CN111831831A - Knowledge graph-based personalized learning platform and construction method thereof - Google Patents
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Abstract
The invention provides an individualized learning platform based on a knowledge graph, which comprises a server and a client, wherein the server is in communication connection with the client, and the server comprises: the knowledge question bank comprises knowledge point data of at least one subject and test question data; the knowledge map is constructed based on knowledge point data in a knowledge question base and test question data; the knowledge point recommending module is used for calculating a learning resource recommending index of the knowledge point according to the familiarity degree of the knowledge point in the knowledge map and recommending the knowledge point of which the learning resource recommending index is larger than a first preset index threshold value to a user; the invention can carry out differential teaching aiming at students in different levels, recommend learning resources and exercises matched with the students in different learning levels, identify the familiarity of individual students with a certain knowledge point and then adopt targeted strengthening training. Correspondingly, the invention further provides a construction method of the personalized learning platform based on the knowledge graph.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a knowledge graph-based personalized learning platform and a construction method thereof.
Background
In the current society, the development of the internet prompts the development of the education industry to be networked and intelligentized. In online teaching, a large number of courses can only achieve video teaching and online answering, and teachers cannot conveniently know the mastery degree of students on some knowledge points and aim at training like offline teaching. The on-line teaching content can only be transmitted by video and used for the theme and sea tactics, which can not only carry out differentiation teaching aiming at students of different levels, but also can not identify the familiarity of a certain knowledge point of individual students and adopt targeted strengthening training, thereby bringing great inconvenience to the teaching work.
Disclosure of Invention
In order to solve the problems that the existing online teaching can not only carry out differential teaching aiming at students in different levels, but also can not identify the familiarity of individual students with a knowledge point and adopt targeted reinforced training, the invention provides a knowledge-graph-based personalized learning platform and a construction method thereof, and the specific technical scheme is as follows:
a knowledge graph-based personalized learning platform comprises a server and a client, wherein the server is in communication connection with the client, and the server comprises:
the knowledge question bank comprises knowledge point data of at least one subject and test question data;
the knowledge map is constructed based on knowledge point data in a knowledge question base and test question data;
the knowledge point recommending module is used for calculating a learning resource recommending index of the knowledge point according to the familiarity degree of the knowledge point in the knowledge map and recommending the knowledge point of which the learning resource recommending index is larger than a first preset index threshold value to a user;
wherein the learning resource recommendation index
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity between the knowledge point and the common sense, r represents the association degree between a topic and the knowledge point, and l represents a topicThe difficulty coefficient of a topic, n represents the total score of a topic, and c represents the actual score obtained by a topic.
Optionally, the server further includes a training module, configured to recommend the test question associated with the knowledge point with the learning resource recommendation index greater than the second preset index threshold to the user.
Optionally, the client includes teacher's end, student's end and administrator's end, the teacher's end is used for task issuing, job issuing, examination question management and activity management, the student's end is used for signing in, receives task, system notice and question training, the administrator's end is used for account number management, class management, job management, examination paper management, file management, question bank management and notice management.
Correspondingly, the invention also provides a construction method of the individualized learning platform based on the knowledge graph, which comprises the following steps:
step 1, acquiring knowledge point data and test question data of at least one subject, processing the knowledge point data and establishing a knowledge question library;
step 2, constructing a knowledge graph according to the knowledge point data and the test question data acquired in the step 1;
and 3, calculating a learning resource recommendation index R, wherein,
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity of the knowledge point and the common knowledge, r represents the association degree of a certain topic and the knowledge point, l represents the difficulty coefficient of the certain topic, n represents the total score of the certain topic, and c represents the actual score obtained by the certain topic;
and 4, recommending the knowledge points with the learning resource recommendation index larger than the first preset index threshold value to the user.
Optionally, the method for constructing the personalized learning platform further includes:
step 5, recommending the test question associated with the knowledge point with the learning resource recommendation index larger than a second preset index threshold value to the user;
and the second preset index threshold value is larger than the first preset index threshold value.
Optionally, in step 2, a specific method for constructing a knowledge graph according to the knowledge point data and the test question data obtained in step 1 is as follows:
step 2a, extracting knowledge from the knowledge point data and integrating the data of the test question to form a preliminary knowledge representation;
and 2b, carrying out entity alignment on the preliminary knowledge representation to form a knowledge graph.
