CN113918812A - Course learning path determination method, device, equipment and storage medium - Google Patents
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Abstract
Provided are a course learning path determination method, a course learning path determination device and a storage medium, wherein the method comprises the following steps: determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; determining basic data of the current user according to the registration information of the current user; determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user; inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; the basic data corresponding to the selected historical users are all between the upper limit value of the basic data and the lower limit value of the basic data; matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining the selected learning paths, and taking the selected learning paths as the learning paths of the current user; wherein the learning path is a learning sequence of the plurality of courses. The text can match the requirements of the user and improve the learning effect of the user.
Description
Technical Field
The present invention relates to the field of learning paths, and in particular, to a method, an apparatus, a device, and a storage medium for determining a course learning path.
Background
With the increase of the public demand for financing, financing education is gradually received by people, and more people choose to learn financing courses on the internet. In the course of financial education, the users need to learn from shallow depth and from deep depth step by step, and the paths of course learning for each user may be different for different users due to the different basic conditions of each user.
However, in the prior art, most of courses are provided to users, and the users determine course learning paths themselves, but since the users themselves do not know all course architectures and the teaching content of each course, the users cannot learn courses according to their actual needs during course learning, which affects learning effect.
Therefore, a method for determining a course learning path is needed, which can determine a learning path of a user in a targeted manner, better match the user needs, and improve the learning effect of the user.
Disclosure of Invention
An object of the embodiments herein is to provide a course learning path determining method, apparatus, device and storage medium, so as to match the needs of a user and improve the learning effect of the user.
To achieve the above object, in one aspect, an embodiment herein provides a course learning path determining method, including:
determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
determining basic data of the current user according to the registration information of the current user;
determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
Preferably, the determining the basic data of the current user according to the registration information of the current user further includes:
obtaining a plurality of basic information of the current user according to the registration information of the current user;
quantizing a plurality of basic information of a current user to obtain a plurality of basic quantized values;
and determining the basic data of the current user according to the plurality of basic quantized values and the weight value corresponding to each basic quantized value.
Preferably, the determining the upper limit value and the lower limit value of the basic data according to the basic data of the current user further includes:
respectively taking basic data of a current user as an initial upper limit value and an initial lower limit value;
circularly inquiring a historical user basic database to determine a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users are all between an initial upper limit value and an initial lower limit value;
judging whether the number of the multiple selected historical users is larger than or equal to the set number;
if not, modifying the initial upper limit value and the initial lower limit value by setting step length to increase the initial upper limit value and reduce the initial lower limit value;
if so, stopping the circular inquiry, determining the initial upper limit value as the upper limit value of the basic data, and determining the initial lower limit value as the lower limit value of the basic data.
Preferably, the modifying the initial upper limit value and the initial lower limit value by setting a step size to increase the initial upper limit value and decrease the initial lower limit value further includes:
determining the sum of the initial upper limit value and the set step length as a modified initial upper limit value;
and determining the difference between the initial lower limit value and the set step length as the modified initial lower limit value.
Preferably, the determining the tag lessons of the current user according to the learning records of the current user on the plurality of lesson pieces further includes:
acquiring any one or more of browsing times, message leaving times, question asking times and collection times of each course segment of the current user, and summing the times;
determining the numerical value obtained by summation as the learning data of the course to which the corresponding course segment belongs;
and selecting the first N data with larger values from the plurality of learning data, and taking the courses corresponding to the first N data as the label courses of the current user.
Preferably, the matching the label courses with the learning paths corresponding to a plurality of selected historical users respectively, and the determining of the selected learning paths further includes:
determining the number of courses matched with the label courses in the multiple courses according to the multiple courses in the learning path corresponding to each selected historical user;
and determining the learning path with the largest number of matched courses as the selected learning path.
Preferably, the method for determining the plurality of lesson pieces comprises:
the courseware corresponding to the course is split, audio splitting is carried out on audio corresponding to the course, and a plurality of courseware fragments and a plurality of audio fragments are obtained;
and combining the courseware fragments and the audio fragments of the same course content to form a plurality of course fragments corresponding to courses.
