Big data analysis system based on teaching process evaluation and learning ability improvement

Abstract

The invention discloses a big data analysis system based on teaching process evaluation and learning ability improvement, and belongs to the technical field of education technology and big data analysis. Data in the teaching process is comprehensively collected through equipment such as a front-end AI behavior analysis camera, an all-purpose card, an intelligent operation system and a comprehensive evaluation system; the performance of students and student learning ability improvement and teacher teaching level can be accurately evaluated, and abundant materials are provided for data analysis. The data processing function can identify learning modes, predict scores, find learning obstacles and assist in making effective learning strategies. The ability improvement module provides personalized suggestions for students and teachers, meets different requirements, and improves the teaching quality. The user interface displays the progress and the effect through a visual chart, user enthusiasm is stimulated, meanwhile, the system is continuously optimized according to feedback, and practicability is improved. The system can improve education quality, promote reasonable utilization of resources, and promote transformation and upgrading of education digitization and intelligence.

Description

Technical Field

[0001] The present invention belongs to the field of educational technology and big data analysis technology, and specifically relates to a big data analysis system based on teaching process evaluation and learning ability improvement. Background Art

[0002] With the widespread application of information technology in the field of education, educational data has shown explosive growth. A large amount of data such as students' learning behaviors, learning outcomes, and teachers' teaching methods are digitally recorded. These data contain great potential for improving teaching effectiveness and optimizing learning paths. Traditional teaching models often rely on experience and intuition to make teaching decisions, but lack targeted and personalized support. Under the modern educational concept, people are increasingly aware that in order to improve teaching quality and students' learning outcomes, it is necessary to accurately evaluate the teaching process and provide targeted learning ability improvement plans based on the evaluation results. At present, big data analysis technology has been applied to multiple industries to discover patterns, associations, and trends in data. However, in the field of education, despite the large amount of teaching data that can be analyzed, existing systems often fail to make full use of these data resources. Some existing educational analysis tools only provide basic analysis functions, such as score statistics and simple feedback summaries, and do not dig deep into the deep information behind the data, such as students' learning habits, potential learning problems, and the influence of teachers' teaching styles.

[0003] In addition, the analysis results of existing systems often lack real-time and personalization, and cannot provide timely and customized feedback and suggestions for each student and teacher. At the same time, these systems are rarely able to integrate third-party educational resources and services, so the implementation of improvement suggestions is limited to the functional scope of a single platform or system. Therefore, it is particularly important and urgent to develop a big data analysis system based on teaching process evaluation and learning ability improvement. This system not only needs to have efficient data collection and processing capabilities, but also should combine advanced machine learning algorithms to identify learning patterns, predict learning outcomes, and provide practical personalized suggestions for students and teachers. By displaying analysis results through a user-friendly interface, collecting user feedback, and continuously adjusting and optimizing, such a system will greatly promote personalized teaching and precision learning in the field of education, and achieve a dual improvement in teaching quality and student learning ability. Summary of the invention

[0004] The purpose of the present invention is to provide a big data analysis system based on teaching process evaluation and learning ability improvement to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: A big data analysis system based on teaching process evaluation and learning ability improvement, comprising:

[0006] a) Data collection module, used to collect students’ learning behavior data, learning outcomes data and teachers’ teaching behavior data during the teaching process;

[0007] b) Data processing module, which cleans, integrates and analyzes the collected data to evaluate students’ learning ability and teachers’ teaching quality;

[0008] c) Ability improvement module, which provides personalized learning suggestions and teaching improvement plans based on the analysis results of the data processing module;

[0009] d) User interface module, used to present analysis results and improvement suggestions to students and teachers, and to receive user feedback.

[0010] Preferably, the data acquisition module further comprises:

[0011] a) Learning behavior tracking submodule, which is used to record students’ learning activities, including but not limited to homework completion, classroom interaction participation, and online learning time;

[0012] b) Learning outcome record submodule, used to store students’ test scores, homework scores and other forms of assessment results;

[0013] c) Teaching behavior recording submodule, which is used to record the teacher’s teaching method, interaction frequency and teaching content arrangement.

[0014] Preferably, the data processing module uses machine learning algorithms and statistical analysis methods to identify students' learning patterns, predict academic performance and discover potential learning obstacles.

[0015] Preferably, the capability enhancement module further comprises:

[0016] a) Learning path recommendation submodule, which recommends appropriate learning resources and learning plans based on students’ learning abilities and preferences;

[0017] b) Teaching method optimization sub-module provides teachers with suggestions for adjusting teaching strategies based on student feedback and learning outcomes.

