CN111353917A - Network teaching management method based on big data - Google Patents
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Abstract
The invention provides a network teaching management method based on big data, which comprises the steps of teaching management, educational administration management and big data analysis processing; the method for teaching management comprises the following steps: after the student signs up through the teaching unit and is subjected to enrollment and review, the student logs in the unified identity authentication system by using a student number generated by the educational administration system; the method for performing educational administration management comprises the following steps: students select an intentional specialty for registration according to enrollment information issued by an enrollment department through a education management unit; the method for analyzing and processing the big data comprises the following steps: constructing a control processing unit based on Hadoop cluster software, and storing and managing data by adopting an HDFS (Hadoop distributed file system) and an HBASE (Hadoop distributed database) columnar data warehouse; the invention solves the problems of separated teaching and management, lack of assessment of teaching quality and difficult data sharing of the prior network teaching platform.
Description
Technical Field
The invention relates to a network teaching management method based on big data, and belongs to the technical field of teaching management equipment.
Background
At present, the network teaching platform mainly provides non-academic education and academic continuation education, the construction of the open university network teaching platform needs to take learners as the center, take network learning as a main learning form, take data fusion and sharing as targets, and aim at improving the traditional talent culture mode and implementing open flexible learning systems such as registration and admission, flexible learning systems and diversified and personalized education service modes, and meanwhile, the open university network teaching platform is also a lifelong learning platform and carrier for mutual communication of professional education and common education and effective connection of pre-job education and post-job education.
The open university network teaching platform follows a design concept of taking disciplines as a lead, taking specialities as a platform and taking courses as a center, adopts a top-level design method, is designed based on a service-oriented technical architecture and an optimized service model, completes fusion with an integral informatization platform through a large number of open interfaces, realizes data sharing with other systems, and reduces a large amount of data maintenance workload on the basis of ensuring the consistency of authoritative data; meanwhile, based on a big data technology, data acquisition, conversion and storage, a data sharing mechanism and data warehouse storage of the network teaching platform are completed, analysis index systems such as students, teachers and fundings are built around topics such as personnel, mechanisms and teaching of the network teaching platform, requirements of data reporting and deep data mining analysis are met, support is provided for improvement of management services, and support is provided for leadership-oriented auxiliary decision making.
Disclosure of Invention
The invention provides a network teaching management method based on big data, aiming at the problems of separated teaching and management, lack of assessment of teaching quality and difficulty in data sharing of the prior art network teaching platform, and the method can provide full-life-cycle learning and management services such as student enrollment, online learning, teaching management, teaching quality assessment and the like on line in a one-stop mode, and simultaneously analyze online learning behaviors of students based on big data technology, assess the network teaching quality, improve the online learning quality of the students and optimize learning experience.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a network teaching management method based on big data comprises the steps of performing teaching management, educational administration management and big data analysis processing;
the method for teaching management comprises the following steps: after the student registers through the teaching unit and revives through enrollment and review, the student logs in a unified identity authentication system by using a student number generated by the educational administration system, enters the teaching unit for study, enters the class of the course according to the result of selecting courses and dividing classes, then checks and reads the course tutorial materials of each section according to the requirement of the course tutor, completes course homework and unit evaluation on time, actively participates in forum discussion of the course, communicates with the tutor on line for the questioned content in the course, records the reading progress, homework completion condition, interactive communication content and the score condition of each course homework by the teaching unit, takes the learning link as an important reference index of the usual shape test score of the student, if the course is a final test course, the course tutor enters the final score of the student to test, and finally synthesizes the score of the course of the student;
the method for performing educational administration management comprises the following steps: the students select the purpose-oriented professions to register the names, the enrollment departments or the points of study according to enrollment information issued by the enrollment departments through the education management unit, submit the enrollment students to the enrollment departments for review after the enrollment students initially examine the names, generate school numbers for the students after the review, and simultaneously perform the review on the student data network for registration of the school books; the students select courses and examination reports according to professional talent culture schemes reported in the educational administration system, pay on-line credit and examination fees, and enter a teaching unit for learning; the educational administration department distributes classes and course guides for students on line, and after the course learning of the students is finished, the course guides record the final examination scores of the students on line according to the learning conditions of the courses of the students; if the course is a final examination course, generating examination room arrangement by the examination affair system, and organizing the students to take paper-pen examinations by the examination points to complete the examination process; if the student meets the course requirements of graduate papers of the academic system and the school score of the academic specialty, the student can apply for graduation; if the student scores the requirements of the academic degree, the students can apply for the professional academic degree;
