CN112837191A - Method, device and equipment for generating intelligent customized course - Google Patents
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Abstract
The invention discloses a method, a device and equipment for generating an intelligent customized course, wherein the method comprises the following steps: before the beginning of a course is detected, acquiring a first course learning state of a student before the current course, and generating outline data of the current course according to the first course learning state; acquiring online courseware data corresponding to the current course according to the outline data; and detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student. The embodiment of the invention can determine the trend of the main line flow and the sub line flow of the class according to the learning condition of the student in the previous system before class and the information of capturing the performance, the test result and the like of the student in real time during class, thereby realizing the customized effect of one person for one class, improving the pertinence of learning courseware and improving the intellectualization of the courseware.
Description
Technical Field
The invention relates to the technical field of online education, in particular to a method, a device and equipment for generating an intelligent customized course.
Background
With the development of science and technology, online education has become an integral part of education.
In the current online education course, the course mode and the content are usually designed in advance by a special course department, and once the design is completed, the content, the mode or the flow cannot be changed in the course stage at the later stage. However, for most students, the learning progress, the receiving speed of the course content and the completion of the post-session work of each student are different, which results in that the course content cannot be taught according to the nature, customized course content cannot be provided for each student, and inconvenience is brought to the learning of the students.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
In view of the above deficiencies of the prior art, an object of the present invention is to provide a method, an apparatus, and a device for generating an intelligent customized course, which are used to solve the technical problems in the prior art that the content of the course is designed in advance, and customized course content cannot be provided for each student, which brings inconvenience to the learning of the student.
The technical scheme of the invention is as follows:
a method of generating an intelligent customized course, the method comprising:
before the beginning of a course is detected, acquiring a first course learning state of a student before the current course, and generating outline data of the current course according to the first course learning state;
acquiring online courseware data corresponding to the current course according to the outline data;
and detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student.
Further, the detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student further comprises:
and acquiring the learning state of the student in the current course, and uploading the learning state to a server.
Further preferably, the acquiring the first course learning state of the student before the current course includes:
acquiring the learning state of a student from a server, and generating a first course learning state before the current course according to the learning state; the first course learning state comprises the work completion condition of the previous course and the classroom test result.
Further preferably, the generating of the outline data of the current course according to the first course learning state includes:
acquiring the mastering condition of the knowledge point in the previous course of the student according to the completion condition of the student work and the classroom test result;
and generating outline data of the current course according to the mastery condition of the knowledge points in the previous course.
Preferably, the acquiring the second course learning state of the student in the current course includes:
and acquiring all knowledge points in the current course, and acquiring the knowledge point mastering conditions of students in real time.
Further, the acquiring knowledge point mastery conditions of the students comprises:
acquiring the number of answers, the answer accuracy and the homework completion condition of students in the current course;
and generating knowledge point mastering conditions according to the number of answers, the answer accuracy and the operation completion conditions in the courses.
Further, the adjusting the courseware details in the online courseware data according to the second course learning state includes:
judging whether knowledge point mastering conditions of students meet preset conditions or not;
if the preset conditions are met, continuously importing courseware corresponding to the next knowledge point;
if the preset conditions are not met, the review courseware of the current knowledge point is imported until the knowledge point mastering conditions of the students are detected to meet the preset conditions.
Another embodiment of the present invention provides an apparatus for generating an intelligent customized course, including:
the outline data generation module is used for acquiring a first course learning state of a student before the current course before the beginning of the course is detected, and generating outline data of the current course according to the first course learning state;
the courseware data acquisition module is used for acquiring online courseware data corresponding to the current course according to the outline data;
and the courseware detail adjusting module is used for detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting the courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student.
Another embodiment of the present invention provides an intelligent customized lesson generation device, comprising at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for intelligent customized lesson generation described above.
Yet another embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer-executable instructions, which when executed by one or more processors, cause the one or more processors to perform the above-mentioned method for generating an intelligent customized course.
Has the advantages that: the embodiment of the invention can determine the trend of the main line flow and the sub line flow of the class according to the learning condition of the student in the previous system before class and the information of capturing the performance, the test result and the like of the student in real time during class, thereby realizing the customized effect of one person for one class, improving the pertinence of learning courseware and improving the intellectualization of the courseware.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flowchart illustrating a method for generating an intelligent customized course according to a preferred embodiment of the present invention;
FIG. 2 is a flow chart of the method for determining the outline data of the current lesson in the preferred embodiment of the method for generating the intelligent customized lesson of the present invention;
FIG. 3 is a flowchart of a method for adjusting the details of the courseware of the current lesson according to the preferred embodiment of the method for generating the intelligent customized lesson of the present invention;
FIG. 4 is a functional block diagram of an apparatus for generating intelligent customized lessons according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a hardware structure of an intelligent course generation device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is described in further detail below. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Embodiments of the present invention will be described below with reference to the accompanying drawings.
