CN110990707A - Learning content pushing method, system, equipment and storage medium - Google Patents
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Abstract
The embodiment of the application provides a method, a system, equipment and a storage medium for pushing learning content, wherein the method comprises the following steps: receiving a specified learning range; acquiring the knowledge state of at least one knowledge point of the student in the learning range; determining knowledge points needing to be learned according to the knowledge state, and pushing learning contents matched with the knowledge points; and acquiring learning data of the learning content, judging whether preset conditions are met, and stopping pushing the learning content when the preset conditions are met. According to the embodiment of the application, the learning content suitable for students can be pushed according to the knowledge states of the students, the mastering probability of the students to the knowledge points is updated in real time, and efficient learning is ensured.
Description
Technical Field
The present application relates to the field of electrical technologies, and in particular, to a method, a system, a device, and a storage medium for pushing learning content.
Background
The education mode of the traditional education is difficult to train aiming at weak knowledge points of each student, so that repeated practice or lack of practice of the students is caused, excellent students progress slowly, weak students cannot progress, and the enthusiasm of the students is influenced after a long time; at present, in the prior art, a system and a method for pushing learning contents according to knowledge mastering conditions of students are not provided, but the learning probability of the knowledge of the students is not acquired and updated in real time so as to judge the learning degree of the knowledge of the students in time, so that the next learning plan of the students can be guided in time.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method, a system, a device, and a storage medium for pushing learning content, so as to push the most suitable learning content according to the knowledge state of a student, and update the learning probability of the student on a knowledge point in real time by using the learning process data of the student.
In a first aspect, an embodiment of the present application provides a method for pushing learning content, including receiving a specified learning range; acquiring the knowledge state of at least one knowledge point of the student in the learning range; determining knowledge points needing to be learned according to the knowledge state, and pushing learning contents matched with the knowledge points; and acquiring learning data of the learning content, judging whether preset conditions are met, and stopping pushing the learning content when the preset conditions are met.
In the implementation process, firstly, the appointed learning range is received, the knowledge states of a plurality of knowledge points of the student in the learning range are obtained, the knowledge points which the student needs to learn are determined according to the determined knowledge states, then, the learning content matched with the determined knowledge points is pushed to the student, and the current mastering degree of the student on each knowledge point can be clearly judged according to the knowledge states of the student, so that the learning content suitable for the current learning of the student can be pushed to the student; furthermore, in the learning process of the students, learning data of the learning contents of the students are acquired in real time, whether preset conditions are met or not is judged, whether pushing of the learning contents to the students is stopped or not is judged, and therefore repeated learning of the students can be avoided.
Further, the acquiring the knowledge state of the student at least one knowledge point in the learning range comprises: reading the evaluation data and the learning process data; and analyzing the evaluation data and the learning process data based on a probability graph model to acquire the knowledge state of the knowledge point of the student in the learning range.
In the implementation process, after the learning range is determined, the evaluation data and the learning process data of the student are read, then the evaluation data and the learning process data are analyzed based on the probability map model, the knowledge states of a plurality of knowledge points of the student in the learning range are obtained through analysis, and the mastering degree of the student on the knowledge points learned in the learning process can be effectively obtained through various data recorded in the evaluation data and the learning process data under the analysis of the probability map model.
Further, the acquiring learning data of the learning content and judging whether a preset condition is met includes: extracting learning data of the learning content; judging whether the student grasps all knowledge points in the learning range or not according to the learning data; or judging whether the number of the questions reaches a preset upper limit or not according to the learning data.
In the implementation process, the learning data of the learning content learned by the students are extracted in real time in the learning process of the students, and then whether the students master all knowledge points needing learning in the learning range or not is judged according to the learning data, or whether the number of the subjects made by the students reaches a preset upper limit or not is judged according to the learning data, so that the learning content matched with the mastered knowledge points can be timely stopped being pushed to the students.
Further, the determining whether the student grasps all knowledge points of the learning range according to the learning data includes: estimating individual parameters of the students according to the learning data; updating knowledge point mastering probability of the students according to the individual parameters of the students; and judging whether the knowledge points are mastered or not according to the curve of the knowledge point mastering probability.
