Ecological classroom teaching system based on smart learning

Abstract

The invention discloses an ecological classroom teaching system based on smart learning. The ecological classroom teaching system comprises a student information acquisition module, a classroom information acquisition module, a teacher information acquisition module, a data processing module and an information sending module. The student information acquisition module is used for acquiring student related information; the teacher information acquisition module is used for acquiring related information of teachers; the classroom information acquisition module is used for acquiring classroom related information; the data processing module is used for processing the student related information to obtain student evaluation information; processing the teacher related information to obtain teacher management information; processing the classroom related information to obtain classroom management information; and the information sending module is used for sending the student evaluation information, the teacher management information and the classroom management information to a preset receiving terminal after the student evaluation information, the teacher management information and the classroom management information are generated. According to the invention, computer teaching courses suitable for students can be pushed to the students, and the personalized learning requirements of the students are met.

Description

Technical Field

[0001] The present invention relates to the field of teaching systems, and in particular to an ecological classroom teaching system based on smart learning. Background Art

[0002] With the rapid development of information technology, the field of education is also undergoing a digital transformation. The traditional teaching model is gradually unable to meet the diverse learning needs of students and the requirements for the efficiency of education management. Educational informatization aims to use modern information technology means, such as the Internet, big data, and artificial intelligence, to optimize the teaching process, improve teaching quality, and enhance the level of education management. This ecological classroom teaching system came into being in this context. With the help of information technology, it realizes the comprehensive collection, processing, and analysis of student, teacher, and classroom information to support more personalized and intelligent teaching and management.

[0003] Different students have different learning styles, knowledge bases, and learning abilities. The traditional one-size-fits-all teaching method is difficult to meet the learning needs of each student. In order to improve students' learning effects and learning enthusiasm, it is necessary to provide students with personalized learning plans and resources. Therefore, an ecological classroom teaching system based on smart learning is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem of poor teaching effect and low intelligence caused by the one-size-fits-all teaching process in the existing teaching system, which has a certain impact on the use of the teaching system, and provide an ecological classroom teaching system based on smart learning.

[0005] The present invention solves the above technical problems through the following technical solutions. The present invention includes a student information collection module, a classroom information collection module, a teacher information collection module, a data processing module and an information sending module; The student information collection module is used to collect student related information; The teacher information collection module is used to collect teacher related information; The classroom information collection module is used to collect classroom related information; The data processing module is used to process student related information and obtain student evaluation information; Process teacher-related information and obtain teacher management information; Process classroom-related information to obtain classroom management information; The information sending module is used to send the student evaluation information, teacher management information and classroom management information to a preset receiving terminal after the above information is generated.

[0006] Furthermore, the specific process of the student information collection module collecting student related information is as follows: A logging script is embedded in the computers in the school's teaching computer room to capture students' operation information in the computer room. The operation information includes real-time capture of learners' click event information, browsing time and search keywords. At the same time, questionnaires are sent to users who use the school's teaching computer room to obtain questionnaire scores; The survey questions include a user information filling form and a basic content test form; The basic content test form is issued after the user completes the user information form; The content of the user information filling form and the score of the basic content test constitute the questionnaire information; Operation information and questionnaire information constitute student-related information.

[0007] Furthermore, the process of obtaining the student evaluation information is as follows: Extracting operation information, extracting click events of learners in real time from the operation information, the click events include click page location information, click page location and click times; Then extract the browsing time, which is the browsing time of the clicked page; Processing the clicked page location information and the browsing time of the clicked page to obtain browsing determination information, analyzing the browsing determination information to obtain a first parameter; Then extract the clicked page position and the number of clicks, process the clicked page position and the number of clicks, and obtain the second parameter; Finally, the search keyword is extracted, the search keyword is processed, and the third parameter is obtained; Calculate the first parameter, the second parameter and the third parameter to obtain the passive basic score; Then extract the questionnaire information, and obtain the content of the user information filling form and the score of the basic content test; The contents of the user information form include those who have been exposed to computers and those who have not been exposed to computers; When the user information form contains information about having been exposed to computers, extract the scores of the basic content test, import them into the preset score mapping set, extract the scores of the corresponding basic content test, and obtain the active basic score. When the score of the basic content test is lower than the preset standard score a1, it means that the student's information is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to not having been exposed to computers is sent to the user for testing; When the user information form contains content that the user has never been exposed to computers, the score of the basic content test is extracted and imported into the preset score mapping set, from which the corresponding basic content test score is extracted to obtain the active basic score. When the score of the basic content test is higher than the preset standard score a2, it means that the student's filling is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to the user who has been exposed to computers is sent to the user for testing; The passive basic scores and active basic scores are processed to obtain a comprehensive score, and student evaluation information is generated based on the comprehensive score.