The embodiment of the invention provides a method for generating an intelligent customized course. Referring to fig. 1, fig. 1 is a flowchart illustrating a method for generating an intelligent customized course according to a preferred embodiment of the present invention. As shown in fig. 1, it includes the steps of:
step S100, before the beginning of a course is detected, a first course learning state of a student before the current course is obtained, and outline data of the current course is generated according to the first course learning state;
s200, acquiring online courseware data corresponding to the current course according to the outline data;
and step S300, detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, and adjusting courseware details in the online courseware data according to the second course learning state to generate a customized course corresponding to the current student.
In specific implementation, the customized course generation benchmark of the embodiment of the invention is based on the pre-class student attending conditions and the in-class student real-time performance. The class front part is mainly divided into a class front part and a class middle part, the class front part depends on the performance of students in the class front and the completion conditions of in-class and after-class homework in the class front taken from a user information system, the data determines the proportion of review contents in all knowledge points in the current class of the students, the better the course front is completed, the lower the proportion of review knowledge points is, the main flow of the class also determines whether the consolidated knowledge points are taken as the main part or the new learning knowledge points are taken as the main part, so that corresponding class courseware is generated according to the condition of each student, and the intelligence of the courseware is improved.
The main line process is to decide whether a review link needs to be arranged for the current course according to the knowledge point mastering condition before the current course, namely the main line course is outline data (review + learning new knowledge points) or (only learning new knowledge points); the branch line flow is a sub-link under each main line flow, i.e. details of courseware, for example, in a new knowledge point learning link, whether students grasp the knowledge point (each course may have a plurality of new knowledge points, and the judgment mode is mainly answering), if the students grasp the knowledge point, the branch line link enters the next knowledge point, and if the students do not grasp the knowledge point, the branch line link is still remained in the current knowledge point reviewing link.
The embodiment of the invention can determine the trend of the main line flow and the sub line flow of the class according to the condition that the student completes the work in the previous system before class and the information such as the performance and the test result of the student captured in real time during class, thereby realizing the customized effect of one person for one class.
Further, it is detected that the student enters the online courseware data, acquires the second course learning state of the student in the current course, adjusts the courseware details in the online courseware data according to the second course learning state, generates the customized course corresponding to the current student, and further comprises:
and acquiring the learning state of the student in the current course, and uploading the learning state to a server.
When the specific implementation is carried out, when the student logs in the course, all learning states of the current course corresponding to the student account are obtained, and the learning states of the student are stored and uploaded to the server. The server for storing the learning state of the student in the embodiment of the invention is recorded as a user information system.
Further, acquiring a first course learning state of the student before the current course, including:
acquiring the learning state of a student from a server, and generating a first course learning state before the current course according to the learning state; the first course learning state comprises the work completion condition of the previous course and the classroom test result.
In specific implementation, the first course learning state mainly refers to a learning state of a previous course of a current course, the first course learning state includes an assignment completion condition of the previous course of a student, and the assignment includes assignments in and after the course. The work in class also includes classroom test results. As shown in fig. 2, the completion of the previous course and the previous work of the student is acquired from the user information system.
Further, generating outline data of the current course according to the first course learning state comprises:
acquiring the mastering condition of the knowledge point in the previous course of the student according to the completion condition of the student work and the classroom test result;
and generating outline data of the current course according to the mastery condition of the knowledge points in the previous course.
When the method is specifically implemented, the mastering condition of the knowledge point in the previous course of the student is obtained according to the homework completion condition and the classroom test result of the student, the proportion of review contents needing to be added in the current course is determined according to the mastering condition of the knowledge point in the previous course, and customized learning is achieved.
For example, a student learns a binary addition and subtraction method of mathematics in a student course before a current course, the main content of the current course is a multi-element addition and subtraction method, knowledge points of the current course are established on the basis of the previous course, the performance, completion conditions of post-lesson homework and the like of the student in the course before the current course can be acquired from a teaching system before the current course, the mastering condition of the student on the knowledge points of the current course is comprehensively evaluated, if the learning is better, the current course can directly enter the learning of the multi-element addition and subtraction content of new knowledge points, and if the learning is general, the current course still needs to contain review the content of the binary addition and subtraction method of the knowledge points before the current course.
Further, acquiring a second course learning state of the student in the current course, including:
and acquiring all knowledge points in the current course, and acquiring the knowledge point mastering conditions of students in real time.
