Online education management system based on big data cloud platform

Abstract

The invention discloses an online education management system based on a big data cloud platform, which comprises a system architecture and a function module constructed based on the big data cloud platform, and is characterized in that the system architecture comprises an infrastructure layer, a data acquisition layer, a data storage and management layer and a data analysis layer. According to the online education management system based on the big data cloud platform, through deep analysis of individual data of students, a learning scheme is customized for each student to achieve the purpose of personalized learning, the learning effect and the learning enthusiasm are improved, comprehensive data insight is provided for education institutions and teachers, and the education efficiency is improved. Based on intelligent management and analysis of teaching resources, accurate pushing and efficient utilization of the resources are achieved, resource waste is reduced, and by means of the advantages of the big data cloud platform, the system can easily cope with concurrent access of a large number of users, and the system is more intelligent. And functions and resources can be flexibly expanded according to service development requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of online education, and in particular to an online education management system based on a big data cloud platform. Background Art

[0002] Online learning is a way of conducting online teaching and learning with teachers in a network virtual classroom through computer Internet or mobile phone wireless network. Online learning is no longer limited to this. There are complete online learning platforms on the Internet. Such learning platform systems can intelligently connect a cloud question bank with the platform to complete your learning goals according to your learning needs. Students can learn online in a course system that is synchronized with their own learning and then answer questions synchronously. After answering, the system intelligently presents the problem-solving process to you to assist you in improving your academic performance. With the development of the Internet, the education industry promoted distance education ten years ago, and realized remote video teaching and electronic document sharing through Internet virtual classrooms, so that teachers and students can form a teaching and learning interaction on the Internet. The advent of the 4G era makes learning more convenient not only through bulky computers. As long as a mobile phone with large traffic is promoted through the fast 4G network, we can more conveniently and directly learn online through mobile phones and other handheld tools, and wireless networks make people's daily interactions more effective.

[0003] Big data, or massive data, refers to the amount of data involved that is so large that it cannot be captured, managed, processed, and organized into information that helps companies make more proactive business decisions within a reasonable time through mainstream software tools. Based on this data, the platform can optimize and update teaching resources, recommend better and more effective teaching resources to students, and improve the utilization value of teaching resources. The characteristics of big data include huge data volume, various data types, fast processing speed, low value density and high commercial value. Big data needs to collect different types of data from different data sources in real time or in a timely manner, and send it to the storage system or data middleware system for subsequent processing. Since the quality of the data has a direct impact on the analysis and mining results, it is necessary to pre-process the data, including data cleaning, conversion, integration and other operations to improve the quality and availability of the data.

[0004] With the rapid development of Internet technology, online education has gradually become an important educational model. However, existing online education systems often have the problem of scattered data, which is difficult to effectively integrate and analyze, and cannot provide strong support for teaching decisions, resulting in a lack of personalized learning path planning and an inability to meet the learning needs of different students. Summary of the invention

[0005] The purpose of the present invention is to provide an online education management system based on a big data cloud platform to solve the problem that the online education system proposed in the above background technology often has scattered data, which is difficult to effectively integrate and analyze, and cannot provide strong support for teaching decisions, thereby leading to a lack of personalized learning path planning and an inability to meet the learning needs of different students.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: an online education management system based on a big data cloud platform, comprising a system architecture and functional modules constructed based on the big data cloud platform, the system architecture comprising an infrastructure layer, a data acquisition layer, a data storage and management layer, and a data analysis layer, the functional modules comprising a teaching management module, a learning management module, a user management module, a resource management module, and an intelligent recommendation module.

[0007] The infrastructure layer provides a hardware and software operating environment for the data collection layer. The data collection layer sends the collected data to the data storage and management layer, and the data analysis layer obtains data from the data storage and management layer.

