Self-adaptive teaching interaction system

Abstract

The invention discloses a self-adaptive teaching interaction system. Based on a Unity engine, a virtual world, a figure and a text information framework are simulated and generated by means of a UPR rendering pipeline; the background information processing module enables a preset task to run in the background; the music design module provides sound simulating a real market; when a student interacts with the NPC, a text interaction panel pops up; the interactive design module constructs a semi-open world, and students can experience the operation mode of financial knowledge in reality in a task scene; the text system is used for students to obtain understanding of financial knowledge through a dialogue panel with the NPC; the event system is used for starting conversations between the students and different NPCs to trigger different event delegation; and the checking system is used for the teacher to observe student behaviors in the background and judge whether the students really understand financial knowledge. According to the invention, an immersive financial knowledge learning environment is created for students, teaching tasks and contents are dynamically adjusted according to learning data of the students, and the students are assisted to better understand and apply financial knowledge.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and specifically refers to an adaptive teaching interaction system. Background Art

[0002] In the modern traditional university education system, university teachers only output information through PPT, blackboard, etc. in the classroom, relying on visual and auditory senses. The classes lack interest, making it difficult to improve students' attention and resulting in low learning efficiency. At the same time, the traditional university education classroom does not combine with modern technology, with slow adaptive development and unable to fully keep students highly focused.

[0003] With the development of virtual reality and artificial intelligence technologies, virtual teaching systems have emerged. However, existing systems have deficiencies in adaptive teaching and interactivity, and cannot provide accurate learning content and feedback based on students' real-time learning situations.

[0004] Therefore, it is necessary to develop a teaching system that can adaptively adjust teaching content and methods and provide a rich interactive experience. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an adaptive teaching interaction system, which, with the cooperation of multiple modules, creates an immersive financial knowledge learning environment for students, dynamically adjusts teaching tasks and content based on students' learning data, helps students better understand and apply financial knowledge, and at the same time provides teachers with a comprehensive tool for monitoring and analyzing students' learning situations.

[0006] To solve the above technical problem, the technical solution provided by the present invention is: an adaptive teaching interaction system, including:

[0007] Image display module: Based on the Unity engine, it simulates and generates a virtual world, characters, and a text information framework with the UPR rendering pipeline, and continuously plays animations and pushes content to adapt to students' adaptive learning needs;

[0008] Background information processing module: Places the preset tasks to run in the background to promote students' adaptive learning process;

[0009] Music design module: Provides sounds that simulate the real market;

[0010] NPC design module: When interacting with NPCs, a text interaction panel pops up, and different NPCs have their own unique behavior designs, including task judgment and corresponding behaviors for task completion situations;

[0011] Interaction Design Module: Build a semi-open world where students can explore and discover different NPCs. The NPCs give players instructions and tasks, and the players go to the task locations according to the instructions. The task locations are set with simulated real market scenarios, and students can experience the operation mode of financial knowledge in reality in the scenarios.

[0012] Text System: It is used for students to indirectly or directly obtain an understanding of financial knowledge through the dialogue panels of NPCs. Different NPCs output different texts containing financial knowledge, showing the impact of financial knowledge on different people's choices.

[0013] Event System: It is used for students to start conversations with different NPCs in the open world to trigger different event commissions, and each commission hides different financial knowledge.

[0014] Inspection System: It is used for teachers to observe students' behaviors in the background to judge whether students really understand financial knowledge.

[0015] Furthermore, the tasks preset in the background information processing module cover various learning objectives and difficulty levels, dynamically adjust the task priorities and progress rhythms according to students' learning data, and provide personalized adaptive learning path planning.

[0016] Furthermore, the music design module simulates the sounds of the real market through multi-channel surround technology, and the volume, timbre and rhythm of the sounds change in real time according to the game scenarios and students' interaction behaviors.

[0017] Furthermore, the NPC design module designs the behaviors of different NPCs based on the decision tree algorithm, judges according to the current task status of students, and adjusts the behavior patterns in combination with students' historical interaction data to provide targeted tasks and feedback.

[0018] Furthermore, the interaction design module constructs the map layout and NPC distribution of the semi-open world, with exploration elements and hidden tasks. The simulated real market scenarios include financial trading elements such as price fluctuations and supply-demand relationships.