In another aspect, embodiments herein provide an apparatus for determining a course learning path, the apparatus including:
a tag course determination module: determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
a basic data determination module: determining basic data of the current user according to the registration information of the current user;
an upper and lower limit value determination module: determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
a selected historical user determination module: inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
a learning path determination module: matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
In yet another aspect, embodiments herein also provide a computer device comprising a memory, a processor, and a computer program stored on the memory, the computer program, when executed by the processor, performing the instructions of any one 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 the instructions of any one of the methods described above.
According to the technical scheme provided by the embodiment, the embodiment of the invention can select the selected historical user which is close to the basic data of the current user from the historical users on the basis of the current user registration information through the method for determining the learning path, and further matches and selects the selected learning path corresponding to the current user from the selected historical users by combining the label courses of the current user on the basis of the selected historical user. Therefore, the user needs can be matched more pertinently, and the learning effect of the user is improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart illustrating a course learning path determination method provided in an embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method for determining basic data of a current user according to an embodiment of the present disclosure;
fig. 3 illustrates a schematic flow chart for determining an upper value and a lower value of basic data provided in an embodiment of the present disclosure;
FIG. 4 is a flow diagram illustrating a process for determining a tag lesson for a current user as provided by an embodiment herein;
FIG. 5 is a flow diagram illustrating a process for determining a learned path of a current user provided by embodiments herein;
FIG. 6 is a flow diagram illustrating a determination of a plurality of curriculum segments provided by an embodiment herein;
fig. 7 is a schematic block diagram illustrating a module structure of a course learning path determining apparatus provided in an embodiment of the present disclosure;
fig. 8 shows a schematic structural diagram of a computer device provided in an embodiment herein.
Description of the symbols of the drawings:
100. a tag course determination module;
200. a basic data determination module;
300. an upper and lower limit value determination module;
400. selecting a historical user determination module;
500. a learning path determination module;
802. a computer device;
804. a processor;
806. a memory;
808. a drive mechanism;
810. an input/output module;
812. an input device;
814. an output device;
816. a presentation device;
818. a graphical user interface;
820. a network interface;
822. a communication link;
824. a communication bus.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments herein without making any creative effort, shall fall within the scope of protection.
In the prior art, most of courses are provided for users, and the users determine course learning paths by themselves, but because the users do not know all course architectures and the teaching content of each course, the users cannot learn courses according to their actual needs when learning courses, and learning effect is affected.
In order to solve the above problem, embodiments herein provide a course learning path determination method. Fig. 1 is a schematic diagram of steps of a course learning path determination method provided in an embodiment herein, and the present specification provides the method operation steps as described in the embodiment or the flowchart, but may include more or less operation steps based on conventional or non-creative labor. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual system or apparatus product executes, it can execute sequentially or in parallel according to the method shown in the embodiment or the figures.
Referring to fig. 1, a course learning path determining method includes:
s101: determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
s102: determining basic data of the current user according to the registration information of the current user;
s103: determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
s104: inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
s105: matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
The learning path refers to the sequence of courses when a user learns, and the user can learn the courses from first to last and from shallow to deep according to the sequence by setting the learning path.
For each course, each course has at least one course segment, the course segments comprise courseware segments and audio segments corresponding to the courseware segments, and for each course segment, the corresponding courseware segment corresponds to a complete teaching content. For example, for a stock course for analyzing the latest trend of a large stock, a course segment corresponding to the course segment is to analyze the trend of a certain stock a, and two pages of courseware are to introduce corresponding contents, so that the two pages of courseware and the audio corresponding to the two pages of courseware need to form the course segment when forming the course segment.
In this embodiment, before the current user determines the course learning path, the tag course of the current user needs to be determined, where the tag course needs to be determined according to the learning record of the current user for the plurality of course segments, that is, some of the course segments expressed by the current user through watching and learning the plurality of course segments have learning preference, and the course corresponding to the course segments is the tag course.
The basic data can be obtained from all or part of the registration information, the basic data can be a score obtained according to the registration information and is used for representing the risk bearing capacity and the learning capacity of the current user, and the higher the score is, the stronger the risk bearing capacity and the learning capacity of the current user are represented.
