Big data and AI-based auxiliary teaching interaction and teaching quality improvement system

Abstract

The invention discloses a big data and AI-based auxiliary teaching interaction and teaching quality improvement system, which comprises a student end, and is characterized in that the student end is composed of a data acquisition and processing module A, an artificial intelligence application module A and a user interface and interaction module A; and the teacher end is composed of a data acquisition module and processing module B, a data analysis and mining module, an artificial intelligence application module B and a user interface and interaction module B. The learning data of students are analyzed through big data, AI-assisted teaching interaction provides personalized learning support for each student by using the data, and the system can understand the learning style, weakness and advantages of each student by analyzing the learning behaviors and performance of the students, so that the learning efficiency of the students is improved. According to the information, a learning plan is customized for the students, suitable learning resources are recommended, personalized learning suggestions and tutoring are provided, and personalized learning support can help the students to learn more effectively.

Description

Technical Field

[0001] The present invention relates to the field of intelligent teaching, and specifically to a system for improving teaching interaction and teaching quality based on big data and AI. Background Art

[0002] The traditional teaching method usually involves teachers, blackboards, students, and books. The teacher lectures at the podium and the students listen and take notes in their seats. Whether there is a good interaction between the two depends on the teacher's teaching level and experience.

[0003] Although the existing teaching quality improvement system with monitoring function can replace teachers to sign in and monitor students' learning status, over-reliance on monitoring may weaken teachers' initiative and teaching ability. If teachers rely too much on the data and feedback provided by the system, and students may feel uncomfortable or disgusted with being filmed and monitored for learning status, and believe that this method infringes on personal privacy and freedom, it will also have a negative impact on the teaching atmosphere and even affect learning enthusiasm and teaching effectiveness. Summary of the invention

[0004] The purpose of the present invention is to provide a system for improving teaching interaction and teaching quality based on big data and AI to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A system for teaching interaction and teaching quality improvement based on big data and AI, including a student end, which is composed of a data acquisition and processing module A, an artificial intelligence application module A and a user interface and interaction module A. The student end is supported by big data analysis and AI technology, allowing students to access educational resources, receive personalized learning guidance, and interact with a teacher end and other student ends; a teacher end, which is composed of a data acquisition module and processing module B, a data analysis and mining module, an artificial intelligence application module B and a user interface and interaction module B. The teacher end is supported by big data analysis and AI technology, allowing teachers to manage teaching content, supervise students' learning progress, and interact with students and other teacher ends.

[0007] Preferably, the data collection and processing module A is responsible for collecting students' study time and browsing history, the artificial intelligence application module A automatically recommends learning content and adjusts learning difficulty based on students' learning data and analysis results, and the user interface and interaction module A enables students to access educational resources, receive personalized learning guidance, and interact with other students through the student terminal.

[0008] Preferably, the data acquisition module and processing module B are responsible for collecting teachers' teaching data, including teaching plans and student grades. The data analysis and mining module uses a deep learning algorithm to analyze the teaching data to help teachers identify key problems in teaching and optimize teaching methods. The artificial intelligence application module B adjusts the teaching content according to the learning data uploaded by the student end of the whole class. The user interface and interaction module B enables teachers to manage the teaching content, supervise the learning progress of the whole class, and interact with other teachers through the teacher end.

[0009] Preferably, the teacher side also includes an AI-assisted teaching interaction module, which uses an AI chat robot to interact with students in real time for teaching, including intelligent question and answer and automatic homework grading.

[0010] Preferably, the student end and the teacher end use a personal computer and an academic computer respectively, and the personal computer and the academic computer are connected to a cloud server respectively. The cloud server is deployed with a microservice architecture to support the efficient operation of AI models and big data analysis. The cloud server is also provided with a data storage and management module, and the data storage and management module stores and manages teaching data in real time, as well as historical data for later analysis and mining.

[0011] Preferably, a system backend is also deployed in the cloud server, and the student terminal and teacher terminal are connected to the system backend via a network, and data interaction and collaboration are performed through the system backend.

[0012] Preferably, the data collection and processing module A collects student learning behavior data through sensors and log recorders, and pre-processes the data using data cleaning tools.

[0013] Preferably, the data analysis and mining module uses a machine learning algorithm library to analyze student learning behavior and evaluate teaching effectiveness.

