CN112699308A - Clustering algorithm-based subject knowledge point recommendation method and system - Google Patents
Clustering algorithm-based subject knowledge point recommendation method and system Download PDFInfo
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Abstract
The application provides a subject knowledge point recommendation method based on a clustering algorithm, which comprises the following steps: s1, acquiring subject history teaching information; s2, clustering the subject history teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a subject knowledge point set; s3, acquiring real-time teaching information of the subject, and clustering the historical teaching information of the subject by using a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set; and S4, fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set. The scheme of this application had both considered the important knowledge point of the past year, had still considered present important knowledge point, and the comprehensiveness of knowledge point both can both be taken into account to the two after fusing, can also take into account in time supplementary and the adjustment to new knowledge point, and then can effectual supplementary teacher formulate reasonable teaching plan, problem plan.
Description
Technical Field
The application relates to the field of intelligent teaching, in particular to a subject knowledge point recommendation method based on a clustering algorithm.
Background
Along with the arrival of big data and the internet era, intelligent education is increasingly emphasized by people, and the situation of rapid development is presented. The exercises play a very important role in intelligent education and are important teaching resources for helping students to consolidate the knowledge points learned in classes after class. For teachers, how to make the set exercises adapt to the real learning level, learning requirement and progress of students is increasingly important in intelligent education, and the setting of the exercises needs to be accurate depending on knowledge points seriously. However, especially for newly-enrolled teachers, it is difficult to precisely find out the knowledge points of the discipline in a short time, and the current situation is that only experienced teachers teach the experience or drill themselves. However, the teaching of the hands of other teachers is completely relied on, so that a great deal of effort is inevitably consumed on the other teachers, and the teaching task of most teachers is very heavy, which is difficult to realize; for self-drilling it obviously takes several years to achieve.
Therefore, how to enable teachers (particularly newly-enrolled teachers) to conveniently acquire accurate subject knowledge point recommendation is a technical problem which needs to be solved urgently in the current intelligent teaching field.
Disclosure of Invention
In order to solve the technical problems, the application provides a subject knowledge point recommendation method based on a clustering algorithm, so that accurate subject knowledge point information is recommended to teachers, and corresponding teaching plans, exercise plans and the like are made in an auxiliary mode.
The first aspect of the application provides a subject knowledge point recommendation method based on a clustering algorithm, and the method comprises the following steps:
s1, acquiring subject history teaching information;
s2, clustering the subject history teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a subject knowledge point set;
s3, acquiring real-time teaching information of the subject, and clustering the historical teaching information of the subject by using a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and S4, fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
Optionally, the subject history teaching information is subject teaching information of past years.
Optionally, the subject real-time teaching information is teaching information of a current school year.
Optionally, the fusing the first set of subject knowledge points and the second set of subject knowledge points into a set of subject knowledge points includes:
and grouping the subject knowledge point set I and the subject knowledge point set II based on chapters respectively, extracting the chapters of the grouped knowledge points with coincidence between the subject knowledge point set II and the subject knowledge point set I, and replacing the grouped knowledge points of the corresponding chapters in the subject knowledge point set I with the grouped knowledge points of the same chapters in the subject knowledge point set II.
Optionally, examination question information of a plurality of years is also acquired, a third knowledge point set is extracted by adopting a third clustering algorithm, an evaluation trend graph of each knowledge point is drawn based on the third knowledge point set, and an importance label of each knowledge point is determined based on the evaluation trend graph;
the replacing the grouped knowledge points of the corresponding chapters in the first discipline knowledge point set with the grouped knowledge points of the same chapters in the second discipline knowledge point set comprises:
and re-determining the grouped knowledge points of the same chapter in the second discipline knowledge point set based on the importance labels.
Optionally, the importance label includes: gradually strengthened, gradually weakened and relatively stable.
The re-determining grouped knowledge points of the same chapter in the second set of discipline knowledge points based on the importance labels comprises:
if the importance label is gradually strengthened or relatively stable, the knowledge point is kept in the chapter group; and if the importance label is gradually weakened, deleting the knowledge point from the chapter group.