the method for analyzing and processing the big data comprises the following steps: a control processing unit is constructed based on Hadoop cluster software, an HDFS distributed file system and an HBASE column type data warehouse are adopted to store and manage data, a keytle software is utilized to extract data of a teaching unit and a teaching management system according to a data dictionary for data integration, a fluorine software of Cloudera is utilized to extract student learning behavior data and streaming log data generated by the system, the collected data are output to the HBASE data warehouse through a Sink receiving end, on the basis of the data warehouse, topics with different student topics, teacher topics, course topics and resource topics are selected according to the requirement of assistant decision to carry out topic analysis, learning behavior analysis and teaching quality analysis multidimensional analysis, the analysis result is stored in a behavior feature library, a learning feature library and a resource feature library, various platform users are fed back through topic analysis, learning behavior analysis and teaching quality analysis, and data support is provided for the assistant decision, and provides reference for updating and improving the functions of the network teaching platform.
Further, according to the big data-based network teaching management method, in the method for teaching management, students complete relevant service courses such as course selection recommendation, course quit application, student status transaction application, score query, graduation application, academic position application, examination registration, examination arrangement and check in the course of academic education through the teaching unit.
Further, according to the big data-based network teaching management method, in the method for teaching management, a course leader can complete course team construction, course resource organization and arrangement, team workload accounting and team teaching inspection work in the teaching process.
According to the method for analyzing and processing the big data, when data of the teaching unit and the teaching management system are extracted according to the data dictionary by using the key software, the data with the time field are directly extracted and written into the data warehouse, and the timestamp field is added after the state data are extracted and then written into the data warehouse, so that the data are analyzed conveniently.
Further, according to the big data based network teaching management method of the present invention, the network teaching management method is implemented by a big data network teaching management platform, and the big data network teaching management platform includes: the teaching unit, the teaching management unit and the control processing unit; the teaching unit is connected with the teaching tube unit, and the teaching tube unit and the teaching unit are connected with the control processing unit; the teaching unit obtains teaching and educational administration related business data through the teaching management unit, and the control processing unit obtains structured, semi-structured and unstructured data through the teaching unit; and the control processing unit feeds the learning behavior characteristics and the teaching quality analysis result back to the teaching unit and the education management unit.
According to the online teaching management method based on big data, the teaching unit comprises a class schedule module, a learning module, an interaction module, a management module and a gear management module; the school timetable module is connected with the learning module and is used for online distribution and playing of course learning resources; the learning module is connected with the interaction module and is used for interactive communication discussion of the teacher and the student on the learned course resources; the learning module is connected with the management module and is used for the students to submit course chapter homework, and teachers can assist in reviewing the student homework on line to complete the synthesis of scores at ordinary times; the file management module is respectively connected with the learning module, the interaction module and the management module and is used for collecting the learning process and the learning behavior of students and counting the scores, and forming teaching business files for archiving the statistics of the teaching process of teachers and students and the interaction process of teachers and students.
According to the network teaching management method based on big data, the teaching management unit comprises a management module, a financial management module and a educational administration module; the admission management module is connected with the financial management module and is used for completing the course selection and payment process of the professional course after the student registers and reviews on line and obtains the student status; the financial administration module is connected with the educational administration module and is used for educational administration related services such as professional culture schemes, on-line coaching, examination management, student status management and the like of students after successful payment.
Further, according to the big data-based network teaching management method, the educational administration management module comprises a subject module, a teaching operation module, a subject module and a test administration module.
