CN112734608A - Method and system for expanding concept of admiration course - Google Patents

Method and system for expanding concept of admiration course Download PDF

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CN112734608A
CN112734608A CN202011583108.XA CN202011583108A CN112734608A CN 112734608 A CN112734608 A CN 112734608A CN 202011583108 A CN202011583108 A CN 202011583108A CN 112734608 A CN112734608 A CN 112734608A
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expansion
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李涓子
于济凡
罗干
侯磊
张鹏
唐杰
许斌
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Tsinghua University
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Abstract

The embodiment of the invention provides a method and a system for expanding a concept of a mu course, wherein the method comprises the following steps: taking an online interactive game as a training environment, and training to obtain a reinforcement learning model; carrying out concept extension based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the extension process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course; and returning the user feedback to the interactive game for re-expansion until a preset target is reached, and obtaining an expansion result. The embodiment of the invention can be applied to newly established courses in a large scale after training on certain specific courses by using the training method of reinforcement learning, and saves a large amount of manual labels compared with the traditional method, thereby having stronger extensibility. Meanwhile, due to the multi-level training mode, the method can keep producing higher-quality extension results when processing courses in fields related to multiple disciplines.

Description

Method and system for expanding concept of admiration course
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for expanding a mu class course concept.
Background
With the popularization and commercialization of a large-scale Online Open Course platform (also called a mu class, Massive Open Online Course, abbreviated as MOOC), this novel education method attracts more and more autonomous learners to perform Online learning according to their needs and preferences. In fact, this mode brings convenience and also has some functional loss: in a traditional classroom, a teacher can supplement knowledge points required to be explained according to the performance and questions of students, and the interaction is difficult to achieve on a admiration class, because an admiration class is mainly a pre-recorded class video, when the students learn the class video, the problems in the course learning process cannot be timely fed back to the teacher, and the problems generally belong to some extended contents and knowledge point extensions in the class, and the interaction harvested between the teacher and the students is very delayed because the teacher cannot acquire the feedback of the students on site. One of the key technologies for supplementing additional online knowledge of courses is to find out knowledge concepts related to courses from various external resources, such as knowledge bases, texts, and search engines, according to the course contents to supplement the concepts of the courses.
However, the existing automatic concept expansion method has two reasons mainly and is difficult to use in the actual admiration course, the concept of the admiration course is often composed of concepts of multiple categories, and the existing method causes a semantic drift phenomenon when expanding the concept set, so that the quality of the expansion result is too low to use; secondly, the existing methods are mainly supervised training methods, and the course of the admiration is frequently updated, so that the performance effect of the methods on the newly-entered course is not ideal, and the teaching requirements cannot be met.
Therefore, there is a need for a method and system for extending the concept of a mu lesson to solve the above problems.
Disclosure of Invention
To solve the problems in the prior art, embodiments of the present invention provide a method and a system for extending a mu lesson concept.
In a first aspect, an embodiment of the present invention provides a method for extending a mu class course concept, including:
taking an online interactive game as a training environment, and training to obtain a reinforcement learning model;
carrying out concept extension based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the extension process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course;
and returning the user feedback to the interactive game for re-expansion until a preset target is reached, and obtaining an expansion result.
Further, the performing concept extension based on the in-class concept set and the reinforcement learning model, and obtaining user feedback in an extension process includes:
acquiring an in-class concept set, and extracting a part from the in-class concept set to be used as an extended seed;
searching in external resources by using the extended seeds, and removing concepts which appear in classes in search results to obtain a candidate concept set;
and inputting the candidate concept set into the reinforcement learning model for concept expansion, and acquiring user feedback in the expansion process.
Further, returning the user feedback to the interactive game for re-expanding until a preset target is reached to obtain an expansion result, including:
judging each concept in the candidate concept set to obtain a result of 0 or 1;
inputting the concept of result 1 into the game interface to obtain user feedback;
taking the user feedback as an independent reward value of the model, and mapping the independent reward value into a preset interval to be used as a model low-order training reward;
when all concepts are judged, summing all obtained independent reward values to obtain high-order training rewards;
and performing self-optimization based on the low-order training reward and the high-order training reward, and repeating the model expansion process until a preset target is reached to obtain an expansion result.
Further, the preset targets are:
the amount of concepts contained in the in-class concept set reaches a preset target, or all candidate concepts have been confirmed.