Optionally, the knowledge extraction includes entity extraction, relationship extraction, and attribute extraction.
Optionally, the entity alignment includes entity disambiguation and coreference resolution.
Accordingly, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the personalized learning platform construction method described above.
The beneficial effects obtained by the invention are as follows: the familiarity of the knowledge points is calculated by utilizing the similarity of the knowledge points and the common knowledge, the association degree of the questions and the knowledge points, the difficulty coefficient of the questions and the total score of the questions, then the learning resource recommendation index is calculated through the familiarity degree of the knowledge points, natural constants and forgetting time, differential teaching can be carried out on students of different levels, learning resources and exercises matched with the students of different learning levels are recommended for the students of different learning levels, the familiarity of a certain knowledge point can be identified for individual students, and then targeted intensive training is adopted. Meanwhile, teachers can also deeply know the knowledge point mastering degree of all students.
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The present invention will be further understood from the following description taken in conjunction with the accompanying drawings, the emphasis instead being placed upon illustrating the principles of the embodiments.
FIG. 1 is a schematic overall structure diagram of a knowledge-graph-based personalized learning platform according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a client in an embodiment of the present invention;
fig. 3 is an overall flowchart of a method for constructing an individualized learning platform based on a knowledge graph in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to embodiments thereof.
The invention relates to a knowledge graph-based personalized learning platform and a construction method thereof, which explain the following embodiments according to the attached drawings:
as shown in fig. 1, the personalized learning platform based on the knowledge graph comprises a server and a client, wherein the server is in communication connection with the client. The server is built based on a B/S framework and with SpringBoot as a core, and comprises a knowledge question bank, a knowledge graph and a knowledge point recommendation module. The knowledge item base comprises knowledge point data and test item data of at least one subject, the knowledge map is constructed based on the knowledge point data and the test item data in the knowledge item base, and the knowledge point recommending module is used for calculating learning resource recommending indexes of the knowledge points according to the familiarity degree of the knowledge points in the knowledge map and recommending the knowledge points with the learning resource recommending indexes larger than a first preset index threshold value to a user. Wherein the learning resource recommendation index
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity of the knowledge point and the common knowledge, r represents the association degree of a certain topic and the knowledge point, l represents the difficulty coefficient of a certain topic, n represents the total score of a certain topic, c represents the actual score obtained by a certain topic, and the error is a positive number. The similarity between the knowledge points and the common knowledge and the association degree between a topic and the knowledge points are obtained through initial presetting.
Through calculating the learning resource recommendation index, the differential teaching can be carried out on students in different levels, the learning resources matched with the students in different learning levels are recommended for the students in different learning levels, and teachers can conveniently and deeply know the knowledge point mastering degree of all students.
In some embodiments, in the personalized learning platform which is just built, a server is used to initially preset the difficulty coefficient of a certain question, and then the number of times of doing the question by the student and the number of times of correctly solving the question are counted to determine, for example, the number of times of doing the question by the student is N, the number of times of correctly solving the question is M, and then the difficulty coefficient l is (1-N/M).
In some examples, the difficulty factor for a topic is calculated by counting the average of the scores obtained when students make a topic and the total score of the topic. If a topic is divided into W and the student scores the topic with X on average, the difficulty coefficient l is (1-X/W).
In some embodiments, the server further includes a training module, configured to recommend the test question associated with the knowledge point with the learning resource recommendation index greater than the second preset index threshold to the user. Through aiming at the training module, the familiarity of individual students with a certain knowledge point can be identified, and then targeted strengthening training is adopted to deepen the understanding of the students on the knowledge point.
In some embodiments, as shown in fig. 2, the client includes a teacher end, a student end and an administrator end, the teacher end is used for task issuing, job issuing, test question management and activity management, the student end is used for signing in, receiving tasks, system notifications and question training, and the administrator end is used for account management, class management, job management, test paper management, file management, question bank management and notification management.
Correspondingly, as shown in fig. 3, the invention further provides a method for constructing a knowledge-graph-based personalized learning platform, which comprises the following steps:
step 1, acquiring knowledge point data and test question data of at least one subject, processing the knowledge point data, and establishing a knowledge question base.
