Intelligent teaching activity design system based on artificial intelligence big data

Abstract

The invention discloses an intelligent teaching activity design system based on artificial intelligence big data, through the analysis of a teaching strategy data set, an interaction data link and a multi-dimensional data cube, the system can automatically identify the demand and ability level of a learner, customize personalized learning paths and resources, improve the pertinence and effectiveness of teaching, and improve the teaching efficiency. A real-time interaction data link is combined with artificial intelligence analysis, learner states, preferences and difficulties are recognized, real-time feedback is provided for teaching interaction, a differentiation teaching module is combined, personalized learning support is provided, teaching data of different dimensions are integrated, comprehensive learner portraits and teaching environment descriptions are formed, resource allocation is optimized, and teaching efficiency is improved. The big data analysis and artificial intelligence technology realize accurate matching and dynamic adjustment of resources, avoid resource waste, ensure that each student enjoys high-quality educational resources, and improve learning efficiency and scores.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching activity design, and in particular to an intelligent teaching activity design system based on artificial intelligence big data. Background Art

[0002] In recent years, the field of education has been undergoing a profound transformation from the traditional education model to a modern and intelligent education model. One of the core driving forces of this transformation is the pursuit of personalized teaching. The traditional "one-size-fits-all" teaching model can no longer meet the diverse learning needs of students. Personalized teaching that customizes teaching content and paths based on individual differences of students has become the key to improving the quality of education.

[0003] In the context of personalized teaching, the integration of teaching strategies and resources has become an important issue. Different students may need different teaching strategies and resources to meet their learning needs. Therefore, how to effectively integrate and manage multiple teaching strategies and their related data, and how to build a system that can respond to student needs in real time and dynamically adjust teaching strategies and resources have become urgent issues to be solved in the field of education. Summary of the invention

[0004] In view of this, the present invention proposes an intelligent teaching activity design system based on artificial intelligence big data, which can accurately identify students' learning needs and intelligently integrate teaching strategies and resources, and customize personalized learning paths and teaching resources for each student.

[0005] The technical solution of the present invention is achieved in this way:

[0006] An intelligent teaching activity design system based on artificial intelligence big data, comprising:

[0007] Teaching strategy dataset module, used to integrate and manage data related to various teaching strategies;

[0008] The interactive data link module is used to build a real-time interactive data link connecting learners, teachers, teaching resources and environment. Through artificial intelligence analysis, it can identify learners' learning status, interest preferences and difficulties from interactive data.

[0009] Multidimensional data cube module, used to integrate and manage teaching data from different dimensions;

[0010] The differentiated teaching module is used to automatically identify and classify learners' different needs and ability levels based on the analysis results of teaching strategy data sets, interactive data links and multidimensional data cubes, customize personalized learning paths and teaching resources for each learner, and implement differentiated teaching strategies.

[0011] As a further optional solution of the intelligent teaching activity design system based on artificial intelligence big data, the teaching strategy data set module integrates and manages relevant data of multiple teaching strategies, specifically including:

[0012] Collect relevant data from various teaching scenarios, student assignments, and test data;

[0013] Based on the collected data, calculate the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index;

[0014] According to the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index, the data that meets the teaching strategy data requirements are screened out;

[0015] The screened data that meets the teaching strategy data requirements are stored in the database.

[0016] As a further optional solution of the intelligent teaching activity design system based on artificial intelligence big data, the interactive data chain module builds a real-time interactive data chain connecting learners, teachers, teaching resources and environment, and identifies learners' learning status, interest preferences and difficulties from interactive data through artificial intelligence analysis, including:

[0017] Obtain learner behavior data, learning resource usage data, and learner feedback data;

[0018] Integrate learner behavior data, learning resource usage data and learner feedback data into one data chain;

[0019] Based on the machine learning algorithm, the real-time interactive data in the data chain is analyzed in real time to calculate the engagement index, interest preference index and difficulty index.

[0020] As a further optional solution of the intelligent teaching activity design system based on artificial intelligence big data, the multidimensional data cube module integrates and manages teaching data from different dimensions, specifically including:

[0021] Collect teaching data from various teaching management systems and online learning platforms and load them into multidimensional data cubes;

[0022] The multidimensional data cube calculates the learning efficiency, teaching resource utilization rate and teacher teaching effect of teaching data according to the preset formula;

[0023] The teaching data is integrated based on its learning efficiency, teaching resource utilization rate and teacher teaching effectiveness.

