CN111831914A - Intelligent question pushing system for online education - Google Patents
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Abstract
The invention provides an intelligent topic pushing system for online education, which comprises a knowledge point mastery degree unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving a topic pushing request initiated by a user, acquiring at least one knowledge point requested by the user and acquiring the mastery degree of the knowledge point; a question-pushing unit: and the method is used for acquiring corresponding questions according to the mastery degrees of all the knowledge points requested by the user and pushing the questions to the user. The system can more accurately position the proficiency of the user on each knowledge point, comprehensively cover the mastering condition of the user on all knowledge points, recommend questions suitable for the user for each user, customize effective personalized learning paths for the user and accurately recommend the suitable personalized questions.
Description
Technical Field
The invention belongs to the technical field of online education, and particularly relates to an intelligent question-pushing system for online education.
Background
At present, an artificial intelligence technology is introduced into the education field, so that an online teaching system becomes an intelligent teaching system. Question brushing is one of effective methods for consolidating knowledge points, and is no exception in the field of online education. However, the learning, accepting and intelligence levels of each user are different, the knowledge points are mastered by different users to different degrees, and if the user brushes the questions according to the unified knowledge point mastered degree, the actual conditions of the user cannot be well fitted.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the intelligent topic pushing system for online education, which can more accurately position the proficiency of each knowledge point of the user and recommend the topic suitable for the user for each user.
An intelligent topic-pushing system for online education, comprising:
knowledge point mastery degree unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving a topic pushing request initiated by a user, acquiring at least one knowledge point requested by the user and acquiring the mastery degree of the knowledge point;
a question-pushing unit: and the method is used for acquiring corresponding questions according to the mastery degrees of all the knowledge points requested by the user and pushing the questions to the user.
Preferably, the knowledge point mastery unit is specifically configured to:
constructing a user image of the user according to the basic information of the user;
modeling the mastery degree of the knowledge points of the user by using a BKT algorithm to obtain a mastery degree model;
acquiring the mastery degree of different knowledge points of each user by using the mastery degree model, and storing the mastery degree of different knowledge points into a user portrait of the user;
receiving a topic pushing request initiated by a user, and acquiring at least one knowledge point requested by the user;
the degree of mastery of the knowledge point is obtained from the user image of the user.
Preferably, the knowledge point mastery unit is further configured to:
constructing a knowledge point relation graph according to the parent-child relation and the front-back position relation of the knowledge points, and importing the knowledge point relation graph into a graph database to form a knowledge point map;
when the mastery degree of the knowledge point requested by a user is recognized, reading the front-back position relation of the knowledge point from the knowledge point map;
sorting the knowledge points according to the front-back position relation of the knowledge points to obtain a knowledge point sorting table;
and filtering the mastery knowledge points in the knowledge point ranking table.
Preferably, the basic information of the user includes a grade, a subject, a region, a subject ability value, and a mastery degree of a knowledge point.
Preferably, the knowledge point mastery unit is specifically configured to:
acquiring the historical answer condition of a user;
training parameters corresponding to the mastery degree model according to the historical answer condition of the user;
and when the mastery degrees of different knowledge points of the user are calculated, bringing the parameters corresponding to the user into the mastery degree model to obtain the mastery degrees of the different knowledge points of the user.
Preferably, the knowledge point mastery unit is further configured to:
and after acquiring the mastery degree of the knowledge points, when an updated answer request is received, updating the mastery degree of different knowledge points of the user by using the mastery degree model, and storing the updated mastery degree of the knowledge points into the user portrait of the user.
Preferably, the question deducting unit is specifically configured to:
constructing a question portrait according to question information of a question and associated information of the question and a knowledge point;
obtaining a question setting rule under a training scene according to the training scene requested by a user, and calculating the number and type of questions corresponding to the training scene according to the question setting rule;
calculating the question difficulty of each knowledge point according to the mastery degree of the knowledge point of the user and by combining the mastery degree distribution of all users in the knowledge point calculated off line;
and acquiring corresponding questions from the question pictures according to the training scenes, the knowledge points, the question difficulty and the question number, and pushing the questions to the user.
Preferably, the question deducting unit is specifically configured to:
and sequencing the obtained topics according to the knowledge point sequencing table, and pushing the sequenced topics to a user.
