CN112001825A - Learning cognitive path planning system based on cognitive map - Google Patents
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Abstract
The invention provides a learning cognitive path planning system based on a cognitive map, which improves the analysis refinement degree of the cognitive map by dividing the cognitive map into a plurality of sub-maps and can also adjust the learning sequence and/or the learning progress of different users to corresponding knowledge point data according to the interest degree of different learners to different sub-maps, thereby effectively improving the teaching quality and the teaching efficiency.
Description
Technical Field
The invention relates to the technical field of intelligent education, in particular to a learning cognitive path planning system based on a cognitive map.
Background
The existing teaching mode is to perform uniform teaching on all learners indiscriminately according to the same cognitive map, and although the teaching mode can be suitable for the learning requirements of most learners and effectively reduce the workload of teaching preparation in advance, the teaching mode cannot perform adaptive learning cognitive path planning on learners with different learning requirements and learning abilities, so that the teaching quality and the teaching efficiency are reduced. Therefore, a teaching mode capable of matching and adjusting learning cognitive paths according to learning requirements and learning interests of different learners is urgently needed in the prior art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a learning cognitive path planning system based on a cognitive map, which comprises a knowledge data acquisition module, a cognitive map construction module, a cognitive map spectrum division module, a user-cognitive map interestingness evaluation module and a learning cognitive path planning module, wherein the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about preset disciplines; the cognitive map construction module is used for preprocessing a plurality of knowledge point data, and constructing and obtaining a cognitive map related to the plurality of knowledge point data according to text semantic information corresponding to the preprocessed knowledge point data; the cognitive map division module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points; the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum; the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users; therefore, the learning cognitive path planning system based on the cognitive map improves the analysis refinement degree of the cognitive map by dividing the cognitive map into a plurality of sub-maps, and can adjust the learning sequence and/or the learning progress of different users to corresponding knowledge point data according to the interest degree of different learners to different sub-maps, thereby effectively improving the teaching quality and the teaching efficiency.
The invention provides a learning cognitive path planning system based on a cognitive map, which is characterized by comprising a knowledge data acquisition module, a cognitive map construction module, a cognitive map spectrum division module, a user-cognitive map interestingness evaluation module and a learning cognitive path planning module; wherein the content of the first and second substances,
the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about preset subject categories;
the cognitive map building module is used for preprocessing the data of the knowledge points and building a cognitive map related to the data of the knowledge points according to text semantic information corresponding to the preprocessed data of the knowledge points;
the cognitive map dividing module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points;
the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum;
the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users;
further, the knowledge data acquisition module comprises a subject gate class determination submodule, a knowledge point data acquisition submodule and a knowledge point data cache submodule; wherein the content of the first and second substances,
the subject department class determination submodule is used for determining a subject department class corresponding to the current learning scheme planning so as to obtain the preset subject department class, wherein the preset subject department class comprises any one of language, mathematics, English, physics, chemistry, biology, politics, geography and history;
the knowledge point data acquisition submodule is used for picking a plurality of knowledge point data matched with the preset discipline categories from a preset discipline knowledge database according to the preset discipline categories;
the knowledge point data caching submodule is used for performing ascending order caching on data length on all knowledge point data according to the data length of the knowledge point data;
further, the cognitive map building module comprises a knowledge point data noise reduction sub-module, a knowledge point data text semantic conversion sub-module and a cognitive map generation sub-module; wherein the content of the first and second substances,
the knowledge point data denoising submodule is used for performing Kalman filtering pretreatment on a plurality of knowledge point data so as to remove noise components of the knowledge point data;
the knowledge point data text semantic conversion submodule is used for converting the knowledge point data into text semantic information with context meaning;