[0019] Furthermore, the text system uses natural language processing algorithms to generate text content and intelligently adjusts the reply content according to students' questions and dialogue contexts.

[0020] Furthermore, the event commissions in the event system follow the logical structure of the financial knowledge system, gradually deepening from basic concepts to complex applications, and the event trigger conditions and reward mechanisms are associated with students' learning achievements.

[0021] Furthermore, the inspection system is used for teachers to view students' learning trajectories, task completion situations, and knowledge mastery levels, and analyzes students' behaviors using data analysis tools.

[0022] The advantages of the present invention compared with the prior art are as follows:

[0023] Through the adaptive task adjustment algorithm of the background information processing module, the present invention dynamically adjusts the task priority and promotion rhythm according to the learning data of students, provides personalized learning paths for each student, and improves the learning effect.

[0024] Through the image display module and the music design module, the present invention jointly creates a realistic virtual learning environment, and the music is adjusted in real time according to the scene and interaction behavior, enhancing the immersion of students.

[0025] Through the NPC design module and the text system, the present invention uses the decision tree algorithm and natural language processing technology to achieve intelligent interaction with students, and provides targeted tasks and feedback.

[0026] Through the inspection system, the present invention provides comprehensive monitoring and analysis tools for teachers on the learning situation of students, helping teachers to adjust teaching strategies in a timely manner. Brief Description of the Drawings

[0027] Figure 1 is a system block diagram of an adaptive teaching interaction system of the present invention.

[0028] Figure 2 is a flowchart of an adaptive teaching interaction system of the present invention. Detailed Embodiments

[0029] The following will refer to the drawings to describe in detail various exemplary embodiments of the present invention. It should be noted that: unless otherwise specifically stated, the relative arrangements, numerical expressions and values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0030] The description of at least one exemplary embodiment below is actually only illustrative and in no way restricts the present invention and its application or use.

[0031] Technologies, methods and devices known to those of ordinary skill in the relevant fields may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification.

[0032] In all the examples shown and discussed here, any specific value should be construed as merely exemplary and not as a limitation. Therefore, other examples of the exemplary embodiments may have different values.

[0033] The following will further describe in detail an adaptive teaching interaction system of the present invention with reference to the drawings.

[0034] Combined with the attached Figure 1-2 , the present invention will be introduced in detail.

[0035] An adaptive teaching interaction system, comprising:

[0036] Image display module: Based on the Unity engine, with the help of the UPR rendering pipeline, it simulates and generates a virtual world, characters, and text information framework, and continuously plays animations and pushes content to adapt to the adaptive learning needs of students. This module can create a realistic and attractive virtual scene, providing students with an immersive learning environment, making students seem to be in a real financial market situation.

[0037] Background information processing module: Places the preset tasks to run in the background, promoting the adaptive learning process of students. The preset tasks cover a variety of learning objectives and difficulty levels, dynamically adjust the task priorities and progress rhythms according to students' learning data, and provide personalized adaptive learning path planning. For example, the system will judge students' mastery of different knowledge points based on data such as the answering accuracy rate and completion time of students in previous tasks, and then push tasks more suitable for their current learning level to students, making the learning process more targeted and efficient. Using an adaptive task adjustment algorithm:

[0038] Let the mastery level of student for knowledge point i be S i , and its calculation method is the ratio of the number of correct answers C i to the total number of answers T i for the tasks related to knowledge point i, that is: Task j is related to knowledge point k, and its priority P j can be expressed as: P j = 1 - S k ; The system sorts all tasks from high to low according to the priority, and preferentially assigns tasks with high priority to students.

[0039] Music design module: Provides sounds that simulate the real market. Simulates the sounds of the real market through multi-channel surround technology, and the volume, timbre, and rhythm of the sounds change in real time according to the game scene and students' interaction behaviors. When students enter different market areas or perform specific trading operations, the system will correspondingly adjust the sound effects, enhancing students' sense of immersion and further improving the learning experience.