[0014] Preferably, the backend system is also deployed with a security and privacy protection module, which is responsible for protecting data in the system and user privacy from unauthorized access and attacks.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] 1. The present invention analyzes students' learning data through big data, and AI-assisted teaching interaction uses data to provide personalized learning support for each student. By analyzing students' learning behaviors and performances, the system can understand each student's learning style, weaknesses and strengths, and tailor a learning plan for them based on this information, recommend suitable learning resources, and provide personalized learning advice and guidance. Personalized learning support can help students learn more effectively because they can obtain teaching content and guidance that matches their learning abilities and interests. Personalized learning support helps to improve students' learning motivation and academic performance, and promote their personal development and growth.

[0017] 2. The present invention is based on big data and AI-assisted teaching interaction, real-time feedback and adjustment. By analyzing students' performance and feedback data in the learning process, it can timely discover students' learning difficulties and problems, and provide corresponding help and guidance. At the same time, teachers monitor students' learning progress and understanding level in real time through AI-assisted teaching interaction, so as to timely adjust teaching content and methods to meet students' learning needs. This helps to improve the pertinence and efficiency of teaching, so as to better understand and master knowledge. Teachers can also adjust teaching strategies in time according to students' learning situation to improve teaching effects and learning outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the system structure of a system based on big data and AI-assisted teaching interaction and teaching quality improvement system of the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] like Figure 1 As shown, the present invention provides a technical solution:

[0021] A system based on big data and AI to assist teaching interaction and improve teaching quality, including

[0022] Student side: The student side consists of data collection and processing module A, artificial intelligence application module A, and user interface and interaction module A. The student side is supported by big data analysis and AI technology, allowing students to access educational resources, receive personalized learning guidance, and interact with the teacher side and other student sides;

[0023] Specifically, the student terminal includes a data collection and processing module A, an artificial intelligence application module A, and a user interface and interaction module A. Students can access educational resources, receive personalized learning guidance, and interact with the teacher terminal and other student terminals through the student terminal;

[0024] Furthermore, when students use the student side, the user interface and interactive module A allow them to access educational resources, receive personalized learning guidance, and interact with other students. At the same time, the data collection and processing module A in the student side collects students' learning time and browsing history in real time, and then uses the artificial intelligence application module A to automatically recommend learning content and adjust the learning difficulty based on the student's learning data and analysis results.

[0025] Teacher side, the teacher side consists of data collection module and processing module B, data analysis and mining module, artificial intelligence application module B, and user interface and interaction module B. The teacher side is supported by big data analysis and AI technology, allowing teachers to manage teaching content, monitor students' learning progress, and interact with students and other teachers;

[0026] Specifically, the teacher end includes a data collection and processing module B, a data analysis and mining module, an artificial intelligence application module B, and a user interface and interaction module B. Teachers can manage teaching content, monitor students' learning progress, and interact with students and other teacher ends;

[0027] Furthermore, when teachers use the teacher side, the user interface and interactive module B enable teachers to manage teaching content, supervise students' learning progress, and interact with other teachers. At the same time, the data collection and processing module B collects the teacher's teaching data, including teaching plans and student grades, and the data analysis and mining module uses deep learning algorithms to analyze teaching data to help teachers discover key problems in teaching and optimize teaching methods. The artificial intelligence application module B will adjust the teaching content based on the learning data of the entire class.

[0028] In this embodiment, the data collection and processing module A is responsible for collecting students' learning time and browsing history. The artificial intelligence application module A automatically recommends learning content and adjusts the learning difficulty based on the students' learning data and analysis results. The user interface and interaction module A enables students to access educational resources, receive personalized learning guidance, and interact with other students through the student end. The data collection module and processing module B are responsible for collecting teachers' teaching data, including teaching plans and student grades. The data analysis and mining module uses deep learning algorithms to analyze teaching data to help teachers find key problems in teaching and optimize teaching methods. The artificial intelligence application module B adjusts the teaching content based on the learning data uploaded by the student end of the whole class. The user interface and interaction module B enables teachers to manage teaching content, supervise the learning progress of the whole class, and interact with other teachers through the teacher end; the teacher end also includes an AI-assisted teaching interaction module, which uses an AI chat robot to interact with students in real time, including intelligent Question and answer and automatic homework grading; the student side and the teacher side use personal computers and teaching computers respectively, and the personal computers and teaching computers are connected to the cloud server respectively. The cloud server is deployed with a microservice architecture to support the efficient operation of AI models and big data analysis. The cloud server is also equipped with a data storage and management module. The data storage and management module stores and manages teaching data in real time, as well as historical data for later analysis and mining. The cloud server is also deployed with a system backend. The student side and the teacher side are connected to the system backend through the network, and data interaction and collaboration are carried out through the system backend. The data acquisition and processing module collects student learning behavior data through sensors and loggers, and uses data cleaning tools to pre-process the data. The data analysis and mining module uses a machine learning algorithm library to analyze student learning behavior and evaluate teaching effectiveness. The back-end system is also deployed with a security and privacy protection module, which is responsible for protecting the data and user privacy in the system from unauthorized access and attacks.