The range of the basic data can be determined by the upper limit value and the lower limit value of the basic data. The basic data range is a range defined based on the basic data, and the data in the basic data range is the same as or slightly different from the basic data.
The historical user basic database stores historical user information which is learned in the past, and the historical user information comprises registration information of historical users, basic data of the historical users obtained from the registration information, and learning paths of the historical users. According to the upper limit value and the lower limit value of the basic data, historical users of which the basic data are in the range of the upper limit value and the lower limit value of the basic data can be inquired in the historical user basic database, and the historical users are selected historical users.
And determining learning paths corresponding to the selected historical users respectively, and matching the label courses of the current user with the learning paths of the selected historical users, wherein the learning paths record the sequence of different courses. If the matching degree is high, namely more label courses exist in the learning path of the selected historical user, the learning path representing the selected historical user is more suitable for the current user to learn, the learning path of the selected historical user can be determined as the selected learning path, and the selected learning path can be used as the learning path of the current user.
Assuming that there are multiple learning paths, any one of the learning paths can be randomly selected as the learning path of the current user.
By the method for determining the learning path, the selected historical user which is similar to the basic data of the current user in the historical users can be selected on the basis of the registration information of the current user, the label course of the current user is combined on the basis of the selected historical user, the selected historical user is further selected in a matching mode, and the selected learning path corresponding to the current user is determined. Therefore, the user needs can be matched more pertinently, and the learning effect of the user is improved.
Referring to fig. 2, further, step S102 further includes:
s201: obtaining a plurality of basic information of the current user according to the registration information of the current user;
s202: quantizing a plurality of basic information of a current user to obtain a plurality of basic quantized values;
s203: and determining the basic data of the current user according to the plurality of basic quantized values and the weight value corresponding to each basic quantized value.
Specifically, the registration information of the user may include a registration name, a registration identification number, a registration history, a registration age, and a registration preference, and the registration history, the registration age, and the registration preference may be determined as basic information of the current user.
The registration preference is used for selecting the course type which is interested by the user, for example, for a financial education course, wherein the course type comprises different course types such as financial, bond, fund, stock and the like, the course type which is interested by the user can be selected by the user as the registration preference when the user registers.
The method comprises the steps of obtaining a registration academic calendar, a registration age and a registration preference of a user, and quantifying the registration academic calendar, the registration age and the registration preference of the user respectively to obtain corresponding basic quantified values respectively. For the registries, the following can be followed: the first and the following, high school, specialty, Benke, Master, doctor and the above six classes are assigned with values respectively. Generally, the higher the academic history is, the higher the risk bearing capacity is, the higher the learning capacity is, so the specific assignment mode is that the assignment is higher according to the higher the academic history is, for example, the maximum value of the registered academic history is 10, and the assignment is sequentially from low to high according to the academic history: 2. 4, 6, 7, 8, 10, and further correspondingly determining the registration academic degree quantization value of the user.
For registering age, the following can be followed: the four categories of 18-25 years old, 25-35 years old, 35-55 years old and over 55 years old are assigned with values respectively. Generally, the risk tolerance of the middle and young years is stronger, and the learning ability is stronger, so the specific assignment mode is that the assignment value is firstly increased and then reduced along with the increase of the age, for example, the maximum value of the registered age quantification is 10, and the assignment values are sequentially from small to large according to the ages: 4. 6, 10 and 3, and correspondingly determining the registered age quantization value of the user.
For registration preferences, the preferences may be divided into three categories, a first category may include: financing and insurance; the second category may include: stocks, securities, funds; the third category may include: trusts, futures. The above three classes are assigned respectively. Generally, the stronger the risk bearing ability of the user is required, the stronger the learning ability is required from the first class course to the third class course. Specifically, the assignment values are increased from the first class to the third class from low to high, for example, the highest quantized value of the registration preference is 10, and the assignment values according to the first class to the third class are: 1. 3, 5, and correspondingly determining the registration preference quantization value of the user.
Of course, the specific quantization mode can be set according to different actual requirements.
