CN110599831A - Big data-based adaptive learning system and learner model construction method - Google Patents
Big data-based adaptive learning system and learner model construction method Download PDFInfo
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Abstract
The invention discloses a big data-based self-adaptive learning system, relates to the field of network self-learning education, and solves the problem that the existing network education system has no pertinence, and the technical scheme is as follows: a big data-based self-adaptive learning system comprises a network course module, a personal information module and a cloud database, wherein the network course module comprises a course pushing unit, a course editing unit, a course extending unit, a post-session exercise unit, a quality supervision unit, a feedback unit, a storage unit, a course evaluation unit, an online evaluation unit and a user authority management unit; the personal information module comprises a basic information unit, a safety information unit and a academic information unit; the big data-based self-adaptive learning system provided by the invention carries out course pushing on a user in a targeted manner through big data processing; the big data-based self-adaptive learning system based on image recognition uses various learning auxiliary tools, so that the user can learn more conveniently.
Description
Technical Field
The invention relates to the field of network self-learning education, in particular to a self-adaptive learning system based on big data and a learner model construction method.
Background
With the deep development of education informatization, the interactive modes of online open learning are increased, the participating learners are continuously increased, and the learning data accumulated by the online learning system is increased explosively. How to mine, analyze and utilize these data is a necessary premise for realizing personalized learning.
In the teaching process in a classroom, the knowledge structure of each learner is large in difference and the learning ability is uneven, and teachers are difficult to give consideration to all learners due to limited energy and time, so that learners with weak learning bases cannot keep pace with learning, and learners with strong learning ability cannot exert the learning autonomy. Meanwhile, the learning process of the learner is a dynamic process, and the traditional description of the learner is in a form of a 'black box', so that the learning process of the learner is difficult to be comprehensively and visually described. Therefore, the method has important significance for comprehensively and visually describing the learning process of the learner so as to construct a more accurate learner model.
Therefore, people have searched for the basic learning models, and the basic learning models currently exist are a coverage model, a deviation model, a cognitive model and a psychological model. However, these models are only focused on some aspects of learners to perform modeling, and in the environment of big educational data, the advantages of various models need to be combined to construct a more complete learner model.
Therefore, the educators need to construct a model of the learner by acquiring data of online learning and quantifying and extracting the learning experience of the learner, so as to visually represent the learning process of the learner under different time and space. But it is difficult to accurately depict the learner's learning process by only relying on the basic static data of the learner's learning process. With the development of big data analysis and mining technology, the learner's learning process will be described more accurately by building a learner model using static data and dynamic data in combination.
Disclosure of Invention
The invention aims to provide a big data-based self-adaptive learning system, which can perfect the self-learning process of learners in the network learning process and realize the self-perfection of the system.
The technical purpose of the invention is realized by the following technical scheme:
a big data-based self-adaptive learning system comprises a network course module and a personal information module, wherein the network course module comprises a course pushing unit, a course editing unit, a course extending unit, a post-session problem unit, a quality supervision unit, a feedback unit, a storage unit, a course evaluation unit, an online evaluation unit and a user authority management unit; the personal information module comprises a basic information unit, a safety information unit and a academic information unit;
the course pushing unit pushes courses to the user according to the information in the personal information module and the information in the cloud database, the course editing unit edits the duration of the courses, the course extending unit edits the contents of the courses, the quality supervision unit is used for performing behavior supervision in the learning process of the user, the after-school exercise unit is used for testing the user after the courses are finished, the storage unit is used for storing the online data modified by the user, the course evaluation unit is used for evaluating the courses by the user after the courses are finished, the online evaluation unit is used for commenting the course content by the user and displaying the course content to other users, the feedback unit is used for receiving and integrating data in the quality supervision unit, the post-session problem unit, the course evaluation unit and the online evaluation unit and sending the integrated data to the cloud database, and the user authority management unit is used for distinguishing the user from an administrator; the basic information unit stores basic information of a user and sends the basic information to the cloud database, the safety information unit is used for storing an account and a password of the user logging in the system, and the academic information unit is used for storing academic information and sending the academic information to the cloud database.
By adopting the technical scheme, the user inputs own basic information through the basic information unit, inputs an account and a password of the user for logging in the system through the safety information unit, and inputs academic records, professional records and related professional records through the academic information unit; the system carries out course pushing for the user through the course pushing unit according to the information input before, the user selects related courses after receiving the pushed courses, and stores the related selections through the storage unit, so that the user can directly click to continue learning next time, after selecting the corresponding courses, the user can edit the duration of each video section through the course editing unit, so that the requirements of different users are met, and the user who works edits the duration of each course section to enable the user to reasonably utilize the own time.