the cognitive map generation submodule is used for generating a cognitive map related to a plurality of knowledge point data according to the text semantic information;
further, the cognitive map dividing module comprises a knowledge point difficulty type determining sub-module and a sub-map spectrum generating sub-module; wherein the content of the first and second substances,
the knowledge point difficulty type determining submodule is used for determining a difficulty evaluation value of each knowledge point data according to a preset knowledge point data difficulty evaluation model;
the sub-graph spectrum generation sub-module is used for dividing the cognitive graph into N sub-graphs according to the difficulty evaluation value, wherein the difficulty evaluation value of all knowledge point data contained in each sub-graph falls in a preset different value interval;
further, the user-sub-graph spectrum interestingness evaluation module comprises a user-sub-graph spectrum interestingness value calculation module and an interestingness value screening sub-module; wherein the content of the first and second substances,
the user-sub-graph interestingness value calculation sub-module is used for calculating the interestingness value of each user in the M users to each sub-graph in the N sub-graphs according to the following formula (1)
In the above formula (1), PgkRepresents the interest value of the kth user on the kth sub-atlas, and g is 1, 2, 3, …, M, k is 1, 2, 3, …, N, TgkRepresents the time, T, of the kth user browsing the kth sub-atlas1The preset browsing upper limit time of each sub-map is represented, and lambda represents a preset external influence factor corresponding to the browsing of the kth sub-map by the kth user, and the value of the external influence factor is [0.05, 0.1 ]]，DkThe period time of carrying out knowledge point data updating on the kth sub-map is represented, and the knowledge point data updating refers to the modification, deletion or addition of the knowledge point data on the kth sub-map;
the interest degree value screening submodule is used for screening a plurality of interest degree values in a preset interest degree qualified range from all the interest degree values obtained through calculation, and the interest degree values are used as effective interest degree values;
further, the learning cognitive path planning module comprises an interest degree sequence generation sub-module, an interest degree sequence processing sub-module, a target population division module and a learning sequence and/or learning progress determination sub-module; wherein the content of the first and second substances,
the interest degree sequence generation submodule is used for performing descending order arrangement on all the effective interest degree values so as to generate a corresponding interest degree sequence;
the interestingness sequence processing submodule is used for calculating the similarity between the interestingness sequence of any one user of the M users and the interestingness sequences of the rest users;
the target crowd division module is used for dividing the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity;
the learning sequence and/or learning progress determining submodule is used for respectively determining different learning sequences and/or learning progresses of knowledge point data aiming at the first target population and the second target population;
further, the interestingness sequence processing sub-module is configured to calculate similarity between the interestingness sequence of any one of the M users and the interestingness sequences of the remaining users according to the following formula (2)
In the above formula (2), sim (a, b) represents the similarity between the sequence of interestingness of the a-th user of the M users and the sequence of interestingness of the b-th user of the remaining users, PaiRepresents the interest value of the ith user to the ith sub-atlas, and i is 1, 2, 3, …, N, PbjRepresents the interest value of the jth sub-atlas of the jth user, and j is 1, 2, 3, …, N, QaRepresents the value of the test score, Q, determined after the normalized test of the a-th userbThe value of the test score determined after the standardized test is carried out on the b-th user is represented, wherein the standardized test refers to the test of knowledge points of the user according to a preset test question bank, K represents a preset experience value and takes the value of (0, 1)]；
Further, the target crowd division module divides the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity,
comparing the calculated similarity with a preset similarity threshold, if the similarity is greater than or equal to the preset similarity threshold, dividing the user corresponding to the current similarity into the first target group, and if the similarity is less than the preset similarity threshold, dividing the user corresponding to the current similarity into the second target group;
the learning sequence and/or learning progress determining submodule specifically determines the learning sequence and/or learning progress of different knowledge point data for the first target population and the second target population respectively, including
Aiming at the first target population, adjusting the learning sequence and/or learning progress of the knowledge point data according to the sequence of difficulty before difficulty;
and aiming at the second target population, adjusting the learning sequence and/or the learning progress of the knowledge point data according to the sequence of easy first and difficult second.