Optionally, the first clustering algorithm, the second clustering algorithm and the third clustering algorithm are one or more of a K-means clustering algorithm, a mean shift clustering algorithm, a DBSCAN clustering algorithm, a coacervation level clustering algorithm and a maximum expected clustering algorithm of a mixed gaussian model.
The second aspect of the present application provides a subject knowledge point recommendation system, the system includes a first acquisition module, a first extraction module, a second acquisition module, a second extraction module, and a fusion module, including:
the first acquisition module is used for acquiring the subject history teaching information;
the first extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm so as to extract corresponding subject knowledge points to form a subject knowledge point set;
the second acquisition module is used for acquiring real-time teaching information of the subject;
the second extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and the fusion module is used for fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
A third aspect of the present application provides an electronic device, characterized in that the device includes:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method as described above.
A fourth aspect of the present application provides a computer storage medium, characterized in that the storage medium stores computer instructions for executing the method as described above when the computer instructions are called.
The invention has the beneficial effects that: according to the technical scheme, a subject knowledge point set is obtained by fusion based on subject historical teaching information and subject real-time teaching information, and is pushed to teachers (particularly newly-entered teachers). According to the scheme, the important knowledge points in the past year and the current important knowledge points are considered, the comprehensiveness of the knowledge points can be considered after the two are fused, the timely supplement and adjustment of new knowledge points can be considered, and therefore a teacher can be effectively assisted to make a reasonable teaching plan and a problem plan.
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 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 for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flow chart of a subject knowledge point recommendation method based on a clustering algorithm disclosed in an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a subject knowledge point recommendation system disclosed in an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within 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.
In the description of the present application, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which the present invention product is usually put into use, it is only for convenience of describing the present application and simplifying the description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation and be operated, and thus, should not be construed as limiting the present application.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present application may be combined with each other without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic flow chart of a subject knowledge point recommendation method based on a clustering algorithm disclosed in an embodiment of the present application. As shown in fig. 1, a first aspect of the present application provides a subject knowledge point recommendation method based on a clustering algorithm, the method including:
s1, acquiring subject history teaching information;
s2, clustering the subject history teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a subject knowledge point set;
s3, acquiring real-time teaching information of the subject, and clustering the historical teaching information of the subject by using a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and S4, fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
When the subject knowledge point set is determined, the subject historical teaching information and the subject real-time teaching information are considered at the same time, and are organically fused. Therefore, the scheme considers the important knowledge points in the past year and the current important knowledge points, after the two are fused, the comprehensiveness of the knowledge points can be considered, the timely supplement and adjustment of the new knowledge points can be considered, and after the new knowledge points are pushed to teachers (particularly newly-entered teachers), the teachers can be effectively assisted to make reasonable teaching plans and problem plans.
Optionally, the subject history teaching information is subject teaching information of past years.
In the embodiment of the application, the time span of the subject historical teaching information is not set too long in order to avoid that the abandoned or weakened knowledge points influence the formulation of the teaching plan and the problem plan of a teacher (particularly a newly-entered teacher) in consideration of the fact that the knowledge points of each subject change greatly along with the development of the era. For example, it may be between ten and fifteen years.
Optionally, the subject real-time teaching information is teaching information of a current school year.
In the embodiment of the present application, the teaching information of the current school year in the present application is not a teaching plan of a complete school year, but is accumulated by the real-time single teaching plan information made by the teacher, for example, if the current teaching progress is the third chapter, the subject real-time teaching information is the teaching plan information of the first to third chapters. Therefore, the fused subject knowledge point set in the application is gradually updated along with the teaching information of the current school year, and the set method is more suitable for newly-entered teachers because the teachers can gradually master relatively more real and reliable subject knowledge points, and further, reasonable teaching plans and exercise plans can be further formulated.
Optionally, the fusing the first set of subject knowledge points and the second set of subject knowledge points into a set of subject knowledge points includes:
and grouping the subject knowledge point set I and the subject knowledge point set II based on chapters respectively, extracting the chapters of the grouped knowledge points with coincidence between the subject knowledge point set II and the subject knowledge point set I, and replacing the grouped knowledge points of the corresponding chapters in the subject knowledge point set I with the grouped knowledge points of the same chapters in the subject knowledge point set II.
