Internet of Things education management platform for overseas study

Abstract

The invention relates to the technical field of digital data processing, in particular to an overseas study Internet of Things education management platform, which is characterized in that an input module of a student end receives a learning request input by a student and sends the learning request of the student to a server end; the display module displays the teaching resources returned by the server side; the data collection module obtains learning data of students; the user identity authentication module of the server side is used for verifying the identity of the overseas student logging in the system; the receiving module receives a learning request of a student end; the processing module retrieves corresponding learning resources according to the learning request; the sending module sends the learning resources to the student side; the behavior recognition module analyzes the question data of the students to obtain question knowledge points; the learning path generation module forms a learning path based on the question knowledge points; and the push module retrieves the learning resources based on the learning path and generates push information, so that a personalized learning path can be generated, and the learning efficiency and the learning effect of the overseas students are improved.

Description

Technical Field

[0001] This invention relates to the field of digital data processing technology, and in particular to an Internet of Things (IoT) education management platform for studying abroad. Background Technology

[0002] Studying abroad refers to a form of education where students choose to study in countries or regions outside their home country in order to receive a higher level of education or gain different cultural experiences. International students typically choose renowned universities or professional colleges around the world for their undergraduate, master's, or doctoral studies. Studying abroad is not just about academic learning, but also an immersive experience of culture and language.

[0003] Current international education systems do not facilitate the online analysis of international students' learning data to provide targeted learning suggestions, thereby reducing learning efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide an IoT-based education management platform for studying abroad, which aims to generate personalized learning paths and improve the learning efficiency and effectiveness of international students.

[0005] To achieve the above objectives, the present invention provides an IoT-based education management platform for studying abroad, comprising a student terminal and a server terminal. The student terminal is used to collect student data and send student learning requests to the server terminal. The server terminal is used to receive learning requests from the student terminal and retrieve corresponding learning resources to feed back to the student terminal.

[0006] The student terminal includes an input module, a display module, and a data collection module;

[0007] The input module is used to receive learning requests input by students and send the students' learning requests to the server.

[0008] The display module is used to display the teaching resources returned by the server.

[0009] The data collection module is used to acquire students' learning data;

[0010] The server-side includes a user authentication module, a receiving module, a processing module, a sending module, a behavior recognition module, a learning path generation module, and a push module;

[0011] The user authentication module verifies the identity of international students logging into the system;

[0012] The receiving module is used to receive learning requests from students.

[0013] The processing module is used to retrieve corresponding learning resources based on the learning request;

[0014] The sending module is used to send learning resources to the student's end;

[0015] The behavior recognition module is used to analyze students' test-taking data and obtain the knowledge points of the problems.

[0016] The learning path generation module is used to generate learning paths based on the knowledge points of the problem;

[0017] The push module is used to retrieve learning resources based on the learning path and generate push information.

[0018] The input module includes an input unit and a checking unit;

[0019] The input unit is used to receive input from students on the interface;

[0020] The inspection unit is used to verify the input data to ensure its integrity and accuracy.

[0021] The data collection module includes an identity information collection unit, an attendance record unit, and a practice record unit.

[0022] The identity information collection unit is used to obtain the student's identity information and use it for login;

[0023] The attendance recording unit is used to record students' attendance data;

[0024] The practice recording unit is used to obtain students' problem-solving data.

[0025] The user authentication module includes a data extraction unit, a data matching unit, and a verification unit.

[0026] The data extraction unit is used to extract the identity information input by international students;

[0027] The data matching unit is used by the system to compare the input user credentials with pre-stored database information to determine whether the match is successful.

[0028] The verification unit is used to further verify the identity information through a secondary verification mechanism after a successful match.

[0029] The processing module includes a learning resource library, an index generation unit, and a retrieval unit.

[0030] The learning resource library is used to store various types of learning resources;

[0031] The index generation unit is used to create an index for the resource library;

[0032] The retrieval unit is used to retrieve learning resources from the learning resource database based on learning requests and indexes.

[0033] The behavior recognition module includes a question-and-answer data acquisition unit, a data processing unit, a classification unit, an analysis unit, and a question knowledge point generation unit.

[0034] The problem-solving data acquisition unit is used to collect data on the problems that students have solved.

[0035] The data processing unit is used to clean the collected data;

[0036] The classification unit is used to categorize all questions according to knowledge points;

[0037] The analysis unit is used to analyze the data of students' incorrect answers and identify the error types.