Further, the online interactive game is used as a training environment, and comprises the following steps:
constructing a game interface for collecting the feedback of the user on the expansion result;
data interaction is performed with user feedback based on the game interface.
Further, the obtaining of the in-class concept set includes:
acquiring a course knowledge concept corresponding to course content from external resources according to the course content of a mullet course to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine;
and taking the course knowledge concept as the content of a course knowledge point, and constructing the in-class concept set so as to carry out concept expansion on the admire to be expanded according to the in-class concept set.
In a second aspect, an embodiment of the present invention provides a system for extending a mu lesson concept, including:
the model training module is used for training an online interactive game as a training environment to obtain a reinforcement learning model;
the user feedback module is used for carrying out concept expansion based on an in-class concept set and the reinforcement learning model and obtaining user feedback in the expansion process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course;
and the expansion module is used for returning the user feedback to the interactive game for re-expansion until a preset target is reached to obtain an expansion result.
Further, the system further comprises:
the acquisition module is used for acquiring a course knowledge concept corresponding to the course content from external resources according to the course content of the admire to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine;
and the processing module is used for taking the course knowledge concept as the content of a course knowledge point, constructing the in-class concept set and performing concept expansion on the admire to be expanded according to the in-class concept set.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method provided in the first aspect when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method as provided in the first aspect.
According to the method and the system for expanding the concept of the mullet course, provided by the embodiment of the invention, the training method of reinforcement learning is used, so that the method and the system can be applied to newly established courses in a large scale after training is carried out on certain specific courses, and compared with the traditional method, a large amount of manual labels are saved, so that the method and the system have stronger extensibility. Meanwhile, due to the multi-level training mode, the method can keep producing higher-quality extension results when processing courses in fields related to multiple disciplines.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for extending a mu lesson concept according to an embodiment of the present invention;
FIG. 2 is a flow chart of reinforcement learning according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a system for extending the concept of a mu lesson according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
Fig. 1 is a schematic flow chart of a method for extending a mu lesson concept according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a method for extending a mu lesson concept, including:
step 101, taking an online interactive game as a training environment, and training to obtain a reinforcement learning model;
102, carrying out concept expansion based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the expansion process, wherein the in-class concept set is formed by the contents of course knowledge points which need to be supplemented and explained in a mullet course;
and 103, returning the user feedback to the interactive game for re-expansion until a preset target is reached to obtain an expansion result.
In the embodiment of the invention, a method capable of carrying out high-quality extension on a course concept set containing multi-class concepts is provided, and meanwhile, the method has stronger generalization capability on the course concepts except supervision, namely, better performance is kept on untrained courses, so that the requirement of automatic extension of the course concepts of the mu lesson is met. Fig. 2 is a flowchart of reinforcement learning according to an embodiment of the present invention, and as shown in fig. 2, first, a game feedback interface needs to be constructed, where the game feedback interface is used for performing data interaction with a model to obtain feedback information of a user; and then, expanding the admiration course based on the collected in-class concept set and the reinforced learning model for expanding the admiration course, sending the expanded content in the expansion process to the user side, repeatedly updating the training learning process according to the feedback of the user until reaching the degree that the expansion cannot be carried out, and outputting to obtain an expanded result. It should be noted that other types of interactive systems can be used as a training environment in the present invention, so as to train and obtain a reinforcement learning model, for example, an interactive data collection system.
The method for expanding the concept of the curriculum of the mullet provided by the embodiment of the invention uses the training method of reinforcement learning, can be applied to newly established curriculum in a large scale after training on certain specific curriculum, saves a large amount of manual labels compared with the traditional method, and has stronger extensibility. Meanwhile, due to the multi-level training mode, the method can keep producing higher-quality extension results when processing courses in fields related to multiple disciplines.
On the basis of the above embodiment, the performing concept extension based on the in-class concept set and the reinforcement learning model, and acquiring user feedback in an extension process includes:
acquiring an in-class concept set, and extracting a part from the in-class concept set to be used as an extended seed;
searching in external resources by using the extended seeds, and removing concepts which appear in classes in search results to obtain a candidate concept set;
and inputting the candidate concept set into the reinforcement learning model for concept expansion, and acquiring user feedback in the expansion process.
In the embodiment of the invention, the in-class concept set is collected firstly, a part of concepts in the in-class concept set is selected as seeds, and meanwhile, the in-class concepts are searched in external resources; then, the existing concept extraction method is used for reserving concepts in the search results, removing concepts which have appeared in the lesson and using the concepts as a candidate expansion set; and finally, based on a reinforcement learning model, judging each concept in the candidate concept set, and expanding again according to the feedback result until reaching an expansion upper limit to obtain an expansion result.