According to the network teaching management method based on big data, the control processing unit comprises a big data acquisition module, a big data storage module, a network teaching analysis module, a student behavior analysis module and a network teaching evaluation module; the big data acquisition module and the big data storage module are respectively connected with the network teaching analysis module, the student behavior analysis module and the network teaching evaluation module by using WebAPI (web application programming interface) and are used for classifying and screening the structured data and the semi-structured data in the storage module according to the network teaching theme, the learning behavior theme and the teaching evaluation theme and providing the classified and screened structured data and the classified and screened data to the modules for big data processing analysis
The invention has the technical effects that:
the invention can provide full-life-cycle learning and management services such as student enrollment registration, online learning, teaching management, teaching quality assessment and the like on line in a one-stop mode, and simultaneously analyzes the online learning behaviors of students based on a big data technology, assesses the network teaching quality, improves the online learning quality of the students and optimizes the learning experience.
Drawings
Fig. 1 is a schematic diagram of a system structure of a big data network teaching management platform for implementing the big data-based network teaching management method of the present invention.
FIG. 2 is a schematic diagram of a teaching unit structure according to the present invention.
FIG. 3 is a schematic diagram of a teaching management system according to the present invention.
FIG. 4 is a schematic diagram of a control processing unit according to the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be described in detail with reference to the accompanying drawings, wherein the technology and products not shown in the embodiments are all conventional products available in the art or commercially.
As shown in FIGS. 1-4, the web-based teaching management method of the present invention realizes a dependent web-based teaching management platform of big data, comprising a teaching unit, a teaching unit and a control processing unit, wherein the WebAPI interface of the teaching unit is bidirectionally connected with the WebAPI interface of the teaching management system via HTTP protocol, and is used for bidirectionally synchronizing the basic data of students and courses, course selection information, examination arrangement, score entry, graduation application and other teaching and teaching related business data, the WebAPI interface of the teaching unit is connected with the WebAPI interface of the control processing unit for collecting the structured, semi-structured and unstructured data in the teaching unit and storing the data in the control processing unit, the WebAPI interface of the teaching unit is connected with the WebAPI interface of the big data analysis system, the control processing unit analyzes and feeds back a WebAPI interface
The teaching unit comprises a school timetable module, a learning module, an interaction module, a management module and a business file management module, wherein the school timetable module is connected with the learning module and used for online distribution and playing of course learning resources. The learning module is connected with the interaction module and used for interactive communication discussion of the learning course resources between teachers and students. The learning module is connected with the management module and used for students to submit course chapter homework, and teachers can assist in reading and amending the student homework on line to complete the synthesis of scores at ordinary times. The gear management module is respectively connected with the learning module, the interaction module and the management module and is used for collecting the learning process and the learning behavior of students and counting the scores, and forming teaching business files for archiving the statistics of the teaching process of teachers and students and the interaction process of teachers and students.
The class schedule module mainly provides related functions of uploading, encrypting, transcoding, classifying, distributing, playing and the like of teaching resources. The video course resource uploading method based on the web online mode is characterized in that uploading functions of video course resources and electronic document resources in client and web online modes are provided for teaching unit teacher role users, the client uploading mode is adopted for video course resource recommendation, functions such as breakpoint continuous transmission are provided, the electronic document resources are in the web online mode, and operation is simple and convenient. The course resource server side deployment mode supports a local and cloud mixed deployment mode and supports mainstream cloud service providers. After the course resources are uploaded, the video resources are sliced and encrypted, the encryption supports the modes of watermarking, anti-theft chain and the like, code stream files in various formats are generated according to the requirements of multi-terminal adaptation, and electronic document resources uniformly transcode different file formats such as PDF, DOCX, XLSX, PPTX and the like into PDF formats, so that students can play the files conveniently by using a player at multiple terminals, and the uniform playing of the video resources and the electronic document resources is realized. The course resources can be classified according to dimensions such as mainstream disciplines, professions, courses and application fields, and students can conveniently and quickly select and search. And course resources are distributed by using the existing CDN, so that better on-line course resource learning experience of students is guaranteed. The course resources are played by adopting a unified online H5 player, and the course resources with proper code streams and formats can be recommended to be played according to the type of the user terminal and the real-time bandwidth condition.