[0038] The problem knowledge point generation unit is used to calculate the average accuracy rate for each error type, and to obtain problem knowledge points when the accuracy rate is lower than a threshold.

[0039] The learning path generation module includes a knowledge graph generation unit and a path generation unit.

[0040] The knowledge graph generation unit is used to construct a knowledge graph based on the logical relationships between knowledge points.

[0041] The path generation unit is used to obtain learning paths based on problem knowledge points and knowledge graphs.

[0042] The push module includes a keyword extraction unit, a search unit, and an information generation unit. The keyword extraction unit is used to extract keywords based on the learning path; the search unit is used to search the learning resource library based on the keywords to obtain the target link; and the information generation unit is used to generate push information based on the retrieved target link.

[0043] This invention discloses an IoT-based education management platform for international students. The student-side interface serves as the direct user interaction interface and includes an input module, a display module, and a data collection module. The input module allows students to input learning requests, whether it's searching for information, watching videos, or submitting assignments, all of which are quickly transmitted to the server. The display module clearly and intuitively presents the teaching resources returned by the server to students, ensuring they can easily and quickly access the information they need. The data collection module collects students' learning data in real time. The server, the core of the platform, processes learning requests from students and retrieves corresponding learning resources. The server includes a user authentication module, a receiving module, a processing module, a sending module, a behavior recognition module, and a learning path generation module. The user authentication module ensures that only legitimate international students can access the platform, protecting educational resources from misuse. The receiving module receives learning requests from students, ensuring smooth information transmission. The processing module uses a powerful search engine to retrieve corresponding learning resources from a vast educational resource database based on the learning requests, ensuring students receive accurate and comprehensive information. The sending module quickly sends the retrieved learning resources back to the student, ensuring the continuity and efficiency of learning. The behavior recognition module enables in-depth analysis of students' learning data. By mining students' problem-solving data, it identifies the knowledge points that students encounter in their learning, providing strong support for the generation of subsequent learning paths. The learning path generation module, based on these knowledge points and combined with students' learning characteristics and needs, forms personalized learning paths. In this way, each student receives a tailor-made learning plan, improving learning efficiency and effectiveness. Finally, the push module retrieves relevant learning resources based on the learning path and generates push notifications. These push notifications may include relevant learning materials, video tutorials, and practice questions, aiming to help students consolidate knowledge points and improve problem-solving abilities. Through regular push notifications, students can maintain a constant learning state and continuously accumulate knowledge and skills. In summary, this IoT-based education management platform for international students provides them with a brand-new learning experience. Through the collaborative work of the student and server sides, the platform can collect students' learning data in real time, analyze learning needs, generate personalized learning paths, and push relevant learning resources. This not only improves the learning efficiency and effectiveness of international students but also provides them with great convenience and support for their study and life abroad. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a structural diagram of an IoT-based education management platform for studying abroad, according to the first embodiment of the present invention.

[0046] Figure 2 This is a structural diagram of the input module according to the second embodiment of the present invention.

[0047] Figure 3 This is a structural diagram of the data collection module according to the second embodiment of the present invention.

[0048] Figure 4 This is a structural diagram of the user identity authentication module according to the second embodiment of the present invention.

[0049] Figure 5 This is a structural diagram of the processing module of the second embodiment of the present invention.

[0050] Figure 6 This is a structural diagram of the behavior recognition module according to the second embodiment of the present invention.

[0051] Figure 7 This is a structural diagram of the learning path generation module according to the second embodiment of the present invention.

[0052] Figure 8 This is a structural diagram of the push module according to the second embodiment of the present invention.

[0053] Student terminal 101, Server terminal 102, Input module 103, Display module 104, Data collection module 105, User authentication module 106, Receiving module 107, Processing module 108, Sending module 109, Behavior recognition module 110, Learning path generation module 111, Push module 112, Input unit 201, Checking unit 202, Identity information collection unit 203, Attendance record unit 204, Practice record unit 205, Data extraction unit 206, Data matching unit 207, Verification unit 208, Learning resource library 209, Index generation unit 210, Retrieval unit 211, Problem data acquisition unit 212, Data processing unit 213, Classification unit 214, Analysis unit 215, Problem knowledge point generation unit 216, Knowledge graph generation unit 217, Path generation unit 218, Keyword extraction unit 219, Search unit 220, Information generation unit 221. Detailed Implementation

