustrative of the methods herein and their core ideas; also, as will be apparent to those of ordinary skill in the art in light of the teachings herein, many variations are possible in the specific embodiments and in the scope of use, and nothing in this specification should be construed as a limitation on the invention.
Claims (9)
1. A method for managing network courses, comprising:
classifying all network courses according to the course types to obtain various courses;
determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
Ordering network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for a user to enable the user to learn from shallow network courses;
wherein, according to the registration information of the user and the learning information of various lead courses, determining the behavior label of the user comprises:
according to the registration academy, registration age and registration preference of the user, risk scoring and capability scoring are carried out on the user; the risk score is used for evaluating financial risk bearing capacity of the user;
determining initial risk orientation and initial learning ability of the user according to the risk scoring result and the ability scoring result of the user;
determining the intention course of the user according to the learning information of the user corresponding to various lead courses;
judging whether the risk scoring result of the user can be corrected or not;
if so, when the risk correction direction corresponding to the user corresponds to the intention course, correcting the initial risk orientation of the user according to the risk correction direction;
judging whether the capability scoring result of the user can be corrected or not;
if so, when the capacity correction direction corresponding to the user corresponds to the intention course, correcting the initial learning capacity of the user according to the capacity correction direction;
The risk scoring for the user according to the registration history, registration age and registration preference of the user comprises:
quantifying the registration academy, the registration age and the registration preference of the user respectively to obtain a registration academy quantified value, a registration age quantified value and a registration preference quantified value of the user;
and scoring the risk of the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial risk orientation respectively.
2. The method for managing network courses according to claim 1, wherein learning information of the user on each kind of lead courses includes:
playing the duration of the pilot course;
and/or, collecting information of the lead course;
and/or, message information of the lead course.
3. The method of claim 2, wherein scoring the user's ability based on the user's registration history, registration age, and registration preferences, comprising:
and scoring the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively.
4. The method for managing network courses of claim 3 wherein said scoring risk for the user based on the user's registration history, registration age, and registration preference, and the weights associated with the registration history, registration age, and registration preference, respectively, in the initial risk orientations includes:
determining a risk score result of the user by the following formula:
X＝a 1 ×A+b 1 ×B+c 1 ×C；
wherein X is risk scoring result of the user, a 1 B for registering weights of the academy in the initial risk orientation 1 To register the weight of age in the initial risk orientation, c 1 The method comprises the steps that A is the registration academic quantization value of a user, B is the registration age quantization value of the user, and C is the registration preference quantization value of the user;
the scoring the ability of the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial learning ability respectively, including:
determining a capability scoring result of the user by the following formula:
Y＝a 2 ×A+b 2 ×B+c 2 ×C；
y is the ability scoring result of the user, a 2 B for registering weights of the academy in the initial learning ability 2 C, for registering the weight of the age in the initial learning ability 2 Weights in the initial learning ability are selected for registration.
5. The method for managing network courses according to claim 1, wherein the ordering network courses corresponding to the required course categories according to relevance includes:
acquiring course content of all network courses in the required course category;
judging whether definition keywords exist in course contents of any network course in the class of courses, wherein the definition keywords are used for defining under calibration words and sentences;
if yes, determining a calibration word and sentence corresponding to the definition class keyword, and judging whether the calibration word and sentence is cited in course contents corresponding to other network courses in the class courses;
if so, then a correlation is set to exist between the network courses with the reference relationship, and the ordering of the referenced network courses is lower than the ordering of the referenced network courses.
6. The method for managing network courses of claim 1, further comprising:
judging whether the current network course is completed in the network courses corresponding to the required course categories;
if so, unlocking the next network course related to the current network course;
If not, locking the next network course related to the current network course.
7. A network curriculum management apparatus, said apparatus comprising:
course classification module: classifying all network courses according to the course types to obtain various courses;
and a lead course determining module: determining the network course with the highest comprehensive score in all network courses corresponding to each class of courses as a lead course of the class of courses, and obtaining various lead courses respectively corresponding to the various classes;
the learning information determining module: providing the various lead courses for the user to learn, and acquiring learning information of the user on the various lead courses;
the behavior label determining module: determining a behavior label of the user according to the registration information of the user and the learning information of various lead courses;
the required course category determining module: matching the behavior label of the user with course labels corresponding to various courses respectively, and determining the category of the courses required by the user;
the course group determining module: ordering network courses corresponding to the required course categories according to the relevance, and providing the ordered network courses for a user to enable the user to learn from shallow network courses;
Wherein, according to the registration information of the user and the learning information of various lead courses, determining the behavior label of the user comprises:
according to the registration academy, registration age and registration preference of the user, risk scoring and capability scoring are carried out on the user; the risk score is used for evaluating financial risk bearing capacity of the user;
determining initial risk orientation and initial learning ability of the user according to the risk scoring result and the ability scoring result of the user;
determining the intention course of the user according to the learning information of the user corresponding to various lead courses;
judging whether the risk scoring result of the user can be corrected or not;
if so, when the risk correction direction corresponding to the user corresponds to the intention course, correcting the initial risk orientation of the user according to the risk correction direction;
judging whether the capability scoring result of the user can be corrected or not;
if so, when the capacity correction direction corresponding to the user corresponds to the intention course, correcting the initial learning capacity of the user according to the capacity correction direction;
the risk scoring for the user according to the registration history, registration age and registration preference of the user comprises:
Quantifying the registration academy, the registration age and the registration preference of the user respectively to obtain a registration academy quantified value, a registration age quantified value and a registration preference quantified value of the user;
and scoring the risk of the user according to the quantized value of the registration academy, the quantized value of the registration age and the quantized value of the registration preference of the user and the weights corresponding to the registration academy, the registration age and the registration preference in the initial risk orientation respectively.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory, characterized in that the computer program, when being executed by the processor, performs the instructions of the method according to any of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor of a computer device, executes instructions of the method according to any one of claims 1-6.
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