CN103440243B - A kind of teaching resource recommendation method and device thereof - Google Patents
A kind of teaching resource recommendation method and device thereof Download PDFInfo
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Abstract
The invention discloses a kind of teaching resource recommendation method and device thereof, comprise the following steps：Obtain the education activities of user terminal；The resource matched with described education activities is obtained from teaching resource, recommendation information is then generated；The recommendation information is sent to user terminal.So as to which the present invention can make user terminal reduce the resource searching time, improve the utilization rate of teaching resource.
Description
Technical Field
The embodiment of the invention relates to the technical field of Internet, in particular to a teaching resource recommendation method and a device thereof.
Background
In a teaching resource network, a teacher may retrieve resources in a variety of ways, such as by keywords, categories (logos, textbooks, grades, media types, topics), or a combination of the two. However, in the face of such a vastly "ocean of resources," it is difficult for users to find the real needed educational resources in a short time. Therefore, it often causes "information lost" and "information overload". On one hand, the utilization rate of teaching resources is low, and on the other hand, the difficulty is brought to the user to apply the teaching resources to carry out information teaching.
Disclosure of Invention
The embodiment of the invention provides a teaching resource recommendation method and a device thereof, which can reduce resource search time of a user terminal and improve the utilization rate of teaching resources.
The teaching resource recommendation method provided by the embodiment of the invention comprises the following steps:
acquiring a teaching activity of a user terminal;
acquiring resources matched with the teaching activities from teaching resources, and then generating recommendation information;
and sending the recommendation information to a user terminal.
Optionally, before the instructional resource recommendation method is executed, instructional data uploaded by at least one user terminal is received, and the received instructional data is integrated into the instructional resource.
Furthermore, the teaching resources are teaching resources with multi-dimensional attributes.
Further, the step of performing multidimensional attribute integration processing on the received teaching data in the teaching resource recommendation method to obtain the teaching resource with multidimensional attributes includes:
receiving teaching data and extracting the teaching data;
selecting a modeling algorithm, and establishing a multi-dimensional teaching data model according to the extracted teaching data;
the multidimensional teaching data model is used as a teaching resource with multidimensional attributes.
Further, the multidimensional teaching data model comprises data in three dimensions: the method comprises the steps of obtaining the type dimension of the teaching data, the content category dimension of the teaching data and the judgment dimension of the user terminal on the teaching data.
Further, in the teaching resource recommendation method, the step of acquiring the resource matched with the teaching activity from the teaching resource and then generating recommendation information includes:
extracting activity data of the teaching activities;
preprocessing the extracted activity data;
selecting a modeling algorithm, and establishing a teaching activity data model according to the preprocessed activity data;
and acquiring data matched with the teaching activity data model in the teaching resources, and then generating recommendation information.
Further, after the step of generating recommendation information, the teaching resource recommendation method can also store the teaching activities and the generated recommendation information.
Further, classifying all the stored teaching activities into groups according to similarity while storing the teaching activities and the generated recommendation information;
and then the recommendation information in each group is respectively sent to the user terminals corresponding to the teaching activities in the group.
Based on the above object, the present invention further provides a teaching resource recommendation device, including:
the teaching activity unit is used for receiving and storing the teaching activity of the user terminal;
the teaching resource unit is used for storing teaching resource data;
and the intelligent recommendation engine is used for acquiring resources matched with the teaching activities in the teaching activity unit from the teaching resource unit, generating recommendation information and sending the recommendation information to the user terminal.
Optionally, the teaching resource unit receives teaching data uploaded by at least one user terminal, and performs integration processing on the received teaching data.
Furthermore, the teaching resource unit is a teaching resource with multi-dimensional attributes.
Further, the teaching resource unit comprises a receiving module, a teaching data extraction module and a modeling module, wherein:
the receiving module is used for receiving the teaching data uploaded by at least one user terminal;
the teaching data extraction module is used for extracting teaching data from the receiving module;
and the modeling module is used for selecting a modeling algorithm and establishing a multi-dimensional teaching data model according to the extracted teaching data.