[0008] Furthermore, the process of obtaining the first parameter is as follows: Extract the clicked page location information and the browsing time of the clicked page, process the clicked page location information, analyze whether the clicked page location is computer learning content, and if the clicked page location information is computer learning content, extract the browsing time of the clicked page. When the browsing time of the clicked page is within a preset time range, it is recorded as valid browsing. When the browsing time of the clicked page exceeds the preset time range, it is recorded as invalid browsing. Extracting all clicked page location information as the number of valid views and the number of invalid views in the computer learning content, and calculating the ratio of the number of valid views to the number of invalid views, that is, obtaining the first parameter; The process of obtaining the second parameter is as follows: Extract the clicked page position, import the clicked page position into the database, analyze whether the clicked page position is a computer learning content position, then extract the number of clicks when the clicked page position is a computer learning content position and the number of clicks when the clicked page position is a non-computer learning content position, calculate the ratio of the number of clicks when the clicked page position is a computer learning content position to the number of clicks when the clicked page position is a non-computer learning content position, and obtain the second parameter; Extracting search keywords, recording the number of computer learning content with the search keyword and the number of non-computer learning content with the search keyword, and calculating the ratio of the number of computer learning content with the search keyword to the number of non-computer learning content with the search keyword, and obtaining a third parameter; The first parameter is recorded as Z1, the second parameter is recorded as Z2, and the third parameter is recorded as Z3; Assign weight Q1 to the first parameter Z1, weight Q2 to the second parameter Z2, and weight Q3 to the third parameter Z3, Q1+Q2+Q3=1, Q1>Q2>Q3; Through the formula Z1*Q1+Z2*Q2+Z3*Q3=Zz, the passive basic score Zz is obtained; Then extract the active basic score, mark it as Yy, assign the passive basic score Zz correction value P1, the active basic score Yy correction value P2, P2>P1, P1+P2=1; The comprehensive score can be obtained through the formula Zz*P1+Yy*P2=Zy.

[0009] Furthermore, the specific process of generating student evaluation information based on the comprehensive score is as follows: The comprehensive score is extracted. When the comprehensive score is greater than the preset value f1, the student evaluation information is generated, and the student is rated as a Class I student; When the comprehensive score is between the preset values ​​f1 and f2, the student evaluation information is generated, and the student is rated as a second-class student; When the comprehensive score is less than the preset value f2, the student evaluation information is generated, and the student is rated as a third-category student; When the student evaluation information is generated, the ecological classroom teaching system pushes the computer teaching courses with corresponding contents according to the content of the student evaluation information.

[0010] Furthermore, the specific process of pushing the computer teaching course of the corresponding content is as follows: Classify courses according to the computer knowledge system, and then add labels to each course, including course difficulty, applicable population, knowledge point coverage and course duration information; Extracting student evaluation information from the ecological classroom teaching system, including students’ comprehensive scores and categories, i.e., first-class students, second-class students, and third-class students; At the same time, the student's operation information and questionnaire information are associated; Use data mining technology to analyze student evaluation information, discover students' learning interests, knowledge gaps and learning abilities, and then formulate and push courses.

[0011] Furthermore, the specific process of the course push is as follows: according to the student's study time habits, set the course push rules, that is, extract the study history within the previous x days, record the time point with the most occurrences, and set the push time point five minutes before the time point for course push; After the course is pushed, learning feedback information is collected, including manual feedback and automatic feedback; Manual feedback is feedback on the difficulty of learning, including difficult, normal and easy; When the manual feedback is that the course is difficult or easy, a course with a higher or lower difficulty level will be pushed to the student again; Automatic feedback is the duration of the learning course tracked by the system. When the duration of the learning course is less than the preset value, a prompt message is generated; When a learning course has not been opened within the preset time after the push time point, a reminder message is generated.

[0012] Furthermore, the process of obtaining the teacher management information is as follows: Extract the collected teacher-related information, including teacher teaching effect information and teacher teaching time information; Extracting teaching effect information, the teaching effect information is the test score of the teacher teaching the computer course; Continuously collect u test scores, the test score is the average test score of all students, analyze the u test scores, take the first test score as the benchmark, perform trend analysis on subsequent test scores, obtain the test score trend, when the test score trend is in a continuous decline, extract the teacher's teaching time information, when the teacher's teaching time information is less than the preset value, generate teacher management information.