In the course of learning courses, along with different learning experiences of different users, different learning materials are learned, different knowledge points are mastered, and a plurality of situations that the learning progress is not matched occur, for example, some theorems or proper nouns occur in the learning materials, but the user does not master the basic principle of the teaching materials, but the principle is necessary for subsequent learning, or in another situation, the user learns too deeply at a certain knowledge point, the course explains the basic principle for a plurality of times, a large amount of time is spent on the user, and the user cannot learn new knowledge, and a plurality of problems also occur in the process of later review, at the moment, the user refines and supplements the knowledge points which are not understood and are not mentioned through the course extending unit, so that the study is facilitated, and the review is also facilitated; deleting the comprehended knowledge points to improve the learning efficiency; after the user uses the course extending unit, the modified content can be selectively disclosed, after the content is disclosed, other users can check the corresponding modified content through the online evaluation unit, the modified content is mainly embodied in an annotation form and cannot influence the normal reading of the text, other users can also modify the local course of the user through the course extending unit by the annotated content so that the user can obtain review content suitable for the user, the quality supervision unit carries out quality supervision on the course of learning the course of the user and uploads the review content to the feedback unit, and the storage unit stores the user progress and information edited by the course editing unit and the course extending unit; the user authority management unit can distinguish a user from an administrator, the administrator can make some modifications to the course text according to the times of the online user modifying the local data based on the user through the course extension unit, and the annotations which are considered to be good by most people are added to the text content, so that the course can meet the requirements of more people.
When the user finishes learning each time of the learning plan, the after-class exercise unit provides corresponding exercises for the user to practice, compares answers answered by the user with standard answers, generates corresponding correction pages, sends the correction pages to the user and sends the correction pages to the feedback unit; after the exercise is finished, the user evaluates the exercise through the exercise evaluation unit, scores and comments, evaluation information is sent to the feedback unit, the feedback unit collects the information and then sends the information to the cloud database and the system manager in a centralized mode, the system can supervise and evaluate the learning state of the user according to exercise conditions and quality of the user, when the score is too low, the user is judged to be a problem, when the score is too high, the system is judged to be a problem, the system can improve pertinence of other information sent by the feedback unit, the system is also continuously improved, similar users are met in the later period, namely, when the information in the personal information module is similar, the exercise pushing unit selectively pushes the information.
The embodiment is further configured that the course pushing unit includes a professional course pushing component, a teacher pushing component, and a section-time division pushing component; the course propelling movement subassembly carries out the propelling movement to the course content, and the teacher's propelling movement subassembly carries out the propelling movement to the different teacher's courses of the same content, and the length of a lesson divides the propelling movement subassembly to carry out the course propelling movement according to the length of a lesson that the user selected, and the user can self-defined selects different courses to study, and can carry out different selections according to self study condition and idle time condition, and is very convenient.
The embodiment is further configured that the quality supervision unit includes a pause number counting component and a course window scaling number counting component; the temporary times counting component counts the temporary times of the user in the learning process, and the course window zooming times counting component counts the learning condition of the user in the learning process so as to know the learning condition of the user, scores the learning state of the user according to the two parameters in the learning process of the user, and can also be used for judging the maturity of the system and the user experience to correspondingly score, so that the user can be facilitated and simultaneously can continuously perfect the system.
The embodiment is further configured that the network course module further includes a learning auxiliary tool, and the learning auxiliary tool includes a screenshot tool, a voice capture tool, a voice recording tool, and an image editing tool; the user can intercept images and voices aiming at different knowledge points, focus points and the like and store the intercepted images and voices to the local so as to achieve the purpose of mastering the knowledge points through multiple times of memory.
The embodiment is further configured that the basic information unit comprises a name input component, a gender input component, an age input component and a household address input component; after the basic information is input, the course pushing unit can push courses according to the basic information of the user, for example, a local guide selects the same language so that the user can understand the basic information, and the same accent can make the user more familiar and the learning efficiency higher when the user finds out in the research; on the other hand, the system judges similar users according to the basic information of other users in the cloud database, and carries out improved course pushing according to the feedback of the previous users.