In the implementation process, the student individuation parameters are estimated based on the probability map model according to the extracted learning data, then the knowledge point mastering probability of the student is updated in real time according to the student individuation parameters, the learning condition of the student on the learned content can be obtained in real time in the learning process of the student, and the knowledge point mastering probability of the student in real time is obtained; whether the student grasps the knowledge point is further judged through a curve of knowledge point grasping probability, the knowledge point grasping probability curve can reflect dynamic change of the knowledge point grasping probability of the student, and therefore the grasping degree of the student on the knowledge point can be judged according to the dynamic change of the curve.
Further, the determining whether to grasp the knowledge point according to the curve of the knowledge point grasping probability includes: when the knowledge point mastering probability curve is converged and is higher than a first threshold value, judging that the knowledge point is mastered; and when the knowledge point mastering probability curve is lower than a first threshold value, judging that the knowledge point is not mastered.
In the implementation process, when judging whether the student grasps the knowledge point, the knowledge point is realized by judging whether the curve of the knowledge point grasping probability is higher than a first threshold value, when the knowledge point grasping probability curve of the student is converged and higher than the first threshold value, the student is judged to grasp the knowledge point, when the knowledge point grasping probability curve of the student is lower than the first threshold value, the student is judged not to grasp the knowledge point, whether the student still needs to learn the knowledge point can be judged efficiently and timely through the setting of the first threshold value, and repeated practice is avoided.
Further, after the determining that the knowledge point is not mastered when the knowledge point mastering probability curve is lower than a first threshold, the method further includes: when the probability that the student grasps the knowledge point within the preset time is larger than a second threshold value, continuously pushing the learning content matched with the knowledge point; and when the probability that the student grasps the knowledge points within the preset time is lower than a second threshold value, pushing the learning content matched with the precondition knowledge points of the knowledge points.
In the implementation process, after the student is judged not to know the knowledge point, whether the probability that the student knows the knowledge point in the preset time is greater than a second threshold value or not is judged, so that the next learning content of the student is determined, when the probability that the student knows the knowledge point in the preset time is greater than the second threshold value, the learning content matched with the knowledge point is continuously pushed to the student, when the probability that the student knows the knowledge point in the preset time is lower than the second threshold value, the learning content matched with the precondition knowledge point of the knowledge point is pushed to the student, and the second threshold value is set so as to prevent the student from pushing the grasped precondition knowledge point to the student under the condition that the student possibly grasps the knowledge point.
Further, after the terminating the pushing of the learning content, the method further includes: and generating a learning report, wherein the learning report is used for displaying the learning result of the student.
In the implementation process, after determining to terminate the pushing of the learning content, a learning report is generated to display the learning result of the student in the process, and the learning result includes: the study data of the students can be comprehensively and finely presented by the aid of question making results, knowledge point mastering conditions and the like.
In a second aspect, an embodiment of the present application provides a system for pushing learning content, including a receiving unit, configured to receive a specified learning range; the acquisition unit is used for acquiring the knowledge state of at least one knowledge point of the student in the learning range; the pushing unit is used for determining the knowledge points needing to be learned according to the knowledge state and pushing the learning content matched with the knowledge points; and the judging unit is used for acquiring the learning data of the learning content, judging whether a preset condition is met or not, and terminating pushing the learning content when the preset condition is met.
Further, the acquisition unit includes: the evaluation result data storage module is used for storing evaluation result data; the learning process data storage module is used for storing learning process data; and the knowledge state updating module is used for updating the knowledge state of the student, and the knowledge state is updated once the learning content is finished according to the dynamic tracking model.
Further, the pushing unit includes: the knowledge graph data storage module is used for storing and updating the discipline knowledge graph; the learning content library module is used for storing electronic learning content; the knowledge point determining module is used for determining the next knowledge point to be learned; and the learning content pushing module is used for pushing the learning content matched with the knowledge points.
Further, the judging unit includes: the dynamic tracking model parameter estimation module is used for estimating individual parameters of the students by using the EM model based on the learning data of the students; and the knowledge point mastering probability updating module is used for updating the knowledge point mastering probability of the students.
Further, the system further comprises: a generating unit for generating a learning report.