[0054] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0055] First Embodiment

[0056] Please see Figure 1 This invention provides an IoT-based education management platform for studying abroad, comprising a student terminal 101 and a server terminal 102. The student terminal 101 collects student data and sends student learning requests to the server terminal 102. The server terminal 102 receives learning requests from the student terminal 101 and retrieves corresponding learning resources to feed back to the student terminal 101. The student terminal 101 includes an input module 103, a display module 104, and a data collection module 105. The input module 103 receives learning requests input by students and sends them to the server terminal 102. The display module 104 displays teaching resources returned by the server terminal 102. The data collection module 105 acquires student learning data.

[0057] The server-side 102 includes a user authentication module 106, a receiving module 107, a processing module 108, a sending module 109, a behavior recognition module 110, and a learning path generation module 111. The user authentication module 106 verifies the identity of international students logging into the system. The receiving module 107 receives learning requests from student terminals 101. The processing module 108 retrieves corresponding learning resources based on the learning requests. The sending module 109 sends the learning resources to the student terminals 101. The behavior recognition module 110 analyzes the students' test-taking data to obtain the relevant knowledge points. The learning path generation module 111 generates a learning path based on the relevant knowledge points. The push module 112 retrieves learning resources based on the learning path and generates push notifications.

[0058] In this embodiment, the student client 101 serves as the user's direct interaction interface, including an input module 103, a display module 104, and a data collection module 105. The input module 103 allows students to input learning requests, whether it's searching for information, watching videos, or submitting assignments, all of which are quickly transmitted to the server 102. The display module 104 is responsible for displaying the teaching resources returned by the server 102 to students in a clear and intuitive way, ensuring they can easily and quickly obtain the information they need. The data collection module 105 collects students' learning data in real time. The server 102 is the core of the entire platform; it is responsible for processing learning requests sent by the student client 101 and retrieving corresponding learning resources. The server 102 includes a user authentication module 106, a receiving module 107, a processing module 108, a sending module 109, a behavior recognition module 110, and a learning path generation module 111. The user authentication module 106 ensures that only legitimate international students can access the platform, protecting educational resources from misuse. The receiving module 107 is responsible for receiving learning requests sent by the student client 101, ensuring smooth information transmission. The processing module 108 retrieves relevant learning resources from a vast educational resource database using a powerful search engine based on the learning request, ensuring students receive accurate and comprehensive information. The sending module 109 quickly sends the retrieved learning resources back to the student's terminal 101, ensuring the continuity and efficiency of learning. The behavior recognition module 110 performs in-depth analysis of student learning data. By mining student problem-solving data, it identifies the problematic knowledge points encountered by students in their learning, providing strong support for the generation of subsequent learning paths. The learning path generation module 111, based on the problematic knowledge points and combined with the student's learning characteristics and needs, forms a personalized learning path. In this way, each student can receive a tailor-made learning plan, improving learning efficiency and effectiveness. Finally, the push module 112 retrieves relevant learning resources based on the learning path and generates push notifications. These push notifications may include relevant learning materials, video tutorials, and practice questions, aiming to help students consolidate knowledge points and improve problem-solving abilities. Through regular push notifications, students can maintain a constant learning state and continuously accumulate knowledge and skills. In summary, this IoT-based education management platform for international students provides them with a brand-new learning experience. Through the collaborative work of student-side 101 and server-side 102, the platform can collect students' learning data in real time, analyze their learning needs, generate personalized learning paths, and push relevant learning resources. This not only improves the learning efficiency and effectiveness of international students, but also provides them with great convenience and support for their study and life in a foreign country.

[0059] Second Embodiment

[0060] Please see Figures 2-8Based on the first embodiment, the present invention also provides an IoT-based education management platform for studying abroad. The input module 103 includes an input unit 201 and a checking unit 202. The input unit 201 receives input from students on the interface; the checking unit 202 verifies the input data to ensure its integrity and accuracy. The input unit 201 receives input from students on the interface. This unit is designed with user-friendliness and ease of operation in mind, allowing students to submit necessary data such as assignments, exam answers, and personal information through various methods, such as text input, image uploads, and audio recording. This functionality not only reduces user frustration caused by complex operations but also greatly improves the efficiency and accuracy of data submission. The checking unit 202 rigorously verifies the input data to ensure the integrity of the content for subsequent retrieval.