Compared with the prior art, the learning cognitive path planning system based on the cognitive map comprises a knowledge data acquisition module, a cognitive map construction module, a cognitive map spectrum division module, a user-cognitive map interestingness evaluation module and a learning cognitive path planning module, wherein the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about preset subject categories; the cognitive map construction module is used for preprocessing a plurality of knowledge point data, and constructing and obtaining a cognitive map related to the plurality of knowledge point data according to text semantic information corresponding to the preprocessed knowledge point data; the cognitive map division module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points; the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum; the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users; therefore, the learning cognitive path planning system based on the cognitive map improves the analysis refinement degree of the cognitive map by dividing the cognitive map into a plurality of sub-maps, and can adjust the learning sequence and/or the learning progress of different users to corresponding knowledge point data according to the interest degree of different learners to different sub-maps, thereby effectively improving the teaching quality and the teaching efficiency.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
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, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a learning cognitive path planning system based on a cognitive atlas provided in the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 structural diagram of a learning cognitive path planning system based on a cognitive atlas according to an embodiment of the present invention. The learning cognitive path planning system based on the cognitive map comprises a knowledge data acquisition module, a cognitive map construction module, a cognitive map division module, a user-cognitive map interestingness evaluation module and a learning cognitive path planning module; wherein the content of the first and second substances,
the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about preset subject categories;
the cognitive map construction module is used for preprocessing a plurality of knowledge point data, and constructing and obtaining a cognitive map related to the plurality of knowledge point data according to text semantic information corresponding to the preprocessed knowledge point data;
the cognitive map division module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points;
the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum;
the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users.
The learning cognitive path planning system based on the cognitive map is different from learning cognitive path design of unified knowledge point data of all users in the prior art, the cognitive map is formed by a plurality of collected knowledge point data, the cognitive map is divided into a plurality of sub-maps, and then according to the interest degrees of different users to different sub-maps, the learning sequence and/or learning progress adjustment of the differentiated knowledge point data is carried out according to the interest degrees of different users, so that the learning quality and/or the learning sequence of the users for learning different knowledge point data are improved, the learning sequence and/or the learning progress of the different users for the corresponding knowledge point data can be adjusted according to the interest degrees of different learners to different sub-maps, and the teaching quality and the teaching efficiency are effectively improved.
Preferably, the knowledge data acquisition module comprises a subject gate class determination submodule, a knowledge point data acquisition submodule and a knowledge point data cache submodule; wherein the content of the first and second substances,
the subject department class determining submodule is used for determining a subject department class corresponding to the current learning scheme planning so as to obtain a preset subject department class, wherein the preset subject department class comprises any one of language, mathematics, English, physics, chemistry, biology, politics, geography and history;
the knowledge point data acquisition submodule is used for picking a plurality of knowledge point data matched with the preset discipline department from a preset discipline knowledge database according to the preset discipline department;
the knowledge point data caching submodule is used for performing ascending order caching on data length on all knowledge point data according to the data length of a plurality of knowledge point data.
The extraction of the knowledge point data by determining the subject department can ensure the correctness of the extraction of the knowledge point data, thereby avoiding the subsequent condition of mistaken extraction of the knowledge point data.
Preferably, the cognitive map building module comprises a knowledge point data noise reduction submodule, a knowledge point data text semantic conversion submodule and a cognitive map generation submodule; wherein the content of the first and second substances,
the knowledge point data denoising submodule is used for performing Kalman filtering pretreatment on a plurality of knowledge point data so as to remove noise components of the knowledge point data;
the knowledge point data text semantic conversion submodule is used for converting the knowledge point data into text semantic information with context meaning;
the cognitive map generation submodule is used for generating a cognitive map related to a plurality of knowledge point data according to the text semantic information.
The data of the knowledge graph is filtered, denoised and converted by text semantics, so that the simplification degree of the data of the cognitive graph and the reliability of the data can be improved.
Preferably, the cognitive map spectrum division module comprises a knowledge point difficulty type determination sub-module and a sub-map spectrum generation sub-module; wherein the content of the first and second substances,
the knowledge point difficulty type determining submodule is used for determining a difficulty evaluation value of each knowledge point data according to a preset knowledge point data difficulty evaluation model;
the sub-graph spectrum generation sub-module is used for dividing the cognitive graph into N sub-graphs according to the difficulty evaluation value, wherein the difficulty evaluation value of all knowledge point data contained in each sub-graph falls in a preset different value interval.
