Information technology personalized learning system based on artificial intelligence

Abstract

The invention relates to an artificial intelligence-based information technology personalized learning system, which can customize a personalized learning path for each student by comprehensively collecting and analyzing learning behaviors, history and interest data of the student and utilizing an advanced machine learning algorithm. The core functions of the system comprise intelligent learning resource recommendation, dynamic learning content difficulty adjustment and providing of an ai teacher assistant and an intelligent question answering system so as to realize real-time tutoring and feedback. In addition, the system also provides an autonomous learning tool to support students to make learning plans, track the progress and perform self-evaluation. The invention aims to improve the learning efficiency and motivation of students in information technology subjects, promote the cultivation of deep learning and autonomous learning ability, and optimize the distribution and use of educational resources. The system not only adapts to individual requirements of students, but also can be expanded and applied to other subjects.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence education technology, and in particular to an information technology personalized learning system based on artificial intelligence. Background Art

[0002] In traditional information technology teaching, a "one-size-fits-all" teaching method is usually adopted, that is, all students receive the same teaching content and progress regardless of their personal interests, learning speed or existing knowledge level. This model ignores the differences between individual students, causing some students to feel frustrated due to inappropriate difficulty, while other students may lose interest due to lack of challenge. In addition, traditional teaching models often make it difficult to update teaching materials in a timely manner and keep up with the rapid changes in information technology.

[0003] With the development of educational concepts, more and more educators and researchers have realized the importance of personalized learning. Personalized learning can adjust teaching content and methods according to the specific needs of each student, and more effectively improve students' learning efficiency and motivation. Especially in information technology subjects, due to the rapid update and wide application of technology, personalized learning paths can help students better master practical skills and stimulate innovation.

[0004] Although there are some products on the market that attempt to provide personalized learning solutions, these solutions often have some shortcomings. For example, they are unable to comprehensively collect and analyze students' learning data to accurately identify students' learning preferences and needs; or their resource recommendation systems are not smart enough to dynamically adjust learning resources according to students' real-time learning situations; or they lack effective real-time feedback and support mechanisms, which are not enough to provide students with immediate help and guidance.

[0005] In recent years, the application of artificial intelligence technology in the field of education has shown great potential. Using AI technology, especially machine learning and data analysis, we can build student models more accurately and achieve truly personalized learning. In addition, AI teacher assistants can provide 24 / 7 learning support, intelligently analyze students' learning progress and problems, and provide targeted feedback and suggestions.

[0006] In view of the shortcomings of the existing technology, this patent proposes an AI-based personalized learning system specifically for the teaching of information technology subjects. The system can comprehensively collect students' learning behavior data, identify students' learning preferences and needs through advanced data analysis technology, and generate personalized learning paths accordingly. Summary of the invention

[0007] In order to solve the problems of the prior art, the present invention provides an information technology personalized learning system based on artificial intelligence.

[0008] In order to solve the above technical problems, the present invention is implemented by the following technical solutions: In the first aspect, an information technology personalized learning system based on artificial intelligence comprises:

[0009] Data collection module, used to collect learners’ basic information, learning history and learning behavior;

[0010] The data analysis module is used to process and analyze the collected data to extract the personalized characteristics of learners' learning habits, ability levels, and the pros and cons of learning each subject content;

[0011] A personalized learning model building module is connected to the data analysis module, and based on the extracted personalized features, uses a machine learning algorithm to build and update the learner's personalized learning model, which can dynamically guide the adjustment of the difficulty, progress and presentation form of the learning content;

[0012] A learning resource recommendation module, connected to the personalized learning model building module, recommends learning resources that meet the learner's needs based on the learner's personalized learning model and current learning status, including but not limited to online courses, textbooks, and exercises;

[0013] Intelligent tutoring and feedback module, which provides AI teacher assistant and intelligent Q&A system tools to provide learners with instant tutoring support, and generates detailed learning reports based on learners' learning performance, providing targeted feedback and suggestions;

[0014] The self-study tool module provides self-study tools including but not limited to study plan formulation, learning progress tracking, and self-testing to help learners better manage the learning process.