[0024] As a further optional solution of the intelligent teaching activity design system based on artificial intelligence big data, the differentiated teaching module automatically identifies and classifies the different needs and ability levels of learners based on the analysis results of the teaching strategy data set, interactive data chain and multidimensional data cube, and customizes personalized learning paths and teaching resources for each learner, specifically including:

[0025] Analyze the teaching strategy data set, interactive data chain, and multidimensional data cube data to form a learner profile;

[0026] Plan personalized learning paths based on learner portraits, combined with learners’ learning history and goals;

[0027] Recommend personalized learning resources based on learners’ learning paths and interest preferences;

[0028] Track learners' learning progress and dynamically adjust learning paths and resource recommendations based on learners' learning status and feedback results.

[0029] A method for designing intelligent teaching activities based on artificial intelligence big data, specifically including:

[0030] Integrate and manage data related to various teaching strategies based on the teaching strategy data set module;

[0031] Based on the interactive data link module, a real-time interactive data link connecting learners, teachers, teaching resources and environment is constructed. Through artificial intelligence analysis technology, the learners' learning status, interest preferences and difficulties encountered in the learning process are identified and extracted from the interactive data;

[0032] Integrate and manage teaching data from different dimensions based on the multidimensional data cube module;

[0033] Using the differentiated teaching module, based on the analysis results of teaching strategy data sets, interactive data chains and multidimensional data cubes, it automatically identifies and classifies learners' different needs and ability levels;

[0034] Based on the identification results, customized learning paths and teaching resources are provided for each learner;

[0035] Implement differentiated teaching strategies, adjust the content and form of teaching activities based on learners' personalized learning paths and resource recommendations to meet the needs and ability levels of different learners.

[0036] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent teaching activity design method based on artificial intelligence big data when executing the computer program.

[0037] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned intelligent teaching activity design method based on artificial intelligence big data.

[0038] The beneficial effects of the present invention are as follows: by integrating and managing the relevant data of multiple teaching strategies, the module provides rich strategy resources for teaching activities. Teachers or systems can select appropriate teaching strategies from the data set according to the actual situation of learners, thereby improving the pertinence and effectiveness of teaching. Based on the analysis results of the teaching strategy data set, interactive data chain and multidimensional data cube, it can automatically identify and classify the different needs and ability levels of learners, which customizes personalized learning paths and teaching resources for each learner, ensures the accurate implementation of teaching strategies, helps to improve learners' learning efficiency and grades, and builds a real-time interactive data link connecting learners, teachers, teaching resources and environment. Through artificial intelligence analysis, it can identify the learners' learning status from the interactive data. The system can provide real-time feedback and adjustment basis for teaching interaction based on the attitudes, interests, preferences and difficulties encountered. Combined with differentiated teaching modules, it can provide learners with personalized learning support and guidance. When learners encounter difficulties, the system can promptly recommend relevant learning resources and answer prompts to help them solve problems and improve learning effects. It integrates and manages teaching data from different dimensions to form a comprehensive learner portrait and teaching environment description, which helps the system to more accurately understand the needs of learners and environmental characteristics, thereby optimizing the configuration and utilization of teaching resources. Through big data analysis and artificial intelligence technology, accurate matching and dynamic adjustment of teaching resources can be achieved, which can not only avoid the waste and duplication of educational resources, but also enable every student to enjoy high-quality educational resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is a schematic diagram of the composition of an intelligent teaching activity design system based on artificial intelligence big data of the present invention;

[0041] Figure 2 A schematic diagram of a flow chart of a method for designing intelligent teaching activities based on artificial intelligence big data according to the present invention;

[0042] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] refer to Figures 1 to 3 , an intelligent teaching activity design system based on artificial intelligence big data, including:

[0045] Teaching strategy dataset module, used to integrate and manage data related to various teaching strategies;

[0046] The interactive data link module is used to build a real-time interactive data link connecting learners, teachers, teaching resources and environment. Through artificial intelligence analysis, it can identify learners' learning status, interest preferences and difficulties from interactive data.

[0047] Multidimensional data cube module, used to integrate and manage teaching data from different dimensions;

[0048] The differentiated teaching module is used to automatically identify and classify learners' different needs and ability levels based on the analysis results of teaching strategy data sets, interactive data links and multidimensional data cubes, customize personalized learning paths and teaching resources for each learner, and implement differentiated teaching strategies.