After the knowledge item base is established, the work of structuring the data, cleaning the data packet and the like is started so as to facilitate the step 2.
Step 2, constructing a knowledge graph according to the knowledge point data and the test question data acquired in the step 1;
and 3, calculating a learning resource recommendation index R, wherein,
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity of the knowledge point and the common knowledge, r represents the association degree of a certain topic and the knowledge point, l represents the difficulty coefficient of the certain topic, n represents the total score of the certain topic, and c represents the actual score obtained by the certain topic;
and 4, recommending the knowledge points with the learning resource recommendation index larger than the first preset index threshold value to the user.
In some embodiments, the method for constructing the personalized learning platform further includes step 5, recommending test question questions associated with knowledge points with learning resource recommendation indexes larger than a second preset index threshold value to the user. And the second preset index threshold value is larger than the first preset index threshold value.
In some embodiments, in step 2, a specific method for constructing a knowledge graph according to the knowledge point data and the test question data obtained in step 1 is as follows:
and 2a, extracting knowledge from the knowledge point data and integrating the data of the test question to form a primary knowledge representation.
The knowledge point data comprises semi-structured knowledge point data and unstructured knowledge description data. And (2) processing the data set collected in the step (1), for example, performing data integration on structured test question data, performing triple extraction (knowledge extraction) on semi-structured knowledge point data and unstructured knowledge description data, performing syntactic analysis, part of speech tagging and the like on the data set, tagging a small part of non-recognizable term data set, and finally sorting the data set into triples to form preliminary knowledge representation.
And 2b, carrying out entity alignment on the preliminary knowledge representation to form a knowledge graph.
In some embodiments, the knowledge extraction includes an entity extraction, a relationship extraction, and an attribute extraction.
In some embodiments, the entity alignment includes entity disambiguation and coreference resolution.
The familiarity of the knowledge points is calculated by utilizing the similarity of the knowledge points and the common knowledge, the association degree of the questions and the knowledge points, the difficulty coefficient of the questions and the total score of the questions, then the learning resource recommendation index is calculated through the familiarity degree of the knowledge points, natural constants and forgetting time, differential teaching can be carried out on students of different levels, learning resources and exercises matched with the students of different learning levels are recommended for the students of different learning levels, the familiarity of a certain knowledge point can be identified for individual students, and then targeted intensive training is adopted. Meanwhile, teachers can also deeply know the knowledge point mastering degree of all students.
Accordingly, the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the personalized learning platform construction method described above.
In summary, the invention discloses an individualized learning platform based on a knowledge graph and a construction method thereof, and the beneficial technical effects are as follows: the familiarity of the knowledge points is calculated by utilizing the similarity of the knowledge points and the common knowledge, the association degree of the questions and the knowledge points, the difficulty coefficient of the questions and the total score of the questions, then the learning resource recommendation index is calculated through the familiarity degree of the knowledge points, natural constants and forgetting time, differential teaching can be carried out on students of different levels, learning resources and exercises matched with the students of different learning levels are recommended for the students of different learning levels, the familiarity of a certain knowledge point can be identified for individual students, and then targeted intensive training is adopted. Meanwhile, teachers can also deeply know the knowledge point mastering degree of all students.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (9)
1. A personalized learning platform based on knowledge graph comprises a server and a client, wherein the server is in communication connection with the client, and the server comprises:
the knowledge question bank comprises knowledge point data of at least one subject and test question data;
the knowledge map is constructed based on knowledge point data in a knowledge question base and test question data;
the knowledge point recommending module is used for calculating a learning resource recommending index of the knowledge point according to the familiarity degree of the knowledge point in the knowledge map and recommending the knowledge point of which the learning resource recommending index is larger than a first preset index threshold value to a user;
wherein the learning resource recommendation index
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity of the knowledge point and the common knowledge, r represents the association degree of a certain topic and the knowledge point, l represents the difficulty coefficient of the certain topic, n represents the total score of the certain topic, and c represents the actual score obtained by the certain topic.
2. The knowledge-graph-based personalized learning platform of claim 1, wherein the server further comprises a training module for recommending to the user test questions associated with knowledge points for which the learning resource recommendation index is greater than a second preset index threshold.
