CN110727822B - Online learning system based on personalized recommendation - Google Patents
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- CN110727822B CN110727822B CN201911134193.9A CN201911134193A CN110727822B CN 110727822 B CN110727822 B CN 110727822B CN 201911134193 A CN201911134193 A CN 201911134193A CN 110727822 B CN110727822 B CN 110727822B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/63—Querying
- G06F16/635—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
- G06F16/64—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/74—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B5/00—Electrically-operated educational appliances
- G09B5/04—Electrically-operated educational appliances with audible presentation of the material to be studied
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- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
Abstract
The utility model discloses an online learning system based on personalized recommendation, which comprises: a decomposition module; a playing module; a feedback module; a distribution module; a teaching module; and a learning module. The utility model also provides an online learning method based on personalized recommendation, electronic equipment and a storage medium. The utility model can improve the interaction and the autonomous learning of students, and leads the learning to become interesting and effective.
Description
Technical Field
The utility model relates to an online learning system. More particularly, the present invention relates to an online learning system based on personalized recommendations.
Background
With the rise of artificial intelligence, the arrival of knowledge economy impacts the existing learning mode. An online education platform is constructed, students use a network to learn online to become a new learning mode, and the online education platform has the advantages that the learning education is humanized: students can learn in a favorite way according to respective levels and speeds, and the learning method has the defects of lack of necessary supervision, interaction and test, easy realization of inexplicable understanding of students in fast-paced life, and just low learning quality and low efficiency.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.
It is still another object of the present invention to provide an online learning system based on personalized recommendation, which can improve the interaction and autonomous learning of students, and make the learning become interesting and effective.
To achieve these objects and other advantages in accordance with the purpose of the utility model, there is provided an online learning system based on personalized recommendations, comprising:
a decomposition module which decomposes the learning audio or video into a plurality of segments according to a time axis and marks the content of each segment;
the playing module plays the learning audio or video and displays the content marks of all the sections on a time axis;
a feedback module that collects user unique annotation tags for each segment, annotation tags including "teach", "learn", "over";
the allocation module is used for grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', the users are allocated to the teaching queue of the section, if the annotation marks of the users are 'learning', the users are allocated to the learning queue of the section, and if the annotation marks of the users are 'passing', the users are not allocated;
the teaching module is used for distributing the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group with a preset time length section by taking each section as a unit, and sending annotation marks made by the intention of the users of the teaching queue of the section after the one-to-one guidance with the preset time length to the feedback module again;
and the learning module collects the users in the learning queue of the section by taking each section as a unit to form a one-to-many discussion group of a preset time length section, simultaneously forms a one-to-one guidance group of the preset time length section according to the distribution of the teaching module, and sends annotation marks made by the learning results of the users in the learning queue of the section after the preset time length to the feedback module again.
Preferably, the method further comprises the following steps:
and the excitation module is used for collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an excitation mechanism corresponding to the user evaluation.
Preferably, the method further comprises the following steps:
and the test module collects the test results of the users in the course test by taking the users as units, and recommends corresponding segments to the users according to the test results.
The online learning method based on personalized recommendation comprises the following steps:
step 1) decomposing learning audio or video into a plurality of segments according to a time axis, and marking the content of each segment;
step 2) playing learning audio or video, and displaying the content marks of all the sections on a time axis;
step 3) collecting unique annotation marks of each section, wherein the annotation marks comprise 'teaching', 'learning' and 'over';
step 4) grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', distributing the users to a teaching queue of the section, if the annotation marks of the users are 'learning', distributing the users to a learning queue of the section, and if the annotation marks of the users are 'passing', not distributing the annotations;
step 5) taking each section as a unit, allocating the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group of a preset time length section, and returning annotation marks made by the intention of the users of the teaching queue of the section after one-to-one guidance of the preset time length to the step 3);
and (3) summarizing the users of the learning queue of each section by taking each section as a unit, forming a one-to-many discussion group of a preset time length section, simultaneously forming a one-to-one guidance group of the preset time length section according to the distribution of the teaching modules, and returning annotation marks made by the users of the learning queue of the section after the learning result of the preset time length to the step 3).
