Digital intelligence teaching method and system based on TPACK framework

Abstract

The embodiment of the invention relates to the technical field of wisdom education, and discloses a digital intelligent teaching method and system based on a TPACK framework, which is applied to a digital intelligent courseware platform based on the TPACK framework, and comprises the following steps: collecting at least two digital intelligent teaching tools; collecting collected data when a user uses the digital intelligent teaching tool; constructing a user training and supporting tool; the collected data are analyzed through the user training and supporting tool, a training interface is provided according to an analysis result, and the training interface comprises recommended intelligent teaching tool introduction, an intelligent teaching tool use course and common intelligent teaching tool use problems. The comprehensive ability of the user in the aspects of education technology, teaching method and subject content is improved, and powerful technical support is provided for intelligent and efficient development of an education system.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of smart education technology, and in particular to a digital intelligence teaching method and system based on the TPACK framework. Background Art

[0002] In recent years, artificial intelligence seems to have had a transformative impact on business and health, however, the potential of artificial intelligence has not yet been fully utilized in the field of education. Although AI-based tools have brought innovative teaching opportunities to the fields of education and learning, for example, these tools are expected to promote a student-centered teaching approach, provide customized support based on insights into learners' cognitive and emotional needs, and enable users to effectively monitor students' learning progress, assist users in evaluating teaching processes, and help with course design and implementation.

[0003] The technological, pedagogical, and content knowledge (TPACK) framework provides a perspective for understanding the requisite knowledge required for users to integrate AI-based tools. TPACK represents the expertise of users to effectively use technology to achieve instructional purposes. TPACK is viewed as a flexible framework that is applicable to a variety of instructional methods and technological tools. When the TPACK framework is combined with the technological and pedagogical advantages of AI, it will provide a solid theoretical framework for a deep understanding of user knowledge for AI-based instruction.

[0004] Only when users have sufficient teaching knowledge and can effectively use AI-based tools can AI technology play its effectiveness in teaching. Therefore, it is crucial to master the knowledge of integrating AI-based tools into education. However, the development speed of AI educational tools in existing technologies is fast, and it is difficult for users to keep up with the update and iteration speed of the tools. Therefore, it is urgent to solve the problem of not being able to integrate AI tools into education in digital intelligence teaching. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a digital intelligence teaching method and system based on the TPACK framework, introduce a courseware platform specifically for AI education applications, provide interactive training, case teaching, real-time monitoring and feedback functions, and support users from a technical level.

[0006] According to some embodiments of the present application, a first aspect of the embodiments of the present application provides a digital teaching method based on the TPACK framework, which is applied to a digital courseware platform based on the TPACK framework, collects at least two digital teaching tools; collects data collected by users when using the digital teaching tools; builds user training and support tools; analyzes the collected data through the user training and support tools, and provides a training interface based on the analysis results, wherein the training interface includes a recommended introduction to intelligent teaching tools, a tutorial on how to use intelligent teaching tools, and common problems in using intelligent teaching tools.

[0007] According to some embodiments of the present application, the second aspect of the embodiments of the present application further provides a digital and intelligent teaching system based on the TPACK framework, including a digital and intelligent courseware platform based on the TPACK framework, the system including: a collection module for collecting at least two digital and intelligent teaching tools; a data acquisition module for collecting collected data when a user uses the digital and intelligent teaching tools; a training and support tool construction module for constructing user training and support tools; an analysis module for analyzing the collected data through the user training and support tools, and providing a training interface based on the analysis results, the training interface including a recommended introduction to intelligent teaching tools, a tutorial for using intelligent teaching tools, and common problems in using intelligent teaching tools.

[0008] According to some embodiments of the present application, a third aspect of the embodiments of the present application further provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the digital intelligence teaching method based on the TPACK framework as described above.

[0009] According to some embodiments of the present application, a fourth aspect of the embodiments of the present application further provides a computer-readable storage medium, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, performs the digital intelligence teaching method based on the TPACK framework as described above.