[0049] In this embodiment, by integrating and managing relevant data of multiple teaching strategies, the module provides rich strategy resources for teaching activities. Teachers or systems can select appropriate teaching strategies from the data set according to the actual situation of learners, thereby improving the pertinence and effectiveness of teaching. Based on the analysis results of the teaching strategy data set, interactive data chain and multidimensional data cube, it can automatically identify and classify the different needs and ability levels of learners, which customizes personalized learning paths and teaching resources for each learner, ensures the accurate implementation of teaching strategies, helps to improve learners' learning efficiency and performance, and builds a real-time interactive data link connecting learners, teachers, teaching resources and environment. Through artificial intelligence analysis, it can identify the learner's learning status from the interactive data, Interest preferences and difficulties encountered provide a basis for real-time feedback and adjustment of teaching interactions. Combined with differentiated teaching modules, it can provide learners with personalized learning support and guidance. When learners encounter difficulties, the system can promptly recommend relevant learning resources and answer prompts to help them solve problems and improve learning outcomes. It integrates and manages teaching data from different dimensions to form a comprehensive learner portrait and teaching environment description, which helps the system to more accurately understand learners' needs and environmental characteristics, thereby optimizing the allocation and utilization of teaching resources. Through big data analysis and artificial intelligence technology, accurate matching and dynamic adjustment of teaching resources can be achieved, which can not only avoid waste and duplication of educational resources, but also enable every student to enjoy high-quality educational resources.

[0050] Preferably, the teaching strategy data set module integrates and manages relevant data of multiple teaching strategies, specifically including:

[0051] Collect relevant data from various teaching scenarios, student assignments, and test data;

[0052] Based on the collected data, calculate the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index;

[0053] According to the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index, the data that meets the teaching strategy data requirements are screened out;

[0054] The screened data that meets the teaching strategy data requirements are stored in the database.

[0055] In this embodiment, relevant data are collected from various teaching scenarios (such as classroom teaching, online learning, experimental training, etc.), student homework, and test data to ensure the breadth and diversity of the data, which helps the system to have a more comprehensive understanding of the learners' learning situation and the actual application effect of the teaching strategy. The collected data includes not only the learners' learning outcomes (such as test scores, homework completion, etc.), but also the behavioral data in the learning process (such as learning time, interaction frequency, error rate, etc.), which provides rich materials for in-depth analysis of the effectiveness of teaching strategies. Through the calculation of the learning effectiveness index, the learners' learning effects can be quantitatively evaluated to provide a scientific basis for the adjustment and optimization of teaching strategies. The calculation of the mastery of knowledge points helps the system to understand the learners' mastery of different knowledge points, so as to recommend relevant learning resources or adjust the teaching strategies in a targeted manner. The teaching strategy adaptability index reflects the effectiveness of the teaching strategy. The system can filter out the teaching strategy that best suits the current learner by calculating the index. Based on the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index, the system can filter out data that meets the teaching strategy data requirements. This step ensures the accuracy and pertinence of the data. Through data screening, the system can remove redundant and invalid data and improve data utilization efficiency, which helps to reduce the time and cost of data processing and improve the overall performance of the system. The data that meets the teaching strategy data requirements is stored in the database, ensuring the security and long-term preservation of the data. This helps the system to call and analyze the data at any time, providing support for the formulation and adjustment of teaching strategies. Database storage also makes data access more convenient. The system can extract relevant data from the database at any time as needed for further analysis and processing.

[0056] It should be noted that the specific formula for calculating the learning effectiveness index is:

[0057]

[0058] Among them, the problem solving rate refers to the proportion of students solving problems, the average problem solving rate and the standard deviation problem solving rate refer to the average and standard deviation of all students in the class respectively;

[0059] Calculate the mastery of knowledge points. The specific formula is:

[0060]

[0061] Among them, n is the total number of knowledge points, the mastery level of knowledge point i is the student's score of the mastery level of the knowledge point, the highest mastery level of knowledge point i is the highest possible score, and the importance coefficient of knowledge point i is the importance weight of the knowledge point in the syllabus;

[0062] Calculate the teaching strategy adaptability index. The specific formula is:

[0063]

[0064] Among them, the score improvement rate is the proportion of improvement in students' scores after the implementation of the teaching strategy, and the teaching strategy cost is the resource investment required to implement the strategy.

[0065] Preferably, the interactive data link module builds a real-time interactive data link connecting learners, teachers, teaching resources and environment, and identifies learners' learning status, interest preferences and difficulties encountered from interactive data through artificial intelligence analysis, specifically including:

[0066] Obtain learner behavior data, learning resource usage data, and learner feedback data;

[0067] Integrate learner behavior data, learning resource usage data and learner feedback data into one data chain;

[0068] Based on the machine learning algorithm, the real-time interactive data in the data chain is analyzed in real time to calculate the engagement index, interest preference index and difficulty index.