The learning module provides the identity verification, resource browsing, resource collection, learning note, learning progress, online test, score query, backlog and other related functions of the student. Student role user carries out user authentication back through SSO single sign-on, can get into learning module, according to the navigation of the plan of choosing lessons or course resource of talent training scheme, go into course resource page browsing resources, can utilize H5 player to browse course resource through learning terminals such as cell-phone APP, PAD, PC, the reading progress of course resource can be synchronous between a plurality of learning terminals, to the key difficult point in the course resource, the student can collect the resource, the classroom note also can online record in the learning process, the resource of collecting and study note all can online convenient and fast's retrieval and reading. The learning progress function can remind the learning tasks needing to be completed by the students at the current stage according to the teaching progress of the teachers, and the learning tasks comprise course resource learning, course operation, teacher-student interaction and the like. The online test function is beneficial supplement of course operation, a course responsibility teacher selects questions from a question bank for key contents of course chapters according to teaching arrangement, organizes online test paper, and students complete online test of corresponding course chapters according to learning progress arrangement to further consolidate course knowledge. The score inquiry function is used for providing score inquiry of all learned courses of students, including revised courses, general exam courses, experimental training courses, graduation design courses and the like. The backlog is that the education management unit utilizes the message system to carry out management information pushing according to education operation arrangement, and the backlog comprises course selection recommendation, examination arrangement, fee refund application, graduation application, academic position application and the like.
The interaction module provides task arrangement, real-time online and offline interactive interaction and communication functions for teachers and students. Aiming at a learning mode with separated teachers and students, a course responsibility teacher regularly carries out online interactive communication for learners according to a teaching progress, because of the system capacity limitation of an interactive communication module, the course responsibility teacher needs to report interactive communication tasks in advance, the modules carry out task arrangement uniformly, so that the course selection students are prevented from participating in interactive communication time conflicts, a task arrangement list is finally generated, and the course selection tasks are pushed to the course responsibility teacher and the course selection students in the modes of short messages, Email, in-station notification and the like through a teaching platform message mechanism. The interactive module adopts real-time online live broadcast and recorded broadcast to explain key difficult points to the student on time, and the student can carry out online questioning to the teacher during interactive communication, and the problem that everybody was concerned is preferably selected to course responsibility teacher explains, because the variety of study object, to the student that can not participate in real-time discussion, can look up the video of historical interactive communication recorded broadcast through the interactive module.
The management module provides functions of job layout, job lookup, job submission, job reading, ordinary score and the like, a course responsibility teacher enters a course management page and can arrange jobs in specific chapters and sections, the job layout can adopt two modes of online editing or word document uploading, and the jobs are displayed on line in a unified mode. The student role user can look up the contents such as the homework requirement, the homework content, the homework score proportion and the like on line, can submit the homework on line after finishing the homework, can edit and submit the image-text mixed homework on line, can design other types of homework such as creation and the like, and can submit the homework through the homework attachment module. The course tutor on-line reviews and scores the homework submitted by the student, and the management module combines the multiple homework scores into the final homework
The file management module provides the function of filing the student study files and the teacher teaching business files. The network teaching mode is to carry out the class-dividing teaching according to the class selection result of the course to form the teaching class of the course, and the administrative class in the common education is lacked, so the teaching business files need to be filed according to the course teaching class. The filing content of the learning archives of the course selection of the students comprises learning materials, homework, teacher-student interaction (BBS forum discussion topic postings), viewing records of course resources, learning scores, training scores, final examination scores and the like of the online course learning of the students. The filing content of the teacher course teaching business file comprises course learning data, homework reading, teacher-student interaction content (BBS discussion subject post and follow post), course training score final examination score and the like. After the teaching business archives are filed, the teaching business archives can be consulted by students, teachers and teaching supervisors, but the filing content of the business archives cannot be changed.
The education management unit comprises a hostage module, a financial management module and a educational administration management module, wherein the hostage module is connected with the financial management module and is used for completing the course selection and payment process of professional courses after students roll on the line to check and pass the acquired student status. The financial administration module is connected with the educational administration module and is used for educational administration related services such as professional culture schemes, on-line coaching, examination management, student status management and the like of students after successful payment.