In a third aspect, an apparatus is provided in an embodiment of the present application, and includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for learning content push according to any one of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a storage medium for storing instructions that, when executed on a computer, cause the computer to perform the method for learning content push according to any one of the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which when run on a computer, causes the computer to execute the method for learning content push according to any one of the first aspect.
Additional features and advantages of the disclosure will be set forth in the description which follows, or in part may be learned by the practice of the above-described techniques of the disclosure, or may be learned by practice of the disclosure.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a system structure diagram of a server to which a method for pushing learning content provided by an embodiment of the present application is applied;
fig. 2 is a schematic flowchart of a method for pushing learning content according to an embodiment of the present application;
fig. 3 is a method for determining whether a preset condition is satisfied according to an embodiment of the present disclosure;
fig. 4 is a schematic flowchart of a method for pushing learning content according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a system for pushing learning content according to an embodiment of the present application;
fig. 6 is a block diagram of a learning content pushing apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
The method for pushing the learning content can be applied to the field of pushing the learning content, the learning content suitable for learning is pushed to students by acquiring the knowledge states of the students, the mastering probability of the knowledge points is updated in real time according to the learning data of the students in the learning process of the students, and the mastering degree of the knowledge points of the students is judged, so that whether the students need to learn the knowledge points or whether the knowledge points are suitable for learning can be judged in real time in the learning process of the students, and the learning efficiency and enthusiasm of the students are improved. Illustratively, the method for pushing learning content according to the embodiment of the present application may be applied to a server. Referring to fig. 1, fig. 1 is a system structure diagram of a server to which the method for pushing learning content provided in the present embodiment is applied, where the server 10 may be a computer or a device that provides and manages network resources on a network, and the teacher terminal 20 and the student terminals 30 may be various devices including (but not limited to) a mobile phone, a personal computer, and a tablet. A terminal may be any data device that communicates with a server via a wireless channel and/or via a wired channel.
Illustratively, please refer to fig. 2, where fig. 2 is a method for pushing learning content according to an embodiment of the present application, and the method includes:
in step S210, a designated learning range is received.
Illustratively, when applied to the server 10, the learning range transmitted by the teacher terminal 20 may be received by the server 10, where the learning range transmitted by the teacher terminal 20 may be a learning range selected by the teacher at the teacher terminal.
Step S220, acquiring the knowledge state of at least one knowledge point of the student in the learning range.
Illustratively, the server 10 acquires knowledge states of a plurality of knowledge points within the knowledge point range from the received learning range.
Optionally, in step S220, a method for pushing learning content provided in an embodiment of the present application includes: reading the evaluation data and the learning process data; and analyzing the evaluation data and the learning process data based on a probability graph model to acquire the knowledge state of the knowledge point of the student in the learning range.
Illustratively, the server 10 reads the evaluation data and the learning process data sent by the student terminals, and updates the knowledge state of the students in the knowledge point range according to the dynamic tracking model based on the probability map model.
Optionally, the assessment data comprises: weak knowledge point list, mastered knowledge point list, evaluation score, evaluation time and knowledge point mastered rate; the learning process data includes: question sequence, question making time, question making result, video watching sequence, watching time, lecture reading sequence and reading time.
Illustratively, the server can determine knowledge points required to be learned by the students according to the weak knowledge point list in the evaluation data, and the knowledge points most suitable for the students to learn at present can be obtained through the knowledge point mastering probability; the server can judge the mastering degree of the knowledge points related to the question according to the question making duration and the question making result in the learning process data.
Illustratively, the knowledge state may be divided into: mastered, better, generally worse.
And step S230, determining the knowledge points needing to be learned according to the knowledge state, and pushing the learning content matched with the knowledge points.
Illustratively, the server 10 determines the knowledge point to be learned next by the student according to the knowledge state obtained by the update, and pushes the learning content matched with the knowledge point to the student terminal 30 according to the determined knowledge point.