The embodiment is further configured that the personal information module further includes a performance information unit, a work information unit and a relationship information unit; the performance information unit is used for storing the normal online learning condition and the process evaluation of each course; the work information unit is used for storing postings, argument and other records which are well-commented by the user in various online discussion and learning activities, and related academic achievements and the like which are obtained by the user in the learning process; the relationship information unit is used for storing the interaction information of the user in the learning resources, the companions, the instructor and the system.
A learner model construction method based on the adaptive learning system comprises the following steps:
s1: initializing a system;
s2: inputting basic information including name, gender, age and household location;
s3: inputting academic information including professional and expected learning courses;
s4: according to the information input in the processes of S2 and S3, the course pushing unit pushes courses to the user;
s5: the user edits the course single-section time through the course editing unit and sets the single-section time;
s6: the user omits and extends the course content through the course extending unit;
s7: the user extracts the exercises in the after-class exercise unit for practice and generates corresponding answers and correct rate;
s8: the user evaluates a single course through the course evaluation unit;
s9: the quality supervision unit acquires the pause times and the course window scaling times in the course using process of a user;
s10: the network course module generates a user learning report through the processes of S7, S8 and S9 and feeds the user learning report back to the cloud database through the feedback unit;
s11: the cloud database receives the data sent by the feedback unit and pushes the data to different courses to the user through the course pushing unit.
The embodiment further provides that the learner model building method further includes an interaction method, and specifically includes the following steps:
d1: the performance information acquires the online learning condition and the process evaluation of each course of the user;
d2: the relationship information unit acquires the interaction information of the user, the learning resources, other users in the same course, teachers and the system;
d3: the work information unit obtains postings, arguments and related academic achievements obtained in the learning process which are well-commented in the online discussion and learning activities of the user.
The embodiment further provides that the learner model building method further includes a course editing method:
k1: a user captures a curriculum through a capture tool, captures a voice through a voice capture tool, inputs the voice of the user through a voice recording tool, and edits the capture through an image editing tool;
k2: and the user compresses or expands the course content through the course extending unit.
In conclusion, the invention has the following beneficial effects:
(1) the big data-based self-adaptive learning system provided by the invention carries out course pushing on a user in a targeted manner through big data processing;
(2) the big data-based self-adaptive learning system provided by the invention enables the use habits and annotations of different users to appear in the course content through the use mode of the online users, is convenient for other users, and simultaneously enables the course content to be improved through data sharing so as to adapt to the learning habits of more users;
(3) the self-adaptive learning system based on big data provided by the invention can continuously perform self-improvement according to the feedback condition of the user, and has very high growth performance;
(4) the self-adaptive learning system based on big data can provide higher degree of freedom for users, and the users can edit courses so as to improve the learning efficiency;
(5) the self-adaptive learning system based on the big data and based on the image recognition uses various learning auxiliary tools, so that the user can learn more conveniently;
(6) the learner model construction method provided by the invention is more scientific, and the learning efficiency and the learning ability of the user are more effectively improved.
Drawings
FIG. 1 is a flow diagram of a big data based adaptive learning system according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an adaptive learning system based on big data according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Example 1:
a big data-based self-adaptive learning system comprises a network course module and a personal information module, wherein the network course module comprises a course pushing unit, a course editing unit, a course extending unit, a post-session problem unit, a quality supervision unit, a feedback unit, a storage unit, a course evaluation unit, an online evaluation unit and a user authority management unit; the personal information module comprises a basic information unit, a safety information unit and a academic information unit;
the system comprises a course pushing unit, a course editing unit, a course extending unit, a quality supervision unit, a post-lesson exercise unit, a storage unit, a quality supervision unit, a feedback unit and a cloud database, wherein the course pushing unit pushes courses for a user according to information in a personal information module and information in the cloud database, the course editing unit edits course duration, the course extending unit edits course content, the quality supervision unit is used for performing behavior supervision in the learning process of the user, the post-lesson exercise unit is used for testing the user after the course is finished, the storage unit is used for storing online data modified by the user, the course evaluating unit is used for evaluating the courses by the user after the course is finished, the feedback unit is used for receiving and integrating data in the quality supervision unit; the basic information unit stores basic information of a user and sends the basic information to the cloud database, the safety information unit is used for storing an account and a password of the user logging in the system, and the academic information unit is used for storing academic information and sending the academic information to the cloud database.