The enrollment module provides functions of enrollment batches, entry form management, generation of school codes and the like, the enrollment batches are generally divided into spring and autumn enrollment batches, and can be adjusted to be enrolled once every quarter or be enrolled all year round according to actual needs. The enrollment table can be input after the pre-student calendar of the enrollment students is initially checked, key attributes of the requirement of the student letter network of the education department and related certificate electronic files of the pre-student calendar are input, and after the audit is passed, the enrollment students generate school numbers for the enrollment students, so that later-stage teaching and educational administration are facilitated.
The financial management module provides functions of charging standard management, payment management, fee returning management, invoice management and the like related to network teaching. The charge standard management is set according to the charge standard approved by the higher-level price bureau, such as the charge standard of each branch of the subject and the special subject, the charge standard of each field of examination, the charge standard of the registration fee and the like. The payment management generates an order according to the course selection result of the student, completes the order payment in the modes of two-dimension code scanning payment, online bank, public-to-public remittance and the like, confirms the course selection result, can complete the refund of the course by self in a set time limit, generates a refund order, supports the full refund and partial refund of the order, and refunds the course through the payment process. And after the set refund period limit is exceeded, the financial affairs are provided for the students to print invoice receipts or provide electronic invoices on line according to payment auditing results.
The educational administration management module comprises a subject module, a teaching operation module, a subject module and a study module, and provides functions of talent culture schemes, course management, teacher and resource management, study management, subject management and the like for each professional. The project of professional talent culture is a guidance file for professional declaration, talent culture, course setting, graduation examination and academic position granting, and is generally divided into three categories of courses such as a professional compulsory course, a professional selection course and a general knowledge course according to different professions, and professional credit requirements and academic position granting conditions. Course management is the key of a network teaching platform and comprises course application levels, course teaching content organization, course students and the like, wherein the course application levels comprise application subject, subject or subject sharing, the course teaching content organization is organized according to chapters or teaching weeks, the course credit is set according to the teaching content and teaching time of a course, the credit is the basic attribute of the course and is also the direct basis for the student to select the course for payment, and the recommended matched teaching materials of the course and the like. The teacher and resource management is used for managing the relevant attributes of teachers and resources in the network teaching platform, comprises information of titles, professional directions, institutions and colleges of the teachers and the like, and is an important reference basis for lessons and workload coefficients of teachers. The examination management comprises two parts, namely examination organization and score management, wherein the examination organization comprises examination batches, examination starting courses, examination inclusion, examination room arrangement, examination paper orders and the like, the score management comprises score entry, score synthesis, score release and the like, and the examination room arrangement is basic data which is in butt joint with a third-party examination system, such as a network examination system. The student status management provides the functions of basic student status, division conversion, graduation management and academic degree management, students can apply division conversion and non-repair and non-examination according to the relevant regulations of a division bank, students who accord with the graduation conditions can apply graduation, and students who accord with the academic degree granting conditions can apply academic degrees.
The control processing unit comprises a big data acquisition module, a big data storage module, a network teaching analysis module, a student learning behavior analysis module and a network teaching evaluation module, wherein the big data acquisition module and the big data storage module are respectively connected with the network teaching platform theme analysis module, the student behavior analysis module and the network teaching evaluation module by using WebAPI (Web application program interface) and are used for classifying and screening the structural data and the semi-structural data in the storage module according to the network teaching theme, the learning behavior theme and the teaching evaluation theme and providing the classification and screening for the modules to perform big data processing analysis.
The acquisition and storage module provides data acquisition and data storage functions of a teaching platform based on a Hadoop cluster, the data acquisition mainly acquires service data and log data of the teaching platform, the acquired contents comprise enrollment, educational administration, teaching administration, student online learning behavior records, operation logs of teaching platform users and the like, the data are stored in an HBase data warehouse, an HDFS file system in the Hadoop cluster provides high-reliability bottom storage support, and a Mapreduce mechanism in the Hadoop cluster provides high-performance computing capability.
The network teaching analysis module is used for carrying out theme analysis on a network teaching platform aiming at a big data analysis scene on the basis of a Hadoop cluster Hbase data warehouse and mainly comprises a personnel theme, an organization theme, a financial theme, a learning theme, a management theme and a message theme. The data acquisition and storage function is to extract business data and log data from the existing teaching platform relational database according to various subjects, load and store the business data and the log data into an Hbase data warehouse after conversion.