Preferably, the knowledge point mastery unit is further configured to:
collecting an abest related log;
and analyzing the abest related logs, and optimizing the mastery model according to an analysis result.
According to the technical scheme, the intelligent topic pushing system for online education provided by the invention can more accurately position the proficiency of the user on each knowledge point, comprehensively cover the mastering conditions of the user on all knowledge points, recommend the topics suitable for the user for each user, customize an effective personalized learning path for the user and realize the function of accurately recommending the appropriate personalized topics.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a block diagram of an intelligent topic-pushing system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a topic pushing concept of the intelligent topic pushing system according to an embodiment of the present invention.
Fig. 3 is a method for arranging topics in an intelligent topic pushing system according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby. It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
The first embodiment is as follows:
an intelligent topic-pushing system for online education, see fig. 1, comprising:
knowledge point mastery degree unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving a topic pushing request initiated by a user, acquiring at least one knowledge point requested by the user and acquiring the mastery degree of the knowledge point;
specifically, the topic request includes the requesting user, knowledge points, training scenarios, and the like. The knowledge points are confirmed according to the basic information of the users, and the knowledge points corresponding to the users of different ages in different classes are different. The mastery degree includes mastery, mastery percentage, and the like. The topic pushing request can be sent by a client or obtained by operating a key arranged in the system. The knowledge point mastery degree unit obtains the mastery degrees of all the knowledge points of different users.
A question-pushing unit: and the method is used for acquiring corresponding questions according to the mastery degrees of all the knowledge points requested by the user and pushing the questions to the user.
Specifically, the topic pushing unit pushes the topic to the user according to the mastery degrees of different knowledge points of the user. For example, when the mastery degree of a certain knowledge point of a user is poor, more questions of the knowledge point are pushed to the user; if the mastery degree of a certain knowledge point of the user is better, few or no topics of the knowledge point are pushed to the user, so that the system can push the topics to the user according to the mastery degrees of different knowledge points of the user.
The system can more accurately position the proficiency of the user on each knowledge point, comprehensively cover the mastering condition of the user on all knowledge points, recommend questions suitable for the user for each user, customize effective personalized learning paths for the user and accurately recommend the suitable personalized questions.
Referring to fig. 2, the knowledge point mastery unit is specifically configured to:
constructing a user image of the user according to the basic information of the user;
modeling the mastery degree of the user knowledge points by using a BKT algorithm (a Bayesian network-based user knowledge point tracking model) to obtain a mastery degree model;
acquiring the mastery degree of different knowledge points of each user by using the mastery degree model, and storing the mastery degree of different knowledge points into a user portrait of the user;
receiving a topic pushing request initiated by a user, and acquiring at least one knowledge point requested by the user;
the degree of mastery of the knowledge point is obtained from the user image of the user.
Specifically, the knowledge point mastery unit tracks the mastery condition of the knowledge point by the user by adopting BKT (a user knowledge point tracking model based on a bayesian network). And the mastery degrees of different user knowledge points output by the mastery degree model are stored in the user portrait, so that personalized intelligent recommendation is realized. The knowledge point mastery degree unit is used for constructing a user portrait by collecting basic information and behavior logs of the user. The system comprises information such as the grade, subject, region, subject ability value, knowledge point mastery degree and the like of the user, and can better perform personalized recommendation based on the information.
Referring to fig. 3, the knowledge point mastery unit is further configured to:
constructing a knowledge point relation graph according to the parent-child relation and the front-back position relation of the knowledge points, and importing the knowledge point relation graph into a graph database to form a knowledge point map;
when the mastery degree of the knowledge point requested by a user is recognized, reading the front-back position relation of the knowledge point from the knowledge point map;
sorting the knowledge points according to the front-back position relation of the knowledge points to obtain a knowledge point sorting table;
and filtering the mastery knowledge points in the knowledge point ranking table.