Preferably, the method further comprises the following steps:
and 6) collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an incentive mechanism corresponding to the user evaluation.
Preferably, the method further comprises the following steps:
and 7) collecting the test result of the user in the course test by taking the user as a unit, and recommending the corresponding section to the user according to the test result.
An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the method.
The utility model at least comprises the following beneficial effects:
the user can independently learn through learning audio or video, can carry out unique annotation mark according to the condition of absorbing knowledge point in the learning process of each section, and the user can find out to each section of knowledge point through one-to-one, one-to-many mode and further learn in the teaching, learning process to obtain new results in the study, make the study no longer single, boring, promote study enjoyment and efficiency greatly.
Additional advantages, objects, and features of the utility model will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the utility model.
Drawings
Fig. 1 is a schematic flow chart of a technical solution of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the utility model by referring to the description text.
It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
It is to be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials, if not otherwise specified, are commercially available; in the description of the present invention, it should be noted that unless otherwise explicitly stated or limited, the terms "mounted," "connected," and "disposed" are to be construed broadly and can, for example, be fixedly connected, disposed, detachably connected, disposed, or integrally connected and disposed. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. The terms "lateral," "longitudinal," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the utility model and to simplify the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the utility model.
The utility model provides an online learning system based on personalized recommendation, which comprises:
the decomposition module is used for decomposing the learning audio or video into a plurality of sections according to a time axis, the duration of each section is not limited, manual division can be carried out according to knowledge points and curriculum plates, and each section is marked with content, so that the section content linkage can be conveniently carried out in the later period;
the playing module plays the learning audio or video, can selectively play according to the requirements of a user, for example, the learning audio is played at the rate of less than or equal to 512Kbps, the learning video is played at more than 1Mbps, and content marks of all sections are displayed on a time axis so as to inform the user of the learning content of the section, so that the user can conveniently check defects and fill leaks at the later stage;
the feedback module is used for collecting unique annotation marks of each section, wherein the annotation marks comprise 'teaching', 'learning' and 'passing', the user needs to carry out unique annotation marks on each section according to the self understanding degree in the learning process, the 'teaching' represents that the user completely understands the knowledge point of the section and is willing to interact with other users for teaching, the 'learning' represents that the user is suspicious of the knowledge point of the section, needs to learn further and is willing to interact with other users for learning, and the 'passing' represents that the user completely understands the knowledge point of the section and is unwilling to interact with other users for directly learning the next section;
the distribution module is used for grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', the users are distributed into the teaching queue of the section, if the annotation marks of the users are 'learning', the users are distributed into the learning queue of the section, if the annotation marks of the users are 'over', the users are not distributed, each learning audio or video comprises a plurality of sections, if the annotation marks of the users at different sections are different, the grouping is different, and after the learning audio or video is finished, the users can enter the queues of different sections for teaching and learning;
the teaching module is used for allocating the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group of a preset time length section by taking each section as a unit, the one-to-one guidance teaching has time length limitation, for example, 15min, the learning users and the teaching users communicate within the preset time length, after the teaching is finished, the teaching users perform unique annotation mark expression intention on the one-to-one learning again, and the annotation mark made by the intention of the users of the teaching queue of the section after the one-to-one guidance of the preset time length is sent to the feedback module again;
the learning module collects the users in the learning queue of the section and forms a one-to-many discussion group with a preset time length section by taking each section as a unit, the one-to-many discussion group has no time length limitation, the learning users can interact in the voice and character communication direction, one discussion group can be specially set for each section, namely, the learning users of the section automatically enter the discussion group, meanwhile, a one-to-one guidance group with the preset time length section is formed according to the distribution of the teaching module, the learning users can communicate with each other and can ask the teaching users to teach, the learning users carry out unique annotation marking on the results of the one-to-many discussion and one-to-one learning again, and the annotation marking made by the learning results of the learning queue of the section after the learning process with the preset time length is sent to the feedback module again.