On the basis of the above embodiment, returning the user feedback to the interactive game for re-expansion until a preset target is reached to obtain an expansion result includes:
judging each concept in the candidate concept set to obtain a result of 0 or 1;
inputting the concept of result 1 into the game interface to obtain user feedback;
taking the user feedback as an independent reward value of the model, and mapping the independent reward value into a preset interval to be used as a model low-order training reward;
when all concepts are judged, summing all obtained independent reward values to obtain high-order training rewards;
and performing self-optimization based on the low-order training reward and the high-order training reward, and repeating the model expansion process until a preset target is reached to obtain an expansion result.
In the embodiment of the invention, from the candidate expansion set, each concept in the candidate expansion set is distinguished according to the expansion seed (as the seed of the expansion of the current round) obtained in the above embodiment and the obtained whole candidate expansion set, a binary result of 0 or 1 is obtained, the concept of 1 is input to the game interface, the feedback condition of the user is collected and used as the independent reward value of the model, and the independent reward value is mapped to an interval of (-1,1) to be used as the low-order training reward of the model. After the model receives the low-level training reward, the low-level part of the model is optimized according to the reward.
Further, when all concepts of the candidate expansion set are judged, all obtained independent reward values are added to obtain the reward value of the round, and the reward value is the high-level training reward of the model. And when all concepts judged to be 1 in the current round are used as expansion results and input into the in-class concept set, the model performs self-optimization on the high-order part according to the high-order training obtained in the current round.
On the basis of the above embodiment, the preset targets are:
the amount of concepts contained in the in-class concept set reaches a preset target, or all candidate concepts have been confirmed.
In the embodiment of the present invention, the concept extension process is a repeated process, and the specific stopping condition is that the amount of concepts contained in the in-class concept set reaches a preset target, or all the found candidate concepts have been confirmed.
On the basis of the above embodiment, the taking of the online interactive game as the training environment includes:
constructing a game interface for collecting the feedback of the user on the expansion result;
data interaction is performed with user feedback based on the game interface.
In the embodiment of the invention, a game interface capable of collecting the feedback of the user on the expansion result is constructed firstly, then the in-class concept set is collected, the set is utilized to carry out seed selection and concept expansion, and finally multi-level training is achieved to obtain the expansion result with higher quality.
On the basis of the above embodiment, the acquiring a set of in-class concepts includes:
acquiring a course knowledge concept corresponding to course content from external resources according to the course content of a mullet course to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine;
and taking the course knowledge concept as the content of a course knowledge point, and constructing the in-class concept set so as to carry out concept expansion on the admire to be expanded according to the in-class concept set.
In the embodiment of the invention, based on the course content of the existing admiration course, knowledge concepts related to the admiration course are found from various external resources, such as a knowledge base, text content and a search engine, so that the knowledge concepts of the course are supplemented.
Fig. 3 is a schematic structural diagram of a system for extending mu course concepts according to an embodiment of the present invention, and as shown in fig. 3, the embodiment of the present invention provides a system for extending mu course concepts, which includes a model training module 301, a user feedback module 302, and an extension module 303, where the model training module 301 is configured to train an online interactive game as a training environment to obtain a reinforcement learning model; the user feedback module 302 is configured to perform concept extension based on an in-class concept set and the reinforcement learning model, and obtain user feedback in an extension process, where the in-class concept set is formed by course knowledge point contents that need to be supplemented with explanation in a mullet course; the expansion module 303 is configured to return the user feedback to the interactive game for re-expansion until a preset target is reached, so as to obtain an expansion result.
The system for expanding the concept of the curriculum of the mullet provided by the embodiment of the invention uses the training method of reinforcement learning, can be applied to newly established curriculum in a large scale after training on certain specific curriculum, saves a large amount of manual labels compared with the traditional method, and has stronger extensibility. Meanwhile, due to the multi-level training mode, the method can keep producing higher-quality extension results when processing courses in fields related to multiple disciplines.
On the basis of the embodiment, the system further comprises an acquisition module and a processing module, wherein the acquisition module is used for acquiring the course knowledge concept corresponding to the course content from external resources according to the course content of the mullet to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine; the processing module is used for taking the course knowledge concept as the content of a course knowledge point, constructing the in-class concept set and performing concept expansion on the admire to be expanded according to the in-class concept set.