The user inputs own basic information through the basic information unit, inputs an account and a password of a user login system through the safety information unit, and inputs academic records, professional records and related professional records through the academic information unit; the system carries out course pushing for the user through the course pushing unit according to the information input before, the user selects related courses after receiving the pushed courses, and stores the related selections through the storage unit, so that the user can directly click to continue learning next time, after selecting the corresponding courses, the user can edit the duration of each video section through the course editing unit, so that the requirements of different users are met, and the user who works edits the duration of each course section to enable the user to reasonably utilize the own time.
In the course of learning courses, along with different learning experiences of different users, different learning materials are learned, different knowledge points are mastered, and a plurality of situations that the learning progress is not matched occur, for example, some theorems or proper nouns occur in the learning materials, but the user does not master the basic principle of the teaching materials, but the principle is necessary for subsequent learning, or in another situation, the user learns too deeply at a certain knowledge point, the course explains the basic principle for a plurality of times, a large amount of time is spent on the user, and the user cannot learn new knowledge, and a plurality of problems also occur in the process of later review, at the moment, the user refines and supplements the knowledge points which are not understood and are not mentioned through the course extending unit, so that the study is facilitated, and the review is also facilitated; deleting the comprehended knowledge points to improve the learning efficiency; after the user uses the course extending unit, the modified content can be selectively disclosed, after the content is disclosed, other users can check the corresponding modified content through the online evaluation unit, the modified content is mainly embodied in an annotation form and cannot influence the normal reading of the text, other users can also modify the local course of the user through the course extending unit by the annotated content so that the user can obtain review content suitable for the user, the quality supervision unit carries out quality supervision on the course of learning the course of the user and uploads the review content to the feedback unit, and the storage unit stores the user progress and information edited by the course editing unit and the course extending unit; the user authority management unit can distinguish a user from an administrator, the administrator can make some modifications to the course text according to the times of the online user modifying the local data based on the user through the course extension unit, and the annotations which are considered to be good by most people are added to the text content, so that the course can meet the requirements of more people.
When the user finishes learning each time of the learning plan, the after-class exercise unit provides corresponding exercises for the user to practice, compares answers answered by the user with standard answers, generates corresponding correction pages, sends the correction pages to the user and sends the correction pages to the feedback unit; after the exercise is finished, the user evaluates the exercise through the exercise evaluation unit, scores and comments, evaluation information is sent to the feedback unit, the feedback unit collects the information and then sends the information to the cloud database and the system manager in a centralized mode, the system can supervise and evaluate the learning state of the user according to exercise conditions and quality of the user, when the score is too low, the user is judged to be a problem, when the score is too high, the system is judged to be a problem, the system can improve pertinence of other information sent by the feedback unit, the system is also continuously improved, similar users are met in the later period, namely, when the information in the personal information module is similar, the exercise pushing unit selectively pushes the information.
Example 2: basically similar to the embodiment 1, the difference is that the course pushing unit includes a professional course pushing component, a teacher pushing component, and a section-time division pushing component; the quality supervision unit comprises a pause time counting component and a course window scaling time counting component; the network course module also comprises a learning auxiliary tool, wherein the learning auxiliary tool comprises a screenshot tool, a voice interception tool, a voice recording tool and an image editing tool; the basic information unit comprises a name input component, a gender input component, an age input component and a household address input component, and the personal information module further comprises a performance information unit, a work information unit and a relationship information unit.
In embodiment 2, the course pushing component pushes the course content, the tutor pushing component pushes different tutor courses with the same content, the subsection duration division pushing component pushes the courses according to the single section duration selected by the user, the user can self-define and select different courses for learning, and different selections can be made according to the self-learning condition and the idle time condition, which is very convenient; the temporary times counting component counts the temporary times of the user in the learning process, and the course window zooming times counting component counts the learning condition of the user in the learning process so as to know the learning condition of the user, scores the learning state of the user according to the two parameters in the learning process of the user, and can also be used for judging the maturity of the system and the user experience to correspondingly score, so that the user can be facilitated and the system can be continuously improved; the user can intercept images and voices aiming at different knowledge points, focus points and the like and store the intercepted images and voices to the local so as to be memorized for multiple times to achieve the aim of mastering the knowledge points, and the user can record the voices, edit the images and input own ideas and corresponding notes so as to be convenient for the user to fast master the knowledge points; after the basic information is input, the course pushing unit can push courses according to the basic information of the user, for example, a local guide selects the same language so that the user can understand the basic information, and the same accent can make the user more familiar and the learning efficiency higher when the user finds out in the research; on the other hand, the system judges similar users according to the basic information of other users in the cloud database, and carries out improved course pushing according to the feedback of the previous users; the performance information unit is used for storing the normal online learning condition and the process evaluation of each course; the work information unit is used for storing postings, argument and other records which are well-commented by the user in various online discussion and learning activities, and related academic achievements and the like which are obtained by the user in the learning process; the relationship information unit is used for storing the interaction information of the user in the learning resources, the companions, the instructor and the system.