The student behavior analysis module can classify, inquire and count the learning behaviors of the students according to the collected data, for example, the basic learning behaviors comprise browsing, clicking, collecting, grading and the like of course resources; the advanced learning behaviors comprise completion of operations, resource evaluation, information release, interaction between teachers and students and the like; advanced learning behaviors include information processing, advanced inquiry, personal learning note arrangement, question tutoring, conclusion and saving, etc. The student behavior analysis function is beneficial to carrying out professional teaching supervision and evaluation of course construction quality.
The network teaching evaluation module is used for evaluating and analyzing the course quality and the teacher teaching quality by utilizing the collected data, evaluating and fastening the following key points, including the professional level and the job title structure of the course teaching teacher team, the category and the grade of course resources and teaching materials, whether the teaching content fastens the outline or not, whether the teaching method adopts various network teaching auxiliary modes or not, the scores of students for evaluating teaching, the scores of teachers for self-evaluating and the scores of colleagues for sampling and grading the courses.
The method is roughly divided into 7 layers according to the processing and using flow of the service. The data source layer comprises a teaching unit, structured service data in a teaching management system operation library, learning behavior data and log streaming data generated by a user in the system operation process; the data integration layer adopts an ETL tool button to collect and convert the structured data, and flow type data are collected and converted by using a Flume software; the data storage layer stores data collected by the button and the flash into a Hadoop distributed cluster, the cluster file system adopts HDFS, and the data are stored into an HBASE columnar data warehouse, so that the storage requirements of a large amount of structured data and streaming data and the subsequent data analysis requirements are met. The data analysis layer classifies and stores the data into a behavior feature library, a learning feature library and a resource feature library by multi-acquired data and a statistical analysis method. And the application analysis layer performs application analysis on a learning theme, a educational administration theme, a learning behavior and teaching quality according to the three types of feature libraries, and performs evaluation feedback on a learning system and an educational administration unit according to an application analysis result, wherein the main feedback comprises personalized intelligent learning feedback on students, teaching quality feedback on teachers and educational administration staff, feedback on resource use conditions in a teaching platform and the like. The network teaching platform user improves the learning method, the management mechanism and the supervision mode according to various evaluation feedback results, promotes the function perfection of the network teaching platform, and provides a better reference mechanism for the subsequent improvement and function update of the platform.
The network teaching management method based on big data comprises a teaching management method, a educational administration management method and a big data analysis processing method.
The method for teaching management comprises the following steps: after the student is registered through the teaching unit and is rescued, the student logs in the unified identity authentication system by using the student number generated by the educational administration system, enters the teaching unit for learning, enters the class of the course according to the result of class selection and class division, then checking and reading the course tutor teaching materials of each chapter according to the requirements of the course tutor, completing course operation and unit evaluation on time, actively participating in forum discussion of the course, for the content in question in the course, the teaching unit can communicate with the instructor on line, the teaching unit can record the reading progress, the homework completion condition and the interactive communication content of the student, and the scoring condition of each course homework, the learning links are used as important reference indexes of the ordinary form examination scores of the students, if the course is a final examination course, the course guide needs to input the final examination scores of the students, and finally the scores of the student's local course are synthesized. Students in the teaching unit can complete related service courses in the course of education, such as course selection recommendation, course withdrawal application, student status transaction application, score query, graduation application, academic position application, examination registration, examination arrangement and check and other related services. The course responsible person can complete the course team building, course resource organization and arrangement, team workload accounting, team teaching inspection and other works in the teaching process.