[0010] The technical solution provided in the embodiment of the present application has at least the following advantages:

[0011] By combining the digital courseware platform and the TPACK framework, and collecting a variety of digital teaching tools, users can obtain rich training resources and technical support, and can systematically learn and apply AI technology, thereby improving users' comprehensive capabilities in educational technology, teaching methods, and subject content; by analyzing the data collected when users use AI tools, and based on the analysis results and the user's personalized needs, provide an introduction to intelligent teaching tools, tutorials on how to use intelligent teaching tools, and training interfaces for related content such as intelligent teaching tools, and recommend training content that is highly matched to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1 It is a flowchart of a digital intelligence teaching method based on the TPACK framework according to an embodiment of the present application;

[0014] Figure 2 is a schematic diagram of the relationship between various factors of the TPACK scale according to an embodiment of the present application;

[0015] Figure 3 is a schematic diagram of a digital intelligence teaching system based on the TPACK framework according to an embodiment of the present application;

[0016] Figure 4 is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] Due to the rise of AI technology, the implementation of teaching methods has changed significantly. In order to fully grasp the opportunities brought by AI in education, users need to be able to effectively use AI-based tools to achieve their effectiveness in teaching. For example, the more users understand the practicality of AI-based tools, the more they are inclined to use these tools to improve learners' motivation and engagement. Similarly, users who have a deeper understanding of AI can more accurately choose AI tools that suit their teaching needs. Therefore, users' understanding of AI enables them to use AI-based tools to achieve personalized learning and instant feedback. It can be seen that it is crucial to master the knowledge of integrating AI-based tools into education.

[0018] The TPACK framework is a model used to describe the professional knowledge structure of users in technology-integrated teaching. The TPACK framework attempts to explain the multiple types of knowledge and their interactions that users need when effectively using technology for teaching. Specifically, TPACK covers the following seven types of knowledge: Technological Knowledge (TK): the ability to understand and use a variety of technological tools and resources (such as software, hardware, the Internet, and other digital tools); Pedagogical Knowledge (PK): a deep understanding of how to teach, including teaching methods, strategies, classroom management, and assessment techniques; Content Knowledge (CK): a deep understanding of a specific subject area, including core concepts, theories, frameworks, and specialized teaching content; Technological-Pedagogical Knowledge (TPK): the ability to apply and integrate technological tools and methods in different teaching environments and scenarios; Pedagogical-Content Knowledge (PCK): the ability to select and implement the most appropriate teaching strategies and methods in combination with specific subject content; Technological-Content Knowledge (TCK): understanding how the content of a certain subject area can be presented, analyzed, and deepened through technological means; Technological-Pedagogical-Content Knowledge (TCK): understanding how the content of a certain subject area can be presented, analyzed, and deepened through technological means; TPACK: The ability to apply all of the above knowledge types, especially in a specific teaching context, to effectively integrate technology, teaching methods and content to teach.

[0019] Based on this, an embodiment of the present application provides a digital and intelligent teaching method and system based on the TPACK framework, which collects at least two digital and intelligent teaching tools; collects collected data when users use the digital and intelligent teaching tools; builds user training and support tools; analyzes the collected data through the user training and support tools, and provides a training interface based on the analysis results, wherein the training interface includes a recommended introduction to intelligent teaching tools, a tutorial on how to use intelligent teaching tools, and common problems in using intelligent teaching tools.

[0020] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0021] An embodiment of the present invention relates to a digital and intelligent teaching method based on the TPACK framework, which is applied to a digital and intelligent courseware platform based on the TPACK framework, and the digital and intelligent courseware platform can be integrated on any electronic device, such as a mobile phone, a computer, a server, etc. The method includes: collecting at least two digital and intelligent teaching tools; collecting collected data when users use the digital and intelligent teaching tools; building user training and support tools; analyzing the collected data through the user training and support tools, and providing a training interface based on the analysis results, and the training interface includes a recommended introduction to intelligent teaching tools, a tutorial on how to use intelligent teaching tools, and common problems in using intelligent teaching tools. The implementation details of the digital and intelligent teaching method based on the TPACK framework of an embodiment of the present invention are described in detail below. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution.