[0015] In this embodiment, the data collection module is the basis of the system. It collects learners' basic information, learning history and behavior data through various online platforms. This module uses database records to ensure that learners' activities can be tracked and analyzed. These data provide the necessary input for the system, so that the data analysis module can further process and analyze to discover learners' habits and ability levels. After data collection, the data analysis module uses data mining techniques such as clustering and predictive modeling to analyze and understand learners' behavior patterns and learning needs. The output of this module helps the system identify the unique learning characteristics of each learner and lays the foundation for building a personalized learning model. Based on the output of the data analysis module, the personalized learning model construction module uses machine learning algorithms to design customized learning paths for individual learners. These models can dynamically adjust the difficulty and presentation of learning content to adapt to the progress and ability of each learner to ensure maximum learning efficiency. According to the constructed personalized learning model, the learning resource recommendation module recommends learners the resources that are most suitable for their current learning status, such as online courses and teaching materials. This module continuously evaluates learners' progress and dynamically adjusts recommended resources to achieve continuous learning optimization. The intelligent tutoring and feedback module provides instant AI teacher assistants and intelligent Q&A support to help learners solve learning problems and provide targeted feedback and suggestions through detailed learning reports. This not only supports learners' real-time learning needs, but also promotes their long-term learning development. The autonomous learning tool module equips learners with self-management tools, such as learning plan formulation and progress tracking functions, so that learners can manage their own learning process more effectively. This enhances learners' autonomy and enables them to learn at their own pace and preferences.

[0016] In one embodiment of the present invention, the data analysis module uses big data analysis technology to deeply mine learner data to discover potential learning patterns and trends. Data is collected from various sources (such as online interactions, homework submitted to the learning platform, real-time test results, etc.) and stored in a big data warehouse. The collected data is cleaned and standardized for more effective analysis. Statistical analysis and predictive modeling methods, including cluster analysis, association rules, and sequential pattern mining methods, are used to identify learners' behavior patterns and learning trends.

[0017] In one embodiment of the present invention, the personalized learning model construction module generates a learning path by identifying learner characteristics, recommends learning resources, and regularly evaluates and adjusts the accuracy and effectiveness of the learning model based on learners' learning feedback and results. The system regularly collects learners' learning feedback, including data such as the grades of completed assignments, scores of online tests, and resource usage, and uses these feedback data to evaluate the accuracy and effectiveness of the current personalized learning model, identify deficiencies in the model, and based on the evaluation results, the system regularly adjusts and optimizes the learning model to better meet the development needs of learners.

[0018] In one embodiment of the present invention, the learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to the learner's learning progress and results to achieve continuous optimization of personalized learning paths. The system continuously monitors learners' learning activities, including completed exercises, watched teaching videos, participated discussions, etc., as well as the results of these activities, such as test scores and homework feedback. Based on the collected data, the system evaluates the learner's mastery of each knowledge point and identifies the learner's strengths and weaknesses. The system intelligently selects the most suitable learning materials from the resource library according to the learner's ability and the requirements of the learning path. These resources may include course content of different difficulty levels, interactive exercises, video lectures, simulation tests, etc.

[0019] In one specific implementation of one aspect, the learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to the learner's learning progress and results, so as to realize the continuous optimization of personalized learning paths. The algorithm is used to track the learner's completed courses, test scores, homework submissions, etc. in real time, and comprehensively evaluate their learning progress. According to the learner's performance, such as accuracy, learning speed, etc., the system analyzes their learning results, identifies the learner's strengths and room for improvement, and based on the above data, the system dynamically adjusts the difficulty (from basic to advanced) and type (videos, articles, interactive exercises, etc.) of the resources recommended to the learner through built-in logic and recommendation algorithms.