[0040] NPC design module: When interacting with NPCs, a text interaction panel pops up, and different NPCs have their own unique behavior designs, including task judgment and corresponding behaviors for task completion situations. Designs the behaviors of different NPCs based on the decision tree algorithm, judges according to the current task status of students, and adjusts the behavior patterns in combination with students' historical interaction data, providing targeted tasks and feedback. For example, a certain NPC may decide the task difficulty and reward method for students next time according to the speed and quality of students' completion of similar tasks before.

[0041] Interactive Design Module: Build a semi-open world where students can explore and discover different NPCs. The NPCs give players instructions and tasks, and players go to the task locations according to the instructions. The task locations are set with simulated real-world market scenarios, where students can experience how financial knowledge operates in reality. The constructed semi-open world has a rich map layout and NPC distribution, including exploration elements and hidden tasks. The simulated real-world market scenarios include financial trading elements such as price fluctuations and supply-demand relationships. Students can actively learn during the exploration process and deeply understand financial knowledge through actual operations.

[0042] Text System: It is used for students to indirectly or directly obtain an understanding of financial knowledge through the dialogue panel with NPCs. Different NPC dialogues output different texts, which contain financial knowledge and show the impact of financial knowledge on different people's choices. Natural language processing algorithms are used to generate text content, and the reply content is intelligently adjusted according to students' questions and dialogue contexts. When students ask questions about financial concepts, the system can give accurate explanations in an easy-to-understand way according to the meaning and context of the questions. The above purpose is achieved by using intelligent reply generation algorithms, and the specific processing method is as follows: Let the student's question be Q, and the knowledge entries in the knowledge base be K1, K2, …, K n . First, use the TF-IDF (Term Frequency-Inverse Document Frequency) method to transform the question Q and the knowledge entry K i into vectors and The similarity Sim(Q, K i ) between the question Q and the knowledge entry K i can be calculated by cosine similarity: Select the knowledge entry K max with the highest similarity as the reply content, that is:

[0043] Event System: It is used for students to trigger different event commissions by starting conversations with different NPCs in the open world. Each commission hides different financial knowledge. The event commissions follow the logical structure of the financial knowledge system, gradually deepening from basic concepts to complex applications. The event trigger conditions and reward mechanisms are associated with students' learning achievements. For example, students can only trigger more advanced investment strategy-related events after mastering the basic financial trading rules.

[0044] Inspection System: It is used for teachers to observe students' behaviors in the background to judge whether students really understand financial knowledge. Teachers can view students' learning trajectories, task completion situations, and knowledge mastery levels, and use data analysis tools to analyze students' behaviors. Through these data, teachers can timely discover the difficulties and problems encountered by students during the learning process and adjust teaching contents and methods accordingly.

[0045] The specific implementation process of an adaptive teaching interaction system of the present invention is as follows:

[0046] In practical applications, leveraging the powerful graphics processing capabilities of the Unity engine and combining with the UPR rendering pipeline, a virtual world is carefully constructed. First, model according to relevant financial market scenarios, including stock exchanges, banks, commercial streets, etc. At the same time, design diverse virtual character images, with each image corresponding to different role settings, such as investors, financial advisors, market analysts, etc. For the text information framework, adopt a concise and clear layout method to ensure that students can quickly obtain key information. In terms of animation production, use professional animation design software to create smooth and natural animation effects for virtual characters and scene elements, such as animations of characters walking, trading operations, etc. During the content push process, according to the students' learning progress and current tasks, accurately push relevant animations and text content. For example, when students are learning stock trading knowledge, the system pushes animations of stock trading scenarios and text descriptions of relevant trading rules.

[0047] Teaching experts and technical personnel jointly formulate a task library with multiple learning objectives and difficulty levels. The tasks cover multiple aspects such as basic financial knowledge, investment strategies, risk management, etc., and each task has clear learning objectives and evaluation criteria. During the operation of the system, real-time collect students' learning data, including answering questions, task completion time, interaction records with NPCs, etc. Through data analysis algorithms, evaluate students' learning abilities and knowledge mastery levels. For example, if a student performs well in multiple tasks related to stock valuation, the system determines that the student has a good grasp of this knowledge point. When pushing the next task, the task difficulty will be increased, or the focus of the task will be shifted to aspects such as stock trading risk control. At the same time, dynamically adjust the task priorities according to the students' learning progress and task completion situations. For tasks that have not been completed but are of high importance, the system will give prompts on the interface to guide students to complete them first.