[0022] The digital and intelligent courseware is an advanced teaching resource that integrates digital and intelligent technologies. It is built on digital technology and converts traditional teaching materials, teaching materials and various educational resources into digital forms that can be processed by computers, which is convenient for storage, management and dissemination. The digital and intelligent courseware uses multimedia, animation, simulation and other means to provide a highly interactive learning experience, enabling students to understand and master knowledge in a dynamic and visual way, and enhance participation and fun in the learning process. In addition, the digital and intelligent courseware combines artificial intelligence and big data analysis technology, and can dynamically adjust and personalize according to the learner's learning behavior, ability level and personalized needs to achieve adaptive teaching. The digital and intelligent courseware also has real-time learning data collection and analysis functions, provides feedback and evaluation, helps course designers optimize teaching strategies, and guides students to improve their learning methods. In addition, the digital and intelligent courseware supports students' autonomous learning and exploratory learning, and cultivates students' innovative thinking ability and independent problem-solving ability. The digital and intelligent courseware aims to empower education through technology, improve teaching quality, promote efficient learning, and meet the development trend of modern education informatization and personalized teaching.

[0023] At the same time, the Intelligent Courseware Design Platform is an online tool or software system that integrates various digital and intelligent technologies. It aims to support educators in creating, managing and optimizing digital courseware platforms. It has multiple functions such as multimedia content creation, interactive courseware design, artificial intelligence support, big data analysis, collaboration and sharing, training and support, etc.

[0024] In the embodiment of the present application, a scale based on the extended TPACK framework is created to systematically evaluate and improve users' technical, pedagogical, and content knowledge and their integration capabilities, and relevant data is collected for analysis.

[0025] Specifically, the TPACK scale includes the digitalization-TK factor, the digitalization-TPK factor, the digitalization-TCK factor, and the digitalization-TPACK factor, wherein the digitalization-TK factor is a knowledge reserve factor for evaluating users' interaction with AI-based tools, and is used to measure users' familiarity with the technical characteristics of AI tools; the digitalization-TPK factor is a factor for evaluating users' ability to use AI-based tools for teaching; the digitalization-TCK factor is a factor for evaluating users' knowledge of AI-based tools in specific fields, and is used to measure users' ability to update their professional knowledge using AI tools in teaching practice; the digitalization-TPACK factor is an ability factor for evaluating users' professional knowledge, and is used to measure users' professional knowledge in selecting and using AI tools suitable for teaching strategies.

[0026] The interrelationships between the various factors of the TPACK scale are as follows: Figure 2 As shown, H1a indicates that digitalization-TK positively affects digitalization-TPK, H1b indicates that digitalization-TK positively affects digitalization-TCK, H1c indicates that digitalization-TK positively affects moral evaluation, and H1d indicates that digitalization-TK positively affects digitalization-TPACK. H2 indicates that digitalization-TPK positively affects digitalization-TPACK, H3 indicates that digitalization-TCK positively affects digitalization-TPACK, H4a indicates that morality positively affects digitalization-TPK, H4b indicates that morality positively affects digitalization-TCK, and H4c indicates that morality positively affects digitalization-TPACK.

[0027] The TPACK scale was developed based on the factors of the TPACK scale and the relationships between the factors.

[0028] The development process of the TPACK scale includes: determining the skills and knowledge required by users in AI teaching based on the teaching principles of artificial intelligence teaching tools; creating a preliminary draft of the teaching project, which includes the dimensions of the TPACK framework and the knowledge and skills for interacting with AI tools; reviewing the TPACK scale and removing items containing invalid dimensions in the preliminary draft; using exploratory factor analysis to explore the latent factor structure of the items in the TPACK scale, removing items with low loadings, and obtaining high latent factor items; using confirmatory factor analysis on the latent factor items to obtain a confirmed version of the TPACK scale.

[0029] In an embodiment of the present application, the dimensions of the TPACK framework are digital-TK factor, digital-TPK factor, digital-TCK factor, and digital-TPACK factor. A preliminary draft of a teaching project is created, which includes the dimensions of the TPACK framework and the knowledge and skills for interacting with AI tools, such as the knowledge and skills for understanding and interpreting AI system alerts.