Further, the multidimensional teaching data model comprises data in three dimensions: the method comprises the steps of obtaining the type dimension of the teaching data, the content category dimension of the teaching data and the judgment dimension of the user terminal on the teaching data.
Optionally, the intelligent recommendation engine includes a teaching activity data extraction module, a modeling module, a matching module and an output module; wherein:
the teaching activity data extraction module is used for extracting activity data from the teaching activity units and preprocessing the activity data;
the modeling module is used for selecting a modeling algorithm and establishing a teaching activity data model according to the preprocessed activity data;
the matching module is used for acquiring data matched with the teaching activity data model from the teaching resource unit and generating recommendation information;
and the output module is used for sending the generated recommendation information to the user terminal.
Furthermore, the intelligent recommendation engine also comprises a group matching module which is connected with the matching module;
and the group matching module receives and stores the teaching activity data model and recommendation information obtained after the teaching activity data model is matched with the data in the teaching resource unit.
Further, the grouping matching module classifies all the stored teaching activities into groups according to similarity, and sends the recommendation information in each group to the user terminal corresponding to each teaching activity in the group.
As can be seen from the above, the teaching resource recommendation method and the device thereof provided in the embodiments of the present invention generate recommendation information by acquiring resources matched with the teaching activities from teaching resources, and send the recommendation information to the user terminal. Therefore, highly accurate recommendation information can be provided for the user terminal, and the search processing work of the user terminal on teaching resources is greatly simplified.
Drawings
FIG. 1 is a schematic flow chart of a teaching resource recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a recommendation information generation method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a teaching resource recommendation device according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a teaching resource unit with multi-dimensional attributes according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an intelligent recommendation engine according to an 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 described in further detail below with reference to specific embodiments and the accompanying drawings.
Referring to fig. 1, a schematic flowchart of a teaching resource recommendation method according to an embodiment of the present invention is shown, including:
step 101, obtaining teaching activities of a user terminal.
And 102, acquiring resources matched with the teaching activities from teaching resources, and then generating recommendation information.
In one embodiment of the invention, before the teaching resource recommendation method is carried out, the teaching data uploaded by at least one user terminal is received, and the received teaching data is integrated into the teaching resource. Preferably, the teaching data source is a teaching resource with multi-dimensional attributes. The teaching resources with multi-dimensional attributes can be obtained by the following method:
the first step is as follows: and receiving teaching data, and extracting and preprocessing the teaching data.
The rule engine is a component embedded in an application program, and the work task of the rule engine is to test and compare the teaching data currently submitted to the rule engine with all business rules loaded in the rule engine, find and activate the business rules in accordance with the current data state, and then trigger corresponding operation according to the execution logic of the activated business rules.
In addition, extracted teaching data can be preprocessed, for example, abnormal value processing that can delete data exceeding a certain range; conversion of the format of the extracted data into a modeled canonical format, and the like.
The second step is that: and selecting a modeling algorithm, and establishing a multi-dimensional teaching data model according to the extracted teaching data.
The selected modeling algorithm may adopt a decision tree algorithm, a cluster analysis algorithm, and the like. As one example, the created multidimensional teaching data model may include data in three dimensions: one dimension is the type of the teaching data, including text, images, animations, courseware, video, audio, etc., and the user terminal can be applied to different types in different application scenarios. One dimension is the content category of the teaching data, including subject, grade, knowledge point, difficulty, keywords, etc. The dimension has strong relevance with the information needed by the user terminal, such as that a teacher makes courseware for a specific subject, a specific grade and a specific knowledge point. The dimension is used for judging teaching data by the user terminal and comprises scoring, evaluation, download rate, adoption rate and the like, and the dimension can ensure that the finally pushed information is teaching resources with high comprehensive judgment.