In the technical scheme, the user can independently learn through learning audio or video, unique annotation marking can be carried out according to the condition of absorbing knowledge points in the learning process of each section, the user can find out each section of knowledge points in a one-to-one or one-to-many mode in the teaching and learning processes to further learn, and new harvest is obtained in the learning process, so that the learning is not single and boring, and the learning interest and efficiency are greatly improved.
In another technical solution, the method further comprises:
and the excitation module is used for collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an excitation mechanism corresponding to the user evaluation.
In the above technical solution, a teaching user interacts with a learning user in a one-to-one guidance process, after learning is completed, the learning user can evaluate the teaching user, for example, make a single or multiple choice evaluation, including the specialty, teaching effect, teaching attitude, and the like of the teaching user, or make a subjective opinion evaluation, can weigh the evaluation of the learning user and the feedback items to obtain a score, or a grade evaluation, and reward the teaching user for the score and the grade evaluation, where the incentive mechanism is pre-stored in the incentive module, and the form of the reward is not limited, for example, the fee paid by the user for learning is reduced to a certain extent, or cash reward is performed, and the like. The subjective teaching of the user of the teaching can be supervised, and the enthusiasm of the user of the teaching is improved.
In another technical solution, the method further comprises:
and the test module collects the test results of the users in the course test by taking the users as units, and recommends corresponding segments to the users according to the test results.
In the technical scheme, after the user finishes learning, the conventional course test is carried out, the subject of the course test aims at the knowledge point of one or more segments, if the user answers the subject, the links between the user and the segments are increased, the results of all the subjects can form a link network between the user and each segment to a certain extent, the corresponding segment is recommended to the user according to the link network, and whether to carry out the relearning is subjectively determined by the user. The learning effect of the user can be ensured, and the learning efficiency of the user is improved.
An online learning method based on personalized recommendation, as shown in fig. 1, includes:
step 1) decomposing learning audio or video into a plurality of segments according to a time axis, and marking the content of each segment;
step 2) playing learning audio or video, and displaying the content marks of all the sections on a time axis;
step 3) collecting unique annotation marks of each section, wherein the annotation marks comprise 'teaching', 'learning' and 'over';
step 4) grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', distributing the users to a teaching queue of the section, if the annotation marks of the users are 'learning', distributing the users to a learning queue of the section, and if the annotation marks of the users are 'passing', not distributing the annotations;
step 5) taking each section as a unit, allocating the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group of a preset time length section, and returning annotation marks made by the intention of the users of the teaching queue of the section after one-to-one guidance of the preset time length to the step 3);
and (3) summarizing the users of the learning queue of each section by taking each section as a unit, forming a one-to-many discussion group of a preset time length section, simultaneously forming a one-to-one guidance group of the preset time length section according to the distribution of the teaching modules, and returning annotation marks made by the users of the learning queue of the section after the learning result of the preset time length to the step 3).
In the technical scheme, the user can independently learn through learning audio or video, unique annotation marking can be carried out according to the condition of absorbing knowledge points in the learning process of each section, the user can find out each section of knowledge points in a one-to-one or one-to-many mode in the teaching and learning processes to further learn, and new harvest is obtained in the learning process, so that the learning is not single and boring, and the learning interest and efficiency are greatly improved.
In another technical solution, the method further comprises:
and 6) collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an incentive mechanism corresponding to the user evaluation.
In the above technical solution, a teaching user interacts with a learning user in a one-to-one guidance process, after learning is completed, the learning user can evaluate the teaching user, for example, make a single or multiple choice evaluation, including the specialty, teaching effect, teaching attitude, and the like of the teaching user, or make a subjective opinion evaluation, can weigh the evaluation of the learning user and the feedback items to obtain a score, or a grade evaluation, and reward the teaching user for the score and the grade evaluation, where the incentive mechanism is pre-stored in the incentive module, and the form of the reward is not limited, for example, the fee paid by the user for learning is reduced to a certain extent, or cash reward is performed, and the like. The subjective teaching of the user of the teaching can be supervised, and the enthusiasm of the user of the teaching is improved.