[0030] In an embodiment of the present application, the latent factors of the items in the TPACK scale are factors identified in exploratory factor analysis (EFA) that can explain the potential structure or characteristics behind the scale items. These factors are not directly measured, but indirectly reflected by the scale items. The purpose of EFA is to explore the potential factor structure of the items in the TPACK scale. By analyzing the correlation between the items, EFA can identify some common factors, which are latent factors. For example, some items may be related to the user's familiarity with the technical characteristics of AI tools, so these items will point to a latent factor together, namely digital intelligence-TK (technical knowledge). Therefore, the latent factor is a key step in the development process of the TPACK scale. It helps researchers understand the deep structure and characteristics hidden behind the scale items, so as to more accurately assess the user's knowledge and skills.

[0031] Exploratory factor analysis was used to explore the latent factor structure of the items in the TPACK scale and remove items with low loading, where loading refers to the degree of correlation between the scale items and the latent factors. It reflects the extent to which each item represents a specific latent factor. During the development of the TPACK scale, EFA calculates the correlation coefficient between each item and each latent factor, which is the loading value. The higher the loading value, the stronger the correlation between the item and the latent factor, and the greater the explanatory power of the item on the latent factor. For example, if an item has a high loading value, then the item represents the corresponding latent factor well. Conversely, if an item has a low loading value, then the item may not be suitable for measuring the latent factor and needs to be considered for deletion or modification. Loading value is one of the important indicators for evaluating the reliability and validity of the scale. Generally speaking, the higher the loading value, the greater the explanatory power of the scale items on the latent factor, and the higher the reliability and validity of the scale.

[0032] like Figure 1 As shown, the digital intelligence teaching method based on the TPACK framework includes the following steps:

[0033] Step S11, collecting at least two digital intelligence teaching tools.

[0034] Collect at least two digital teaching tools through the digital courseware platform, among which at least two digital teaching tools include: learning tools, including AI-driven dashboards, virtual tutoring systems, and digital human dialogue agents; teaching tools, including adaptive learning systems, automated scoring systems, and intelligent content recommendation systems; evaluation tools, including formative and summative evaluation tools and learning analysis tools.

[0035] In an embodiment of the present application, an AI-driven dashboard is used to monitor student learning engagement and provide visual data; a virtual tutoring system is used to provide personalized tutoring using machine learning algorithms and natural language processing technology; and a digital human dialogue agent is used to provide services to users, such as answering questions, solving problems, and learning planning.

[0036] In the embodiments of the present application, adaptive learning technology is used to provide personalized learning paths and resources based on the student's learning situation; the automated grading system is used to automatically grade homework and exams and provide timely feedback; and intelligent content recommendation is used to recommend relevant learning content based on the student's learning progress and interests.

[0037] In the embodiments of the present application, formative and summative assessment tools are used to track student learning progress and evaluate learning outcomes; learning analysis tools are used to analyze student learning data, identify learning problems, and provide improvement suggestions.

[0038] Step S12, collecting data collected by the user when using the digital intelligence teaching tool.

[0039] Specifically, the user may be a teacher or a training worker. The collected data of the user when using the digital intelligence teaching tool may include data related to the user's courseware production, subject attention data, AI assistant interaction data, and personal information.

[0040] The relevant data on courseware production specifically include the subjects of courseware production, modifications and adjustments to AI-generated courseware, login time, modification time for specific questions, etc.; subject attention data include the subject areas that users are concerned about, and the frequency and method of using AI tools in this field; AI assistant interaction data include the types and frequency of questions users ask to AI assistants, and the method of interacting with AI assistants; personal information includes users' personal information, teaching experience, subject background, etc., which can be used to analyze the use of AI tools by users from different backgrounds.

[0041] Step S13: construct user training and support tools.

[0042] The user training and support tools include: the Digital-TPACK scale, which is used to assess the user's level in technology, teaching methods, content knowledge and ethical knowledge; the case teaching tool, which provides real teaching cases to demonstrate the application scenarios and effects of AI tools in teaching; the intelligent tutor system, which provides real-time guidance and assistance to users in using AI tools; the teacher dashboard, which provides real-time feedback on student learning progress data and alert notifications; and the real-time feedback tool, which provides feedback on users' use of AI tools and suggestions.

