Personalized teaching method and system based on multi-dimensional learning style analysis

Abstract

The invention discloses a personalized teaching method and system based on multi-dimensional learning style analysis, and relates to the technical field of teaching. The personalized teaching system based on multi-dimensional learning style analysis comprises a learning style analysis module, and the learning style analysis module is connected with a teaching content management module. According to the system, a personalized learning plan can be made according to the learning style and progress of students through a personalized learning path and an intelligent recommendation engine, learning is flexible and efficient, in addition, rich teaching resources such as videos, interactive courses and textbooks in the system enhance the diversity and interestingness of learning, and the learning efficiency is improved. The real-time feedback and multi-dimensional evaluation mechanism ensures continuous support in the learning process and helps students to adjust strategies in time to optimize the learning effect, the system also combines online and offline mixed teaching, provides wider learning choices, enhances interaction and communication opportunities between teachers and students, and improves the learning efficiency. Therefore, the teaching effect and the satisfaction degree of students are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of teaching technology, and in particular to a personalized teaching method and system based on multi-dimensional learning style analysis. Background Art

[0002] Teaching is a systematic process that aims to promote the all-round development of students' knowledge, skills and emotions through the guidance of teachers. It starts with setting clear educational goals and is achieved by selecting and organizing appropriate teaching materials and learning content. Teachers need to select appropriate teaching methods and strategies, such as lectures, discussions and practical operations, based on the characteristics of the subject and the needs of students, to effectively impart knowledge and cultivate abilities. During the teaching process, teachers also use assessments and other methods to evaluate and provide feedback to support students' learning progress and improve teaching strategies. In general, teaching not only focuses on the imparting of knowledge, but also pays more attention to cultivating students' creativity, critical thinking and moral qualities to help them grow in an all-round way.

[0003] Traditional teaching methods are usually conducted in the classroom under the guidance of teachers according to the predetermined course outline, mainly in the form of lectures, questions and answers, and discussions. This method emphasizes teacher-led, relatively fixed course organization, and basically the same learning progress for all students. In order to enhance the teaching effect, multimedia tools (such as PPT, videos, etc.) are often used to assist classroom teaching, improve the vividness of the content, and increase student participation. In addition, through experiments and practical activities, students can understand theoretical knowledge more deeply in practice. Another method is online learning, where students can watch video courses through online platforms, arrange their own study time, complete homework, and communicate and discuss with teachers and other students. Nowadays, there are also hybrid teaching methods that combine online and offline, using the Internet to expand learning outside the classroom and provide more flexible learning options. However, these traditional methods still have some shortcomings. Courses are usually advanced at a uniform pace, which makes it difficult to meet the personalized learning needs of each student, especially when students' learning styles vary greatly, their learning effects may be affected. In the case of large classes, individual students have fewer opportunities to get the attention of teachers, and classroom interaction is not sufficient, which limits the learning effect and the implementation of personalized guidance. In addition, traditional teaching often relies on mid-term and final exams to evaluate learning progress, and fails to provide timely feedback and support. Diverse teaching resources are not fully utilized and are usually limited to textbooks and classroom lectures, failing to maximize their effectiveness. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a personalized teaching system based on multi-dimensional learning style analysis, including a learning style analysis module, the learning style analysis module is connected to a teaching content management module, the teaching content management module is connected to a personalized teaching design module, and the personalized teaching design module is connected to feedback and evaluation;

[0005] The learning style analysis module includes a data collection subsystem and a multidimensional analysis engine, the teaching content management module includes a teaching material library construction and an intelligent recommendation engine, the personalized teaching design module includes diversified teaching strategies and dynamic learning path design, and the feedback and evaluation includes real-time feedback and multidimensional evaluation.

[0006] Preferably, the data collection subsystem includes questionnaires and tests and behavioral data collection, wherein the questionnaires and tests are for evaluating students' learning preferences through standardized learning style questionnaires (such as the VARK model, Kolb learning style), and the behavioral data collection is for recording learning behavior data such as learning time, activity frequency, and participation methods through smart devices.

[0007] Preferably, the multidimensional analysis engine includes comprehensive analysis and dynamic adjustment. The comprehensive analysis is to combine the questionnaire results with the behavioral data, use a machine learning algorithm to perform cluster analysis, and identify the students' learning style types. The dynamic adjustment is to dynamically update the learning style assessment as the learning process progresses to reflect the students' growth and changes.

[0008] Preferably, the teaching material library is constructed to include diversified content and cases and practices. The diversified content is to prepare learning materials in various formats (text, video, audio, interactive exercises) covering different forms of expression of the same topic. The cases and practices are to provide cases and practical activities combined with real life to help students apply theory to practice.