In the embodiment of the application, the specific fusion mode may be a coverage replacement mode, that is, the knowledge points in the subject real-time teaching information replace the corresponding knowledge points in the subject historical teaching information. The knowledge points are replaced by sections, so that the grouping and classification of the knowledge points are facilitated, the replacement behavior can be limited to a reasonable limit, and the gradual distortion of the obtained knowledge point set caused by unlimited coverage replacement is avoided.
Optionally, examination question information of a plurality of years is also acquired, a third knowledge point set is extracted by adopting a third clustering algorithm, an evaluation trend graph of each knowledge point is drawn based on the third knowledge point set, and an importance label of each knowledge point is determined based on the evaluation trend graph;
the replacing the grouped knowledge points of the corresponding chapters in the first discipline knowledge point set with the grouped knowledge points of the same chapters in the second discipline knowledge point set comprises:
and re-determining the grouped knowledge points of the same chapter in the second discipline knowledge point set based on the importance labels.
Optionally, the importance label includes: gradually strengthened, gradually weakened and relatively stable.
The re-determining grouped knowledge points of the same chapter in the second set of discipline knowledge points based on the importance labels comprises:
if the importance label is gradually strengthened or relatively stable, the knowledge point is kept in the chapter group; and if the importance label is gradually weakened, deleting the knowledge point from the chapter group.
In the embodiments of the present application, in addition to the covering substitution of chapter knowledge points, the applicant of the present application has realized that the teaching is intended to embody comprehensiveness, especially in the early stage of the teaching of academic year, for example, a teacher in the early stage of the academic year basically teaches all knowledge points, wherein obviously many unimportant knowledge points are included, and obviously, the knowledge points currently being learned do not necessarily embody the importance of the knowledge points completely and truly. Therefore, it is necessary to finely adjust the contents of the knowledge points in the set of the second disciplinary knowledge point set for covering. Specifically, the teaching result is usually embodied by the examination result, and the examination points of the examination inevitably influence the teaching plan obviously, so that the examination questions of the past year are clustered, the evaluation trend graph of each knowledge point at the angle of the examination question maker is drawn, the importance degree of each knowledge point can be visually determined based on the graph, and the method is adopted to finely adjust part of the knowledge points in the second set.
In addition, since the current school year is lack of examination questions, in order to further improve the accuracy of the drawn evaluation trend graph or the referential property to the current school year (especially the first/third school year), the phase change substitution of the examination question information of the current school year should be set. The specific mode can be as follows: acquiring the forecast information of the knowledge points of the current school year from multiple channels by adopting a preset algorithm, extracting a third knowledge point set by adopting a clustering algorithm based on the true examination question information of a plurality of years and the forecast information of the knowledge points of the current school year, drawing an evaluation trend graph of each knowledge point based on the third knowledge point set, determining the importance labels of the knowledge points based on the evaluation trend graph, and then executing the subsequent fine tuning method. The predetermined algorithm can be a crawler algorithm, and accordingly, the knowledge point test point prediction information of the current school year is crawled through the internet, or the test point prediction information of teachers in a predetermined range on the knowledge point of the current school year is directly obtained, for example, the prediction of teachers in the same school or the same region and the same age of the subject is obtained; when the point-to-point prediction information is multi-source, the clustered knowledge point sets may be fused in a weighted manner, for example, the respective weights may be determined based on the popularity or success rate of the predictor.
Optionally, the first clustering algorithm, the second clustering algorithm and the third clustering algorithm are one or more of a K-means clustering algorithm, a mean shift clustering algorithm, a DBSCAN clustering algorithm, a coacervation level clustering algorithm and a maximum expected clustering algorithm of a mixed gaussian model
Example 2
Referring to fig. 2, fig. 2 is a schematic structural diagram of a subject knowledge point recommendation system disclosed in an embodiment of the present application. As shown in fig. 2, a second aspect of the present application provides a subject knowledge point recommendation system, which corresponds to the method of one embodiment. Specifically, the system includes a first obtaining module, a first extracting module, a second obtaining module, a second extracting module, and a fusing module, including:
the first acquisition module is used for acquiring the subject history teaching information;
the first extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm so as to extract corresponding subject knowledge points to form a subject knowledge point set;
the second acquisition module is used for acquiring real-time teaching information of the subject;
the second extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and the fusion module is used for fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
When the system determines the subject knowledge point set, the subject historical teaching information and the subject real-time teaching information are considered at the same time, and the subject historical teaching information and the subject real-time teaching information are organically fused. Therefore, the scheme considers the important knowledge points in the past year and the current important knowledge points, after the two are fused, the comprehensiveness of the knowledge points can be considered, the timely supplement and adjustment of the new knowledge points can be considered, and after the new knowledge points are pushed to teachers (particularly newly-entered teachers), the teachers can be effectively assisted to make reasonable teaching plans and problem plans.