[0043] In the embodiments of the present application, the Digital-TPACK scale is used to evaluate the user's level of technology, teaching methods, content knowledge and ethical knowledge, to help users understand their own strengths and weaknesses in AI teaching, and to develop targeted learning plans. Use case teaching tools to provide real teaching cases, demonstrate the application scenarios and effects of AI tools in teaching, and help users learn how to integrate AI tools into teaching practice. The intelligent tutor system provides real-time guidance and assistance to users in using AI tools, helping users solve technical problems encountered in the process of using AI tools. The teacher dashboard provides real-time feedback on student learning progress data and alert notifications to help users understand students' learning situation in a timely manner and make targeted teaching adjustments. Real-time feedback tools provide feedback on users' use and suggestions of AI tools, and continuously improve and optimize the functions and experience of AI tools based on user feedback.

[0044] Step S14, analyzing the collected data through the user training and support tool, and providing a training interface according to the analysis results, wherein the training interface includes a recommended introduction to the intelligent teaching tool, a tutorial for using the intelligent teaching tool, and common problems in using the intelligent teaching tool.

[0045] Among them, analyzing the collected data through the user training and support tools includes: analyzing the relationship between the various factors of Digital Intelligence-TPACK; evaluating the effectiveness of user AI tool use; and predicting user behavior.

[0046] Specifically, the collected data can be analyzed using a structural equation model (SEM), which is a model for analyzing the knowledge structure of users when using a variety of digital intelligence teaching tools. It helps to deeply understand the knowledge structure of users when using AI tools, and provides a scientific basis for user training, teaching strategy optimization and ethical evaluation. It can evaluate the impact of this knowledge on teaching effectiveness, thereby effectively guiding users to use AI tools and improving teaching effectiveness. The structural equation model includes latent variables, observed variables and paths.

[0047] The latent variables include technical knowledge (TK), pedagogical knowledge (PK), content knowledge (CK), technical-pedagogical knowledge (TPK), technical-content knowledge (TCK), technical-pedagogical-content knowledge (TPACK), and ethical evaluation (ETHICS). The observed variables include the items in the Digitalization-TPACK scale, each of which corresponds to a different latent variable. The path represents the hypothesized relationship between the latent variables, for example, TK has a positive impact on TPK.

[0048] The input of the structural equation model is the digital-TPACK scale data: the user's rating of the scale items, reflecting the level of different users in different knowledge areas; monitoring and feedback results: data from the digital courseware platform, such as student learning progress, user tool usage frequency, etc. Data processing methods include: Exploratory Factor Analysis (EFA): used to verify the structure of the scale, determine the number of potential factors and item attribution; Confirmatory Factor Analysis (CFA): used to verify the hypothesized model structure and evaluate the model fit; Structural Equation Model (SEM): used to analyze the relationship between latent variables and evaluate the impact of different knowledge on teaching effectiveness. Model output channels include the digital courseware platform: used to display the results of user knowledge structure analysis and provide personalized training suggestions; teacher dashboard: used to display student learning progress and user tool usage, and provide real-time feedback and guidance.

[0049] The application areas of the structural equation model include user training, teaching strategy optimization and ethical assessment. User training is to provide users with personalized training plans based on the analysis results, and to provide personalized guidance and suggestions based on the users' knowledge structure and usage, to help them improve their weak knowledge areas, share excellent teaching cases, and demonstrate how to effectively integrate AI tools; teaching strategy optimization is to adjust teaching strategies based on the analysis results, such as selecting more suitable digital teaching tools, optimizing teaching content and methods, and providing timely feedback on students' learning progress and users' use of tools to help users adjust their teaching strategies; ethical assessment is to help users understand the ethical issues of AI tools, such as transparency and fairness, and make more informed teaching decisions.