[0069] In this embodiment, interactive data is comprehensively collected from multiple dimensions (learner behavior data, learning resource usage data, learner feedback data) to ensure the comprehensiveness and richness of the data, which helps the system to have a deeper understanding of the learners' learning situation and interactive behavior. By building a real-time interactive data link, the system can collect and process data in real time to ensure the timeliness and accuracy of the data, which helps the system to promptly identify changes in learners' learning status and provide timely support for teaching adjustments; integrating learner behavior data, learning resource usage data and learner feedback data into a data link helps the system to uniformly manage and analyze data, which improves the efficiency of data processing and reduces data redundancy and repeated processing. The integrated data link makes data access more convenient. The system can extract relevant data from the data link for analysis and processing as needed, which improves the flexibility and convenience of data utilization; real-time analysis of real-time interactive data in the data link is performed based on machine learning algorithms, which can It can automatically identify learners' learning status, interest preferences and difficulties encountered, which improves the accuracy and intelligence level of analysis. By calculating the engagement index, interest preference index and difficulty index, the system can quantitatively evaluate learners' learning status, interest preferences and difficulty level, which provides a scientific basis for the adjustment and optimization of teaching strategies. The engagement index reflects learners' learning enthusiasm and participation, the interest preference index helps to understand learners' learning interests and preferences, and the difficulty index reveals the difficulties and challenges encountered by learners in the learning process. Based on the results of artificial intelligence analysis, the system can provide learners with personalized learning support and guidance. For example, when the system identifies that learners have difficulties in a certain knowledge point, it can promptly recommend relevant learning resources and answer prompts to help them solve the problem. Teachers can also adjust teaching strategies and teaching methods based on the data and analysis results provided by the system to better meet the needs of learners, which helps to improve teaching effectiveness and learners' satisfaction.

[0070] It should be noted that the learning status evaluation formula is:

[0071] Engagement Index EI = (study time / total study time)*0.5+(exercise completion rate*0.3)+(number of speeches in the interactive discussion area / average number of speeches)*0.2;

[0072] Among them, the completion rate of exercises = the number of completed exercises / the total number of exercises;

[0073] The higher the engagement index, the better the learner's learning status;

[0074] Interest preference evaluation formula:

[0075] Interest preference index IP I = ​​Σ(number of clicks on resource i * interest weight i) / total number of clicks;

[0076] Among them, the number of clicks on resource i indicates the number of times learners click on a certain teaching resource, and the interest weight i indicates the importance of the resource in the overall teaching resources (which can be determined by expert scoring or learner feedback);

[0077] The higher the interest preference index is, the more interested the learner is in this type of teaching resources;

[0078] Difficulty identification formula:

[0079] Difficulty Index DI = (number of unfinished exercises / total number of exercises)*0.6+(number of repeated resource viewings / total number of resource viewings)*0.3+(number of requests for help / total study time)*0.1;

[0080] Among them, the number of unfinished exercises refers to the number of exercises that learners failed to complete correctly, the number of repeated resource viewings refers to the number of times learners viewed the same teaching resource multiple times, and the number of requests for help refers to the number of times learners asked teachers or classmates for help;

[0081] The higher the difficulty index, the greater the difficulty the learner encounters in the learning process.

[0082] Preferably, the multidimensional data cube module integrates and manages teaching data from different dimensions, specifically including:

[0083] Collect teaching data from various teaching management systems and online learning platforms and load them into multidimensional data cubes;

[0084] The multidimensional data cube calculates the learning efficiency, teaching resource utilization rate and teacher teaching effect of teaching data according to the preset formula;

[0085] The teaching data is integrated based on its learning efficiency, teaching resource utilization rate and teacher teaching effectiveness.