[0061] The data collection module 105 includes an identity information collection unit 203, an attendance record unit 204, and a practice record unit 205; the identity information collection unit 203 is used to obtain the student's identity information and use it for login; the attendance record unit 204 is used to record the student's attendance data; and the practice record unit 205 is used to obtain the student's problem-solving data.

[0062] The system collects students' identity information, such as name, student ID, and gender, and ensures the accuracy of this information. This information is not only a crucial credential for students to log in to the system, but also the basis for subsequent data collection and analysis. The security of this identity information is paramount; therefore, measures such as encrypted storage and access control are necessary to protect student privacy.

[0063] Secondly, the attendance recording unit 204 is responsible for tracking student attendance in real time. By employing biometric technologies such as facial recognition and fingerprint recognition, this unit can accurately record students' learning progress. Finally, the practice recording unit 205 focuses on collecting students' test-taking data. This unit automatically collects students' answer data during homework assignments and exams by connecting with various learning platforms.

[0064] The user identity authentication module 106 includes a data extraction unit 206, a data matching unit 207, and a verification unit 208. The data extraction unit 206 is used to extract the identity information input by the international student. The data matching unit 207 is used by the system to compare the input user credentials with pre-stored database information to determine whether the match is successful. The verification unit 208 is used to further verify the identity information through a secondary verification mechanism after a successful match.

[0065] Data extraction unit 206 is the foundation of the entire authentication process. This unit is responsible for extracting key identity data, such as name, student ID, and passport number, from the information entered by international students. This data needs to be extracted accurately to ensure the smooth progress of subsequent authentication processes. Furthermore, data extraction unit 206 also needs to have a certain degree of error tolerance, capable of handling input errors or non-standard formatting, providing a user-friendly interactive experience.

[0066] Next, the data matching unit 207 is responsible for comparing the extracted identity information with the information pre-stored in the system's database. This step involves information comparison and verification, requiring the system to complete the comparison operation quickly and accurately to determine whether the information entered by the user matches the records in the database. To ensure the accuracy of the matching results, the system also needs to employ advanced encryption algorithms and confidentiality measures to protect the security and privacy of user data.

[0067] Finally, verification unit 208 is a crucial step in ensuring the authenticity of the user's identity. After successful data matching, verification unit 208 will initiate a secondary verification mechanism to further verify the identity information. This step employs multiple methods, such as sending a verification code to the user's registered mobile phone number or email address, requiring the user to enter the correct verification code to complete the verification.

[0068] The processing module 108 includes a learning resource library 209, an index generation unit 210, and a retrieval unit 211; the learning resource library 209 is used to store various types of learning resources; the index generation unit 210 is used to create an index for the resource library; and the retrieval unit 211 is used to retrieve learning resources from the learning resource library based on learning requests and the index.

[0069] First, the Learning Resource Repository 209 is the core of this module, designed to store and manage a vast amount of learning resources. These resources include, but are not limited to, e-books, online courses, instructional videos, audio lectures, and interactive exercises, covering all levels and fields from basic education to higher education. At the same time, to ensure the quality and timeliness of the resources, we have established a strict review mechanism and update process to ensure that users can access the latest and most authoritative learning materials.

[0070] The index generation unit 210 is responsible for creating indexes for these learning resources. It extracts key information from the resources using machine learning techniques, generating indexes that are easy to query and understand. These indexes not only include basic information such as the resource's title, author, and publication date, but also delve into details such as keywords, topic categories, and target audience. This allows users to locate the resources they need more quickly during searches, improving learning efficiency.

[0071] When a user issues a learning request, the retrieval unit 211 responds quickly and performs a fast and accurate search in the learning resource library 209 based on the user's request and the generated index.

[0072] The behavior recognition module 110 includes a problem-solving data acquisition unit 212, a data processing unit 213, a classification unit 214, an analysis unit 215, and a problem knowledge point generation unit 216. The problem-solving data acquisition unit 212 is used to collect data on exercises that students have done. The data processing unit 213 is used to clean the collected data. The classification unit 214 is used to classify all questions according to knowledge points. The analysis unit 215 is used to analyze the data of questions that students answered incorrectly and identify the error types. The problem knowledge point generation unit 216 is used to calculate the average accuracy rate for each error type, and obtain the problem knowledge point when the accuracy rate is lower than a threshold.