In this embodiment, the determining the basic data of the current user according to the quantified value of the registration academic record, the quantified value of the registration age, and the quantified value of the registration preference of the user, and the weights corresponding to the registration academic record, the registration age, and the registration preference respectively includes:
determining the basic data of the current user by the following formula:
X＝a1×A+b1×B+c1×C；
wherein X is the basic data of the current user, a1To register the weight of the calendar, b1As a weight of registered age, c1And the weight value of the registration preference is A, the registration academic record quantized value of the user, B, the registration age quantized value of the user and C, the registration preference quantized value of the user.
According to the actual situation, the weights of the registration academic calendar, the registration age and the registration preference can be equal, and the weights of the three can also be determined according to different proportions. For example, the weights of the registration scholastic, the registration age and the registration preference are respectively determined as: 20%, 30% and 50%, the basic data of the current user is determined by the above formula.
Referring to fig. 3, in this embodiment, step S103 further includes:
s301: respectively taking basic data of a current user as an initial upper limit value and an initial lower limit value;
s302: circularly inquiring a historical user basic database to determine a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users are all between an initial upper limit value and an initial lower limit value;
s303: judging whether the number of the multiple selected historical users is larger than or equal to the set number;
s304: if not, modifying the initial upper limit value and the initial lower limit value by setting step length to increase the initial upper limit value and reduce the initial lower limit value;
s305: if so, stopping the circular inquiry, determining the initial upper limit value as the upper limit value of the basic data, and determining the initial lower limit value as the lower limit value of the basic data.
The basic data of the current user are respectively used as an initial upper limit value and an initial lower limit value, and the determined basic data of the current user is assumed to be 13, and at this time, the initial upper limit value and the initial lower limit value are both 13. Because the basic data corresponding to each historical user is recorded in the historical user basic database, whether the historical user with the basic data of 13 exists in the historical user basic database or not is inquired, and if the historical user with the basic data of 13 exists in the historical user basic database, the historical user is the historical user selected firstly.
The set number can be set according to actual conditions, and in order to increase the diversity of the selected historical users and also consider the calculation speed, the set number can be determined to be 10-15.
If the number of the selected historical users is larger than or equal to the set number, the query can be stopped, the initial upper limit value is determined as the upper limit value of the basic data, and the initial lower limit value is determined as the lower limit value of the basic data.
If the number of the selected historical users is less than the set number, modifying the initial upper limit value and the initial lower limit value, specifically:
determining the sum of the initial upper limit value and the set step length as a modified initial upper limit value;
and determining the difference between the initial lower limit value and the set step length as the modified initial lower limit value.
The setting step size may be set according to actual conditions, but may be determined to be 0.1-0.5 in consideration of the accuracy of the balance calculation and the speed of the calculation.
Assume that the set step size is 0.5, the modified initial upper limit value is 13+0.5 to 13.5, and the modified initial lower limit value is 13-0.5 to 12.5. Further, whether historical users with basic data between 12.5 and 13.5 exist in the historical user basic database or not is inquired, and if yes, the historical users are selected historical users.
Searching by the cyclic query method until the number of the selected historical users is greater than or equal to the set number, and further determining the upper limit value and the lower limit value of the basic data.
The upper limit value and the lower limit value of the basic data may be determined by other methods, and after the upper limit value and the lower limit value of the basic data are determined, the historical users whose basic data are between the upper limit value and the lower limit value of the basic data may be searched in the historical user basic database, where the historical users are selected historical users. It is obvious that if the method for determining the upper limit value and the lower limit value of the basic data described herein is used, the selected historical user is determined while the upper limit value and the lower limit value of the basic data are determined.
In one embodiment herein, the learning record for a plurality of lesson fragments comprises:
the duration of the playing of the curriculum segment;
and/or, collection information of the course segments;
and/or message leaving the lesson snippets.
When the user watches the course segment, the total playing time length is recorded, and if the user does not watch the course segment, the time length of the course segment is 0. Whether the user collects the course fragments or not and whether the user leaves a message on the course fragments or not can be recorded, the message content of the user can be identified, whether the message content of the user leaves a message positively or negatively can be determined through a keyword identification technology, and keywords corresponding to the message positively comprise: good, benefited, inspired, etc.; the keywords corresponding to the negative messages include: bad, boring, not understood, etc.