Example 3
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 3, a third aspect of the present application provides an electronic device, comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method according to the first embodiment.
Example 4
The present embodiment provides a computer storage medium, wherein the storage medium stores computer instructions, and when the computer instructions are called, the computer instructions are used to execute the method according to the first embodiment.
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 changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A discipline question knowledge point recommendation method based on a clustering algorithm is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring subject history teaching information;
s2, clustering the subject history teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a subject knowledge point set;
s3, acquiring real-time teaching information of the subject, and clustering the historical teaching information of the subject by using a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and S4, fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
2. The method of claim 1, wherein: the subject history teaching information is subject teaching information of past years.
3. The method of claim 1, wherein: the subject real-time teaching information is teaching information of the current school year.
4. The method of claim 1, wherein: the fusing the first set of subject knowledge points and the second set of subject knowledge points into a set of subject knowledge points comprises:
and grouping the subject knowledge point set I and the subject knowledge point set II based on chapters respectively, extracting the chapters of the grouped knowledge points with coincidence between the subject knowledge point set II and the subject knowledge point set I, and replacing the grouped knowledge points of the corresponding chapters in the subject knowledge point set I with the grouped knowledge points of the same chapters in the subject knowledge point set II.
5. The method of claim 4, wherein: acquiring examination question information of a plurality of years, extracting a knowledge point set III by adopting a clustering algorithm III, drawing an evaluation trend graph of each knowledge point based on the knowledge point set III, and determining an importance label of each knowledge point based on the evaluation trend graph;
the replacing the grouped knowledge points of the corresponding chapters in the first discipline knowledge point set with the grouped knowledge points of the same chapters in the second discipline knowledge point set comprises:
and re-determining the grouped knowledge points of the same chapter in the second discipline knowledge point set based on the importance labels.
6. The method of claim 5, wherein: the importance label includes: gradually strengthened, gradually weakened and relatively stable.
The re-determining grouped knowledge points of the same chapter in the second set of discipline knowledge points based on the importance labels comprises:
if the importance label is gradually strengthened or relatively stable, the knowledge point is kept in the chapter group; and if the importance label is gradually weakened, deleting the knowledge point from the chapter group.
7. The method according to any one of claims 1-6, wherein: the first clustering algorithm, the second clustering algorithm and the third clustering algorithm are one or more of a K-means clustering algorithm, a mean shift clustering algorithm, a DBSCAN clustering algorithm, a coacervation level clustering algorithm and a maximum expected clustering algorithm of a Gaussian mixture model.
8. The utility model provides a subject question knowledge point recommendation system, the system includes first acquisition module, first extraction module, second acquisition module, second extraction module, fuses the module, includes:
the first acquisition module is used for acquiring the subject history teaching information;
the first extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm so as to extract corresponding subject knowledge points to form a subject knowledge point set;
the second acquisition module is used for acquiring real-time teaching information of the subject;
the second extraction module is used for clustering the subject historical teaching information by adopting a clustering algorithm to extract corresponding subject knowledge points to form a second subject knowledge point set;
and the fusion module is used for fusing the first discipline knowledge point set and the second discipline knowledge point set into a discipline knowledge point set.
9. An electronic device, characterized in that the device comprises:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to perform the method of any of claims 1-7.