[0020] In the second aspect, an information technology personalized learning method based on artificial intelligence comprises the following steps:

[0021] Step 1: Collect relevant data of learners through the data collection module;

[0022] Step 2: Use the data analysis module to process and analyze the collected data and extract the learner's personalized characteristics;

[0023] Step 3: Based on the extracted personalized features, build the learner's personalized learning model through the personalized learning model building module;

[0024] Step 4: Recommend appropriate learning resources through the learning resource recommendation module based on the learner's personalized learning model and current learning status;

[0025] Step 5: Use the intelligent coaching and feedback module to provide learners with instant coaching and support, and generate learning reports and feedback;

[0026] Step 6: Use the self-study tool module to help learners develop study plans, track their learning progress and conduct self-tests.

[0027] The beneficial effects of the present invention are:

[0028] 1. By accurately analyzing students' learning behaviors, histories, and interests, the system can customize a learning plan that best suits each student's individual needs. This personalized approach can significantly reduce ineffective learning time and ensure that students learn the most appropriate content at the most appropriate time, thereby improving overall learning efficiency. The system effectively stimulates students' learning interest and participation by recommending resources and learning paths that match students' interests and abilities. Especially for information technology courses, practical and creative projects can make students more engaged, thereby enhancing their learning motivation;

[0029] 2. The intelligent tutoring and feedback module provides real-time Q&A and personalized feedback to help students solve difficult problems instantly during the learning process and deepen their understanding and mastery of complex concepts. In addition, the system can dynamically adjust the difficulty and content according to the students' learning performance to ensure the occurrence of deep learning. The self-learning tool module allows students to make learning plans, track progress and conduct self-assessment. This not only promotes students' independence and sense of responsibility, but also enables them to learn at their own pace and style, further enhancing their self-learning ability;

[0030] 3. The systematic interdisciplinary project design encourages students to combine information technology with other subject knowledge to solve practical problems. This educational model not only enhances students' understanding and application of information technology, but also promotes the cultivation of innovative thinking and interdisciplinary skills. By accurately matching learning resources and student needs, this system maximizes the efficiency of educational resources. Online courses, teaching materials and exercises are optimally allocated, reducing resource waste and increasing the return on educational investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the internal structure of the system of the present invention.

[0032] Figure 2 It is a schematic diagram of the system connection structure of the present invention.

[0033] Figure 3 It is a schematic diagram of the system flow structure of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] like Figures 1 to 3 The illustrated personalized learning system of information technology based on artificial intelligence includes: a data collection module for collecting learners' information including but not limited to basic information, learning history and learning behavior; a data analysis module for processing and analyzing the collected data to extract personalized features of learners' learning habits, ability levels and the learning pros and cons of each subject content; a personalized learning model construction module, which is connected to the data analysis module and uses a machine learning algorithm to construct and update the learner's personalized learning model based on the extracted personalized features, and the model can dynamically guide the adjustment of the difficulty, progress and presentation form of the learning content; a learning resource recommendation module, which is connected to the personalized learning model construction module and recommends learning resources that meet the learner's needs according to the learner's personalized learning model and current learning status, including but not limited to online courses, teaching materials, and exercises; an intelligent tutoring and feedback module, which provides AI teacher assistants and intelligent question-answering system tools to provide learners with instant tutoring support, and generates detailed learning reports based on the learner's learning performance, and provides targeted feedback and suggestions; an autonomous learning tool module, which provides but not limited to learning plan formulation, learning progress tracking, and self-testing autonomous learning tools to help learners better manage the learning process.

[0036] The data analysis module uses big data analysis technology to conduct in-depth mining of learner data to discover potential learning patterns and trends. The personalized learning model construction module generates learning paths by identifying learner characteristics, recommends learning resources, and regularly evaluates and adjusts the accuracy and effectiveness of the learning model based on learners' learning feedback and results. The learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to learners' learning progress and results to achieve continuous optimization of personalized learning paths. The learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to learners' learning progress and results to achieve continuous optimization of personalized learning paths.