In specific implementation, the second course learning state refers to a learning state of the student in the current course. The second learning state includes but is not limited to the number of answers, the answer accuracy rate and the job completion condition. And acquiring all knowledge points in the current course and acquiring the mastery conditions of the knowledge points of the students.
Further, acquiring knowledge point grasping conditions of students comprises the following steps:
acquiring the number of answers, the answer accuracy and the homework completion condition of students in the current course;
and generating knowledge point mastering conditions according to the number of answers, the answer accuracy and the operation completion conditions in the courses.
In specific implementation, data for mastering the knowledge points of the courses can be reflected through the number of answers of students in the current classroom, the accuracy rate of answers in the courses, the completion condition of post-lesson homework and the like, and certainly, the data is not limited to the above, and all the data capable of reflecting the understanding condition of students on the knowledge points of the current courses can be used as conditions.
In the middle stage, depending on the activity degree (interaction times with courseware contents and the like) of a user in a course, the accuracy rate of answer tests in the course and the like, the data determine the mastering conditions of knowledge points in the current course of a student, if the system judges that the student does not master the current teaching knowledge points well, the content in the next section of the course still consolidates the current knowledge points, and if the system judges that the student master the current knowledge points well, the content in the next section of the course mainly learns new knowledge points.
Further, adjusting the courseware details in the online courseware data according to the second course learning state includes:
judging whether knowledge point mastering conditions of students meet preset conditions or not;
if the preset conditions are met, continuously importing courseware corresponding to the next knowledge point;
if the preset conditions are not met, the review courseware of the current knowledge point is imported until the knowledge point mastering conditions of the students are detected to meet the preset conditions.
In specific implementation, as shown in fig. 3, the courseware carries out teaching of the current knowledge point, obtains the number of times of interaction between the student and the course, tests the completion condition, judges whether to be consolidated again, if so, continues to stop at the teaching of the current knowledge point, and if not, carries out teaching of the next knowledge point.
For example, the full score is 100 points, the activity in the class, the answering accuracy in the class and the post-class work completion respectively account for 20 points, 40 points and 40 points, the activity in the class can give out scores according to the proportion of the number of times of answering questions by teachers to the total number of times of questioning, the answering accuracy in the class can give out scores according to the actual accuracy, the post-class work completion can give out scores according to the number of times of batch changes by teachers, and finally the total score mastered by knowledge points is obtained, and the concrete reference needs to be given by teachers according to experience values, for example, 60 points pass, 80 points are good, and 90 points are excellent.
According to the method, the main line flow and the branch line flow of the intelligent customized course are automatically set according to the learning condition of each student, so that the effect of one person for one course is achieved, and the effectiveness of learning is improved for each student as much as possible.
It should be noted that, a certain order does not necessarily exist between the above steps, and those skilled in the art can understand, according to the description of the embodiments of the present invention, that in different embodiments, the above steps may have different execution orders, that is, may be executed in parallel, may also be executed interchangeably, and the like.
Another embodiment of the present invention provides an apparatus for generating an intelligent customized course, as shown in fig. 4, the apparatus 1 includes:
the outline data generation module 11 is used for acquiring a first course learning state 12 of the student before the current course before the beginning of the course is detected, and generating outline data of the current course according to the first course learning state;
the courseware data acquisition module is used for acquiring online courseware data corresponding to the current course according to the outline data;
and the courseware detail adjusting module 13 is used for detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting the courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student.
The specific implementation is shown in the method embodiment, and is not described herein again.
Another embodiment of the present invention provides an apparatus for generating an intelligent customized course, as shown in fig. 5, the apparatus 10 includes:
one or more processors 110 and a memory 120, where one processor 110 is illustrated in fig. 5, the processor 110 and the memory 120 may be connected by a bus or other means, and where fig. 5 illustrates a connection by a bus.
The memory 120 is a non-volatile computer-readable storage medium, and can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions corresponding to the method for generating the intelligent customized course in the embodiment of the present invention. The processor 110 executes various functional applications and data processing of the device 10, namely, implements the method for generating the intelligent customized lesson in the above-described method embodiment, by executing the nonvolatile software program, instructions and units stored in the memory 120.
The memory 120 may include a storage program area and a storage data area, wherein the storage program area may store an application program required for operating the device, at least one function; the storage data area may store data created according to the use of the device 10, and the like. Further, the memory 120 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 120 optionally includes memory located remotely from processor 110, which may be connected to device 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more units are stored in the memory 120, and when executed by the one or more processors 110, perform the method for generating intelligent customized lessons in any of the above-described method embodiments, for example, performing the above-described method steps S100 to S300 in fig. 1.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer-executable instructions for execution by one or more processors, for example, to perform method steps S100-S300 of fig. 1 described above.