The difficulty evaluation value of each knowledge point data is calculated, so that each knowledge point data can be accurately classified into a proper sub-map, and the partition reliability of the sub-map is improved.
Preferably, the user-sub-graph spectrum interestingness evaluation module comprises a user-sub-graph spectrum interestingness value calculation module and an interestingness value screening sub-module; wherein the content of the first and second substances,
the user-sub-graph interestingness value calculation sub-module is used for calculating the interestingness value of each user in the M users to each sub-graph in the N sub-graphs according to the following formula (1)
In the above formula (1), PgkRepresents the interest value of the kth user on the kth sub-atlas, and g is 1, 2, 3, …, M, k is 1, 2, 3, …, N, TgkRepresents the time, T, of the kth user browsing the kth sub-atlas1The preset browsing upper limit time of each sub-map is represented, and lambda represents a preset external influence factor corresponding to the browsing of the kth sub-map by the kth user, and the value of the external influence factor is [0.05, 0.1 ]]，DkThe period time of carrying out knowledge point data updating on the kth sub-map is represented, and the knowledge point data updating refers to the modification, deletion or addition of the knowledge point data on the kth sub-map;
the interest degree value screening submodule is used for screening a plurality of interest degree values in a preset interest degree qualified range from all the interest degree values obtained through calculation, and the interest degree values are used as effective interest degree values.
Preferably, the learning cognitive path planning module comprises an interest degree sequence generation sub-module, an interest degree sequence processing sub-module, a target population division module and a learning sequence and/or learning progress determination sub-module; wherein the content of the first and second substances,
the interest degree sequence generation submodule is used for performing descending order arrangement on all the effective interest degree values so as to generate a corresponding interest degree sequence;
the interestingness sequence processing submodule is used for calculating the similarity between the interestingness sequence of any one user of the M users and the interestingness sequences of the rest users;
the target crowd division module is used for dividing the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity;
the learning sequence and/or learning progress determining submodule is used for respectively determining different learning sequences and/or learning progresses of knowledge point data aiming at the first target population and the second target population.
Preferably, the interestingness sequence processing sub-module is configured to calculate similarity between the interestingness sequence of any one of the M users and the interestingness sequences of the remaining users according to the following formula (2)
In the above formula (2), sim (a, b) represents the similarity between the sequence of interestingness of the a-th user of the M users and the sequence of interestingness of the b-th user of the remaining users, PaiRepresents the interest value of the ith user to the ith sub-atlas, and i is 1, 2, 3, …, N, PbjRepresents the interest value of the jth sub-atlas of the jth user, and j is 1, 2, 3, …, N, QaRepresents the value of the test score, Q, determined after the normalized test of the a-th userbThe value of the test score determined after the standardized test is carried out on the b-th user is shown, wherein the standardized test refers to the test of the knowledge point of the user according to a preset test question bank, K represents a preset experience value and takes the value of (0, 1)]。
The learning sequence and/or learning progress of each user on the knowledge point data is determined by calculating the interest value of each user on different sub-maps in the cognitive map, the time occupied by useless parts is removed, the learning efficiency of the knowledge point data can be effectively improved, the experience of the user is improved, a reasonable learning sequence and/or learning progress planning scheme can be generated for the user in a targeted manner, the waiting time of the user is reduced, the user can obtain the learning planning scheme at the first time, and the experience of the user is further improved.
Preferably, the target crowd division module divides the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity,
comparing the calculated similarity with a preset similarity threshold, if the similarity is greater than or equal to the preset similarity threshold, dividing the user corresponding to the current similarity into the first target group, and if the similarity is less than the preset similarity threshold, dividing the user corresponding to the current similarity into the second target group;
the learning order and/or learning progress determining submodule specifically determines the learning order and/or learning progress of the different knowledge point data for the first target population and the second target population respectively, including
Aiming at the first target population, adjusting the learning sequence and/or the learning progress of the knowledge point data according to the sequence of difficulty before difficulty;
and aiming at the second target population, adjusting the learning sequence and/or the learning progress of the data of the knowledge point according to the sequence of easy first and difficult second.