[0008] The user management module receives operation signals provided by the learning management module and the teaching management module, and the user management module transmits the signals to the resource management module to call the corresponding data. The user management module simultaneously transmits the operation signals of the teaching management module and the learning management module to the intelligent recommendation module, and the intelligent recommendation module feeds back the recommended data to the teaching management module and the learning management module, and the teaching management module and the learning management module transmit operation signals to each other.

[0009] Preferably, the system architecture includes: a system architecture and functional modules constructed based on a big data cloud platform, so as to improve the efficiency of data utilization.

[0010] Infrastructure layer: Utilize the computing resources, storage resources, and network resources of the cloud platform to provide a stable operating environment for the entire system, and adopt distributed storage technology to ensure high reliability and scalability of data.

[0011] Data collection layer: collects data through multiple channels, including students' learning behavior data, teachers' teaching behavior data, teaching resource data and system operation data.

[0012] Data storage and management layer: Use big data storage technology HDFS to store the massive data collected, use data warehouse technology to classify, organize and index the data to facilitate subsequent query and analysis, and use the data management module to clean, convert and integrate the data to ensure data quality.

[0013] Data analysis layer: Use data mining and machine learning algorithms to conduct in-depth analysis of stored data, classify students according to their learning styles and levels through cluster analysis; discover the relationship between different teaching resources and learning outcomes through association rule mining; and predict students' academic performance and possible difficulties through predictive models.

[0014] The above technical solution is adopted, through the computing power of the big data cloud platform, it is convenient to exchange data and applications, and improves the practicality of use for students and teachers.

[0015] Preferably, the functional modules include:

[0016] Teaching management module: Teachers use this module to create, edit and publish courses. During the course creation process, they use the references provided by big data analysis, the best teaching methods of similar courses, and the difficulty distribution of knowledge points to make teaching plans. The system recommends appropriate teaching progress, assigns homework and organizes exams based on the overall data analysis results of the class students. The system automatically corrects objective questions and gives correction suggestions for subjective questions. At the same time, it generates a detailed performance analysis report based on the students' answers.

[0017] Learning management module: Students can log in to the system to view their own course schedules, learning tasks and learning progress, and learn course content online. The system dynamically adjusts the video playback speed and provides key prompts based on the students' learning behavior. Students can complete homework and take exams, view their own performance analysis and ranking, and understand their learning position in the class. Students record their learning experiences and problems. The system analyzes these contents through natural language processing technology and provides feedback to teachers so that teachers can adjust teaching strategies in a timely manner.

[0018] User management module: realizes the registration, login and information modification functions of teachers, students and administrators, and assigns different permissions according to user roles. Teachers can manage their own courses and student grades, students participate in learning-related activities, and administrators are responsible for the overall setting and maintenance of the system.

[0019] Resource management module: Unified management of teaching resources, including course videos, audios, documents, pictures, test questions and other types of resources. Based on the results of big data analysis, resources are labeled, such as knowledge point labels, difficulty labels, suitable student group labels, etc., to achieve resource version control and update reminders, ensuring that teachers and students use the latest and most accurate teaching resources.

[0020] Intelligent recommendation module: recommends personalized courses, learning paths and learning resources to students based on their learning history, learning style, performance data, etc.

[0021] By adopting the above technical solution, resources are effectively allocated and used for students through the teaching management module and the learning management module.

[0022] Preferably, the data collection layer in the system architecture collects students' learning behavior data, including login time, learning time, course browsing records, and homework completion status; teachers' teaching behavior data includes course design, teaching time, and homework grading status; teaching resource data includes course videos, documents, and test questions; and system operation data includes server load and network bandwidth.

[0023] By adopting the above technical solution, the learning data is analyzed and processed through the data collection layer, and at the same time, the teaching behavior data of teachers is collected, which is conducive to tailoring learning plans for each student.

[0024] Preferably, the intelligent recommendation module in the functional module sends test exercises of recent learning content to students from time to time, records the students' test scores, feeds back the students' scores to the teachers of the corresponding subjects, and summarizes the wrong question data to assist teachers in understanding the students' learning situation. At the same time, the system recommends relevant tutoring courses, exercises and learning skills to students, recommends teaching method improvement suggestions and supplementary information of teaching resources to teachers, and helps teachers improve the quality of teaching.