And determining the course corresponding to the course fragment with the playing time length exceeding the set time length, the collected course fragment or the course fragment with the message left as the front message as the label course of the user. Specifically, the set duration may be determined according to the total duration of the curriculum segments, and the set duration may be determined as the total duration according to the actual situation
Or
And so on.
Referring to fig. 4, in another embodiment herein, determining the tag lessons of the current user according to the learning records of the current user for the plurality of lesson pieces further comprises:
s401: acquiring any one or more of browsing times, message leaving times, question asking times and collection times of each course segment of the current user, and summing the times;
s402: determining the numerical value obtained by summation as the learning data of the course to which the corresponding course segment belongs;
s403: and selecting the first N data with larger values from the plurality of learning data, and taking the courses corresponding to the first N data as the label courses of the current user.
Specifically, in order to ensure the calculation accuracy, when the browsing times are acquired, the browsing time needs to be set, when the browsing time of the current user for the course segment is greater than or equal to the set browsing time, the browsing times are increased by one, and if the current user logs out immediately after touching the course segment by mistake, the browsing time is less than the set browsing time, so that the calculation accuracy can be improved, and the specific value of the set browsing time can be set according to the actual situation.
The number of left messages is the number of left messages on the front side, and the judgment method of the left messages on the front side is similar to the above.
After the sum is obtained to obtain the learning data of the current user for each course, the courses corresponding to the first N data may be selected as the label courses of the current user. The specific value of N may be determined by the number of courses with learning data greater than 0, for example, 16 learning data greater than 0 in all the course learning data corresponding to the current user may be selected, and the number of courses may be taken as the number
Or
As a value of N, in
For example, the lessons corresponding to the first 8 data with larger values of learning data should be taken as the targetsAnd (6) signing courses.
Referring to fig. 5, in this embodiment, step S105 further includes:
s501: determining the number of courses matched with the label courses in the multiple courses according to the multiple courses in the learning path corresponding to each selected historical user;
s502: and determining the learning path with the largest number of matched courses as the selected learning path.
Furthermore, according to the label courses of the current user, querying a historical user basic database, screening out the historical users containing all the label courses of the current user, and determining the sequence of the label courses corresponding to each screened historical user to obtain a plurality of sequences. And determining the probability corresponding to each sort of arrangement sequence, and taking the arrangement sequence with the probability greater than the set probability as the set arrangement sequence, wherein the set probability can be determined as 50% according to the requirement.
For example, if the tag courses are a and C, 10 historical users including a and C are provided, wherein the ranking order of 6 historical users is C, A, and the ranking order of 4 historical users is A, C, then C, A is the set ranking order.
And comparing and matching the current user's tag courses with the multiple courses in the learning path corresponding to each selected historical user, wherein the current user's tag courses are A, C and F, and the multiple courses in the learning path corresponding to the first selected historical user are A, B, C, D, E in sequence. A, C and F are matched with the multiple courses, the matched courses are A and C, and the number of the matched courses is 2. The multiple courses in the learning path corresponding to the second selected historical user are C, B, D, A in sequence, the courses matched with A, C and F and the multiple courses are A and C, and the number of the matched courses is 2. The courses in the learning path corresponding to the third selected historical user are C, F, B, D, A in sequence, the courses matching A, C and F with the courses in the learning path are A, C and F, and the number of the matched courses is 3. The courses in the learning path corresponding to the fourth selected historical user are A, B, C, D, E, F in sequence, the courses matching A, C and F with the courses in the learning path are A, C and F, and the number of the matched courses is 3.
At this time, if there are a plurality of learning paths matching the largest number of courses, the set arrangement order of the tag courses may be further determined, and the learning path matching the set arrangement order may be used as the selected learning path. Assuming that the set arrangement order of A, C and F is A, C, F, the learned path corresponding to the fourth selected historical user is determined as the selected learned path.
Referring to fig. 6, in this embodiment, the method for determining a plurality of curriculum segments includes:
s601: the courseware corresponding to the course is split, audio splitting is carried out on audio corresponding to the course, and a plurality of courseware fragments and a plurality of audio fragments are obtained;
s602: and combining the courseware fragments and the audio fragments of the same course content to form a plurality of course fragments corresponding to courses.