[0013] Furthermore, the specific process of obtaining the classroom management information is as follows: Extracting classroom related information, the classroom related information includes first classroom information and second classroom information; The first classroom information is the classroom information when there is no one teaching, including the duration of the course interface and the volume of classroom voices; When the duration of the course interface stays less than the preset value or the volume of classroom voices exceeds the preset value, classroom management information is generated; The second classroom information is classroom information when someone is teaching, including teacher's speaking speed information and teacher's voice clarity; When the teacher's speaking speed information exceeds the preset range or the teacher's voice clarity is less than the preset value, classroom management information is generated.

[0014] Compared with the prior art, the present invention has the following advantages: the ecological classroom teaching system based on intelligent learning, With the help of detailed student information collection and in-depth analysis, a learning path is tailored for each student. Through operation information, students' learning preferences are understood, such as the pages clicked and the keywords searched, which can reflect their interests; combined with questionnaires to understand the knowledge base. Based on this, the student evaluation information generated can accurately locate the strengths and weaknesses of students. Then, computer teaching courses that match the students' levels and interests are pushed, avoiding the drawbacks of traditional teaching, allowing students to focus on areas where they need to improve and improve learning efficiency. For example, for students who perform poorly in the basic programming test but are interested in game development, the system will push courses that combine basic programming knowledge with game development cases to make learning more targeted.

[0015] Personalized course push can stimulate students' interest in learning and curiosity. When students are exposed to courses that suit their interests and abilities, they will be more willing to take the initiative to study. In addition, the system will dynamically adjust the course difficulty based on students' learning feedback. When students overcome difficulties and complete challenges or feel that the course difficulty is appropriate, they will gain a sense of accomplishment and further enhance their learning motivation. At the same time, in the process of independently selecting and studying courses that suit them, students gradually develop independent learning and self-management abilities.

[0016] The course not only focuses on students' knowledge learning, but also cultivates students' information literacy and problem-solving ability through analysis of operation information and classroom performance. In the process of using the teaching computer room, students need to explore and operate computers independently, which helps to improve their computer operation skills and information processing ability. In addition, the practical projects and case analysis in the course can also exercise students' logical thinking, teamwork and innovation ability, and promote the overall improvement of students' comprehensive quality.

[0017] Student evaluation information, teacher management information, and classroom management information provide rich data support for education managers. Based on this information, managers can understand the teaching effects of different courses and teachers and reasonably allocate teaching resources, such as adjusting teacher allocation, optimizing course settings, and updating teaching equipment. For example, if it is found that the student participation and learning effect of a computer course are not ideal, managers can consider adjusting the teaching teacher or reforming the course content.

[0018] Real-time collection and analysis of classroom management information enables education managers to keep abreast of classroom teaching dynamics. By monitoring classroom information when no one is teaching and when someone is teaching, such as the length of time the course interface stays, the volume of classroom voices, the teacher's speaking speed and voice clarity, managers can find problems in the classroom and intervene in time to ensure teaching order and teaching quality. At the same time, long-term tracking and analysis of teachers' teaching effectiveness and students' learning conditions will help establish a scientific teaching quality monitoring system and promote the improvement of the overall teaching quality of the school. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0020] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given, but the protection scope of the present invention is not limited to the following embodiment.

[0021] like Figure 1As shown, this embodiment provides a technical solution: an ecological classroom teaching system based on smart learning, including a student information collection module, a classroom information collection module, a teacher information collection module, a data processing module and an information sending module; The student information collection module is used to collect student related information; The teacher information collection module is used to collect teacher related information; The classroom information collection module is used to collect classroom related information; The data processing module is used to process student related information and obtain student evaluation information; Process teacher-related information and obtain teacher management information; Process classroom-related information to obtain classroom management information; The information sending module is used to send the student evaluation information, teacher management information and classroom management information to a preset receiving terminal after the above information is generated.

[0022] Furthermore, the specific process of the student information collection module collecting student related information is as follows: A logging script is embedded in the computers in the school's teaching computer room to capture students' operation information in the computer room. The operation information includes real-time capture of learners' click event information, browsing time and search keywords. At the same time, questionnaires are sent to users who use the school's teaching computer room to obtain questionnaire scores; The survey questions include a user information filling form and a basic content test form; The basic content test form is issued after the user completes the user information form; The content of the user information filling form and the score of the basic content test constitute the questionnaire information; Operation information and questionnaire information constitute student-related information.