The third step: the multidimensional teaching data model is used as a teaching resource with multidimensional attributes.
In another embodiment of the present invention, as shown in fig. 2, the teaching resource recommendation method adopts the following method when generating recommendation information:
step 201, extracting activity data of the teaching activities.
Step 202, preprocessing the extracted activity data.
And 203, selecting a modeling algorithm, and establishing a teaching activity data model according to the preprocessed activity data. The modeling algorithm may adopt a decision tree algorithm, a cluster analysis algorithm, etc., and then a teaching activity data model is established according to the selected modeling algorithm and the activity data preprocessed in step 202.
And 204, acquiring data matched with the teaching activity data model from the multi-dimensional teaching data model. Wherein, the multidimensional teaching data model can be obtained by the method in step 102.
In step 205, recommendation information is generated.
And 103, sending the recommendation information to the user terminal.
It should be further noted that, in another embodiment of the present invention, after performing step 204, the teaching resource recommendation method may store the teaching activity data model and the recommendation information obtained after matching the teaching activity data model with the multidimensional teaching data model, and perform similarity classification grouping on all the stored teaching activity data models. And then, sending all recommendation information of each teaching activity data model in each group to the user terminal corresponding to each teaching activity data model in the group.
Fig. 3 is a schematic structural diagram of a teaching resource recommendation device according to an embodiment of the present invention. The teaching resource recommendation device comprises: a teaching activity unit 301, a teaching resource unit 302 and an intelligent recommendation engine 303; wherein,
the teaching activity unit 301 is connected with the intelligent recommendation engine 303, can receive teaching activities of the user terminal, and can store the teaching activities.
The teaching resource unit 302 is connected with the intelligent recommendation engine 303 and stores teaching resource data.
The teaching resource unit 302 receives teaching data uploaded from at least one user terminal, and can perform integration processing on the received teaching data.
In one embodiment of the present invention, as shown in FIG. 4, the tutorial resource unit 302 includes a receiving module 401, a tutorial data extraction module 402, and a modeling module 403. The receiving module 401 receives teaching data uploaded by at least one user terminal. The teaching data extraction module 402 can extract the teaching data in the receiving module 401 through a data extraction rule set by a rule engine, the rule engine is a component embedded in an application program, and the work task of the rule engine is to test and compare the teaching data currently submitted to the rule engine with all business rules loaded in the rule engine, find and activate the business rules in accordance with the current data state, and then trigger corresponding operations according to the execution logic of the activated business rules. For example; the data is extracted for the purpose of making courseware by teachers. In addition, the teaching data extraction module 402 can also preprocess the extracted teaching data. The modeling module 403 selects a modeling algorithm, which may be a decision tree algorithm, a cluster analysis algorithm, or the like, and then builds a multi-dimensional teaching data model based on the data extracted by the teaching data extraction module 402.
Preferably, the multidimensional teaching data model may include data of three dimensions, namely, a type dimension of teaching data, a content category dimension of teaching data, and an evaluation dimension of the teaching data by the user terminal. The type dimension of the teaching data comprises text, images, animations, courseware, videos, audios and the like, and the user terminal can be applied to different types in different application scenes. The content category dimension of the teaching data comprises subject, grade, knowledge point, difficulty degree, keyword and the like. The dimension has strong relevance with the information needed by the user terminal, such as that a teacher makes courseware for a specific subject, a specific grade and a specific knowledge point. The evaluation dimensionality of the user terminal for the teaching data comprises scoring, evaluation, download rate, adoption rate and the like, and the dimensionality can guarantee that the finally pushed information is teaching resources with high comprehensive evaluation.
The intelligent recommendation engine 303 can acquire resources matched with the teaching activities in the teaching activity unit 301 from the teaching resource unit 302 with the multidimensional attribute, then generate recommendation information, and finally send the recommendation information to the user terminal.