[0018] Preferably, the user interface module provides interactive charts and reports to intuitively display students' learning progress and teachers' teaching effectiveness.

[0019] Preferably, it further includes regularly updating the analysis algorithm in the data processing module to improve the accuracy of the evaluation and the effectiveness of the suggestions.

[0020] Preferably, it further includes integration with a third-party education platform to expand the scope of data collection and enhance the implementation channels of recommendations.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] First, the system can comprehensively collect students' learning behavior data, learning outcomes data and teachers' teaching behavior data, ensuring the comprehensiveness and diversity of the data. This not only helps to more accurately evaluate students' learning ability and teachers' teaching quality, but also provides rich raw materials for subsequent data analysis. Secondly, through the cleaning, integration and analysis of the data processing module, the system can identify students' learning patterns, predict academic performance and discover potential learning obstacles. This in-depth data analysis will help reveal the problems and challenges in students' learning process, thereby providing a basis for formulating more effective learning strategies. Furthermore, the capacity improvement module provides each student with personalized learning suggestions based on the analysis results of the data processing module, such as appropriate learning resources and learning plans. At the same time, it also provides teachers with teaching strategy adjustment suggestions based on student feedback and learning effects. This personalized service will help meet the learning needs of different students and promote their personalized development. , and for teachers, they can also adjust teaching methods and strategies according to feedback to improve teaching quality. Finally, the user interface module intuitively displays students' learning progress and teachers' teaching effects in the form of interactive charts and reports, which not only makes it easier for students and teachers to understand the analysis results and improvement suggestions, but also stimulates their enthusiasm and initiative. At the same time, the system also has a feedback mechanism that can adjust the analysis model and improvement suggestions according to user feedback. This continuous optimization process will make the system closer to actual needs and improve its practicality and effectiveness. In summary, the big data analysis system in this case will bring many beneficial effects to the field of education. It will help teachers make more accurate teaching decisions and improve teaching quality; help students discover and overcome learning difficulties and improve learning outcomes; promote the rational allocation and utilization of educational resources and optimize the educational ecology. In addition, the implementation of the system will also promote the digital transformation and intelligent upgrading of the education field, and make positive contributions to the construction of a modern education system. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present invention will be described clearly and completely below in combination with 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.

[0024] The present invention provides a big data analysis system based on teaching process evaluation and learning ability improvement, including:

[0025] a) Data collection module, used to collect students’ learning behavior data, learning outcomes data and teachers’ teaching behavior data during the teaching process;

[0026] b) Data processing module, which cleans, integrates and analyzes the collected data to evaluate students’ learning ability and teachers’ teaching quality;

[0027] c) Ability improvement module, which provides personalized learning suggestions and teaching improvement plans based on the analysis results of the data processing module;

[0028] d) User interface module, used to present analysis results and improvement suggestions to students and teachers, and to receive user feedback.

[0029] In this embodiment, the data acquisition module further includes:

[0030] a) Learning behavior tracking submodule, which is used to record students’ learning activities, including but not limited to homework completion, classroom interaction participation, and online learning time;

[0031] b) Learning outcome record submodule, used to store students’ test scores, homework scores and other forms of assessment results;

[0032] c) Teaching behavior recording submodule, which is used to record the teacher’s teaching method, interaction frequency and teaching content arrangement.

[0033] Furthermore, by tracking students' learning activities such as homework completion, classroom interaction participation, online learning time, etc., the system can understand students' learning habits and activity in detail. At the same time, by recording academic performance and teachers' teaching methods, the teaching process and learning effects can be grasped in detail. This specific data collection not only helps to more accurately analyze and evaluate the teaching and learning status of individuals and groups, but also provides richer dimensions in future data analysis, providing support for the formulation of personalized teaching and learning strategies. In addition, this detailed data record also makes it easier to identify problems in a timely manner and take corresponding measures, thereby improving the quality and efficiency of teaching and learning.

[0034] In this embodiment, the data processing module uses machine learning algorithms and statistical analysis methods to identify students' learning patterns, predict academic performance, and discover potential learning obstacles.

[0035] Furthermore, analytical technology can not only identify students' learning patterns and predict academic performance, but also discover potential obstacles that affect students' learning. Through comprehensive analysis of large amounts of data, the system can reveal the deep information and trends hidden behind the data, which is crucial for optimizing teaching strategies and learning paths. This intelligent data processing method significantly improves the efficiency and accuracy of data processing, provides educators with a scientific basis for decision-making, and helps them better understand student needs and optimize teaching methods. At the same time, this also supports more personalized and precise educational services, so that educational resources can be more reasonably allocated and utilized.