Specifically, the course segments include courseware segments and audio segments corresponding to the courseware segments, and for each course segment, the corresponding courseware segment corresponds to a complete teaching content. The method comprises the following steps of carrying out keyword extraction on courseware corresponding to courses, wherein the process of keyword extraction specifically comprises the following steps: the courseware content is converted into words by OCR (Optical Character Recognition), and then the words are subjected to keyword extraction. And then comparing whether repeated keywords exist between two adjacent pages of courseware, and if so, determining the two pages of courseware as the same courseware segment. The keywords can be determined according to the courseware template, if the courseware template mostly sets titles and further explains the content corresponding to the titles, the keywords can be the titles of each page of courseware, and if the courseware template mostly summarizes the courseware content at the tail of each page, the keywords can be the tail sentences of each page of courseware. Through the method, a plurality of courseware fragments corresponding to the courses can be obtained.
Further, firstly, the audio corresponding to the course is converted into text information, then the text information corresponding to each courseware segment is selected, the audio segment corresponding to the selected text information is intercepted, and the corresponding courseware segment and the corresponding audio segment are combined to form the course segment.
And intercepting and obtaining a plurality of course segments corresponding to each course, and then randomly selecting one course segment from the plurality of course segments as the course segment corresponding to the course, wherein for all courses, each course is correspondingly provided with one course segment. Before the learning path is determined, all the course segments are pushed to the user to be browsed so as to determine the label course of the user, and the accuracy and the adaptability of course path determination are improved.
It should be noted that, the user information (including but not limited to user device 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 method for determining a course learning path, an embodiment herein further provides a device for determining a course learning path. The apparatus may include systems (including distributed systems), software (applications), modules, components, servers, clients, etc. that employ the methods described herein in embodiments, in conjunction with any necessary apparatus to implement the hardware. Based on the same innovative concepts, embodiments herein provide an apparatus as described in the following embodiments. Since the implementation scheme of the apparatus for solving the problem is similar to that of the method, the specific apparatus implementation in the embodiment of the present disclosure may refer to the implementation of the foregoing method, and repeated details are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Specifically, fig. 7 is a schematic block configuration diagram of an embodiment of the device for determining a course learning path provided in the embodiment of the present disclosure, and referring to fig. 7, the device for determining a course learning path provided in the embodiment of the present disclosure includes: the system comprises a label course determining module 100, a basic data determining module 200, an upper and lower limit value determining module 300, a selected history user determining module 400 and a learning path determining module 500.
Tag course determination module 100: determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
the basic data determination module 200: determining basic data of the current user according to the registration information of the current user;
upper and lower limit determination module 300: determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
selected historical user determination module 400: inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
learning path determination module 500: matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
Referring to fig. 8, a computer device 802 is further provided in an embodiment of the present disclosure based on the above-described method for determining a course learning path, wherein the above-described method is executed on the computer device 802. Computer device 802 may include one or more processors 804, 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 802 may also include any memory 806 for storing any kind of information, such as code, settings, data, etc., and in a particular embodiment a computer program on the memory 806 and executable on the processor 804, which computer program when executed by the processor 804 may perform instructions according to the above-described method. For example, and without limitation, memory 806 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent fixed or removable components of computer device 802. In one case, when the processor 804 executes the associated instructions, which are stored in any memory or combination of memories, the computer device 802 can perform any of the operations of the associated instructions. The computer device 802 also includes one or more drive mechanisms 808, such as a hard disk drive mechanism, an optical disk drive mechanism, etc., for interacting with any memory.
Corresponding to the methods in fig. 1-6, the 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-described method.
Embodiments herein also provide computer readable instructions, wherein when executed by a processor, a program thereof causes the processor to perform the method as shown in fig. 1-6.
It should be understood that, in various embodiments herein, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments herein.
It should also be understood that, in the embodiments herein, the term "and/or" is only one kind of association relation describing an associated object, meaning that three kinds of relations may exist. For example, a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly 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 implementation. 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 is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided herein, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed 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 units can be selected according to actual needs to achieve the purposes of the embodiments herein.