As can be seen from the content of the above embodiment, the learning cognitive path planning system based on the cognitive atlas comprises a knowledge data acquisition module, a cognitive atlas construction module, a cognitive atlas division module, a user-cognitive atlas interestingness evaluation module and a learning cognitive path planning module, wherein the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about a preset disciplinary department; the cognitive map construction module is used for preprocessing a plurality of knowledge point data, and constructing and obtaining a cognitive map related to the plurality of knowledge point data according to text semantic information corresponding to the preprocessed knowledge point data; the cognitive map division module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points; the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum; the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users; therefore, the learning cognitive path planning system based on the cognitive map improves the analysis refinement degree of the cognitive map by dividing the cognitive map into a plurality of sub-maps, and can adjust the learning sequence and/or the learning progress of different users to corresponding knowledge point data according to the interest degree of different learners to different sub-maps, thereby effectively improving the teaching quality and the teaching efficiency.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. The learning cognitive path planning system based on the cognitive map is characterized by comprising a knowledge data acquisition module, a cognitive map construction module, a cognitive map spectrum division module, a user-cognitive map interestingness evaluation module and a learning cognitive path planning module; wherein the content of the first and second substances,
the knowledge data acquisition module is used for acquiring a plurality of knowledge point data about preset subject categories; the cognitive map building module is used for preprocessing the data of the knowledge points and building a cognitive map related to the data of the knowledge points according to text semantic information corresponding to the preprocessed data of the knowledge points;
the cognitive map dividing module is used for dividing the cognitive map into a plurality of sub-maps according to the difficulty type of the knowledge points;
the user-sub-graph spectrum interestingness evaluation module is used for evaluating the learning interestingness value of each user in a plurality of different users on each sub-graph spectrum;
the learning cognitive path planning module is used for determining the learning sequence and/or the learning progress of a plurality of knowledge point data of different users according to the learning interest values corresponding to all the users.
2. The cognitive map-based learning cognitive path planning system of claim 1, wherein:
the knowledge data acquisition module comprises a subject gate class determination submodule, a knowledge point data acquisition submodule and a knowledge point data cache submodule; wherein the content of the first and second substances,
the subject department class determination submodule is used for determining a subject department class corresponding to the current learning scheme planning so as to obtain the preset subject department class, wherein the preset subject department class comprises any one of language, mathematics, English, physics, chemistry, biology, politics, geography and history;
the knowledge point data acquisition submodule is used for picking a plurality of knowledge point data matched with the preset discipline categories from a preset discipline knowledge database according to the preset discipline categories;
the knowledge point data caching submodule is used for performing ascending order caching on data length on all knowledge point data according to the data length of the knowledge point data.
3. The cognitive map-based learning cognitive path planning system of claim 1, wherein:
the cognitive map building module comprises a knowledge point data noise reduction submodule, a knowledge point data text semantic conversion submodule and a cognitive map generation submodule; wherein the content of the first and second substances,
the knowledge point data denoising submodule is used for performing Kalman filtering pretreatment on a plurality of knowledge point data so as to remove noise components of the knowledge point data;
the knowledge point data text semantic conversion submodule is used for converting the knowledge point data into text semantic information with context meaning;
and the cognitive map generation submodule is used for generating a cognitive map related to a plurality of knowledge point data according to the text semantic information.
4. The cognitive map-based learning cognitive path planning system of claim 1, wherein:
the cognitive map dividing module comprises a knowledge point difficulty type determining sub-module and a sub-map generating sub-module; wherein the content of the first and second substances,
the knowledge point difficulty type determining submodule is used for determining a difficulty evaluation value of each knowledge point data according to a preset knowledge point data difficulty evaluation model;
and the sub-graph spectrum generation sub-module is used for dividing the cognitive graph into N sub-graphs according to the difficulty evaluation value, wherein the difficulty evaluation value of all knowledge point data contained in each sub-graph falls in a preset different value interval.