The method for performing educational administration comprises the following steps: the student selects an intentional specialty to register the name according to the information issued by the student-inviting department through the education management unit, the student-inviting department or the point of study, submits the student-inviting department to review after the student registers the name for the first time, generates a student number for the student after the review, and registers the student status through a review data communication network. The students select courses and examination reports according to professional talent training schemes reported in the educational administration system, pay on-line student sharing fees and examination fees, and enter the teaching unit for learning. The teaching affair management department needs to distribute classes and course guides for students on line, after the course learning of the students is finished, the course guides can input the final examination scores of the students on line according to the learning conditions of the courses of the students, if the courses are the final examination courses, the examination affair system needs to generate examination room arrangement, and the examination points organize the students to take paper-pen examinations to complete the examination process. If the student meets the course requirement of the graduate paper of the academic system and the school score of the academic specialty, the student can apply for graduation, and if the school score meets the requirement of the academic degree, the student can apply for the academic degree of the academic specialty.
The method for analyzing big data comprises the following steps: a control processing unit is constructed based on Hadoop cluster software, and an HDFS (Hadoop distributed file system) distributed file system and an HBASE (Hadoop distributed file system) columnar data warehouse are adopted for data storage and management. Data integration utilizes the key software to extract teaching unit and teaching management system's data according to the data dictionary, to having time field data, can directly extract and write into the warehouse, to state type data, need add the timestamp field when the extraction, is convenient for carry out the analysis to data. The student learns the behavioral data and the streaming log generated by the system, extracts the streaming data by using the flash software of Cloudera, and outputs the acquired data to an HBASE data warehouse through a Sink receiving terminal. On the basis of a data warehouse, according to the requirement of assistant decision, selecting different themes to carry out multidimensional analysis, such as the themes of students, the themes of teachers, the themes of courses, the themes of resources, messages and the like, carrying out theme analysis, learning behavior analysis and teaching quality analysis, and storing the analysis results in a behavior characteristic library, a learning characteristic library, a resource characteristic library and other data analysis libraries, wherein the data analysis library adopts a Mysql general relational database, and feeds back various platform users through the theme analysis, the learning behavior analysis and the teaching quality analysis, such as personalized intelligent learning feedback of students, teaching quality feedback of teachers, resource construction quality feedback of management and the like, thereby providing data support for assistant decision and providing reference for updating and improving functions of a network teaching platform.
While the present invention has been described with reference to the accompanying drawings, it is to be understood that the invention is not limited thereto, and that various changes and modifications may be made without departing from the spirit and scope of the invention as those skilled in the art will understand.
Claims (9)
1. A network teaching management method based on big data is characterized in that: the method comprises the steps of performing teaching management, educational administration management and big data analysis processing;
the method for teaching management comprises the following steps: after the student registers through the teaching unit and revives through enrollment and review, the student logs in a unified identity authentication system by using a student number generated by the educational administration system, enters the teaching unit for study, enters the class of the course according to the result of selecting courses and dividing classes, then checks and reads the course tutorial materials of each section according to the requirement of the course tutor, completes course homework and unit evaluation on time, actively participates in forum discussion of the course, communicates with the tutor on line for the questioned content in the course, records the reading progress, homework completion condition, interactive communication content and the score condition of each course homework by the teaching unit, takes the learning link as an important reference index of the usual shape test score of the student, if the course is a final test course, the course tutor enters the final score of the student to test, and finally synthesizes the score of the course of the student;
the method for performing educational administration management comprises the following steps: the students select the purpose-oriented professions to register the names, the enrollment departments or the points of study according to enrollment information issued by the enrollment departments through the education management unit, submit the enrollment students to the enrollment departments for review after the enrollment students initially examine the names, generate school numbers for the students after the review, and simultaneously perform the review on the student data network for registration of the school books; the students select courses and examination reports according to professional talent culture schemes reported in the educational administration system, pay on-line credit and examination fees, and enter a teaching unit for learning; the educational administration department distributes classes and course guides for students on line, and after the course learning of the students is finished, the course guides record the final examination scores of the students on line according to the learning conditions of the courses of the students; if the course is a final examination course, generating examination room arrangement by the examination affair system, and organizing the students to take paper-pen examinations by the examination points to complete the examination process; if the student meets the course requirements of graduate papers of the academic system and the school score of the academic specialty, the student can apply for graduation; if the student scores the requirements of the academic degree, the students can apply for the professional academic degree;
the method for analyzing and processing the big data comprises the following steps: a control processing unit is constructed based on Hadoop cluster software, an HDFS distributed file system and an HBASE column type data warehouse are adopted to store and manage data, a keytle software is utilized to extract data of a teaching unit and a teaching management system according to a data dictionary for data integration, a fluorine software of Cloudera is utilized to extract student learning behavior data and streaming log data generated by the system, the collected data are output to the HBASE data warehouse through a Sink receiving end, on the basis of the data warehouse, topics with different student topics, teacher topics, course topics and resource topics are selected according to the requirement of assistant decision to carry out topic analysis, learning behavior analysis and teaching quality analysis multidimensional analysis, the analysis result is stored in a behavior feature library, a learning feature library and a resource feature library, various platform users are fed back through topic analysis, learning behavior analysis and teaching quality analysis, and data support is provided for the assistant decision, and provides reference for updating and improving the functions of the network teaching platform.