[0025] The adoption of the above technical solution, through the intelligent recommendation module in the functional module, is conducive to analyzing and recording students' scores and assisting teachers in implementing teaching plans.

[0026] Compared with the prior art, the beneficial effects of the present invention are: the online education management system based on the big data cloud platform:

[0027] 1. Through in-depth analysis of individual student data, tailor-made learning plans for each student to achieve personalized learning, improve learning outcomes and learning enthusiasm, provide comprehensive data insights for educational institutions and teachers, and help them make more scientific teaching decisions. Based on intelligent management and analysis of teaching resources, accurate push and efficient use of resources can be achieved, reducing resource waste. With the advantages of the big data cloud platform, the system can easily cope with concurrent access by a large number of users, and can flexibly expand functions and resources according to business development needs;

[0028] 2. By using big data to assist in the management of online education, the quality and efficiency of online education have been improved. Through personalized learning path planning and intelligent recommendation, students' learning interest and potential have been stimulated, and strong decision-making support has been provided for educational institutions and teachers, which has promoted the improvement of teaching methods and the optimal allocation of teaching resources. The stability and scalability of the system under the use of a large number of users have been guaranteed, meeting the growing needs of the online education market.

[0029] 3. Based on cloud computing technology, users can access the system through the Internet anytime and anywhere, without being restricted by time and space, making it convenient for teachers and students to carry out teaching and learning activities according to their own arrangements, better meeting personalized learning needs, and reducing the tedious work of educational institutions in teaching management, student management, resource management, etc. through automated and integrated management methods, greatly improving teaching and management efficiency, and saving manpower, material resources and time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the system flow structure of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] See also Figure 1 The present invention provides a technical solution: an online education management system based on a big data cloud platform, including a system architecture and functional modules built based on the big data cloud platform, the system architecture includes an infrastructure layer, a data acquisition layer, a data storage and management layer, and a data analysis layer, and the functional modules include a teaching management module, a learning management module, a user management module, a resource management module, and an intelligent recommendation module.

[0033] The infrastructure layer provides a hardware and software operating environment for the data collection layer. The data collection layer sends the collected data to the data storage and management layer, and the data analysis layer obtains data from the data storage and management layer.

[0034] The user management module receives operation signals provided by the learning management module and the teaching management module, and the user management module transmits the signals to the resource management module to call the corresponding data. The user management module simultaneously transmits the operation signals of the teaching management module and the learning management module to the intelligent recommendation module, and the intelligent recommendation module feeds back the recommended data to the teaching management module and the learning management module, and the teaching management module and the learning management module transmit operation signals to each other.

[0035] The system architecture includes:

[0036] Infrastructure layer: Utilize the computing resources, storage resources, and network resources of the cloud platform to provide a stable operating environment for the entire system, and adopt distributed storage technology to ensure high reliability and scalability of data.

[0037] Data collection layer: collects data through multiple channels, including students' learning behavior data, teachers' teaching behavior data, teaching resource data and system operation data.

[0038] Data storage and management layer: Use big data storage technology HDFS to store the massive data collected, use data warehouse technology to classify, organize and index the data to facilitate subsequent query and analysis, and use the data management module to clean, convert and integrate the data to ensure data quality.

[0039] Data analysis layer: Use data mining and machine learning algorithms to conduct in-depth analysis of stored data, classify students according to their learning styles and levels through cluster analysis; discover the relationship between different teaching resources and learning outcomes through association rule mining; and predict students' academic performance and possible difficulties through predictive models.

[0040] Functional modules include:

[0041] Teaching management module: Teachers use this module to create, edit and publish courses. During the course creation process, they use the references provided by big data analysis, the best teaching methods of similar courses, and the difficulty distribution of knowledge points to make teaching plans. The system recommends appropriate teaching progress, assigns homework and organizes exams based on the overall data analysis results of the class students. The system automatically corrects objective questions and gives correction suggestions for subjective questions. At the same time, it generates a detailed performance analysis report based on the students' answers.