As one embodiment of the present invention, as shown in FIG. 5, the intelligent recommendation engine 303 includes an instructional activity data extraction module 501, a modeling module 502, a matching module 503, and an output module 504. Among them, the data extraction module 501 extracts activity data from the teaching activity unit 301 by a data extraction rule set by the rule engine. In addition, the data extraction module 501 may also perform preprocessing on the extracted teaching data. The modeling module 502 selects a modeling algorithm and then builds a teaching activity data model from the pre-processed activity data. The matching module 503 obtains data matched with the teaching activity data model from the multidimensional teaching data model to generate recommendation information. The output module 504 transmits the recommendation information generated by the matching module 503 to the user terminal.
For example, the teacher's behavior during courseware making is represented by: 1. the teacher likes the courseware style, 2, subject and grade of teacher's teaching, 3, the knowledge point concerned by teacher, 4, the data type frequently downloaded by teacher. And establishing a teaching activity data model according to the interesting activities of the teacher in the courseware making process, and then acquiring teaching resources matched with the teaching activity data model from the established multidimensional teaching data model to generate recommendation information.
It should be further noted that, in another embodiment of the present invention, the intelligent recommendation engine 303 further includes a group matching module 505, connected to the matching module 503, for receiving and storing the teaching activity data model and the recommendation information obtained by matching the teaching activity data model with the multidimensional teaching data model. The grouping matching module 505 classifies the similarity of all the stored teaching activity data models into groups, and then sends all the recommendation information of each teaching activity data model in each group to the user terminal corresponding to each teaching activity data model in the group through the output module 504. Therefore, potential new interesting teaching resources can be found for each user terminal, and information recommended by the intelligent recommendation engine is expanded.
Therefore, the teaching resource recommendation method and the device thereof creatively provide a multidimensional teaching data model which is established by integrating the teaching resource data, a teaching activity model which is established by processing the teaching activity of the user terminal, and finally the teaching resource data which is matched with the teaching activity data model is obtained from the multidimensional teaching data model, and recommendation information is generated and pushed to the user terminal; the accuracy and the satisfaction degree of the information pushed by the user terminal are greatly improved; in addition, the teaching resource data are divided into multiple dimensions according to the characteristics of the teaching resources in practical application, and the data can be screened from each dimension; meanwhile, the user terminals with the teaching activity models with the similarity can form groups with the same interest, so that potential teaching resource information is provided for the user terminals through the teaching activity models of other user terminals in the groups, and the value-added service of the invention to each user terminal is improved; finally, the whole teaching resource recommendation method and the device thereof are simple, convenient and compact, are easy to realize, and provide the most convenient and practical experience for the user terminal.
It should be noted that, in the respective components of the controller of the present invention, the components therein are logically divided according to the functions to be implemented, but the present invention is not limited thereto, and the respective components may be re-divided or combined as needed, for example, some components may be combined into a single component, or some components may be further decomposed into more sub-components.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a controller according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (2)
1. A teaching resource recommendation method is characterized by comprising the following steps:
acquiring a teaching activity of a user terminal;
acquiring resources matched with the teaching activities from teaching resources, and then generating recommendation information;
sending the recommendation information to a user terminal; before the teaching resource recommendation method is executed, receiving teaching data uploaded by at least one user terminal, and integrating the received teaching data into the teaching resources;
after the step of generating recommendation information, the teaching resource recommendation method can also store the teaching activities and the generated recommendation information; classifying all the stored teaching activities into groups according to the similarity while storing the teaching activities and the generated recommendation information; then, the recommendation information in each group is respectively sent to the user terminals corresponding to the teaching activities in the group;
the teaching resources are teaching resources with multi-dimensional attributes;
the teaching resource recommendation method comprises the following steps of integrating the received teaching data with the multidimensional attribute to obtain the teaching resource with the multidimensional attribute:
receiving teaching data and extracting the teaching data;
selecting a modeling algorithm, and establishing a multi-dimensional teaching data model according to the extracted teaching data;
the multidimensional teaching data model is used as a teaching resource with multidimensional attribute;
the multi-dimensional teaching data model comprises data of three dimensions: the method comprises the steps of obtaining type dimension of teaching data, content category dimension of the teaching data and evaluation dimension of a user terminal on the teaching data;
in the teaching resource recommendation method, the steps of acquiring resources matched with the teaching activities from teaching resources and then generating recommendation information comprise:
extracting activity data of the teaching activities;
preprocessing the extracted activity data;
selecting a modeling algorithm, and establishing a teaching activity data model according to the preprocessed activity data;
and acquiring data matched with the teaching activity data model in the teaching resources, and then generating recommendation information.