[0048] Utilize multi-channel surround technology to configure professional sound simulation devices and software in the system. For different game scenarios, such as the noisy trading sounds in a stock exchange and the quiet atmosphere sounds in a bank lobby, collect sound samples in the real environment and perform post-processing and optimization to make them realistically reproduced in the system. At the same time, adjust the sound parameters in real time according to the students' interaction behaviors. When a student approaches the trading area, the volume of the trading sounds will gradually increase; when a student successfully completes a transaction, a cheerful prompt sound will be played, and the tone color and rhythm will be slightly adjusted according to the profit situation of the transaction. The more profitable the transaction, the more cheerful the prompt sound.

[0049] Based on the decision tree algorithm, design unique behavioral logics for each NPC. First, analyze the role characteristics and functions of different NPCs in the financial market to determine their possible interaction methods and task types with students. For example, the main task of the financial advisor NPC is to provide investment advice and guidance to students. Its behavior decision tree will decide what kind of investment advice to give to students and what investment products to recommend based on factors such as the students' asset status, investment goals, and previous trading records. During the interaction with students, the NPC will continuously collect the students' historical interaction data and adjust its own behavior pattern according to these data. If it is found that the student shows a high interest in a certain investment product, the next time they interact, the NPC will provide more detailed information about the product and related tasks.

[0050] When constructing a semi-open world, design a rich and diverse map layout, including different areas such as urban blocks, financial business districts, trading markets, etc. Reasonably distribute NPCs on the map, and each NPC has its specific activity range and task trigger conditions. For example, in the area of the stock exchange, there will be multiple NPCs related to stock trading, and students can interact with them to obtain stock trading tasks. In the simulated real market scenario of the task location, detailedly simulate financial trading elements such as price fluctuations and supply-demand relationships. Through the real-time data simulation algorithm, dynamically adjust the commodity prices and supply-demand quantities according to the market conditions and the students' trading behaviors. Students can perform operations such as buying and selling commodities and investment transactions in the scenario, and truly experience the operation mode of financial knowledge in reality. At the same time, set some hidden tasks and exploration elements to stimulate the students' exploration desire and learning enthusiasm. For example, in a certain hidden corner, there is a mysterious financial expert NPC hidden. After the students find him, they can trigger a series of high-difficulty but extremely challenging financial knowledge learning tasks.

[0051] Utilize natural language processing algorithms to build a text generation and response system. During the system training phase, collect a large amount of text data in the financial field, including financial textbooks, news reports, professional papers, etc., and train the algorithm so that it can understand and generate text content related to finance. When a student has a conversation with an NPC, the system first performs semantic analysis on the text input by the student to understand the student's question intention and context. Then, according to the analysis result, select appropriate response content from the pre-constructed text library, or use the algorithm to generate response text in real time. For example, when the student asks "What is the price-earnings ratio", the system will quickly retrieve the knowledge base and explain the concept of the price-earnings ratio, calculation method, and its application in investment decisions in a clear and easy-to-understand language. At the same time, intelligently adjust the response content according to the progress of the conversation and the student's feedback. If the student still has doubts about the explanation, the system will further give examples or guide the student to perform relevant task operations to deepen the understanding of the concept.

[0052] During the system design process, event delegation is carefully designed based on the logical structure of the financial knowledge system. Starting from basic financial concepts such as the time value of money and interest calculation, to complex applications such as portfolio management and risk management strategies, an event chain is gradually constructed. Each event has clear triggering conditions and reward mechanisms, and is closely related to the learning outcomes of students. For example, when a student completes a series of learning tasks on basic financial concepts and the answering accuracy rate reaches a certain standard, the "Preliminary Investment Practice" event is triggered. In this event, the student can conduct small-scale investment operations in a simulated market and receive corresponding rewards according to the investment results. The rewards can be virtual currency, learning props, or unlocking more advanced learning content. The setting of event triggering conditions fully considers the student's knowledge mastery and operation proficiency, ensuring that students can gradually improve their financial knowledge level during the process of completing event tasks.