For example, when determining knowledge points that need to be learned, the server 10 may determine knowledge points that the next student has learning conditions based on the knowledge map and the current knowledge state of the student. For example, the server 10 may determine the knowledge points whose knowledge states are "general" and "poor" as the knowledge points that need to be learned currently. Optionally, the knowledge-graph comprises: the system comprises a knowledge point list, a knowledge point related relation list and a knowledge point hierarchical relation list. And further defining specific knowledge points to be learned according to the knowledge graph.
The correlation between the knowledge points corresponding to 'general' and 'poor' in the knowledge state of the student and the mastered knowledge points can be judged through the knowledge point correlation series list and the knowledge point hierarchical relation list, the knowledge points suitable for being mastered by the student at present are determined, and more systematic and efficient learning is achieved.
In addition, when pushing the learning content matched with the knowledge point, the server 10 may push the learning content suitable for the student according to the learning preference of the student and the associated knowledge point, and optionally, the learning content includes: title, video, text lecture, learning card.
Through the questions in the learning content can be effectively exercised on the knowledge points, and the videos can help students to better understand the learning content.
Step S240, acquiring learning data of the learning content, determining whether a preset condition is met, and terminating pushing the learning content when the preset condition is met.
Illustratively, the server 10 may acquire learning data of learning content of the student transmitted by the student terminal 30 when determining whether the preset condition is satisfied, and the server 10 determines whether to terminate the pushing of the learning content according to the acquired learning data.
Referring to fig. 3, fig. 3 is a method for determining whether a preset condition is satisfied according to an embodiment of the present disclosure, including:
in step S310, learning data of the learning content is extracted.
Step S320, judging whether the student grasps all knowledge points in the learning range according to the learning data.
Optionally, in step S320, the method for pushing learning content provided in the embodiment of the present application includes: step S321, estimating individual parameters of the students according to the learning data; step S322, updating knowledge point mastering probability of students according to the student individuation parameters; and step S323, judging whether to master the knowledge points according to the curve of the knowledge point master probability.
Illustratively, the server 10 may acquire learning data of the student from the student terminal 30 when determining whether the student grasps all knowledge points of the learning range, and then estimate individualization parameters of the student using the EM model based on the probabilistic graph model, and further, the server 10 may update the knowledge point grasping probability of the student according to the estimated student individualization parameters, and then determine whether the student grasps the knowledge point according to a curve of the knowledge point grasping probability.
Optionally, the student individualization parameters include: the learning rate of different learning materials, and the discrimination of different knowledge points and different learning contents.
Illustratively, the server can intuitively derive the learning probability of the knowledge points by the students according to the learning rates of different learning materials in the individual parameters of the students.
Optionally, in step S323, the method for pushing learning content provided in the embodiment of the present application includes: step S323a, when the knowledge point mastering probability curve is converged and is higher than a first threshold value, judging that the knowledge point is mastered; in step S323b, when the knowledge point mastering probability curve is lower than a first threshold, it is determined that the knowledge point is not mastered.
Alternatively, the first threshold may be 90%.
Optionally, after step S323b, the method for pushing learning content provided in the embodiment of the present application further includes: step S323b1, when the probability that the student grasps the knowledge point within the preset time is larger than a second threshold, continuously pushing the learning content matched with the knowledge point; and step S323b2, when the probability that the student grasps the knowledge point within the preset time is lower than a second threshold, pushing the learning content matched with the precondition knowledge point of the knowledge point.
Alternatively, the second threshold may be 80%; alternatively, the server 10 may predict the probability that the student grasps the knowledge point within a preset time through a mixed effect regression model.
And step S330, judging whether the number of the questions reaches a preset upper limit or not according to the learning data.
And step S340, meeting the preset condition and terminating the pushing of the learning content.
Illustratively, the meeting of the preset condition is that the student grasps all knowledge points in the learning range, or the number of subjects reaches a preset upper limit.
Optionally, after step S240, the method for learning content recommendation provided in the embodiment of the present application further includes: and generating a learning report.
Illustratively, after the server 10 terminates the pushing of the learning content, a learning report is generated and sent to the teacher terminal 20 and the student terminals 30 for the teacher and the students to view.
In one embodiment, when the server 10 determines that the preset condition is not satisfied, the learning content push is not terminated. Referring to fig. 4, fig. 4 is a schematic flowchart of a method for pushing learning content according to an embodiment of the present application, and further, in an embodiment, the method for pushing learning content further includes:
and step S250, acquiring learning data of the learning content, judging whether preset conditions are met, and if not, continuously pushing the learning content.