[0036] In this embodiment, the capability enhancement module further includes:

[0037] a) Learning path recommendation submodule, which recommends appropriate learning resources and learning plans based on students’ learning abilities and preferences;

[0038] b) Teaching method optimization sub-module provides teachers with suggestions for adjusting teaching strategies based on student feedback and learning outcomes.

[0039] Furthermore, we recommend the most suitable learning resources and learning plans based on the learning patterns and abilities of different students, helping students make the best progress on their learning paths. At the same time, we provide feedback and adjustment suggestions for teachers' teaching methods to help them optimize their teaching strategies to meet the needs of different students. This highly customized advice and service not only enhances the relevance and effectiveness of learning, but also promotes the professional growth and development of teachers. In this way, every student and teacher can get support that meets their unique needs, greatly improving the quality of teaching and learning.

[0040] In this embodiment, the user interface module provides interactive charts and reports to intuitively display the students' learning progress and the teachers' teaching effectiveness.

[0041] Furthermore, the intuitive display method enables students and teachers to easily understand complex data analysis results and academic suggestions, stimulating their enthusiasm and initiative. Users can directly see their progress and areas for improvement on the interface, as well as specific guidance on how to make improvements. In addition, the module also supports user feedback, allowing students and teachers to evaluate and provide feedback on the suggestions provided by the system. The system continuously adjusts and optimizes based on these feedbacks, making the services and functions provided closer to the actual needs of users. This interactivity and adaptability strengthens the user's sense of participation and increases the motivation for continued use of the system, thereby maximizing educational effectiveness.

[0042] In this embodiment, a big data analysis system based on teaching process evaluation and learning ability improvement includes the following steps:

[0043] a) Collect data during the teaching process through the data acquisition module;

[0044] b) Analyze and process the collected data using the data processing module;

[0045] c) Generate personalized improvement suggestions through the capability improvement module based on the analysis results;

[0046] d) presenting the analysis results and suggestions to the user through the user interface module and collecting user feedback information;

[0047] e) Adjust analysis models and improvement suggestions based on user feedback to achieve continuous teaching process evaluation and learning ability improvement.

[0048] Furthermore, this process not only systematizes and standardizes the processing and use of data, ensuring that each step can be effectively connected and executed, but also forms a closed-loop optimization model. In this model, the use of the system is constantly adjusted and improved based on user feedback to ensure that the system's functions and services can be continuously optimized and meet user needs. This dynamic optimization mechanism makes the system not rigid, but constantly evolves, improving its adaptability and effectiveness. At the same time, by integrating third-party education platforms, the system has further expanded its scope of application and influence, and can provide services to more users, promoting the efficient use of educational resources and the overall improvement of education quality.

[0049] This embodiment further includes regularly updating the analysis algorithm in the data processing module to improve the accuracy of the evaluation and the effectiveness of the suggestions.

[0050] Furthermore, with the rapid development of machine learning and statistical analysis technology, the emergence of new algorithms and technologies may greatly improve the accuracy and efficiency of data analysis. By regularly updating these algorithms, the system can maintain the most cutting-edge technology level, more accurately identify learning patterns, predict learning outcomes, and effectively discover and solve problems in the learning process. This continuous technical update and optimization not only ensures the efficiency and accuracy of the system in processing and analyzing data, but also ensures that the system can adapt to the ever-changing educational needs and challenges, thereby providing users with better services and support.

[0051] This embodiment further includes integration with a third-party education platform to expand the scope of data collection and enhance the implementation channels of recommendations.

[0052] Furthermore, through this integration, the system can not only access a wider range of educational resources and services, but also directly implement analysis results and improvement suggestions into students' actual learning process. This open and interconnected feature greatly improves the practicality and influence of the system, making educational improvements not limited to the system, but forming a broad educational ecological improvement network. In addition, this integration can also promote resource sharing and the dissemination of best practices, and promote the modernization and intelligent upgrading of the entire education system. In short, this strategy not only improves the effectiveness of a single system, but also contributes to the innovation and development of the entire education industry.