[0009] Preferably, the intelligent recommendation engine includes personalized recommendations and adaptive adjustments, wherein the personalized recommendations are based on the results of learning style analysis to recommend teaching materials and learning activities suitable for students, and the adaptive adjustments are to continuously optimize the recommendation model using feedback data to adapt to students' progress and changes in interests.

[0010] Preferably, the diversified teaching strategy includes tiered teaching and interactive learning experience. The tiered teaching is to provide teaching content of different depths and difficulties according to the students' cognitive levels and learning styles. The interactive learning experience is to design highly interactive courseware and activities to promote students' active participation and in-depth thinking.

[0011] Preferably, the dynamic learning path design includes a customized learning path and real-time adjustment, wherein the customized learning path allows students to choose a learning path according to their interests and goals and provides guidance to maintain learning progress, and the real-time adjustment dynamically adjusts the teaching plan and learning path according to test and quiz feedback.

[0012] Preferably, the real-time feedback utilizes the instant feedback mechanism of the learning platform to help students identify weak links, and teachers obtain real-time data on students' learning status through the system to adjust teaching strategies. The multi-dimensional evaluation is a comprehensive evaluation based on students' usual performance, test scores, and class participation, and generates personalized learning reports for students, which are provided to students, teachers, and parents for tracking learning progress.

[0013] A personalized teaching method based on multi-dimensional learning style analysis, specifically comprising the following steps:

[0014] S1. Preparation

[0015] Determine the target user groups for the system, including grades and subjects, determine the main learning function requirements and corresponding technical requirements, collect and organize the required teaching materials, video and audio resources, and use the provided learning style questionnaire tool to understand the user's learning preferences;

[0016] S2. Preliminary collection

[0017] Log in to the learning management system, access the learning style analysis module, complete the online learning style questionnaire, and use the system to automatically collect and analyze learning behavior data;

[0018] S3. Get study suggestions

[0019] View the learning style report generated by the system based on learning style and behavior data, and accept the personalized learning resources and teaching plans recommended by the system;

[0020] S4. Resource Usage

[0021] Choose learning resources that suit you from the textbook library, and use the intelligent recommendation engine provided by the system to obtain cases and practical activities;

[0022] S5. Personalized learning

[0023] Automatically adjust course content according to learning style and progress, input learning feedback, participate in interactive courses provided by the system, obtain instant feedback from the system based on learning progress, view comprehensive learning evaluation reports, and adaptively adjust learning plans.

[0024] The beneficial effects of the present invention are embodied in:

[0025] This system breaks through the limitations of traditional teaching through personalized learning paths and diversified teaching resources, and provides a new learning experience. First of all, the system can generate personalized learning plans based on each student's unique learning style and progress, so that learning is no longer limited to a unified course rhythm and can better meet individual learning needs. Through the intelligent recommendation engine, students can obtain rich learning resources, including textbooks, videos and interactive courses, which enhances the flexibility and fun of learning. In addition, the system integrates instant feedback and evaluation mechanisms, which can provide continuous support during the learning process, help students adjust their learning strategies in time, and improve learning effects. Through the online platform, students can not only access learning materials anytime and anywhere, but also communicate and interact effectively with teachers and classmates. This hybrid teaching method that combines online and offline teaching makes full use of the advantages of the Internet, greatly enhances the learning experience outside the classroom, provides better opportunities to obtain teacher attention and personalized guidance, and comprehensively improves teaching effectiveness and student learning satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0027] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0028] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0029] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0030] Example 1

[0031] like Figure 1 As shown, an embodiment of the present invention provides a personalized teaching system based on multi-dimensional learning style analysis, including a learning style analysis module, the learning style analysis module is connected to a teaching content management module, the teaching content management module is connected to a personalized teaching design module, and the personalized teaching design module is connected to feedback and evaluation;

[0032] The learning style analysis module includes a data collection subsystem and a multi-dimensional analysis engine; the teaching content management module includes textbook library construction and an intelligent recommendation engine; the personalized teaching design module includes diversified teaching strategies and dynamic learning path design; and feedback and evaluation include real-time feedback and multi-dimensional evaluation.

[0033] The data collection subsystem includes questionnaires and tests and behavioral data collection. Questionnaires and tests are used to assess students' learning preferences through standardized learning style questionnaires (such as the VARK model and Kolb learning style). Behavioral data collection is used to record learning behavior data through smart devices, such as learning time, activity frequency, and participation methods.