[0029] Student operation process:

[0030] Students log in to the student application through their personal computers. In the user interface, students can browse educational resources, including course videos, documents, and exercises. The student application will collect students' study time and browsing history, and automatically recommend learning content and adjust learning difficulty based on students' learning data and analysis results to provide personalized learning guidance. Students can interact with other classmates through the student application, including discussing course content and sharing learning resources.

[0031] Teacher operation process:

[0032] Teachers log in to the teacher-side application through the academic affairs computer. In the user interface, teachers can manage teaching content, including uploading courseware and assigning homework. The teacher-side collects students' learning data, including learning time and browsing history. The data analysis and mining module uses deep learning algorithms to analyze student learning data to help teachers understand students' learning progress and learning status, and adjust teaching content based on the learning data of all students in the class to help teachers optimize teaching methods and content. Teachers can interact with other teachers through the teacher-side, including sharing teaching experiences and discussing teaching strategies.

[0033] The interaction process between students and teachers:

[0034] The teacher side also includes an AI-assisted teaching interaction module, which uses AI robots to interact with students in real time, including intelligent question and answer and automatic homework grading. Students can ask questions to the AI ​​robots during the learning process to get instant learning help and answers. Teachers can use AI robots to answer students' questions and automatically grade and evaluate students' homework. Teachers adjust teaching content and methods based on students' learning data and feedback to better meet students' learning needs and improve teaching effectiveness.

[0035] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0036] 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. A system for teaching interaction and teaching quality improvement based on big data and AI. Features: include Student side, which consists of a data collection and processing module A, an artificial intelligence application module A, and a user interface and interaction module A. The student side is supported by big data analysis and AI technology, allowing students to access educational resources, receive personalized learning guidance, and interact with the teacher side and other student sides; The teacher side is composed of a data acquisition module and a processing module B, a data analysis and mining module, an artificial intelligence application module B, and a user interface and interaction module B. The teacher side is supported by big data analysis and AI technology, allowing teachers to manage teaching content, supervise students' learning progress, and interact with students and other teacher sides.

2. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The data collection and processing module A is responsible for collecting students' study time and browsing history. The artificial intelligence application module A automatically recommends learning content and adjusts learning difficulty based on students' learning data and analysis results. The user interface and interaction module A enables students to access educational resources, receive personalized learning guidance, and interact with other students through the student terminal.

3. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The data acquisition module and processing module B are responsible for collecting teachers' teaching data, including teaching plans and student grades. The data analysis and mining module uses deep learning algorithms to analyze teaching data to help teachers identify key problems in teaching and optimize teaching methods. The artificial intelligence application module B adjusts the teaching content based on the learning data uploaded by the student end of the whole class. The user interface and interaction module B enables teachers to manage teaching content, supervise the learning progress of the whole class, and interact with other teachers through the teacher end.

4. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The teacher side also includes an AI-assisted teaching interaction module, which uses an AI chat robot to interact with students in real time for teaching, including intelligent question and answer and automatic homework grading.

5. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The student end and teacher end use a personal computer and an academic computer respectively, and the personal computer and the academic computer are connected to a cloud server respectively. The cloud server is deployed with a microservice architecture to support the efficient operation of AI models and big data analysis. The cloud server is also provided with a data storage and management module, which stores and manages teaching data in real time, as well as historical data for later analysis and mining.

6. According to claim 5, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The cloud server also has a system backend deployed therein. The student and teacher terminals are connected to the system backend via a network and perform data interaction and collaboration via the system backend.

7. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The data collection and processing module A collects student learning behavior data through sensors and log recorders, and uses data cleaning tools to pre-process the data.

8. According to claim 1, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The data analysis and mining module uses a machine learning algorithm library to analyze student learning behavior and evaluate teaching effectiveness.

9. According to claim 6, a system for teaching interaction and teaching quality improvement based on big data and AI, Features: The backend system is also deployed with a security and privacy protection module, which is responsible for protecting the data and user privacy in the system from unauthorized access and attacks.

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