[0053] After the teacher logs in to the system in the background, they can intuitively view the learning trajectories of students, including information such as the movement paths of students in the virtual world, the interaction order with NPCs, the tasks and events participated in, etc. At the same time, the system details the task completion status of students, such as the task completion time and completion quality (whether the task requirements are correctly completed, the efficiency of completing the task, etc.). For the student's knowledge mastery level, a comprehensive evaluation is conducted through the student's performance in task answering, conversations with NPCs, and the completion of event tasks. Teachers use the data analysis tools provided by the system to deeply analyze the student behavior data. For example, through data visualization charts, the distribution of students' mastery of different knowledge points is intuitively displayed, and the common learning difficulties and problems of students are discovered. Based on the analysis results, teachers can timely adjust teaching strategies. For example, for the knowledge points where students are weak, key explanations are given in class, or more targeted learning tasks and tutoring materials are pushed to students in the system.

[0054] The above describes the present invention and its implementation manners. Such a description is not restrictive, and what is shown in the drawings is only one of the implementation manners of the present invention. The actual structure is not limited thereto. Generally speaking, if those of ordinary skill in the art are inspired by it and design similar structural modes and embodiments to this technical solution without creative efforts without departing from the purpose of the present invention creation, they shall fall within the protection scope of the present invention.

Claims

1. An adaptive teaching interaction system, characterized in that: including Image display module: Based on the Unity engine, it uses the UPR rendering pipeline to simulate and generate a virtual world, characters, and text information framework, and continuously plays animations and pushes content to adapt to the adaptive learning needs of students; Background information processing module: Runs the pre-set tasks in the background to promote the adaptive learning process of students; Music design module: Provides sounds that simulate the real market; NPC design module: When interacting with NPCs, a text interaction panel pops up, and different NPCs have their own unique behavior designs, including task judgment and corresponding behaviors for task completion; Interaction design module: Builds a semi-open world where students can explore and discover different NPCs. The NPCs give students instructions and tasks, and the students go to the task locations according to the instructions. The task locations are set with scenes that simulate the real market, and students can experience the operation mode of financial knowledge in reality in the scenes; Text system: Used for students to indirectly or directly obtain an understanding of financial knowledge through the dialogue panel with NPCs. Different NPC dialogues output different texts, which contain financial knowledge and show the influence of financial knowledge on different people's choices; Event system: Used for students to trigger different event commissions by starting conversations with different NPCs in the open world, and each commission hides different financial knowledge; Inspection system: Used for teachers to observe students' behaviors in the background to judge whether students truly understand financial knowledge.

2. An adaptive teaching interaction system according to claim 1, wherein: The pre-set tasks in the background information processing module cover various learning objectives and difficulty levels, dynamically adjust the task priorities and progress rhythms according to students' learning data, and provide personalized adaptive learning path planning.

3. An adaptive teaching interaction system according to claim 2, wherein: The music design module simulates the sounds of the real market through multi-channel surround technology, and the volume, timbre, and rhythm of the sounds change in real time according to the game scene and students' interaction behaviors.

4. An adaptive teaching interaction system according to claim 3, characterized in that: The NPC design module designs the behaviors of different NPCs based on the decision tree algorithm, judges according to the current task status of students, and adjusts the behavior patterns in combination with students' historical interaction data to provide targeted tasks and feedback.

5. An adaptive teaching interaction system according to claim 4, characterized in that: The interaction design module constructs the map layout and NPC distribution of the semi-open world, with exploration elements and hidden tasks. The simulated real market scene includes financial trading elements such as price fluctuations and supply-demand relationships.

6. The adaptive teaching interaction system according to claim 5, wherein: The text system uses natural language processing algorithms to generate text content and intelligently adjusts the reply content according to students' questions and dialogue contexts.

7. An adaptive teaching interaction system according to claim 6, characterized in that: The event commissions in the event system follow the logical structure of the financial knowledge system, gradually deepening from basic concepts to complex applications, and the event trigger conditions and reward mechanisms are associated with students' learning outcomes.

8. An adaptive teaching interaction system according to claim 7, characterized in that: The inspection system is used for teachers to view students' learning trajectories, task completion situations, and knowledge mastery levels, and analyzes students' behaviors using data analysis tools.

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