[0073] First, the problem-solving data acquisition unit 212 forms the foundation of the behavior recognition module 110. This unit is responsible for collecting data on the problems students have solved, including the type and difficulty of the problems, the students' answering time, and whether the answers were correct. This data is a crucial foundation for subsequent analysis, providing educators with a window into understanding students' learning progress.

[0074] Next, the data processing unit 213 cleans and organizes the collected data. Since the raw data may contain errors, omissions, or inconsistent formats, the task of the data processing unit 213 is to perform operations such as filtering, deduplication, and format conversion on this data to ensure data quality and consistency. Only after this step can subsequent analysis yield accurate results.

[0075] Next, classification unit 214 categorizes the cleaned data according to knowledge points. This step is crucial because it provides educators with information on students' performance across different knowledge points. Through classification, educators can clearly see which knowledge points students have mastered well and which they struggle with, allowing them to adjust their teaching strategies accordingly.

[0076] Analysis Unit 215 is the core component of Behavior Recognition Module 110. This unit uses categorized data to analyze students' incorrect answers and identify the error types. For example, some errors may be due to a student's lack of understanding of the knowledge points, while others may be due to carelessness or improper answering techniques. By deeply analyzing these error types, Analysis Unit 215 provides educators with accurate problem diagnosis.

[0077] Finally, the problem knowledge point generation unit 216 calculates the average accuracy rate for each error type based on the results of the analysis unit 215. When the accuracy rate for a certain error type is lower than a preset threshold, this knowledge point is marked as a problem knowledge point. This step not only helps educators quickly locate students' learning difficulties but also provides a clear direction for subsequent teaching improvements.

[0078] The learning path generation module 111 includes a knowledge graph generation unit 217 and a path generation unit 218; the knowledge graph generation unit 217 is used to construct a knowledge graph based on the logical relationship between knowledge points; the path generation unit 218 is used to obtain a learning path based on the problem knowledge points and the knowledge graph.

[0079] In today's era of knowledge explosion, effective learning path planning is particularly important. To meet this need, the learning path generation module 111 was developed. This module mainly includes a knowledge graph generation unit 217 and a path generation unit 218, which work together to provide users with personalized learning paths.

[0080] First, the knowledge graph generation unit 217 is the foundation of the entire learning path generation module 111. It utilizes natural language processing technology and machine learning algorithms to deeply mine and analyze the connections between knowledge points. These knowledge points come from various sources, including textbooks, online courses, and forum discussions, covering knowledge systems across various fields. By constructing logical relationships between knowledge points, the knowledge graph generation unit 217 forms a comprehensive and accurate knowledge network.

[0081] Building upon this foundation, the path generation unit 218 begins to function. Based on the user's questions and learning needs, it filters relevant knowledge points and paths from the knowledge graph. These paths can be linear, branching, or even circular, to accommodate different learning styles and needs. Simultaneously, the path generation unit 218 also considers the difficulty level of the knowledge points and the user's learning progress, recommending the most suitable learning path for the user.

[0082] To better illustrate the working principle of the learning path generation module 111, let's take a concrete example. Suppose a student is preparing for a math exam and uses the learning path generation module 111 to plan their study schedule. First, the knowledge graph generation unit 217 constructs a knowledge graph based on mathematical knowledge points, showing the connections and patterns between these points. Then, the path generation unit 218 selects exam-related knowledge points and paths from the knowledge graph based on the student's questions and learning needs. For example, the student might first learn basic concepts and then gradually delve into problem-solving techniques and test-taking strategies. During the learning process, the path generation unit 218 also adjusts the learning path based on the student's mastery and progress, ensuring that the student can learn efficiently and achieve good results.

[0083] The push module 112 includes a keyword extraction unit 219, a search unit 220, and an information generation unit 221. The keyword extraction unit 219 is used to extract keywords based on the learning path. The search unit 220 is used to search the learning resource library 209 based on the keywords to obtain the target link. The information generation unit 221 is used to generate push information based on the retrieved target link.

[0084] Keyword extraction unit 219, based on the learning path, intelligently extracts keywords closely related to the learner's current learning status and goals through natural language processing and machine learning technologies.

[0085] Next, the search unit 220 is responsible for efficiently retrieving information from the vast learning resource database 209 based on keywords. This unit relies on advanced search engine technology and optimized management of the learning resource database 209. Through intelligent algorithms, the search unit 220 can quickly filter out learning resources matching the target keywords and generate target links. These links point to high-quality resources closely related to the learner's current learning needs, such as online courses, instructional videos, and academic literature.