Exemplarily, in step S250, a method for pushing learning content provided by the embodiment of the present application includes: extracting learning data of the learning content; estimating individual parameters of the students according to the learning data; updating knowledge point mastering probability of the students according to the individual parameters of the students; judging that the students grasp the knowledge points according to the curve of the knowledge point grasping probability; further judging that the student does not master all knowledge points in the learning range, and updating the knowledge state of the student; determining a knowledge point required to be learned by the next student according to the updated knowledge state; and pushing the learning content matched with the knowledge points to a student terminal.
Illustratively, after the server 10 judges that the student grasps the knowledge point according to the curve of the knowledge point grasping probability, further, judges that the student does not grasp all the knowledge points in the learning range, and the number of the questions does not reach the preset upper limit, the server 10 may update the knowledge state of the student, determine the next knowledge point to be learned by the student according to the updated knowledge state, and push the learning content matched with the determined knowledge point to the student terminal 30 according to the determined knowledge point.
Illustratively, the number of the knowledge points in the learning range received by the server 10 may be 10, the preset upper limit of the number of the questions may be 30, and after the student learns one of the knowledge points through learning, the student is determined to know that the number of the knowledge points in the learning range mastered by the student is 7, and the number of the made questions is 23, and none of the knowledge points reaches the preset condition, the knowledge state of the student is updated, the next knowledge point to be learned is determined, and the learning content matched with the knowledge point is pushed to the student terminal 30.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a learning content pushing system according to an embodiment of the present application. It should be understood that the system in fig. 5 corresponds to the method embodiments in fig. 2 to 3, and can perform the steps related to the method embodiments, and the specific functions of the system can be referred to the description above, and the detailed description is appropriately omitted here to avoid redundancy. The system includes at least one software functional module that can be stored in memory in the form of software or firmware (firmware) or solidified in the Operating System (OS) of the system. Specifically, the system comprises:
a receiving unit 510 for receiving a specified learning range;
an obtaining unit 520, configured to obtain a knowledge state of at least one knowledge point of the student in the learning range;
a pushing unit 530, configured to determine a knowledge point to be learned according to the knowledge state, and push learning content matched with the knowledge point;
a determining unit 540, configured to obtain learning data of the learning content, determine whether a preset condition is met, and terminate pushing the learning content when the preset condition is met;
a generating unit 550 is used for generating the learning report.
In a possible embodiment, the obtaining unit 520 includes:
the evaluation result data storage module is used for storing evaluation data;
the learning process data storage module is used for storing learning process data;
and the knowledge state updating module is used for updating the knowledge state of the student, and the knowledge state is updated once the learning content is finished according to the dynamic tracking model.
In a possible embodiment, the pushing unit 530 includes:
the knowledge graph data storage module is used for storing and updating the discipline knowledge graph;
the learning content library module is used for storing electronic learning content;
the knowledge point determining module is used for determining the next knowledge point to be learned;
and the learning content pushing module is used for pushing the learning content matched with the knowledge points.
In a possible embodiment, the determining unit 540 includes:
the dynamic tracking model parameter estimation module is used for estimating individual parameters of the students by using the EM model based on the learning data of the students;
and the knowledge point mastering probability updating module is used for updating the knowledge point mastering probability of the students.
Fig. 6 shows a structural block diagram of an apparatus for pushing learning content according to an embodiment of the present application. The device may include a processor 610, a communication interface 620, a memory 630, and at least one communication bus 640. Wherein communication bus 640 is used to enable direct, coupled communication of these components. The communication interface 620 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The processor 610 may be an integrated circuit chip having signal processing capabilities.
The Processor 610 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor 610 may be any conventional processor or the like.
The Memory 630 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Read Only Memory (EPROM), an electrically Erasable Read Only Memory (EEPROM), and the like. The memory 630 stores computer readable instructions that, when executed by the processor 610, cause the device to perform the steps associated with the method embodiments of fig. 2-3 described above.
Optionally, the device may further include a memory controller, an input output unit.