3. The knowledge-graph-based personalized learning platform as claimed in claim 2, wherein the client comprises a teacher end, a student end and an administrator end, the teacher end is used for task issuing, job issuing, test question management and activity management, the student end is used for signing in, receiving tasks, system notifications and question training, and the administrator end is used for account management, class management, job management, test paper management, file management, question bank management and notification management.
4. A construction method of an individualized learning platform based on a knowledge graph is characterized by comprising the following steps:
step 1, acquiring knowledge point data and test question data of at least one subject, processing the knowledge point data and establishing a knowledge question library;
step 2, constructing a knowledge graph according to the knowledge point data and the test question data acquired in the step 1;
and 3, calculating a learning resource recommendation index R, wherein,
s represents the familiarity of the knowledge point, t represents forgetting time, e represents a natural constant, a represents the similarity of the knowledge point and the common knowledge, r represents the association degree of a certain topic and the knowledge point, l represents the difficulty coefficient of the certain topic, n represents the total score of the certain topic, and c represents the actual score obtained by the certain topic;
and 4, recommending the knowledge points with the learning resource recommendation index larger than the first preset index threshold value to the user.
5. The method for constructing a knowledge-graph-based personalized learning platform according to claim 4, further comprising:
step 5, recommending the test question associated with the knowledge point with the learning resource recommendation index larger than a second preset index threshold value to the user;
and the second preset index threshold value is larger than the first preset index threshold value.
6. The method for constructing the knowledge-graph-based personalized learning platform according to claim 5, wherein in the step 2, the specific method for constructing the knowledge graph according to the knowledge point data and the test question data obtained in the step 1 comprises the following steps:
step 2a, extracting knowledge from the knowledge point data and integrating the data of the test question to form a preliminary knowledge representation;
and 2b, carrying out entity alignment on the preliminary knowledge representation to form a knowledge graph.
7. The method according to claim 6, wherein the knowledge extraction comprises entity extraction, relationship extraction and attribute extraction.
8. The method of claim 7, wherein the entity alignment comprises entity disambiguation and coreference resolution.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the personalized learning platform construction method according to any one of claims 4 to 8.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112612909A (en) * | 2021-01-06 | 2021-04-06 | 杭州恒生数字设备科技有限公司 | Intelligent test paper quality evaluation method based on knowledge graph |
| CN112905660A (en) * | 2021-02-05 | 2021-06-04 | 广东金融学院 | System and method for cultivating and managing talents of mid-high-post and local departments |
| CN112966121A (en) * | 2021-03-02 | 2021-06-15 | 华南师范大学 | Artificial intelligence autonomous learning education robot for overcoming food preference |
| CN113793239A (en) * | 2021-08-13 | 2021-12-14 | 华南理工大学 | Personalized knowledge tracking method and system fusing learning behavior characteristics |
| CN113851020A (en) * | 2021-11-04 | 2021-12-28 | 华南师范大学 | Self-adaptive learning platform based on knowledge graph |
| CN114020929A (en) * | 2021-11-03 | 2022-02-08 | 北京航空航天大学 | Intelligent education system platform design method based on course knowledge graph |
| CN116340625A (en) * | 2023-03-15 | 2023-06-27 | 武汉博奥鹏程教育科技有限公司 | Course recommendation method and device combining learning state fitness and course collocation degree |
| CN116662578A (en) * | 2023-08-02 | 2023-08-29 | 中国标准化研究院 | End-to-end-based large-scale knowledge graph construction and storage method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512214A (en) * | 2015-11-28 | 2016-04-20 | 华中师范大学 | Knowledge database, construction method and learning situation diagnosis system |