Specifically, the parent-child relationship and the front-back position relationship of the knowledge points can be established in the service background by the teaching and research and set by an education institution or a school. And the knowledge map constructed by the knowledge point mastery degree unit is used by the item pushing unit. For example, when a knowledge point a, a knowledge point B, and a knowledge point C are obtained from a request of a user, and the knowledge point a, the knowledge point B, and the knowledge point C are input into a knowledge point map, the knowledge map outputs that the knowledge point a and the knowledge point C are two parallel knowledge points, and the knowledge point B is a child node of the knowledge point a, the front-rear positional relationship of the knowledge point a, the knowledge point B, and the knowledge point C can be obtained. In order to improve the accuracy of pushing the questions, the system also sequences the knowledge points, filters the mastery degree into the mastered knowledge points, and pushes the questions with the mastered knowledge points to the user.
Preferably, the knowledge point mastery unit is specifically configured to:
acquiring the historical answer condition of a user;
training parameters corresponding to the mastery degree model according to the historical answer condition of the user;
and when the mastery degrees of different knowledge points of the user are calculated, bringing the parameters corresponding to the user into the mastery degree model to obtain the mastery degrees of the different knowledge points of the user.
Specifically, when the knowledge point mastery degree unit tracks the mastery condition of the user on the knowledge point by using the BKT algorithm, the parameters corresponding to the mastery degree model are trained according to the historical answering condition of the user, and then the parameters are brought into the mastery degree model when the user answers, so that the mastery degree of the user on the knowledge point corresponding to the current question is calculated.
Preferably, the knowledge point mastery unit is further configured to:
and after acquiring the mastery degree of the knowledge points, when an updated answer request is received, updating the mastery degree of different knowledge points of the user by using the mastery degree model, and storing the updated mastery degree of the knowledge points into the user portrait of the user.
Specifically, after the user answers the question, an updated answer request can be sent, the mastery degree of the knowledge point of the user is required to be updated according to the current answer condition, and the updated mastery degree is stored in the user portrait, so that the mastery degree of each current knowledge point of the user is shown in the user portrait.
Preferably, the question deducting unit is specifically configured to:
constructing a question portrait according to question information of a question and associated information of the question and a knowledge point;
obtaining a question setting rule under a training scene according to the training scene requested by a user, and calculating the number and type of questions corresponding to the training scene according to the question setting rule;
calculating the question difficulty of each knowledge point according to the mastery degree of the knowledge point of the user and by combining the mastery degree distribution of all users in the knowledge point calculated off line;
and acquiring corresponding questions from the question pictures according to the training scenes, the knowledge points, the question difficulty and the question number, and pushing the questions to the user.
Specifically, the question pushing unit integrates the question information and the associated information of the question and the knowledge point by using a big data technology to obtain a question portrait, more accurately describes the characteristics of the question, including the difficulty of the question, the main and auxiliary knowledge points, the scene and other information, and conveniently provides corresponding services for different learning links. When the method is used for pushing the questions, the number N of the questions in the training scene is obtained according to the training scene (for example, pre-learning, practice, homework and the like), namely, N questions need to be pushed in the training scene. And then calculating the problem difficulty suitable for each knowledge point according to the mastery degree of the knowledge points, wherein the mastery degree of the knowledge points corresponds to the problem difficulty, and for the knowledge points with poor mastery, the simple problem can be started firstly. And then acquiring N titles corresponding to the knowledge points from the title images according to the training scene, the knowledge points and the title difficulty.
The system adopts a BKT algorithm to track the mastery degree of a user on knowledge points, and simultaneously constructs a knowledge map, a user portrait and a subject portrait. The system can be applied to multiple links of online education, comprises pre-class exercises, pre-study courseware, post-class work, intelligent assessment, teacher lesson preparation, question exercise and the like, can completely track the mastery degree of a user on knowledge points, is purposeful, and provides intelligent and personalized question pushing services comprehensively.
Preferably, the question deducting unit is specifically configured to:
and sequencing the obtained topics according to the knowledge point sequencing table, and pushing the sequenced topics to a user.
Specifically, the topic pushing unit also sequences the obtained topics according to the sequenced knowledge points and pushes the ranked topics to the user. The sorting order is set by the administrator. The sorting can be performed according to knowledge points with better mastery to poorer mastery, and can be performed according to the difficulty degree of the questions or can be performed randomly.
Preferably, the knowledge point mastery unit is further configured to:
collecting an abest related log;
and analyzing the abest related logs, and optimizing the mastery model according to an analysis result.