[0050] In the embodiments of the present application, the structural equation model can be used to analyze the relationship between the components of Digital Intelligence-TPACK, analyze the relationship between technical knowledge, teaching method knowledge, content knowledge, ethical knowledge, and the knowledge they integrate (TPK, PCK, TCK, TPACK), and their impact on teaching effectiveness. Evaluate the effectiveness of user AI tool use: Analyze the user's use of AI tools, evaluate their effectiveness, and provide targeted improvement suggestions. Predict user behavior: Based on the user's behavioral data, predict their future use of AI tools, and conduct corresponding intervention and guidance.

[0051] In the embodiments of the present application, in addition to using structural equation model analysis, other data analysis methods can also be used, such as: descriptive statistical analysis: analyzing the basic characteristics of the data such as the central tendency and degree of dispersion to understand the overall situation of users using AI tools; cluster analysis: grouping users according to their behavioral characteristics, identifying different types of user groups, and conducting targeted training and support; association rule mining: discovering the behavioral patterns of users using AI tools. For example, users who use AI assistants to ask questions are more inclined to modify AI-generated courseware.

[0052] In one embodiment of the present application, the method may further include counting the usage of the digital and intelligent teaching tools by users, and guiding the users to use the digital and intelligent teaching tools based on the statistical results. The usage includes the duration, frequency, and purpose of use, etc. By utilizing the multiple digital and intelligent teaching tools for teaching monitoring and feedback, the problems existing in the process of users using digital and intelligent teaching tools can be better explored, and users can be further guided to integrate digital and intelligent teaching tools into teaching practice.

[0053] In the embodiments of the present application, through the combination of the digital courseware platform and the TPACK framework, a variety of digital teaching tools are collected, and users can obtain rich training resources and technical support, and can systematically learn and apply AI technology, thereby improving users' comprehensive abilities in educational technology, teaching methods, and subject content; by analyzing the data collected when users use AI tools, and based on the analysis results and the personalized needs of users, a training interface for intelligent teaching tools introductions, intelligent teaching tool usage tutorials, and related content such as intelligent teaching tools is provided, and training content with a high degree of match with the user is recommended to the user.

[0054] The steps of the above method are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0055] In addition, the examples mentioned in the above embodiments can be freely combined, and any combination can be understood as an embodiment. The "embodiment" or "example" appearing in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It can be understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] Another embodiment of the present invention relates to a digital intelligence teaching system 300 based on the TPACK framework, including a digital intelligence courseware platform based on the TPACK framework, such as Figure 3 As shown, the system comprises:

[0057] A collection module 301 is used to collect at least two digital intelligence teaching tools;

[0058] A data collection module 302 is used to collect data collected by the user when using the digital intelligence teaching tool;

[0059] A training and support tool building module 303 is used to build user training and support tools;

[0060] The analysis module 304 is used to analyze the collected data through the user training and support tool, and provide a training interface based on the analysis results. The training interface includes a recommended introduction to the intelligent teaching tool, a tutorial on how to use the intelligent teaching tool, and common problems in using the intelligent teaching tool.

[0061] In some embodiments, the TPACK scale includes a digital-TK factor, a digital-TPK factor, a digital-TCK factor, and a digital-TPACK factor; wherein the digital-TK factor is a knowledge reserve factor for evaluating teachers' interaction with AI-based tools, and is used to measure users' familiarity with the technical characteristics of AI tools; the digital-TPK factor is a factor for evaluating users' ability to use AI-based tools for teaching; the digital-TCK factor is a factor for evaluating users' knowledge of AI-based tools in specific fields, and is used to measure users' ability to update their professional knowledge using AI tools in teaching practice; the digital-TPACK factor is an ability factor for evaluating users' professional knowledge, and is used to measure users' professional knowledge in selecting and using AI tools suitable for teaching strategies.

[0062] In some embodiments, the development process of the TPACK scale includes: determining the skills and knowledge required by users in AI teaching based on the teaching principles of artificial intelligence teaching tools; creating a preliminary draft of the teaching project, the preliminary draft including the dimensions of the TPACK framework and the knowledge and skills for interacting with AI tools; reviewing the TPACK scale and removing items containing invalid dimensions in the preliminary draft; using exploratory factor analysis to explore the latent factor structure of the items in the TPACK scale, removing items with lower loadings, and obtaining high latent factor items; using confirmatory factor analysis on the latent factor items to obtain a confirmed version of the TPACK scale.