Example 3:
a learner model construction method based on the adaptive learning system comprises the following steps:
s1: initializing a system;
s2: inputting basic information including name, gender, age and household location;
s3: inputting academic information including professional and expected learning courses;
s4: according to the information input in the processes of S2 and S3, the course pushing unit pushes courses to the user;
s5: the user edits the course single-section time through the course editing unit and sets the single-section time;
s6: the user omits and extends the course content through the course extending unit;
s7: the user extracts the exercises in the after-class exercise unit for practice and generates corresponding answers and correct rate;
s8: the user evaluates a single course through the course evaluation unit;
s9: the quality supervision unit acquires the pause times and the course window scaling times in the course using process of a user;
s10: the network course module generates a user learning report through the processes of S7, S8 and S9 and feeds the user learning report back to the cloud database through the feedback unit;
s11: the cloud database receives the data sent by the feedback unit and pushes the data to different courses to the user through the course pushing unit.
As a preferred embodiment, the learner model building method further includes an interaction method, and specifically includes the following steps:
d1: the performance information acquires the online learning condition and the process evaluation of each course of the user;
d2: the relationship information unit acquires the interaction information of the user, the learning resources, other users in the same course, teachers and the system;
d3: the work information unit obtains postings, arguments and related academic achievements obtained in the learning process which are well-commented in the online discussion and learning activities of the user.
As a preferred embodiment, the learner model building method further comprises a course editing method:
k1: a user captures a curriculum through a capture tool, captures a voice through a voice capture tool, inputs the voice of the user through a voice recording tool, and edits the capture through an image editing tool;
k2: and the user compresses or expands the course content through the course extending unit.
The working principle is as follows: the user inputs own basic information through the basic information unit, inputs an account and a password of a user login system through the safety information unit, and inputs academic records, professional records and related professional records through the academic information unit; the system carries out course pushing for the user through the course pushing unit according to the information input before, the user selects related courses after receiving the pushed courses, and stores the related selections through the storage unit, so that the user can directly click to continue learning next time, after selecting the corresponding courses, the user can edit the duration of each video section through the course editing unit, so that the requirements of different users are met, and the user who works edits the duration of each course section to enable the user to reasonably utilize the own time.
In the course of learning courses, along with different learning experiences of different users, different learning materials are learned, different knowledge points are mastered, and a plurality of situations that the learning progress is not matched occur, for example, some theorems or proper nouns occur in the learning materials, but the user does not master the basic principle of the teaching materials, but the principle is necessary for subsequent learning, or in another situation, the user learns too deeply at a certain knowledge point, the course explains the basic principle for a plurality of times, a large amount of time is spent on the user, and the user cannot learn new knowledge, and a plurality of problems also occur in the process of later review, at the moment, the user refines and supplements the knowledge points which are not understood and are not mentioned through the course extending unit, so that the study is facilitated, and the review is also facilitated; deleting the comprehended knowledge points to improve the learning efficiency; after the user uses the course extending unit, the modified content can be selectively disclosed, after the content is disclosed, other users can check the corresponding modified content through the online evaluation unit, the modified content is mainly embodied in an annotation form and cannot influence the normal reading of the text, other users can also modify the local course of the user through the course extending unit by the annotated content so that the user can obtain review content suitable for the user, the quality supervision unit carries out quality supervision on the course of learning the course of the user and uploads the review content to the feedback unit, and the storage unit stores the user progress and information edited by the course editing unit and the course extending unit; the user authority management unit can distinguish a user from an administrator, the administrator can make some modifications to the course text according to the times of the online user modifying the local data based on the user through the course extension unit, and the annotations which are considered to be good by most people are added to the text content, so that the course can meet the requirements of more people.