[0023] The process of obtaining the student evaluation information is as follows: Extracting operation information, extracting click events of learners in real time from the operation information, the click events include click page location information, click page location and click times; Then extract the browsing time, which is the browsing time of the clicked page; Processing the clicked page location information and the browsing time of the clicked page to obtain browsing determination information, analyzing the browsing determination information to obtain a first parameter; Then extract the clicked page position and the number of clicks, process the clicked page position and the number of clicks, and obtain the second parameter; Finally, the search keyword is extracted, the search keyword is processed, and the third parameter is obtained; Calculate the first parameter, the second parameter and the third parameter to obtain the passive basic score; Then extract the questionnaire information, and obtain the content of the user information filling form and the score of the basic content test; The contents of the user information form include those who have been exposed to computers and those who have not been exposed to computers; When the user information form contains information about having been exposed to computers, extract the scores of the basic content test, import them into the preset score mapping set, extract the scores of the corresponding basic content test, and obtain the active basic score. When the score of the basic content test is lower than the preset standard score a1, it means that the student's information is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to not having been exposed to computers is sent to the user for testing; When the user information form contains content that the user has never been exposed to computers, the score of the basic content test is extracted and imported into the preset score mapping set, from which the corresponding basic content test score is extracted to obtain the active basic score. When the score of the basic content test is higher than the preset standard score a2, it means that the student's filling is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to the user who has been exposed to computers is sent to the user for testing; a2>a1; Process the passive basic scores and active basic scores to obtain comprehensive scores, and generate student evaluation information based on the comprehensive scores; This process comprehensively considers students' operational behavior information (click events, browsing time, search keywords) and questionnaire information (basic content test scores, computer exposure). Operational behavior information reflects students' behavior patterns and interest tendencies in the actual learning process, while questionnaire information can reflect students' knowledge base and learning background. The combination of the two can more comprehensively and accurately evaluate students' learning status and abilities, avoiding the limitations of single-dimensional evaluation.

[0024] Through detailed analysis of operation information and questionnaire information, we can evaluate the specific situation of each student. Different students have different clicked page locations, browsing time, search keywords, and basic content test scores. The system can generate personalized evaluation information for each student based on these differences, providing a basis for subsequent personalized teaching and resource push.

[0025] The entire evaluation process is based on a large amount of data collection and analysis, making the evaluation results more objective and scientific. Through the quantitative processing and analysis of various data, specific parameters and scores can be obtained, providing educators with clear decision-making references, such as judging students' learning levels and determining teaching courses suitable for students.

[0026] An abnormality detection mechanism is set up during the assessment process. When a student's basic content test score is lower than the preset standard score, it will be reclassified and sent to a different test form for testing. This helps to detect abnormalities in the information filled in by students, ensure the accuracy of the assessment results, and avoid distortion of the assessment results due to students filling in incorrect information by mistake or deliberately filling in incorrect information.

[0027] The process of obtaining the first parameter is as follows: Extract the clicked page location information and the browsing time of the clicked page, process the clicked page location information, analyze whether the clicked page location is computer learning content, and if the clicked page location information is computer learning content, extract the browsing time of the clicked page. When the browsing time of the clicked page is within a preset time range, it is recorded as valid browsing. When the browsing time of the clicked page exceeds the preset time range, it is recorded as invalid browsing. Extracting all clicked page location information as the number of valid views and the number of invalid views in the computer learning content, and calculating the ratio of the number of valid views to the number of invalid views, that is, obtaining the first parameter; The process of obtaining the second parameter is as follows: Extract the clicked page position, import the clicked page position into the database, analyze whether the clicked page position is a computer learning content position, then extract the number of clicks when the clicked page position is a computer learning content position and the number of clicks when the clicked page position is a non-computer learning content position, calculate the ratio of the number of clicks when the clicked page position is a computer learning content position to the number of clicks when the clicked page position is a non-computer learning content position, and obtain the second parameter; Extracting search keywords, recording the number of computer learning content with the search keyword and the number of non-computer learning content with the search keyword, and calculating the ratio of the number of computer learning content with the search keyword to the number of non-computer learning content with the search keyword, and obtaining a third parameter; The first parameter is recorded as Z1, the second parameter is recorded as Z2, and the third parameter is recorded as Z3; Assign weight Q1 to the first parameter Z1, weight Q2 to the second parameter Z2, and weight Q3 to the third parameter Z3, Q1+Q2+Q3=1, Q1>Q2>Q3; Through the formula Z1*Q1+Z2*Q2+Z3*Q3=Zz, the passive basic score Zz is obtained; Then extract the active basic score, mark it as Yy, assign the passive basic score Zz correction value P1, the active basic score Yy correction value P2, P2>P1, P1+P2=1; The comprehensive score can be obtained through the formula Zz*P1+Yy*P2=Zy; The first parameter: By distinguishing between effective browsing and ineffective browsing, the first parameter can accurately measure students' concentration and learning investment in computer learning content. More effective browsing means that students study the relevant content seriously, and a high parameter reflects that students have a high degree of concentration and learning enthusiasm for computer learning; on the contrary, too much ineffective browsing indicates that students may be distracted or not really engaged in learning.