[0086] In this embodiment, teaching data is collected from various teaching management systems and online learning platforms to ensure the breadth and diversity of the data. These data sources include students' homework scores, online learning time, course participation, teaching resource access records, etc., which provide rich materials for the construction of multidimensional data cubes. Data from different sources are loaded into multidimensional data cubes to achieve unified management and integration of data, which helps to eliminate data silos and improve data availability and analyzability. As an efficient data organization method, multidimensional data cubes can support rapid query and analysis of data. By calculating learning efficiency, teaching resource utilization and teacher teaching effectiveness through preset formulas, the system can generate key indicators reflecting the teaching status in real time. Multidimensional data cubes can also be used to collect teaching data from online learning platforms, and can also be used to collect teaching data from online learning platforms. The cube not only integrates data from different sources, but also achieves deep integration of data by calculating key indicators, which helps to reveal the inherent connections and patterns between data and provide strong support for teaching decisions. By integrating the learning efficiency, teaching resource utilization and teacher teaching effectiveness of teaching data, the system can analyze the correlation between different factors. For example, it can explore the relationship between the use of learning resources and students' learning efficiency, or analyze the relationship between teachers' teaching effectiveness and teaching resource utilization. Through in-depth analysis of teaching data, the system can accurately locate problems and bottlenecks in teaching. For example, it can identify student groups with low learning efficiency or courses with insufficient utilization of teaching resources, so as to take targeted measures to improve them.

[0087] It should be noted that the preset formulas include the learning efficiency formula, the teaching resource utilization formula and the teacher teaching effect evaluation formula, which are as follows:

[0088] Learning efficiency formula: Learning efficiency = learning performance / learning time, used to evaluate students' learning efficiency, that is, the learning results achieved per unit time;

[0089] The formula for teaching resource utilization rate is: teaching resource utilization rate = teaching resource usage time / total learning time, which is used to evaluate students' utilization of teaching resources and the effectiveness of teaching resources;

[0090] The formula for evaluating teacher teaching effectiveness is: teaching effectiveness = student average score / course difficulty coefficient, where the course difficulty coefficient can be calculated based on historical course scores, student feedback and other data, and is used to evaluate the teacher's teaching effectiveness and the rationality of the course setting.

[0091] Preferably, the differentiated teaching module automatically identifies and classifies the different needs and ability levels of learners based on the analysis results of the teaching strategy data set, interactive data chain and multidimensional data cube, and customizes personalized learning paths and teaching resources for each learner, specifically including:

[0092] Analyze the teaching strategy data set, interactive data chain, and multidimensional data cube data to form a learner profile;

[0093] Plan personalized learning paths based on learner portraits, combined with learners’ learning history and goals;

[0094] Recommend personalized learning resources based on learners’ learning paths and interest preferences;

[0095] Track learners' learning progress and dynamically adjust learning paths and resource recommendations based on learners' learning status and feedback results.

[0096] In this embodiment, multi-dimensional data from teaching strategy data sets, interactive data links, and multi-dimensional data cubes are integrated. These data cover multiple dimensions such as learners' learning behaviors, learning outcomes, learning resource usage, and learning feedback. Through in-depth analysis and mining of these data, the system can form a comprehensive and accurate learner portrait that accurately reflects the learner's learning characteristics, interest preferences, and ability levels. Based on the learner portrait, the system combines the learner's learning history and goals to plan a personalized learning path that meets the learner's characteristics and needs. This path takes into account both the learner's current level and the learner's future development direction, which helps to achieve learners' continuous progress and all-round development. The personalized learning path has certain flexibility and adaptability, and can be adjusted according to the learner's learning progress and feedback. The system can dynamically adjust the situation according to the learner's actual needs, which ensures that the learning path can always be close to the learner's actual needs and improve the pertinence and effectiveness of learning. According to the learner's learning path and interest preferences, the system can recommend matching learning resources, including textbooks, videos, online courses, etc. These resources not only meet the learner's learning needs, but also stimulate the learner's interest and motivation in learning. The system can track the learner's learning progress in real time, including the completion of learning tasks, browsing of learning resources, etc. By collecting learners' learning feedback and achievements, the system can comprehensively evaluate the learner's learning situation. According to the learner's learning situation and feedback results, the system can dynamically adjust the learning path and resource recommendations. This dynamic adjustment and optimization helps to improve the efficiency and effectiveness of learning and achieve the learner's personalized development goals.

[0097] A method for designing intelligent teaching activities based on artificial intelligence big data, specifically including:

[0098] Integrate and manage data related to various teaching strategies based on the teaching strategy data set module;

[0099] Based on the interactive data link module, a real-time interactive data link connecting learners, teachers, teaching resources and environment is constructed. Through artificial intelligence analysis technology, the learners' learning status, interest preferences and difficulties encountered in the learning process are identified and extracted from the interactive data;

[0100] Integrate and manage teaching data from different dimensions based on the multidimensional data cube module;

[0101] Using the differentiated teaching module, based on the analysis results of teaching strategy data sets, interactive data chains and multidimensional data cubes, it automatically identifies and classifies learners' different needs and ability levels;

[0102] Based on the identification results, customized learning paths and teaching resources are provided for each learner;

[0103] Implement differentiated teaching strategies, adjust the content and form of teaching activities based on learners' personalized learning paths and resource recommendations to meet the needs and ability levels of different learners.