[0086] Finally, the information generation unit 221 is the output of the entire push module 112. Based on the retrieved target links and the learner's personalized needs and learning preferences, this unit generates targeted push information. This information may include course recommendations, learning suggestions, resource links, etc., aiming to help learners better plan their learning paths and improve learning outcomes. Simultaneously, the information generation unit 221 continuously optimizes the push strategy based on learner feedback and interaction data, improving the accuracy and appeal of the information.

[0087] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes for implementing the above embodiments, and equivalent changes made in accordance with the claims of the present invention, are still within the scope of the invention.

Claims

1. An IoT-based education management platform for studying abroad, characterized in that, It includes a student client and a server client. The student client is used to collect student data and send student learning requests to the server client. The server is used to receive learning requests from students and retrieve corresponding learning resources to send back to the students. The student terminal includes an input module, a display module, and a data collection module; The input module is used to receive learning requests input by students and send the students' learning requests to the server. The display module is used to display the teaching resources returned by the server. The data collection module is used to acquire students' learning data; The server-side includes a user authentication module, a receiving module, a processing module, a sending module, a behavior recognition module, a learning path generation module, and a push module; The user authentication module verifies the identity of international students logging into the system; The receiving module is used to receive learning requests from students. The processing module is used to retrieve corresponding learning resources based on the learning request; The sending module is used to send learning resources to the student's end; The behavior recognition module is used to analyze students' test-taking data and obtain the knowledge points of the problems. The learning path generation module is used to generate learning paths based on the knowledge points of the problem; The push module is used to retrieve learning resources based on the learning path and generate push information.

2. The IoT-based education management platform for studying abroad as described in claim 1, characterized in that, The input module includes an input unit and a checking unit; The input unit is used to receive input from students on the interface; The inspection unit is used to verify the input data to ensure its integrity and accuracy.

3. The IoT-based education management platform for studying abroad as described in claim 2, characterized in that, The data collection module includes an identity information collection unit, an attendance record unit, and a practice record unit; The identity information collection unit is used to obtain the student's identity information and use it for login; The attendance recording unit is used to record students' attendance data; The practice recording unit is used to obtain students' problem-solving data.

4. The IoT-based education management platform for studying abroad as described in claim 3, characterized in that, The user authentication module includes a data extraction unit, a data matching unit, and a verification unit. The data extraction unit is used to extract the identity information input by international students; The data matching unit is used by the system to compare the input user credentials with pre-stored database information to determine whether the match is successful. The verification unit is used to further verify the identity information through a secondary verification mechanism after a successful match.

5. The IoT-based education management platform for studying abroad as described in claim 4, characterized in that, The processing module includes a learning resource library, an index generation unit, and a retrieval unit; The learning resource library is used to store various types of learning resources; The index generation unit is used to create an index for the resource library; The retrieval unit is used to retrieve learning resources from the learning resource database based on learning requests and indexes.

6. The IoT-based education management platform for studying abroad as described in claim 5, characterized in that, The behavior recognition module includes a question-answering data acquisition unit, a data processing unit, a classification unit, an analysis unit, and a question knowledge point generation unit; The problem-solving data acquisition unit is used to collect data on the problems that students have solved. The data processing unit is used to clean the collected data; The classification unit is used to categorize all questions according to knowledge points; The analysis unit is used to analyze the data of students' incorrect answers and identify the error types. The problem knowledge point generation unit is used to calculate the average accuracy rate for each error type, and to obtain problem knowledge points when the accuracy rate is lower than a threshold.

7. A study abroad IoT education management platform as described in claim 6, characterized in that, The learning path generation module includes a knowledge graph generation unit and a path generation unit; The knowledge graph generation unit is used to construct a knowledge graph based on the logical relationships between knowledge points. The path generation unit is used to obtain learning paths based on problem knowledge points and knowledge graphs.

8. The IoT-based education management platform for studying abroad as described in claim 7, characterized in that, The push module includes a keyword extraction unit, a search unit, and an information generation unit. The keyword extraction unit is used to extract keywords based on the learning path. The search unit is used to search the learning resource library based on the keywords to obtain the target link. The information generation unit is used to generate push information based on the retrieved target links.

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