By way of example, non-volatile storage media can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as Synchronous RAM (SRAM), dynamic RAM, (DRAM), Synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The disclosed memory components or memory of the operating environment described herein are intended to comprise one or more of these and/or any other suitable types of memory.
Another embodiment of the present invention provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method for intelligent customized lesson generation of the above-described method embodiment. For example, the method steps S100 to S300 in fig. 1 described above are performed.
The above-described embodiments are merely illustrative, and 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 modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions essentially or contributing to the related art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Conditional language such as "can," "might," or "may" is generally intended to convey that a particular embodiment can include (yet other embodiments do not include) particular features, elements, and/or operations, among others, unless specifically stated otherwise or otherwise understood within the context as used. Thus, such conditional language is also generally intended to imply that features, elements, and/or operations are in any way required for one or more embodiments or that one or more embodiments must include logic for deciding, with or without input or prompting, whether such features, elements, and/or operations are included or are to be performed in any particular embodiment.
What has been described herein in the specification and drawings includes examples of generation methods and apparatus capable of providing intelligent customized lessons. It will, of course, not be possible to describe every conceivable combination of components and/or methodologies for purposes of describing the various features of the disclosure, but it can be appreciated that many further combinations and permutations of the disclosed features are possible. It is therefore evident that various modifications can be made to the disclosure without departing from the scope or spirit thereof. In addition, or in the alternative, other embodiments of the disclosure may be apparent from consideration of the specification and drawings and from practice of the disclosure as presented herein. It is intended that the examples set forth in this specification and the drawings be considered in all respects as illustrative and not restrictive. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation.
Claims (10)
1. A method for generating an intelligent customized course, the method comprising:
before the beginning of a course is detected, acquiring a first course learning state of a student before the current course, and generating outline data of the current course according to the first course learning state;
acquiring online courseware data corresponding to the current course according to the outline data;
and detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student.
2. The method for generating intelligent customized lesson according to claim 1, wherein the step of detecting that the student enters the online courseware data, acquiring a second lesson learning status of the student in the current lesson, adjusting the courseware details in the online courseware data according to the second lesson learning status, and generating the customized lesson corresponding to the current student further comprises:
and acquiring the learning state of the student in the current course, and uploading the learning state to a server.
3. The method for generating intelligent customized lesson according to claim 2, wherein the step of obtaining the first lesson learning status of the student before the current lesson comprises:
acquiring the learning state of a student from a server, and generating a first course learning state before the current course according to the learning state; the first course learning state comprises the work completion condition of the previous course and the classroom test result.
4. The method for generating intelligent customized course according to claim 3, wherein the generating outline data of the current course according to the first course learning state comprises:
acquiring the mastering condition of the knowledge point in the previous course of the student according to the completion condition of the student work and the classroom test result;
and generating outline data of the current course according to the mastery condition of the knowledge points in the previous course.
5. The method for generating intelligent customized lesson according to claim 1, wherein the step of obtaining the second lesson learning status of the student in the current lesson comprises:
and acquiring all knowledge points in the current course, and acquiring the knowledge point mastering conditions of students in real time.
6. The method for generating intelligent customized lesson according to claim 5, wherein the step of obtaining knowledge points mastery of students comprises:
acquiring the number of answers, the answer accuracy and the homework completion condition of students in the current course;
and generating knowledge point mastering conditions according to the number of answers, the answer accuracy and the operation completion conditions in the courses.
7. The method for generating intelligent customized lesson, according to claim 6, wherein the adjusting the courseware details in the online courseware data according to the second lesson learning status comprises:
judging whether knowledge point mastering conditions of students meet preset conditions or not;
if the preset conditions are met, continuously importing courseware corresponding to the next knowledge point;
if the preset conditions are not met, the review courseware of the current knowledge point is imported until the knowledge point mastering conditions of the students are detected to meet the preset conditions.
8. An apparatus for generating an intelligent customized course, the apparatus comprising:
the outline data generation module is used for acquiring a first course learning state of a student before the current course before the beginning of the course is detected, and generating outline data of the current course according to the first course learning state;
the courseware data acquisition module is used for acquiring online courseware data corresponding to the current course according to the outline data;
and the courseware detail adjusting module is used for detecting that the student enters the online courseware data, acquiring a second course learning state of the student in the current course, adjusting the courseware details in the online courseware data according to the second course learning state, and generating a customized course corresponding to the current student.
9. An intelligent customized lesson generation device, characterized in that said device comprises at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for intelligent customized lesson generation of any of claims 1-7.
10. A non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform the method for intelligent customized lesson generation as recited in any one of claims 1-7.
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