2. The big data-based network teaching management method according to claim 1, wherein: in the method for teaching management, students complete relevant service courses such as course selection recommendation, course returning application, student status transaction application, score query, graduation application, academic position application, examination registration, examination place arrangement and check in the course of education through a teaching unit.
3. The big data-based network teaching management method according to claim 1, wherein: in the method for teaching management, the course responsible person can complete course team construction, course resource organization and arrangement, team workload accounting and team teaching inspection work in the teaching process.
4. The big data-based network teaching management method according to claim 1, wherein: according to the method for analyzing and processing the big data, when the keytle software is used for extracting the data of the teaching unit and the teaching management system according to the data dictionary, the data with the time field is directly extracted and written into the data warehouse, and the timestamp field is added after the state data is extracted and then written into the data warehouse, so that the data can be analyzed conveniently.
5. The big data based network teaching management method according to any of claims 1-4, wherein: the network teaching management method is realized through a big data network teaching management platform, and the big data network teaching management platform comprises: the teaching unit, the teaching management unit and the control processing unit; the teaching unit is connected with the teaching tube unit, and the teaching tube unit and the teaching unit are connected with the control processing unit; the teaching unit obtains teaching and educational administration related business data through the teaching management unit, and the control processing unit obtains structured, semi-structured and unstructured data through the teaching unit; and the control processing unit feeds the learning behavior characteristics and the teaching quality analysis result back to the teaching unit and the education management unit.
6. The big-data-based network teaching management method according to claim 5, wherein: the teaching unit comprises a school timetable module, a learning module, an interaction module, a tube making module and a tube blocking module; the school timetable module is connected with the learning module and is used for online distribution and playing of course learning resources; the learning module is connected with the interaction module and is used for interactive communication discussion of the teacher and the student on the learned course resources; the learning module is connected with the management module and is used for the students to submit course chapter homework, and teachers can assist in reviewing the student homework on line to complete the synthesis of scores at ordinary times; the file management module is respectively connected with the learning module, the interaction module and the management module and is used for collecting the learning process and the learning behavior of students and counting the scores, and forming teaching business files for archiving the statistics of the teaching process of teachers and students and the interaction process of teachers and students.
7. The big-data-based network teaching management method according to claim 5, wherein: the education management unit comprises a public education management module, a financial management module and a educational administration management module; the admission management module is connected with the financial management module and is used for completing the course selection and payment process of the professional course after the student registers and reviews on line and obtains the student status; the financial administration module is connected with the educational administration module and is used for educational administration related services such as professional culture schemes, on-line coaching, examination management, student status management and the like of students after successful payment.
8. The big-data-based network teaching management method according to claim 5, wherein: the educational administration module comprises a subject module, a teaching operation module, a subject module and a test administration module.
9. The big-data-based network teaching management method according to claim 5, wherein: the control processing unit comprises a big data acquisition module, a big data storage module, a network teaching analysis module, a student behavior analysis module and a network teaching evaluation module; the big data acquisition module and the big data storage module are respectively connected with the network teaching analysis module, the student behavior analysis module and the network teaching evaluation module by using WebAPI (web application programming interface) and are used for classifying and screening the structured data and the semi-structured data in the storage module according to the network teaching theme, the learning behavior theme and the teaching evaluation theme and providing the classified and screened structured data to the modules for big data processing analysis.
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