[0034] The multi-dimensional analysis engine includes comprehensive analysis and dynamic adjustment. Comprehensive analysis combines questionnaire results with behavioral data, uses machine learning algorithms to perform cluster analysis, and identifies students' learning style types. Dynamic adjustment dynamically updates learning style assessments as the learning process progresses to reflect students' growth and changes.

[0035] The construction of the teaching material library includes diversified content and cases and practices. Diversified content is to prepare learning materials in multiple formats (text, video, audio, interactive exercises), covering different forms of expression of the same topic. Cases and practices are to provide cases and practical activities combined with real life to help students apply theory to practice.

[0036] The intelligent recommendation engine includes personalized recommendations and adaptive adjustments. Personalized recommendations are based on the results of learning style analysis to recommend teaching materials and learning activities suitable for students. Adaptive adjustments use feedback data to continuously optimize the recommendation model to adapt to students’ progress and changes in interests.

[0037] Diversified teaching strategies include differentiated teaching and interactive learning experiences. Differentiated teaching provides teaching content of different depths and difficulties based on students' cognitive levels and learning styles. Interactive learning experiences design highly interactive courseware and activities to promote students' active participation and in-depth thinking.

[0038] Dynamic learning path design includes customized learning paths and real-time adjustments. Customized learning paths allow students to choose learning paths based on their interests and goals, and provide guidance to maintain learning progress. Real-time adjustments dynamically adjust teaching plans and learning paths based on exam and test feedback.

[0039] Real-time feedback utilizes the learning platform's instant feedback mechanism to help students identify their weak links, and teachers obtain real-time data on students' learning status through the system to adjust teaching strategies. Multi-dimensional evaluation conducts a comprehensive evaluation based on students' daily performance, test scores, and class participation, and generates personalized learning reports for students, which are provided to students, teachers, and parents for tracking learning progress.

[0040] Specifically, first, we conducted a needs survey with education experts, teachers, and students. Through workshops and interviews, we clarified the system goals and core functions and designed an overall framework. In this process, we applied the VARK and Kolb learning style models to identify different learning preferences.

[0041] Next, in terms of hardware and software, select data collection equipment and analysis platforms. Use Python's machine learning library for data analysis, build a content management system (CMS), and use cloud infrastructure to store and process large amounts of data. Digitize the VARK questionnaire to collect data quickly and efficiently, and define a standard data collection format to ensure that the data can be seamlessly input into the analysis module. Deploy smart devices to monitor students' learning activities in class and on online platforms, tracking learning time, engagement, and interaction.

[0042] Then, we develop teaching materials and resources based on different learning styles, including text, video, audio and interactive materials. At the same time, we design interdisciplinary projects and real-life application cases to promote students' hands-on skills and practical understanding. This process requires collaboration with content developers and education experts to ensure the quality and applicability of materials.

[0043] During system operation, collaborative filtering and content recommendation algorithms based on machine learning technology are used to customize personalized learning paths based on student characteristics. Teachers can regularly extract student progress reports from the system to evaluate teaching effectiveness and make course adjustments. Effective teaching models and areas that need improvement can be identified by regularly summarizing and analyzing system data.

[0044] Finally, establish a feedback collection mechanism to maintain communication with teachers, students and parents. Through questionnaires and online discussions, collect user feedback to help optimize and iterate the system. Use artificial intelligence and data mining technology to identify potential learning barriers, provide improvement suggestions for the teaching process, and ensure the continuous improvement and effective implementation of personalized teaching.

[0045] Example 2

[0046] A personalized teaching method based on multi-dimensional learning style analysis, specifically comprising the following steps:

[0047] S1. Preparation

[0048] Determine the target user groups for the system, including grades and subjects, determine the main learning function requirements and corresponding technical requirements, collect and organize the required teaching materials, video and audio resources, and use the provided learning style questionnaire tool to understand the user's learning preferences;

[0049] S2. Preliminary collection

[0050] Log in to the learning management system, access the learning style analysis module, complete the online learning style questionnaire, and use the system to automatically collect and analyze learning behavior data;

[0051] S3. Get study suggestions

[0052] View the learning style report generated by the system based on learning style and behavior data, and accept the personalized learning resources and teaching plans recommended by the system;

[0053] S4. Resource Usage

[0054] Choose learning resources that suit you from the textbook library, and use the intelligent recommendation engine provided by the system to obtain cases and practical activities;

[0055] S5. Personalized learning

[0056] Automatically adjust course content according to learning style and progress, input learning feedback, participate in interactive courses provided by the system, obtain instant feedback from the system based on learning progress, view comprehensive learning evaluation reports, and adaptively adjust learning plans.