In addition, functional units in the embodiments herein may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present invention may be implemented in a form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The principles and embodiments of this document are explained herein using specific examples, which are presented only to aid in understanding the methods and their core concepts; meanwhile, for the general technical personnel in the field, according to the idea of this document, there may be changes in the concrete implementation and the application scope, in summary, this description should not be understood as the limitation of this document.
Claims (10)
1. A method for determining a course learning path, comprising:
determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
determining basic data of the current user according to the registration information of the current user;
determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
2. The method for determining a course learning path as claimed in claim 1, wherein the determining the basic data of the current user according to the registration information of the current user further comprises:
obtaining a plurality of basic information of the current user according to the registration information of the current user;
quantizing a plurality of basic information of a current user to obtain a plurality of basic quantized values;
and determining the basic data of the current user according to the plurality of basic quantized values and the weight value corresponding to each basic quantized value.
3. The method for determining a course learning path as claimed in claim 1, wherein the determining the upper limit value and the lower limit value of the basic data according to the basic data of the current user further comprises:
respectively taking basic data of a current user as an initial upper limit value and an initial lower limit value;
circularly inquiring a historical user basic database to determine a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users are all between an initial upper limit value and an initial lower limit value;
judging whether the number of the multiple selected historical users is larger than or equal to the set number;
if not, modifying the initial upper limit value and the initial lower limit value by setting step length to increase the initial upper limit value and reduce the initial lower limit value;
if so, stopping the circular inquiry, determining the initial upper limit value as the upper limit value of the basic data, and determining the initial lower limit value as the lower limit value of the basic data.
4. The method for determining a course learning path as claimed in claim 3, wherein the modifying the initial upper limit value and the initial lower limit value by setting a step size to increase the initial upper limit value and decrease the initial lower limit value further comprises:
determining the sum of the initial upper limit value and the set step length as a modified initial upper limit value;
and determining the difference between the initial lower limit value and the set step length as the modified initial lower limit value.
5. The method for determining a lesson learning path as claimed in claim 1, wherein said determining the tagged lesson of the current user based on the learning record of the current user for the plurality of lesson segments further comprises:
acquiring any one or more of browsing times, message leaving times, question asking times and collection times of each course segment of the current user, and summing the times;
determining the numerical value obtained by summation as the learning data of the course to which the corresponding course segment belongs;
and selecting the first N data with larger values from the plurality of learning data, and taking the courses corresponding to the first N data as the label courses of the current user.
6. The method for determining a course learning path as claimed in claim 5, wherein the step of matching the labeled course with the learning paths corresponding to a plurality of selected historical users respectively further comprises:
determining the number of courses matched with the label courses in the multiple courses according to the multiple courses in the learning path corresponding to each selected historical user;
and determining the learning path with the largest number of matched courses as the selected learning path.
7. The method for determining lesson learning path as claimed in claim 1, wherein said method for determining said plurality of lesson steps comprises:
the courseware corresponding to the course is split, audio splitting is carried out on audio corresponding to the course, and a plurality of courseware fragments and a plurality of audio fragments are obtained;
and combining the courseware fragments and the audio fragments of the same course content to form a plurality of course fragments corresponding to courses.
8. An apparatus for determining a course learning path, the apparatus comprising:
a tag course determination module: determining the label courses of the current user according to the learning records of the current user on the plurality of course segments; the label course is used for reflecting the learning preference of the current user;
a basic data determination module: determining basic data of the current user according to the registration information of the current user;
an upper and lower limit value determination module: determining a basic data upper limit value and a basic data lower limit value according to basic data of a current user;
a selected historical user determination module: inquiring a historical user basic database according to the basic data upper limit value and the basic data lower limit value, and determining a plurality of selected historical users; wherein the basic data corresponding to the plurality of selected historical users is between a basic data upper limit value and a basic data lower limit value;
a learning path determination module: matching the label courses with learning paths corresponding to a plurality of selected historical users respectively, determining a selected learning path, and taking the selected learning path as the learning path of the current user; wherein the learning path is a learning sequence of the plurality of lessons.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory, wherein the computer program, when executed by the processor, performs the instructions of the method of any one of claims 1-7.
10. 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, is adapted to carry out the instructions of the method according to any one of claims 1-7.
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