[0063] In some embodiments, the at least two digital teaching tools include: learning tools, including AI-driven dashboards, virtual tutoring systems, and digital human dialogue agents; teaching tools, including adaptive learning systems, automated scoring systems, and intelligent content recommendation systems; assessment tools, including formative and summative assessment tools and learning analysis tools.

[0064] In some embodiments, the data collected by the digital courseware platform when the user uses the digital teaching tool includes: collecting data of the user in the process of using the digital courseware platform, including courseware production related data, subject attention data, AI assistant interaction data and personal information.

[0065] In some embodiments, the user training and support tools include: a digital-TPACK scale for assessing the user's level of technology, teaching methods, content knowledge, and ethical knowledge; a case teaching tool that provides real teaching cases to demonstrate the application scenarios and effects of AI tools in teaching; an intelligent tutor system that provides real-time guidance and assistance to teachers in using AI tools; a teacher dashboard that provides real-time feedback on student learning progress data and alert notifications; and a real-time feedback tool that provides feedback on users' use of AI tools and suggestions.

[0066] In some embodiments, analyzing the collected data through the user training and support tool includes: analyzing the relationship between the various factors of Digital Intelligence-TPACK; evaluating the effectiveness of user AI tool use; and predicting user behavior.

[0067] In the embodiments of the present application, through the combination of the digital courseware platform and the TPACK framework, a variety of digital teaching tools are collected, and users can obtain rich training resources and technical support, and can systematically learn and apply AI technology, thereby improving their comprehensive capabilities in educational technology, teaching methods and subject content; by analyzing the data collected when users use AI tools, according to the analysis results and according to the user's personalized needs, a training interface for intelligent teaching tool introductions, intelligent teaching tool usage tutorials, and related content such as intelligent teaching tools is provided, and training content with a high degree of user matching is recommended to users.

[0068] It is not difficult to find that this embodiment is a device embodiment corresponding to the above method embodiment, and this embodiment can be implemented in conjunction with the above method embodiment. The relevant technical details mentioned in the above method embodiment are still valid in this embodiment, and in order to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied in the above method embodiment.

[0069] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, this embodiment does not introduce units that are not closely related to solving the technical problem proposed by the present invention, but this does not mean that there are no other units in this embodiment.

[0070] Another embodiment of the present application relates to an electronic device 400, see Figure 4 , including: a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401, such as a data processing program. When the processor 401 executes the computer program, the steps in the above-mentioned embodiments of the digital intelligence teaching method based on the TPACK framework are implemented, such as Figure 1 Steps S11-S16 are shown.

[0071] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 402 and executed by the processor 401 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0072] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The electronic device may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art will appreciate that the schematic diagram is merely an example of an electronic device and does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the electronic device may also include an input / output device, a network access device, a bus, etc.

[0073] The processor 401 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor 401 may also be any conventional processor, etc. The processor 401 is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0074] The memory 402 can be used to store the computer program and / or module, and the processor 401 realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory 402, and calling the data stored in the memory 402. The memory 402 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 402 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0075] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor 401. Wherein, the computer program includes a computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium.

[0076] Another embodiment of the present application provides a computer-readable storage medium, on which programs or instructions are stored. When the programs or instructions are executed by a processor, the various processes of the embodiments of the digital intelligence teaching method based on the TPACK framework as described above are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0077] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0078] Another embodiment of the present application further provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of any of the embodiments of the digital intelligence teaching method based on the TPACK framework shown in the figure can achieve the same technical effect. To avoid repetition, they will not be described here.

[0079] It should be noted that, in this article, the terms "include", "comprises" or any other variations 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. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0080] It should be noted that the collection, collection, updating, analysis, processing, use, transmission, storage and other aspects of user personal information involved in the technical solution of this disclosure are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information and network security.