[0037] An information technology personalized learning method based on artificial intelligence includes the following steps:

[0038] Step 1: Collect relevant data of learners through the data collection module; Step 2: Use the data analysis module to process and analyze the collected data and extract the personalized characteristics of learners; Step 3: Based on the extracted personalized characteristics, build the learner's personalized learning model through the personalized learning model construction module; Step 4: According to the learner's personalized learning model and current learning status, recommend appropriate learning resources through the learning resource recommendation module; Step 5: Use the intelligent tutoring and feedback module to provide learners with instant tutoring and support, and generate learning reports and feedback; Step 6: Help learners develop learning plans, track learning progress and conduct self-tests through the self-learning tool module.

[0039] Example 1: Design of personalized learning path for primary school information technology courses

[0040] Data collection and analysis

[0041] Basic information collection:

[0042] Collect information about students’ grade, gender, and family background.

[0043] Use questionnaires to learn about students' interests, such as interest in computer games, animation or programming.

[0044] Learning behavior tracking:

[0045] Use educational technology tools, such as learning management systems, to record student computer behavior during class, including the software and websites they use most often.

[0046] Track students’ interactive behaviors on online platforms, such as forum posts, assignment submission history, and peer assessment activities.

[0047] Learning History Integration:

[0048] Collect and analyze students' previous IT class grades, specific feedback from teachers, and observation reports from classmates and parents.

[0049] Personalized learning model construction

[0050] Feature recognition and learning path generation:

[0051] By analyzing the data, students are divided into different learning groups, such as "programming interest group", "design interest group", etc.

[0052] Based on the grouping results, specific learning goals and plans are customized for each student. For example, students in the programming group will have more learning resources about Scratch and Python.

[0053] Dynamically adjust learning content:

[0054] The course difficulty is adjusted based on learning feedback each semester. For example, more visual programming exercises are provided for students who are slower in understanding, and more practical projects and advanced programming languages ​​are introduced for students who are making rapid progress.

[0055] Learning resource recommendations and intelligent tutoring

[0056] Recommended resources:

[0057] Based on students' learning progress and interests, the system recommends programming challenges and projects of appropriate levels, such as recommending simple Scratch game production tutorials for beginners.

[0058] Provide customized reading materials, such as books on computer history and fundamentals.

[0059] AI Teacher Assistant:

[0060] The AI ​​teacher assistant can answer students' questions in real time and provide targeted learning suggestions and tips.

[0061] Automatically grade student submissions and provide personalized suggestions for improvement.

[0062] Application of self-learning tools

[0063] Study plan and progress tracking:

[0064] Students can view their learning goals, upcoming tasks, and progress bars on a personalized dashboard.

[0065] Provide self-assessment tools, such as programming quizzes, to help students evaluate their own learning.

[0066] With learning content customized to their interests and abilities, students are more likely to feel engaged and excited, thereby increasing their motivation to learn. Personalized learning paths ensure that each student can learn at a pace and depth that suits them, which helps to better understand and master knowledge points. By dynamically adjusting learning resources and difficulty, students can continuously improve in challenges that suit their current level and gradually develop problem-solving skills. The application of self-learning tools enables students to learn to set goals, track progress, and self-evaluate, thereby enhancing their ability to self-manage and self-learn.

[0067] Example 2: Problem-solving-oriented interdisciplinary project learning

[0068] Integrated analysis of interdisciplinary data

[0069] Comprehensive subject data:

[0070] Collect data on students' performance in math and science classes, as well as their responses to experiments and logic problems.

[0071] Problem Solving Skills Assessment:

[0072] By designing a series of small projects involving programming and scientific experiments, students can observe how they solve real-world problems, such as using programming to control a car to find the exit of a maze.

[0073] Personalized project design

[0074] Project recommendation:

[0075] Select or recommend project topics for students based on their interests and previous performance, such as "The Impact of Climate Change on Local Ecosystems" (combining geography and programming).