[0042] Learning management module: Students can log in to the system to view their own course schedules, learning tasks and learning progress, and learn course content online. The system dynamically adjusts the video playback speed and provides key prompts based on the students' learning behavior. Students can complete homework and take exams, view their own performance analysis and ranking, and understand their learning position in the class. Students record their learning experiences and problems. The system analyzes these contents through natural language processing technology and provides feedback to teachers so that teachers can adjust teaching strategies in a timely manner.

[0043] User management module: realizes the registration, login and information modification functions of teachers, students and administrators, and assigns different permissions according to user roles. Teachers can manage their own courses and student grades, students participate in learning-related activities, and administrators are responsible for the overall setting and maintenance of the system.

[0044] Resource management module: Unified management of teaching resources, including course videos, audios, documents, pictures, test questions and other types of resources. Based on the results of big data analysis, resources are labeled, such as knowledge point labels, difficulty labels, suitable student group labels, etc., to achieve resource version control and update reminders, ensuring that teachers and students use the latest and most accurate teaching resources.

[0045] Intelligent recommendation module: recommends personalized courses, learning paths and learning resources to students based on their learning history, learning style, performance data, etc.

[0046] The data collection layer in the system architecture collects students' learning behavior data, including login time, learning time, course browsing records, and homework completion. Teachers' teaching behavior data includes course design, teaching time, and homework correction. Teaching resource data includes course videos, documents, and test questions. System operation data includes server load and network bandwidth. When using this system, first deploy the system, deploy the online education management system on the big data cloud platform, and configure the corresponding computing, storage, and network resources. According to the scale and needs of the educational institution, determine the number of servers, storage capacity and other parameters, then initialize the data, import the initial teaching resource data, teacher and student information, etc. At the same time, start collecting system operation data and user's initial behavior data.

[0047] The intelligent recommendation module in the functional module sends students test exercises of recent learning content from time to time, records students' test scores, feeds back students' scores to teachers of corresponding subjects, and summarizes wrong question data to assist teachers in understanding students' learning situation. At the same time, the system recommends relevant tutoring courses, exercises and learning skills to students, and recommends suggestions for improving teaching methods and supplementary information on teaching resources to teachers to help teachers improve teaching quality. As this system begins to be used, teachers, students and administrators enter the system through their respective login portals. Teachers begin to create courses and arrange teaching plans; students study according to the course schedule, complete homework and exams; administrators perform daily maintenance and management of the system. As users use it, the system continuously collects new data and regularly updates and analyzes the data. Based on the analysis results, each functional module is continuously optimized and adjusted to provide users with better services.

[0048] Through the intelligent recommendation module of this system, the system will recommend personalized courses, learning paths and learning resources to students based on their learning history, learning style, performance data, etc. If they have difficulty learning a certain subject or knowledge point, they can conduct targeted learning according to the recommended content, such as learning recommended tutoring courses, doing related exercises, and checking learning skills. Students can also actively search for courses or learning resources of interest in the system to broaden their knowledge, and teachers can view student learning feedback collected by the system through irregular system tests, including learning experiences and problems recorded by students. The system analyzes these contents through natural language processing technology, so that teachers can understand students' learning difficulties and needs, adjust teaching methods in a timely manner or provide tutoring for individual students, and finally optimize the teaching process through the system's suggestions for improving teaching methods and supplementary information on teaching resources based on big data analysis.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online education management system based on a big data cloud platform, including a system architecture and functional modules built on the big data cloud platform, characterized in that: The system architecture includes an infrastructure layer, a data collection layer, a data storage and management layer, and a data analysis layer, and the functional modules include a teaching management module, a learning management module, a user management module, a resource management module, and an intelligent recommendation module; The infrastructure layer provides a hardware and software operating environment for the data collection layer, the data collection layer sends the collected data to the data storage and management layer, and the data analysis layer obtains data from the data storage and management layer; The user management module receives operation signals provided by the learning management module and the teaching management module, and the user management module transmits the signals to the resource management module to call the corresponding data. The user management module simultaneously transmits the operation signals of the teaching management module and the learning management module to the intelligent recommendation module, and the intelligent recommendation module feeds back the recommended data to the teaching management module and the learning management module, and the teaching management module and the learning management module transmit operation signals to each other.