[0053] The working principle and use process of the present invention:

[0054] First of all, we start with data collection. The system comprehensively collects students' learning behavior data, learning achievement data and teachers' teaching behavior data during the teaching process through the data collection module. This includes students' learning activity records such as homework completion, classroom interaction participation, online learning time, etc. At the same time, it records students' test scores, homework scores and other forms of evaluation results, as well as teachers' teaching methods, interaction frequency and teaching content arrangement. The comprehensive collection of these data provides rich raw materials for subsequent data processing and analysis. The collected data is then transmitted to the data processing module, where necessary cleaning, integration and in-depth analysis are carried out. This module uses machine learning algorithms and statistical analysis methods to deeply mine the data to identify students' learning patterns, predict academic performance and discover potential learning obstacles. This stage is the core of the system. It reveals the information behind the data through intelligent algorithms, providing a scientific basis for formulating more effective teaching and learning strategies. Based on the analysis results of the data processing module, the capacity improvement module further provides personalized suggestions for each student and teacher. For students, it includes but is not limited to recommending appropriate learning resources and customizing learning plans; for teachers, it provides adjustment of teaching The strategic suggestions of the method ensure the personalization and accuracy of teaching and learning, meet the specific needs of different users, promote the all-round development of students and the improvement of teachers' teaching quality. Finally, the analysis results and various suggestions are intuitively displayed to students and teachers in the form of interactive charts and reports through the user interface module. This intuitive display method not only facilitates the understanding of the analysis results, but also stimulates the enthusiasm and initiative of users, and encourages them to participate in the process of teaching improvement and personal learning improvement. In addition, the system also has a feedback mechanism that allows users to give feedback on the analysis results and suggestions. The system continuously adjusts and optimizes the analysis model and improves the suggestions based on these feedbacks, making the system closer to actual needs and improving its practicality and effectiveness. The entire operation process forms a closed loop: from data collection, processing and analysis to capacity improvement and then to user feedback, each step is to optimize the output of the previous step, to achieve continuous teaching process evaluation and learning ability improvement. This teaching analysis and improvement system based on big data not only improves the efficiency and pertinence of teaching and learning, but also promotes the rational use of educational resources, promotes the digital transformation and intelligent upgrading of the education field, and makes positive contributions to the construction of a modern education system.

[0055] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A big data analysis system based on teaching process evaluation and learning ability improvement, characterized in that: include: a) Front-end data collection module, used to collect students’ learning behavior data, learning outcomes data and teachers’ teaching behavior data during the teaching process; b) Data processing module, which cleans, integrates and analyzes the collected data to evaluate the improvement of students’ learning ability and teachers’ teaching quality; c) Ability improvement module, which provides personalized learning suggestions and teaching improvement plans based on the analysis results of the data processing module; d) User interface module, used to present analysis results and improvement suggestions to students and teachers, and to receive user feedback.

2. According to claim 1, a big data analysis system based on teaching process evaluation and learning ability improvement is characterized in that: The data acquisition module further includes: a) Learning behavior tracking submodule, which is used to record students’ learning activities, including but not limited to homework completion, classroom interaction participation, and online learning time; b) Learning outcome record submodule, used to store students’ test scores, homework scores and other forms of assessment results; c) Teaching behavior recording submodule, which is used to record the teacher’s teaching method, interaction frequency and teaching content arrangement.

3. A big data analysis system based on teaching process evaluation and learning ability improvement according to claim 2, characterized in that: The data processing module uses machine learning algorithms and statistical analysis methods to identify students' learning patterns, predict academic performance and discover potential learning barriers.

4. A big data analysis system based on teaching process evaluation and learning ability improvement according to claim 3, characterized in that: The capability enhancement module further comprises: a) Learning path recommendation submodule, which recommends appropriate learning resources and learning plans based on students’ learning abilities and preferences; b) Teaching method optimization sub-module provides teachers with suggestions for adjusting teaching strategies based on student feedback and learning outcomes.

5. A big data analysis system based on teaching process evaluation and learning ability improvement according to claim 4, characterized in that: The user interface module provides interactive charts and reports to intuitively display students' learning progress and teachers' teaching effectiveness.

6. A big data analysis system based on teaching process evaluation and learning ability improvement according to claim 5, characterized in that: The following steps are involved: a) Collect data during the teaching process through the data acquisition module; b) Analyze and process the collected data using the data processing module; c) Generate personalized improvement suggestions through the capability improvement module based on the analysis results; d) presenting the analysis results and suggestions to the user through the user interface module and collecting user feedback information; e) Adjust analysis models and improvement suggestions based on user feedback to achieve continuous teaching process evaluation and learning ability improvement.

7. A big data analysis system based on teaching process evaluation and learning ability improvement according to claim 6, characterized in that: Further including regularly updating the analysis algorithms in the data processing module to improve the accuracy of the evaluation and the effectiveness of the recommendations.

8. The big data analysis system based on teaching process evaluation and learning ability improvement according to claim 7 is characterized in that: It further includes integration with third-party education platforms to expand the scope of data collection and enhance the implementation channels of recommendations.