When the user finishes learning each time of the learning plan, the after-class exercise unit provides corresponding exercises for the user to practice, compares answers answered by the user with standard answers, generates corresponding correction pages, sends the correction pages to the user and sends the correction pages to the feedback unit; after the exercise is finished, the user evaluates the exercise through the exercise evaluation unit, scores and comments, evaluation information is sent to the feedback unit, the feedback unit collects the information and then sends the information to the cloud database and the system manager in a centralized mode, the system can supervise and evaluate the learning state of the user according to exercise conditions and quality of the user, when the score is too low, the user is judged to be a problem, when the score is too high, the system is judged to be a problem, the system can improve pertinence of other information sent by the feedback unit, the system is also continuously improved, similar users are met in the later period, namely, when the information in the personal information module is similar, the exercise pushing unit selectively pushes the information.
Claims (9)
1. A big data-based self-adaptive learning system comprises a network course module, a personal information module and a cloud database, and is characterized in that the network course module comprises a course pushing unit, a course editing unit, a course extending unit, a post-session exercise unit, a quality supervision unit, a feedback unit, a storage unit, a course evaluation unit, an online evaluation unit and a user permission management unit; the personal information module comprises a basic information unit, a safety information unit and a academic information unit;
the course pushing unit pushes courses to the user according to the information in the personal information module and the information in the cloud database, the course editing unit edits the duration of the courses, the course extending unit edits the contents of the courses, the quality supervision unit is used for performing behavior supervision in the learning process of the user, the after-school exercise unit is used for testing the user after the courses are finished, the storage unit is used for storing the online data modified by the user, the course evaluation unit is used for evaluating the courses by the user after the courses are finished, the online evaluation unit is used for commenting the course content by the user and displaying the course content to other users, the feedback unit is used for receiving and integrating data in the quality supervision unit, the post-session problem unit, the course evaluation unit and the online evaluation unit and sending the integrated data to the cloud database, and the user authority management unit is used for distinguishing the user from an administrator; the basic information unit stores basic information of a user and sends the basic information to the cloud database, the safety information unit is used for storing an account and a password of the user logging in the system, and the academic information unit is used for storing academic information and sending the academic information to the cloud database.
2. The adaptive learning system of claim 1, wherein the course pushing unit comprises a professional course pushing component, a teacher pushing component, and a section-time division pushing component.
3. The adaptive learning system of claim 2, wherein the quality supervision unit comprises a pause number statistics component and a course window scaling number statistics component.
4. The adaptive learning system of claim 3, wherein the network lesson module further comprises learning aides including screenshot tools, voice capture tools, voice recording tools, and image editing tools.
5. The adaptive learning system of claim 1, wherein the basic information element comprises a name input component, a gender input component, an age input component, and a household input component.
6. The adaptive learning system of claim 5, wherein the personal information module further comprises a performance information element, a work information element, and a relationship information element.
7. A learner model construction method based on the adaptive learning system of any one of claims 1 to 6, comprising the steps of:
s1: initializing a system;
s2: inputting basic information including name, gender, age and household location;
s3: inputting academic information including professional and expected learning courses;
s4: according to the information input in the processes of S2 and S3, the course pushing unit pushes courses to the user;
s5: the user edits the course single-section time through the course editing unit and sets the single-section time;
s6: the user omits and extends the course content through the course extending unit;
s7: the user extracts the exercises in the after-class exercise unit for practice and generates corresponding answers and correct rate;
s8: the user evaluates a single course through the course evaluation unit;
s9: the quality supervision unit acquires the pause times and the course window scaling times in the course using process of a user;
s10: the network course module generates a user learning report through the processes of S7, S8 and S9 and feeds the user learning report back to the cloud database through the feedback unit;
s11: the cloud database receives the data sent by the feedback unit and pushes the data to different courses to the user through the course pushing unit.
8. The learner model building method according to claim 7, further comprising an interactive method, specifically comprising the steps of:
d1: the performance information acquires the online learning condition and the process evaluation of each course of the user;
d2: the relationship information unit acquires the interaction information of the user, the learning resources, other users in the same course, teachers and the system;
d3: the work information unit obtains postings, arguments and related academic achievements obtained in the learning process which are well-commented in the online discussion and learning activities of the user.
9. The learner model building method of claim 8, further comprising a course editing method:
k1: a user captures a curriculum through a capture tool, captures a voice through a voice capture tool, inputs the voice of the user through a voice recording tool, and edits the capture through an image editing tool;
k2: and the user compresses or expands the course content through the course extending unit.
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