2. The online education management system based on the big data cloud platform according to claim 1, characterized in that: The system architecture includes: Infrastructure layer: Utilize the computing resources, storage resources, and network resources of the cloud platform to provide a stable operating environment for the entire system, and use distributed storage technology to ensure high reliability and scalability of data; Data collection layer: collects data through multiple channels, including students' learning behavior data, teachers' teaching behavior data, teaching resource data, and system operation data; Data storage and management: Use big data storage technology HDFS to store the massive data collected, use data warehouse technology to classify, organize and index the data to facilitate subsequent query and analysis, and use the data management module to clean, convert and integrate the data to ensure data quality; Data analysis layer: Use data mining and machine learning algorithms to conduct in-depth analysis of stored data, classify students according to their learning styles and levels through cluster analysis; discover the relationship between different teaching resources and learning outcomes through association rule mining; and predict students' academic performance and possible difficulties through predictive models.

3. The online education management system based on the big data cloud platform according to claim 1, characterized in that: The functional modules include: Teaching management module: Teachers use this module to create, edit and publish courses. During the course creation process, teachers use the reference provided by big data analysis, the best teaching methods of similar courses, the difficulty distribution of knowledge points, and make teaching plans. The system recommends appropriate teaching progress, assigns homework and organizes exams based on the overall data analysis results of the class students. The system automatically corrects objective questions and gives correction suggestions for subjective questions. At the same time, it generates a detailed performance analysis report based on the students' answers. Learning management module: Students can log in to the system to view their own course schedules, learning tasks and learning progress, and learn course content online. The system dynamically adjusts the video playback speed and provides key reminders based on the students' learning behavior. Students can complete homework and take exams, view their own performance analysis and ranking, and understand their learning position in the class. Students record their learning experience and problems. The system analyzes these contents through natural language processing technology and provides feedback to teachers so that teachers can adjust teaching strategies in a timely manner. User management module: realizes the registration, login and information modification functions of teachers, students and administrators. Different permissions are assigned according to user roles. Teachers can manage their own courses and student grades, students participate in learning-related activities, and administrators are responsible for the overall setting and maintenance of the system; Resource management module: Unified management of teaching resources, including course videos, audios, documents, pictures, test questions and other types of resources. Based on the results of big data analysis, resources are labeled, such as knowledge point labels, difficulty labels, suitable student group labels, etc., to achieve resource version control and update reminders, ensuring that teachers and students use the latest and most accurate teaching resources; Intelligent recommendation module: recommends personalized courses, learning paths and learning resources to students based on their learning history, learning style, performance data, etc.

4. The online education management system based on the big data cloud platform according to claim 2 is characterized by: The data collection layer in the system architecture collects students' learning behavior data, including login time, learning time, course browsing history, and homework completion status; teachers' teaching behavior data includes course design, teaching time, and homework grading status; teaching resource data includes course videos, documents, and test questions; and system operation data includes server load and network bandwidth.

5. The online education management system based on the big data cloud platform according to claim 3 is characterized by: The intelligent recommendation module in the functional module sends test exercises of recent learning content to students from time to time, records students' test scores, feeds back students' scores to teachers of corresponding subjects, and summarizes wrong question data to assist teachers in understanding students' learning situation. At the same time, the system recommends relevant tutoring courses, exercises and learning skills to students, recommends teaching method improvement suggestions and supplementary information of teaching resources to teachers, and helps teachers improve teaching quality.

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