[0057] The present invention can recommend the most suitable learning content and path for each student based on their learning style and preferences, ensure that the learning content is more in line with personal needs, and can adjust the learning plan and content in real time according to the student's progress and feedback, and flexibly respond to changes in the student's learning process. Through data analysis and intelligent recommendation, the interactive elements of the course are increased to stimulate students' interest and initiative in learning. It can provide feedback and help to students in real time, and teachers can also understand students' learning status in real time and make adjustments, which helps to quickly discover and solve learning difficulties. By comprehensively examining learning style, participation, daily performance and test scores, a multi-dimensional student evaluation is provided to help students and teachers fully understand the progress of learning. Through personalized learning strategies and diversified teaching resources, learning efficiency is maximized and teaching students in accordance with their aptitude is achieved.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A personalized teaching system based on multi-dimensional learning style analysis, including a learning style analysis module, characterized in that: The learning style analysis module is connected to the teaching content management module, the teaching content management module is connected to the personalized teaching design module, and the personalized teaching design module is connected to feedback and evaluation; The learning style analysis module includes a data collection subsystem and a multidimensional analysis engine, the teaching content management module includes a teaching material library construction and an intelligent recommendation engine, the personalized teaching design module includes diversified teaching strategies and dynamic learning path design, and the feedback and evaluation includes real-time feedback and multidimensional evaluation.

2. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The data collection subsystem includes questionnaires and tests and behavioral data collection. The questionnaires and tests are for evaluating students' learning preferences through standardized learning style questionnaires, and the behavioral data collection is for recording learning behavior data such as learning time, activity frequency, and participation methods through smart devices.

3. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The multidimensional analysis engine includes comprehensive analysis and dynamic adjustment. The comprehensive analysis is to combine the questionnaire results with the behavioral data, use the machine learning algorithm to perform cluster analysis, and identify the students' learning style types. The dynamic adjustment is to dynamically update the learning style assessment as the learning process progresses to reflect the students' growth and changes.

4. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The construction of the teaching material library includes diversified content and cases and practices. The diversified content is to prepare learning materials in various formats, covering different forms of expression of the same topic. The cases and practices are to provide cases and practical activities combined with real life to help students apply theory to practice.

5. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The intelligent recommendation engine includes personalized recommendations and adaptive adjustments. The personalized recommendations are based on the results of learning style analysis to recommend teaching materials and learning activities suitable for students. The adaptive adjustments are to continuously optimize the recommendation model using feedback data to adapt to students' progress and changes in interests.

6. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The diversified teaching strategy includes differentiated teaching and interactive learning experience. The differentiated teaching provides teaching content of different depths and difficulties according to students' cognitive levels and learning styles. The interactive learning experience designs highly interactive courseware and activities to promote students' active participation and in-depth thinking.

7. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The dynamic learning path design includes customized learning paths and real-time adjustments. The customized learning path allows students to choose a learning path based on their interests and goals, and provides guidance to maintain learning progress. The real-time adjustment is to dynamically adjust the teaching plan and learning path based on exam and test feedback.

8. The personalized teaching system based on multi-dimensional learning style analysis according to claim 1, characterized in that: The real-time feedback utilizes the instant feedback mechanism of the learning platform to help students identify their weak links, and teachers obtain real-time data on students' learning status through the system to adjust teaching strategies. The multi-dimensional evaluation is a comprehensive evaluation based on students' daily performance, test scores, and class participation, and generates personalized learning reports for students, which are provided to students, teachers, and parents for tracking learning progress.

9. A personalized teaching method based on multi-dimensional learning style analysis according to any one of claims 1 to 8, characterized in that: The specific steps include: S1. Preparation Determine the target user groups for the system, including grades and subjects, determine the main learning function requirements and corresponding technical requirements, collect and organize the required teaching materials, video and audio resources, and use the provided learning style questionnaire tool to understand the user's learning preferences; S2. Preliminary collection Log in to the learning management system, access the learning style analysis module, complete the online learning style questionnaire, and use the system to automatically collect and analyze learning behavior data; S3. Get study suggestions View the learning style report generated by the system based on learning style and behavior data, and accept the personalized learning resources and teaching plans recommended by the system; S4. Resource Usage Choose the learning resources that suit you from the textbook library, and use the intelligent recommendation engine provided by the system to obtain cases and practical activities; S5. Personalized learning Automatically adjust course content according to learning style and progress, input learning feedback, participate in interactive courses provided by the system, obtain instant feedback from the system based on learning progress, view comprehensive learning evaluation reports, and adaptively adjust learning plans.

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