In another technical solution, the method further comprises:
and 7) collecting the test result of the user in the course test by taking the user as a unit, and recommending the corresponding section to the user according to the test result.
In the technical scheme, after the user finishes learning, the conventional course test is carried out, the subject of the course test aims at the knowledge point of one or more segments, if the user answers the subject, the links between the user and the segments are increased, the results of all the subjects can form a link network between the user and each segment to a certain extent, the corresponding segment is recommended to the user according to the link network, and whether to carry out the relearning is subjectively determined by the user. The learning effect of the user can be ensured, and the learning efficiency of the user is improved.
An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method. And forming a professional personalized recommended online learning device.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the method. And forming a professional personalized recommended online learning storage medium.
The number of apparatuses and the scale of the process described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the present invention will be apparent to those skilled in the art.
While embodiments of the utility model have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the utility model pertains, and further modifications may readily be made by those skilled in the art, it being understood that the utility model is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (8)
1. The online learning system based on personalized recommendation is characterized by comprising:
a decomposition module which decomposes the learning audio or video into a plurality of segments according to a time axis and marks the content of each segment;
the playing module plays the learning audio or video and displays the content marks of all the sections on a time axis;
a feedback module that collects user unique annotation tags for each segment, annotation tags including "teach", "learn", "over";
the allocation module is used for grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', the users are allocated to the teaching queue of the section, if the annotation marks of the users are 'learning', the users are allocated to the learning queue of the section, and if the annotation marks of the users are 'passing', the users are not allocated;
the teaching module is used for distributing the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group with a preset time length section by taking each section as a unit, and sending annotation marks made by the intention of the users of the teaching queue of the section after the one-to-one guidance with the preset time length to the feedback module again;
and the learning module collects the users in the learning queue of the section by taking each section as a unit to form a one-to-many discussion group of a preset time length section, simultaneously forms a one-to-one guidance group of the preset time length section according to the distribution of the teaching module, and sends annotation marks made by the learning results of the users in the learning queue of the section after the preset time length to the feedback module again.
2. The online learning system based on personalized recommendations as claimed in claim 1, further comprising:
and the excitation module is used for collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an excitation mechanism corresponding to the user evaluation.
3. The online learning system based on personalized recommendations as claimed in claim 1, further comprising:
and the test module collects the test results of the users in the course test by taking the users as units, and recommends corresponding segments to the users according to the test results.
4. The online learning method based on personalized recommendation is characterized by comprising the following steps:
step 1) decomposing learning audio or video into a plurality of segments according to a time axis, and marking the content of each segment;
step 2) playing learning audio or video, and displaying the content marks of all the sections on a time axis;
step 3) collecting unique annotation marks of each section, wherein the annotation marks comprise 'teaching', 'learning' and 'over';
step 4) grouping the users according to the annotation marks of the users, if the annotation marks of the users are 'teaching', distributing the users to a teaching queue of the section, if the annotation marks of the users are 'learning', distributing the users to a learning queue of the section, and if the annotation marks of the users are 'passing', not distributing the annotations;
step 5) taking each section as a unit, allocating the users of the teaching queue of the section to the users of the learning queue to form a one-to-one guidance group of a preset time length section, and returning annotation marks made by the intention of the users of the teaching queue of the section after one-to-one guidance of the preset time length to the step 3);
and (3) summarizing the users of the learning queue of each section by taking each section as a unit, forming a one-to-many discussion group of a preset time length section, simultaneously forming a one-to-one guidance group of the preset time length section according to the distribution of the teaching modules, and returning annotation marks made by the users of the learning queue of the section after the learning result of the preset time length to the step 3).
5. The method for online learning based on personalized recommendations according to claim 4, further comprising:
and 6) collecting user evaluation obtained by one-to-one guidance of the user in the teaching queue by taking the user as a unit, and prestoring an incentive mechanism corresponding to the user evaluation.
6. The method for online learning based on personalized recommendations according to claim 4, further comprising:
and 7) collecting the test result of the user in the course test by taking the user as a unit, and recommending the corresponding section to the user according to the test result.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 4-6.
8. Storage medium on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 4-6.
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