[0104] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned intelligent teaching activity design method based on artificial intelligence big data when executing the computer program.

[0105] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned intelligent teaching activity design method based on artificial intelligence big data.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent teaching activity design system based on artificial intelligence big data, characterized in that: include: Teaching strategy dataset module, used to integrate and manage data related to various teaching strategies; The interactive data link module is used to build a real-time interactive data link connecting learners, teachers, teaching resources and environment. Through artificial intelligence analysis, it can identify learners' learning status, interest preferences and difficulties from interactive data. Multidimensional data cube module, used to integrate and manage teaching data from different dimensions; The differentiated teaching module is used to automatically identify and classify learners' different needs and ability levels based on the analysis results of teaching strategy data sets, interactive data links and multidimensional data cubes, customize personalized learning paths and teaching resources for each learner, and implement differentiated teaching strategies.

2. According to claim 1, an intelligent teaching activity design system based on artificial intelligence big data is characterized in that: The teaching strategy data set module integrates and manages the relevant data of various teaching strategies, including: Collect relevant data from various teaching scenarios, student assignments, and test data; Based on the collected data, calculate the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index; According to the learning effectiveness index, knowledge point mastery and teaching strategy adaptability index, the data that meets the teaching strategy data requirements are screened out; The screened data that meets the teaching strategy data requirements are stored in the database.

3. The intelligent teaching activity design system based on artificial intelligence big data according to claim 2 is characterized in that: The interactive data link module builds a real-time interactive data link connecting learners, teachers, teaching resources and environment, and identifies learners' learning status, interest preferences and difficulties from interactive data through artificial intelligence analysis, including: Obtain learner behavior data, learning resource usage data, and learner feedback data; Integrate learner behavior data, learning resource usage data and learner feedback data into one data chain; Based on the machine learning algorithm, the real-time interactive data in the data chain is analyzed in real time to calculate the engagement index, interest preference index and difficulty index.

4. The intelligent teaching activity design system based on artificial intelligence big data according to claim 3 is characterized in that The multidimensional data cube module integrates and manages teaching data from different dimensions, specifically including: Collect teaching data from various teaching management systems and online learning platforms and load them into multidimensional data cubes; The multidimensional data cube calculates the learning efficiency, teaching resource utilization rate and teacher teaching effect of teaching data according to the preset formula; The teaching data is integrated based on its learning efficiency, teaching resource utilization rate and teacher teaching effectiveness.

5. The intelligent teaching activity design system based on artificial intelligence big data according to claim 4 is characterized in that: The differentiated teaching module automatically identifies and classifies learners’ different needs and ability levels based on the analysis results of the teaching strategy data set, interactive data chain and multi-dimensional data cube, and customizes personalized learning paths and teaching resources for each learner, including: Analyze the teaching strategy data set, interactive data chain, and multidimensional data cube data to form a learner profile; Plan personalized learning paths based on learner portraits, combined with learners’ learning history and goals; Recommend personalized learning resources based on learners’ learning paths and interest preferences; Track learners' learning progress and dynamically adjust learning paths and resource recommendations based on learners' learning status and feedback results.

6. A method for designing intelligent teaching activities based on artificial intelligence big data, characterized in that: Specifically include: Integrate and manage data related to various teaching strategies based on the teaching strategy data set module; Based on the interactive data link module, a real-time interactive data link connecting learners, teachers, teaching resources and environment is constructed. Through artificial intelligence analysis technology, the learners' learning status, interest preferences and difficulties encountered in the learning process are identified and extracted from the interactive data; Integrate and manage teaching data from different dimensions based on the multidimensional data cube module; Using the differentiated teaching module, based on the analysis results of teaching strategy data sets, interactive data chains and multidimensional data cubes, it automatically identifies and classifies learners' different needs and ability levels; Based on the identification results, customized learning paths and teaching resources are provided for each learner; Implement differentiated teaching strategies, adjust the content and form of teaching activities based on learners' personalized learning paths and resource recommendations to meet the needs and ability levels of different learners.

7. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the intelligent teaching activity design method based on artificial intelligence big data as described in claim 6 are implemented.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent teaching activity design method based on artificial intelligence big data as described in claim 6.

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