The memory 630, the memory controller, the processor 610, the peripheral interface, and the input/output unit are electrically connected to each other directly or indirectly to implement data transmission or interaction. For example, these components may be electrically coupled to each other via one or more communication buses 640. The processor 610 is adapted to execute executable modules stored in the memory 630, such as software functional modules or computer programs comprised by the device.
The input and output unit is used for providing a task for a user to create and start an optional time period or preset execution time for the task creation so as to realize the interaction between the user and the server. The input/output unit may be, but is not limited to, a mouse, a keyboard, and the like.
It will be appreciated that the configuration shown in figure 6 is merely illustrative and that the apparatus may also include more or fewer components than shown in figure 6 or have a different configuration than shown in figure 6. The components shown in fig. 6 may be implemented in hardware, software, or a combination thereof.
The embodiment of the present application further provides a storage medium, where the storage medium stores instructions, and when the instructions are run on a computer, when the computer program is executed by a processor, the method in the method embodiment is implemented, and in order to avoid repetition, details are not repeated here.
The present application also provides a computer program product which, when run on a computer, causes the computer to perform the method of the method embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 method according to the embodiments of the present application. 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 above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of learning content push, comprising:
receiving a specified learning range;
acquiring the knowledge state of at least one knowledge point of the student in the learning range;
determining knowledge points needing to be learned according to the knowledge state, and pushing learning contents matched with the knowledge points;
and acquiring learning data of the learning content, judging whether preset conditions are met, and stopping pushing the learning content when the preset conditions are met.
2. The method for pushing learning content according to claim 1, wherein the obtaining the knowledge state of at least one knowledge point of the student in the learning scope comprises:
reading the evaluation data and the learning process data;
and analyzing the evaluation data and the learning process data based on a probability graph model to acquire the knowledge state of the knowledge point of the student in the learning range.
3. The method for pushing learning content according to claim 1, wherein the obtaining learning data of the learning content and determining whether a preset condition is met comprises:
extracting learning data of the learning content;
judging whether the student grasps all knowledge points in the learning range or not according to the learning data;
or judging whether the number of the questions reaches a preset upper limit or not according to the learning data.
4. The method for pushing learning content according to claim 3, wherein the determining whether the student grasps all knowledge points of the learning range according to the learning data comprises:
estimating individual parameters of the students according to the learning data;
updating knowledge point mastering probability of the students according to the individual parameters of the students;
and judging whether the knowledge points are mastered or not according to the curve of the knowledge point mastering probability.
5. The method of claim 4, wherein the determining whether to grasp the knowledge point according to the curve of the knowledge point grasping probability comprises:
when the knowledge point mastering probability curve is converged and is higher than a first threshold value, judging that the knowledge point is mastered;
and when the knowledge point mastering probability curve is lower than a first threshold value, judging that the knowledge point is not mastered.
6. The method for pushing learning content according to claim 5, further comprising, after determining that the knowledge point is not mastered when the knowledge point mastering probability curve is lower than a first threshold value, the method further comprising:
when the probability that the student grasps the knowledge point within the preset time is larger than a second threshold value, continuously pushing the learning content matched with the knowledge point;
and when the probability that the student grasps the knowledge points within the preset time is lower than a second threshold value, pushing the learning content matched with the precondition knowledge points of the knowledge points.
7. The method for pushing learning content according to claim 1, further comprising, after terminating pushing learning content, the steps of:
and generating a learning report, wherein the learning report is used for displaying the learning result of the student.
8. A system for learning content push, comprising:
a receiving unit configured to receive a specified learning range;
the acquisition unit is used for acquiring the knowledge state of at least one knowledge point of the student in the learning range;
the pushing unit is used for determining the knowledge points needing to be learned according to the knowledge state and pushing the learning content matched with the knowledge points;
the judging unit is used for acquiring learning data of the learning content, judging whether preset conditions are met or not, and stopping pushing the learning content when the preset conditions are met;
a generating unit for generating a learning report.
9. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of learning content push according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium for storing instructions which, when executed on a computer, cause the computer to perform the method of learning content push according to any one of claims 1 to 7.
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Application publication date: 20200410 |