5. The cognitive map-based learning cognitive path planning system of claim 4, wherein:
the user-sub-graph spectrum interest degree evaluation module comprises a user-sub-graph spectrum interest degree value calculation module and an interest degree value screening sub-module; wherein the content of the first and second substances,
the user-sub-graph interestingness value calculation sub-module is used for calculating the interestingness value of each user in the M users to each sub-graph in the N sub-graphs according to the following formula (1)
In the above formula (1), PgkRepresents the interest value of the kth user on the kth sub-atlas, and g is 1, 2, 3, …, M, k is 1, 2, 3, …, N, TgkRepresents the time, T, of the kth user browsing the kth sub-atlas1The preset browsing upper limit time of each sub-map is represented, and lambda represents that the g-th user is out of the preset corresponding to the browsing of the k-th sub-mapA boundary influence factor of [0.05, 0.1 ]]，DkThe period time of carrying out knowledge point data updating on the kth sub-map is represented, and the knowledge point data updating refers to the modification, deletion or addition of the knowledge point data on the kth sub-map;
and the interest degree value screening submodule is used for screening a plurality of interest degree values in a preset interest degree qualified range from all the interest degree values obtained through calculation, and the interest degree values are used as effective interest degree values.
6. The cognitive map-based learning cognitive path planning system of claim 5, wherein:
the learning cognitive path planning module comprises an interest degree sequence generation sub-module, an interest degree sequence processing sub-module, a target crowd division module and a learning sequence and/or learning progress determination sub-module; wherein the content of the first and second substances,
the interest degree sequence generation submodule is used for performing descending order arrangement on all the effective interest degree values so as to generate a corresponding interest degree sequence;
the interestingness sequence processing submodule is used for calculating the similarity between the interestingness sequence of any one user of the M users and the interestingness sequences of the rest users;
the target crowd division module is used for dividing the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity;
the learning sequence and/or learning progress determining submodule is used for respectively determining different learning sequences and/or learning progresses of knowledge point data aiming at the first target crowd and the second target crowd.
7. The cognitive map-based learning cognitive path planning system of claim 6, wherein:
the interestingness sequence processing sub-module is used for calculating the similarity between the interestingness sequence of any one user of the M users and the interestingness sequences of the other users according to the following formula (2)
In the above formula (2), sim (a, b) represents the similarity between the sequence of interestingness of the a-th user of the M users and the sequence of interestingness of the b-th user of the remaining users, PaiRepresents the interest value of the ith user to the ith sub-atlas, and i is 1, 2, 3, …, N, PbjRepresents the interest value of the jth sub-atlas of the jth user, and j is 1, 2, 3, …, N, QaRepresents the value of the test score, Q, determined after the normalized test of the a-th userbThe value of the test score determined after the standardized test is carried out on the b-th user is represented, wherein the standardized test refers to the test of knowledge points of the user according to a preset test question bank, K represents a preset experience value and takes the value of (0, 1)]。
8. The cognitive map-based learning cognitive path planning system of claim 7, wherein:
the target crowd division module divides the M users into a first target crowd and a second target crowd which are not overlapped with each other according to the similarity,
comparing the calculated similarity with a preset similarity threshold, if the similarity is greater than or equal to the preset similarity threshold, dividing the user corresponding to the current similarity into the first target group, and if the similarity is less than the preset similarity threshold, dividing the user corresponding to the current similarity into the second target group;
the learning sequence and/or learning progress determining submodule specifically determines the learning sequence and/or learning progress of different knowledge point data for the first target population and the second target population respectively, including
Aiming at the first target population, adjusting the learning sequence and/or learning progress of the knowledge point data according to the sequence of difficulty before difficulty;
and aiming at the second target population, adjusting the learning sequence and/or the learning progress of the knowledge point data according to the sequence of easy first and difficult second.
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| CN106156365A (en) * | 2016-08-03 | 2016-11-23 | 北京智能管家科技有限公司 | A kind of generation method and device of knowledge mapping |
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