[0076] Dynamically adjust project difficulty:

[0077] Provide extended or simplified tasks as appropriate based on student performance in the project to ensure that all students can grow through challenges.

[0078] Feedback and self-directed learning support

[0079] Comprehensive feedback system:

[0080] After each project, teachers and the AI ​​system jointly provide feedback covering students' innovative thinking, problem-solving skills, and teamwork.

[0081] Self-directed learning tools:

[0082] Encourage students to use learning journals to record their learning and reflection process, and regularly share their progress with the AI ​​system to obtain feedback.

[0083] Through these detailed steps, each implementation example is closer to a complete and real case, which helps to effectively apply the AI-based personalized learning system in actual teaching.

[0084] Combining content from different disciplines, such as programming and scientific experiments, encourages students to develop interdisciplinary thinking skills and innovative methods. By facing real-world problems, students learn to apply their knowledge to solve specific problems, which helps to cultivate their ability to analyze problems and think critically. Projects often require teamwork, which not only enhances students' collaborative ability, but also improves their communication and expression skills. Project learning makes learning content closely related to real life, increases the fun of learning, and enables students to participate more actively in the learning process.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An information technology personalized learning system based on artificial intelligence, It is characterized in that include: Data collection module, used to collect learners’ basic information, learning history and learning behavior; The data analysis module is used to process and analyze the collected data to extract the personalized characteristics of learners' learning habits, ability levels, and the pros and cons of learning each subject content; A personalized learning model building module is connected to the data analysis module, and based on the extracted personalized features, uses a machine learning algorithm to build and update the learner's personalized learning model, which can dynamically guide the adjustment of the difficulty, progress and presentation form of the learning content; A learning resource recommendation module, connected to the personalized learning model building module, recommends learning resources that meet the learner's needs based on the learner's personalized learning model and current learning status, including but not limited to online courses, textbooks, and exercises; Intelligent tutoring and feedback module, which provides AI teacher assistant and intelligent Q&A system tools to provide learners with instant tutoring support, and generates detailed learning reports based on learners' learning performance, providing targeted feedback and suggestions; The self-study tool module provides self-study tools including but not limited to study plan formulation, learning progress tracking, and self-testing to help learners better manage the learning process.

2. According to claim 1, an information technology personalized learning system based on artificial intelligence, Features: The data analysis module uses big data analysis technology to conduct in-depth mining of learner data to discover potential learning patterns and trends.

3. According to claim 1, an information technology personalized learning system based on artificial intelligence, Features: The personalized learning model building module generates a learning path by identifying learner characteristics, recommends learning resources, and regularly evaluates and adjusts the accuracy and effectiveness of the learning model based on learners' learning feedback and results.

4. According to claim 1, an information technology personalized learning system based on artificial intelligence, Features: The learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to the learner's learning progress and results, so as to achieve continuous optimization of personalized learning paths.

5. According to claim 1, an information technology personalized learning system based on artificial intelligence, Features: The learning resource recommendation module can dynamically adjust the difficulty and type of recommended resources according to the learners' learning progress and results, thereby achieving continuous optimization of personalized learning paths.

6. The information technology personalized learning system based on artificial intelligence according to claim 1, Features: The intelligent coaching and feedback module also includes an emotion recognition function that can identify the learner's emotional state and provide appropriate encouragement and support based on emotional changes.

7. A personalized learning method for information technology based on artificial intelligence, It is characterized in that The following steps are involved: Step 1: Collect relevant data of learners through the data collection module; Step 2: Use the data analysis module to process and analyze the collected data and extract the learner's personalized characteristics; Step 3: Based on the extracted personalized features, build the learner's personalized learning model through the personalized learning model building module; Step 4: Recommend appropriate learning resources through the learning resource recommendation module based on the learner's personalized learning model and current learning status; Step 5: Use the intelligent coaching and feedback module to provide learners with instant coaching and support, and generate learning reports and feedback; Step 6: Use the self-study tool module to help learners develop study plans, track their learning progress and conduct self-tests.

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