2. An instructional resource recommendation apparatus, comprising:
the teaching activity unit is used for receiving and storing the teaching activity of the user terminal;
the teaching resource unit is used for storing teaching resource data;
the intelligent recommendation engine is used for acquiring resources matched with the teaching activities in the teaching activity unit from the teaching resource unit, generating recommendation information and sending the recommendation information to the user terminal; the teaching resource unit receives teaching data uploaded by at least one user terminal and integrates the received teaching data;
the intelligent recommendation engine also comprises a group matching module which is connected with the matching module; the group matching module receives and stores the teaching activity data model and recommendation information obtained after the teaching activity data model is matched with data in the teaching resource unit; the group matching module classifies all the stored teaching activities into groups according to the similarity, and respectively sends the recommendation information in each group to the user terminal corresponding to each teaching activity in the group;
the teaching resource unit is a teaching resource with multi-dimensional attributes;
the teaching resource unit comprises a receiving module, a teaching data extraction module and a modeling module, wherein:
the receiving module is used for receiving the teaching data uploaded by at least one user terminal;
the teaching data extraction module is used for extracting teaching data from the receiving module;
the modeling module is used for selecting a modeling algorithm and establishing a multi-dimensional teaching data model according to the extracted teaching data;
the multi-dimensional teaching data model comprises data of three dimensions: the method comprises the steps of obtaining type dimension of teaching data, content category dimension of the teaching data and evaluation dimension of a user terminal on the teaching data;
the intelligent recommendation engine comprises a teaching activity data extraction module, a modeling module, a matching module and an output module; wherein:
the teaching activity data extraction module is used for extracting activity data from the teaching activity units and preprocessing the activity data;
the modeling module is used for selecting a modeling algorithm and establishing a teaching activity data model according to the preprocessed activity data;
the matching module is used for acquiring data matched with the teaching activity data model from the teaching resource unit and generating recommendation information;
and the output module is used for sending the generated recommendation information to the user terminal.
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| CN105787099A (en) * | 2016-03-18 | 2016-07-20 | 广东小天才科技有限公司 | Method and device for acquiring learning data |
| CN108628967B (en) * | 2018-04-23 | 2020-07-28 | 西安交通大学 | Network learning group division method based on learning generated network similarity |
| CN110019862B (en) * | 2018-09-28 | 2021-09-10 | 广州视源电子科技股份有限公司 | Courseware recommendation method, device, equipment and storage medium |
| CN111260165B (en) * | 2018-11-30 | 2022-07-08 | 北京嘀嘀无限科技发展有限公司 | Service efficiency control method and device |
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Effective date of registration: 20140812 Address after: C1104 room 100085 Beijing city Haidian District Third Street No. 9 C Applicant after: HONGHE TECHNOLOGY CO., LTD. Address before: Baoan District Xin'an street Shenzhen city Guangdong province 518100 Liuxian road two Honghui Industrial Park No. four building 1 floor Applicant before: Shenzhen Honghe Innovation Information Technology Co., Ltd. |
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Address after: C1104 room 100085 Beijing city Haidian District Third Street No. 9 C Patentee after: Hitevision Polytron Technologies Inc Address before: C1104 room 100085 Beijing city Haidian District Third Street No. 9 C Patentee before: HONGHE TECHNOLOGY CO., LTD. |
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