| CN108596472A (en) * | 2018-04-20 | 2018-09-28 | 贵州金符育才教育科技有限公司 | A kind of the artificial intelligence tutoring system and method for natural sciences study |
| CN109255031A (en) * | 2018-09-20 | 2019-01-22 | 苏州友教习亦教育科技有限公司 | The data processing method of knowledge based map |
| CN109388744A (en) * | 2017-08-11 | 2019-02-26 | 北京龙之门网络教育技术股份有限公司 | A kind of adaptive learning recommended method and device |
| CN109903617A (en) * | 2017-12-11 | 2019-06-18 | 北京三好互动教育科技有限公司 | Individualized exercise method and system |
| CN110197452A (en) * | 2019-06-11 | 2019-09-03 | 合肥明信软件技术有限公司 | A kind of intelligence adaptation on-line study and examination platform based on artificial intelligence technology |
| CN110378818A (en) * | 2019-07-22 | 2019-10-25 | 广西大学 | Personalized exercise recommended method, system and medium based on difficulty |
-
2020
- 2020-07-17 CN CN202010694378.1A patent/CN111831831A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105512214A (en) * | 2015-11-28 | 2016-04-20 | 华中师范大学 | Knowledge database, construction method and learning situation diagnosis system |
| CN109388744A (en) * | 2017-08-11 | 2019-02-26 | 北京龙之门网络教育技术股份有限公司 | A kind of adaptive learning recommended method and device |
| CN109903617A (en) * | 2017-12-11 | 2019-06-18 | 北京三好互动教育科技有限公司 | Individualized exercise method and system |
| CN108596472A (en) * | 2018-04-20 | 2018-09-28 | 贵州金符育才教育科技有限公司 | A kind of the artificial intelligence tutoring system and method for natural sciences study |
| CN109255031A (en) * | 2018-09-20 | 2019-01-22 | 苏州友教习亦教育科技有限公司 | The data processing method of knowledge based map |
| CN110197452A (en) * | 2019-06-11 | 2019-09-03 | 合肥明信软件技术有限公司 | A kind of intelligence adaptation on-line study and examination platform based on artificial intelligence technology |
| CN110378818A (en) * | 2019-07-22 | 2019-10-25 | 广西大学 | Personalized exercise recommended method, system and medium based on difficulty |
Non-Patent Citations (1)
| Title |
|---|
| 中公教育研究生考试研究院: "《考研轻松学 英语（二）的奥秘 2020中公版》", 31 August 2019, pages: 11 - 12 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN112612909A (en) * | 2021-01-06 | 2021-04-06 | 杭州恒生数字设备科技有限公司 | Intelligent test paper quality evaluation method based on knowledge graph |
| CN112905660A (en) * | 2021-02-05 | 2021-06-04 | 广东金融学院 | System and method for cultivating and managing talents of mid-high-post and local departments |
| CN112905660B (en) * | 2021-02-05 | 2022-12-20 | 广东金融学院 | System and method for culturing and managing high-post talents and domestic talents |
| CN112966121A (en) * | 2021-03-02 | 2021-06-15 | 华南师范大学 | Artificial intelligence autonomous learning education robot for overcoming food preference |
| CN112966121B (en) * | 2021-03-02 | 2023-01-03 | 华南师范大学 | Artificial intelligence autonomous learning education robot for overcoming food preference |
| CN113793239A (en) * | 2021-08-13 | 2021-12-14 | 华南理工大学 | Personalized knowledge tracking method and system fusing learning behavior characteristics |
| CN113793239B (en) * | 2021-08-13 | 2023-12-19 | 华南理工大学 | Personalized knowledge tracking method and system integrating learning behavior characteristics |
| CN114020929A (en) * | 2021-11-03 | 2022-02-08 | 北京航空航天大学 | Intelligent education system platform design method based on course knowledge graph |
| CN114020929B (en) * | 2021-11-03 | 2024-05-28 | 北京航空航天大学 | Intelligent education system platform design method based on course knowledge graph |
| CN113851020A (en) * | 2021-11-04 | 2021-12-28 | 华南师范大学 | Self-adaptive learning platform based on knowledge graph |
| CN116340625A (en) * | 2023-03-15 | 2023-06-27 | 武汉博奥鹏程教育科技有限公司 | Course recommendation method and device combining learning state fitness and course collocation degree |
| CN116340625B (en) * | 2023-03-15 | 2024-04-05 | 武汉博奥鹏程科技投资有限公司 | Course recommendation method and device combining learning state fitness and course collocation degree |
| CN116662578A (en) * | 2023-08-02 | 2023-08-29 | 中国标准化研究院 | End-to-end-based large-scale knowledge graph construction and storage method and system |
| CN116662578B (en) * | 2023-08-02 | 2023-10-31 | 中国标准化研究院 | End-to-end-based large-scale knowledge graph construction and storage method and system |
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