The system provided by the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 4, the electronic device may include: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a communication bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the communication bus 404. Processor 401 may call logic instructions in memory 403 to perform the following method: taking an online interactive game as a training environment, and training to obtain a reinforcement learning model; carrying out concept extension based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the extension process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course; and returning the user feedback to the interactive game for re-expansion until a preset target is reached, and obtaining an expansion result.
In addition, the logic instructions in the memory 403 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes 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 invention. 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.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for extending the mu course concept provided in the foregoing embodiments, for example, the method includes: taking an online interactive game as a training environment, and training to obtain a reinforcement learning model; carrying out concept extension based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the extension process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course; and returning the user feedback to the interactive game for re-expansion until a preset target is reached, and obtaining an expansion result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for extending mu lesson concepts, comprising:
taking an online interactive game as a training environment, and training to obtain a reinforcement learning model;
carrying out concept extension based on an in-class concept set and the reinforcement learning model, and acquiring user feedback in the extension process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course;
and returning the user feedback to the interactive game for re-expansion until a preset target is reached, and obtaining an expansion result.
2. The method for extending mu lesson concept according to claim 1, wherein the concept extension based on the in-class concept set and the reinforcement learning model and obtaining user feedback during the extension process comprises:
acquiring an in-class concept set, and extracting a part from the in-class concept set to be used as an extended seed;
searching in external resources by using the extended seeds, and removing concepts which appear in classes in search results to obtain a candidate concept set;
and inputting the candidate concept set into the reinforcement learning model for concept expansion, and acquiring user feedback in the expansion process.
3. The method of expanding mu lesson concepts as claimed in claim 2, wherein said returning said user feedback to said interactive game for re-expansion until reaching a predetermined goal, resulting in an expanded result, comprises:
judging each concept in the candidate concept set to obtain a result of 0 or 1;
inputting the concept of result 1 into the game interface to obtain user feedback;
taking the user feedback as an independent reward value of the model, and mapping the independent reward value into a preset interval to be used as a model low-order training reward;
when all concepts are judged, summing all obtained independent reward values to obtain high-order training rewards;
and performing self-optimization based on the low-order training reward and the high-order training reward, and repeating the model expansion process until a preset target is reached to obtain an expansion result.
4. The method for extending mu lesson concepts according to claim 1, wherein the preset goals are:
the amount of concepts contained in the in-class concept set reaches a preset target, or all candidate concepts have been confirmed.
5. The method of extending mu lesson concepts of claim 1, wherein said using an online interactive game as a training environment comprises:
constructing a game interface for collecting the feedback of the user on the expansion result;
data interaction is performed with user feedback based on the game interface.
6. The method for extending mu lesson concepts according to claim 2, wherein said obtaining a set of in-lesson concepts comprises:
acquiring a course knowledge concept corresponding to course content from external resources according to the course content of a mullet course to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine;
and taking the course knowledge concept as the content of a course knowledge point, and constructing the in-class concept set so as to carry out concept expansion on the admire to be expanded according to the in-class concept set.
7. A system for extending mu lesson concepts, comprising:
the model training module is used for training an online interactive game as a training environment to obtain a reinforcement learning model;
the user feedback module is used for carrying out concept expansion based on an in-class concept set and the reinforcement learning model and obtaining user feedback in the expansion process, wherein the in-class concept set is formed by course knowledge point contents needing to be supplemented and explained in a mullet course;
and the expansion module is used for returning the user feedback to the interactive game for re-expansion until a preset target is reached to obtain an expansion result.
8. The system for extending mu lesson concepts according to claim 7, further comprising:
the acquisition module is used for acquiring a course knowledge concept corresponding to the course content from external resources according to the course content of the admire to be expanded, wherein the external resources comprise a knowledge base, text content and a search engine;
and the processing module is used for taking the course knowledge concept as the content of a course knowledge point, constructing the in-class concept set and performing concept expansion on the admire to be expanded according to the in-class concept set.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of extending a mu course concept according to any one of claims 1 to 6.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method of extending a mu course concept according to any one of claims 1 to 6.
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CN114567815A (en) * 2022-01-20 2022-05-31 清华大学 Pre-training-based admiration class self-adaptive learning system construction method and device
CN114567815B (en) * 2022-01-20 2023-05-02 清华大学 Pre-training-based adaptive learning system construction method and device for lessons

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