Specifically, the knowledge point mastery degree unit also verifies the quality of the mastery degree model by collecting the attest related logs and performing processing and comparative analysis by using a big data related technology, and provides effective support for the mastery degree model optimization.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (9)
1. An intelligent question-pushing system for online education, comprising:
knowledge point mastery degree unit: the system comprises a data processing module, a data processing module and a data processing module, wherein the data processing module is used for receiving a topic pushing request initiated by a user, acquiring at least one knowledge point requested by the user and acquiring the mastery degree of the knowledge point;
a question-pushing unit: and the method is used for acquiring corresponding questions according to the mastery degrees of all the knowledge points requested by the user and pushing the questions to the user.
2. The system of claim 1, wherein the knowledge point mastery unit is specifically configured to:
constructing a user image of the user according to the basic information of the user;
modeling the mastery degree of the knowledge points of the user by using a BKT algorithm to obtain a mastery degree model;
acquiring the mastery degree of different knowledge points of each user by using the mastery degree model, and storing the mastery degree of different knowledge points into a user portrait of the user;
receiving a topic pushing request initiated by a user, and acquiring at least one knowledge point requested by the user;
the degree of mastery of the knowledge point is obtained from the user image of the user.
3. The system of claim 2, wherein the knowledge point mastery unit is further configured to:
constructing a knowledge point relation graph according to the parent-child relation and the front-back position relation of the knowledge points, and importing the knowledge point relation graph into a graph database to form a knowledge point map;
when the mastery degree of the knowledge point requested by a user is recognized, reading the front-back position relation of the knowledge point from the knowledge point map;
sorting the knowledge points according to the front-back position relation of the knowledge points to obtain a knowledge point sorting table;
and filtering the mastery knowledge points in the knowledge point ranking table.
4. The intelligent question-pushing system of online education as claimed in claim 2,
the basic information of the user comprises grade, subject, region, subject ability value and mastery degree of knowledge points.
5. The system of claim 2, wherein the knowledge point mastery unit is specifically configured to:
acquiring the historical answer condition of a user;
training parameters corresponding to the mastery degree model according to the historical answer condition of the user;
and when the mastery degrees of different knowledge points of the user are calculated, bringing the parameters corresponding to the user into the mastery degree model to obtain the mastery degrees of the different knowledge points of the user.
6. The system of claim 3, wherein the knowledge point mastery unit is further configured to:
and after acquiring the mastery degree of the knowledge points, when an updated answer request is received, updating the mastery degree of different knowledge points of the user by using the mastery degree model, and storing the updated mastery degree of the knowledge points into the user portrait of the user.
7. The system for intelligently pushing questions of online education as claimed in any one of claims 1-6, wherein the question pushing unit is specifically configured to:
constructing a question portrait according to question information of a question and associated information of the question and a knowledge point;
obtaining a question setting rule under a training scene according to the training scene requested by a user, and calculating the number and type of questions corresponding to the training scene according to the question setting rule;
calculating the question difficulty of each knowledge point according to the mastery degree of the knowledge point of the user and by combining the mastery degree distribution of all users in the knowledge point calculated off line;
and acquiring corresponding questions from the question pictures according to the training scenes, the knowledge points, the question difficulty and the question number, and pushing the questions to the user.
8. The system of claim 7, wherein the topic pushing unit is specifically configured to:
and sequencing the obtained topics according to the knowledge point sequencing table, and pushing the sequenced topics to a user.
9. The system for intelligently reasoning on topics of online education as claimed in any one of claims 1-6, wherein the knowledge point mastery unit is further configured to:
collecting an abest related log;
and analyzing the abest related logs, and optimizing the mastery model according to an analysis result.
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| WO2024016695A1 (en) * | 2022-07-22 | 2024-01-25 | 山东浪潮科学研究院有限公司 | Multiview learning-based teaching knowledge graph construction and retrieval method and system |
| CN117672027A (en) * | 2024-02-01 | 2024-03-08 | 青岛培诺教育科技股份有限公司 | VR teaching method, device, equipment and medium |
| CN117672027B (en) * | 2024-02-01 | 2024-04-30 | 青岛培诺教育科技股份有限公司 | VR teaching method, device, equipment and medium |
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