[0081] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a service classification device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0083] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A digital teaching method based on the TPACK framework, applied to a digital courseware platform based on the TPACK framework, characterized in that: The method comprises: Collect at least two digital teaching tools; Collecting data collected by users when using the digital intelligence teaching tool; Building user training and support tools; The collected data is analyzed by the user training and support tool, and a training interface is provided according to the analysis result, wherein the training interface includes a recommended introduction to the intelligent teaching tool, a tutorial for using the intelligent teaching tool, and common problems in using the intelligent teaching tool.

2. The TPACK-based digital intelligence teaching method as claimed in claim 1, characterized in that: The TPACK scale includes a digitalization-TK factor, a digitalization-TPK factor, a digitalization-TCK factor, and a digitalization-TPACK factor; The Digitalization-TK factor is a knowledge reserve factor for evaluating the user's interaction with AI-based tools, and is used to measure the user's familiarity with the technical characteristics of AI tools; The digitalization-TPK factor is a factor for evaluating the user's ability to use AI-based tools for teaching; The Digital Intelligence-TCK factor is a factor for evaluating a user's AI-based tool knowledge in a specific field, and is used to measure the user's ability to update his or her professional knowledge using AI tools in teaching practice; The Digitalization-TPACK factor is an ability factor for evaluating user expertise, and is used to measure the user's expertise in selecting and using AI tools that are suitable for teaching strategies.

3. The digital intelligence teaching method based on the TPACK framework as claimed in claim 1 is characterized by: The development process of the TPACK scale included: Based on the teaching principles of AI-based teaching tools, determine the skills and knowledge required by users in AI teaching; Create a preliminary draft of the teaching project that includes the dimensions of the TPACK framework and the knowledge and skills to interact with the AI ​​tool; The TPACK scale was reviewed to remove items containing invalid dimensions from the preliminary draft; Exploratory factor analysis was used to explore the latent factor structure of the items in the TPACK scale, and items with low loadings were removed to obtain items with high latent factors; The latent factor items were analyzed using confirmatory factor analysis to obtain a confirmed version of the TPACK scale.

4. The digital intelligence teaching method based on the TPACK framework as claimed in claim 1 is characterized by: The at least two digital intelligence teaching tools include: Learning tools, including AI-driven dashboards, virtual tutoring systems, and digital human conversational agents; Teaching tools, including adaptive learning systems, automated grading systems, and intelligent content recommendation systems; Assessment tools, including formative and summative assessment tools and learning analytics tools.

5. The digital intelligence teaching method based on the TPACK framework as claimed in claim 1 is characterized by: The collecting of data collected by the user when using the digital intelligence teaching tool includes: Collect data from users when they use the digital courseware platform, including courseware production-related data, subject attention data, AI assistant interaction data, and personal information.

6. The digital intelligence teaching method based on the TPACK framework as claimed in claim 1 is characterized by: The user training and support tools include: Digitalization-TPACK scale, which is used to assess the user's level of technology, pedagogy, content knowledge and ethical knowledge; Case teaching tools provide real teaching cases to demonstrate the application scenarios and effects of AI tools in teaching; Intelligent tutor system, providing real-time guidance and assistance to users in using AI tools; Teacher dashboard, providing real-time feedback on student learning progress data and alert notifications; Real-time feedback tool to provide users with feedback on the use of AI tools and suggestions.

7. The digital intelligence teaching method based on the TPACK framework as claimed in claim 1 is characterized by: Analyzing the collected data through the user training and support tool includes: Analyze the relationship between the various factors of Digital Intelligence-TPACK; Evaluate the effectiveness of user AI tools; Predict user behavior.

8. A digital intelligence teaching system based on the TPACK framework, including a digital intelligence courseware platform based on the TPACK framework, characterized in that: The system comprises: A collection module, used to collect at least two digital intelligence teaching tools; A data collection module, used to collect data collected by users when using the digital intelligence teaching tool; Training and support tool building modules for building user training and support tools; The analysis module is used to analyze the collected data through the user training and support tool, and provide a training interface according to the analysis results, wherein the training interface includes a recommended introduction to the intelligent teaching tool, a tutorial for using the intelligent teaching tool, and common problems in using the intelligent teaching tool.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method as claimed in any one of claims 1 to 7.

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