10. A computer storage medium, characterized in that the storage medium stores computer instructions which, when invoked, are adapted to perform the method according to any of claims 1-7.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100159432A1 (en) * | 2008-12-19 | 2010-06-24 | Xerox Corporation | System and method for recommending educational resources |
| US20120041901A1 (en) * | 2007-10-19 | 2012-02-16 | Quantum Intelligence, Inc. | System and Method for Knowledge Pattern Search from Networked Agents |
| US20160224564A1 (en) * | 2013-09-29 | 2016-08-04 | Peking University Founder Group Co., Ltd. | Method and system for key knowledge point recommendation |
| US20170011642A1 (en) * | 2015-07-10 | 2017-01-12 | Fujitsu Limited | Extraction of knowledge points and relations from learning materials |
| CN108804543A (en) * | 2018-05-16 | 2018-11-13 | 华南理工大学 | A kind of knowledge-ID analysis method based on FP-Growth algorithms |
| CN110147428A (en) * | 2019-04-11 | 2019-08-20 | 北京任学教育科技有限公司 | Update method, device, equipment and the storage medium of knowledge point master degree |
| CN110659311A (en) * | 2019-09-30 | 2020-01-07 | 浙江学海教育科技有限公司 | Topic pushing method and device, electronic equipment and storage medium |
| CN110851723A (en) * | 2019-11-14 | 2020-02-28 | 上海钦文信息科技有限公司 | English exercise recommendation method based on large-scale knowledge point labeling result |
| CN111081106A (en) * | 2019-12-13 | 2020-04-28 | 北京爱论答科技有限公司 | Job pushing method, system, equipment and storage medium |
| CN111460145A (en) * | 2020-03-18 | 2020-07-28 | 天闻数媒科技（北京）有限公司 | Learning resource recommendation method, device and storage medium |
| CN111563156A (en) * | 2020-04-30 | 2020-08-21 | 广东小天才科技有限公司 | Intelligent knowledge point diagnosis method and device, electronic equipment and storage medium |
| CN112000714A (en) * | 2020-08-21 | 2020-11-27 | 扬州大学 | Mining method for extracting association of teaching knowledge points |
| CN112148890A (en) * | 2020-09-23 | 2020-12-29 | 中国科学院自动化研究所 | Teaching knowledge point spectrum system based on network group intelligence |
-
2021
- 2021-01-13 CN CN202110039634.8A patent/CN112699308A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120041901A1 (en) * | 2007-10-19 | 2012-02-16 | Quantum Intelligence, Inc. | System and Method for Knowledge Pattern Search from Networked Agents |
| US20100159432A1 (en) * | 2008-12-19 | 2010-06-24 | Xerox Corporation | System and method for recommending educational resources |
| US20160224564A1 (en) * | 2013-09-29 | 2016-08-04 | Peking University Founder Group Co., Ltd. | Method and system for key knowledge point recommendation |
| US20170011642A1 (en) * | 2015-07-10 | 2017-01-12 | Fujitsu Limited | Extraction of knowledge points and relations from learning materials |
| CN108804543A (en) * | 2018-05-16 | 2018-11-13 | 华南理工大学 | A kind of knowledge-ID analysis method based on FP-Growth algorithms |
| CN110147428A (en) * | 2019-04-11 | 2019-08-20 | 北京任学教育科技有限公司 | Update method, device, equipment and the storage medium of knowledge point master degree |
| CN110659311A (en) * | 2019-09-30 | 2020-01-07 | 浙江学海教育科技有限公司 | Topic pushing method and device, electronic equipment and storage medium |
| CN110851723A (en) * | 2019-11-14 | 2020-02-28 | 上海钦文信息科技有限公司 | English exercise recommendation method based on large-scale knowledge point labeling result |
| CN111081106A (en) * | 2019-12-13 | 2020-04-28 | 北京爱论答科技有限公司 | Job pushing method, system, equipment and storage medium |
| CN111460145A (en) * | 2020-03-18 | 2020-07-28 | 天闻数媒科技（北京）有限公司 | Learning resource recommendation method, device and storage medium |
| CN111563156A (en) * | 2020-04-30 | 2020-08-21 | 广东小天才科技有限公司 | Intelligent knowledge point diagnosis method and device, electronic equipment and storage medium |
| CN112000714A (en) * | 2020-08-21 | 2020-11-27 | 扬州大学 | Mining method for extracting association of teaching knowledge points |
| CN112148890A (en) * | 2020-09-23 | 2020-12-29 | 中国科学院自动化研究所 | Teaching knowledge point spectrum system based on network group intelligence |
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