[0028] Second parameter: Analyzing the clicks on the page positions can clearly understand the students' attention distribution between computer learning content and non-learning content. If the second parameter is high, it means that students pay more attention to pages related to computer learning content, reflecting their inclination towards computer learning; if the parameter is low, it may mean that students are easily attracted by non-learning content.

[0029] The third parameter: The analysis of search keywords can provide insights into students’ learning interests and needs. A high third parameter means that students’ search behavior mainly revolves around computer learning content, reflecting their strong interest in computer learning and their willingness to actively explore.

[0030] The student's operation behavior is converted into specific parameters, and then the passive basic score and comprehensive score are further calculated, realizing the scientific quantification of the student's learning performance. This quantification method avoids the arbitrariness and uncertainty of subjective evaluation, and provides teachers and systems with objective and accurate student learning data for easy comparison and analysis.

[0031] By assigning corresponding weights to different parameters and basic scores, the student's operation behavior (passive basic score) and the knowledge base reflected in the questionnaire (active basic score) are comprehensively considered. This comprehensive evaluation method can fully and multi-angle understand the student's learning status, paying attention to the student's performance in actual operation and combining their knowledge reserves, providing a more reliable basis for personalized teaching and resource push.

[0032] Based on these quantitative scores and parameters, teachers and systems can promptly identify students’ learning problems and needs. For example, if a student’s first parameter is low, it means that the student is not focused enough on computer learning. Teachers can take corresponding measures, such as reminding students, adjusting teaching content or methods, etc., to improve students’ learning outcomes.

[0033] Suppose a student named Xiao Zhang is studying in a teaching computer room, and the system collects and analyzes his operation information.

[0034] Xiao Zhang clicked on 10 pages, 6 of which were computer learning content. Among these 6 pages, the browsing time of 4 pages was within the preset time range and was recorded as valid browsing; the browsing time of the other 2 pages exceeded the preset range and was invalid browsing.

[0035] Then the first parameter Z1=4 / 2=2; The page locations clicked by Xiao Zhang can be divided into computer learning content locations and non-computer learning content locations. He clicked on computer learning content locations 15 times and non-computer learning content locations 5 times.

[0036] Then the second parameter Z2=15 / 5=5; Xiao Zhang searched for 8 keywords, 6 of which were related to computer learning content and 2 were related to non-computer learning content.

[0037] Then the third parameter Z3=6 / 2=3; Assume weights Q1=0.5, Q2=0.3, Q3=0.2; Passive basic score Zz=2*0.5+5*0.3+3*0.2=2.5; Xiao Zhang filled in the questionnaire that he had been exposed to computer content, and his basic content test score was 75 points. Through the preset score mapping set, he obtained the active basic score Yy=8; Assume the correction values, P1=0.3, P2=0.7; Comprehensive score Zy=2.5*0.3+8*0.7=6.35 Based on this comprehensive score, the system can determine Xiao Zhang's learning status and recommend computer courses suitable for his level. For example, if the preset values ​​f1=8, f2=6, and Xiao Zhang's comprehensive score is 6<6.35<8, he will be rated as a second-class student, and the system will recommend courses suitable for second-class students, such as intermediate courses in computer programming, to meet his learning needs.

[0038] The specific process of generating student evaluation information based on comprehensive scores is as follows: The comprehensive score is extracted. When the comprehensive score is greater than the preset value f1, the student evaluation information is generated, and the student is rated as a Class I student; When the comprehensive score is between the preset values ​​f1 and f2, the student evaluation information is generated, and the student is rated as a second-class student; When the comprehensive score is less than the preset value f2, the student evaluation information is generated, and the student is rated as a third-category student; When the student evaluation information is generated, the ecological classroom teaching system pushes the corresponding computer teaching courses according to the content of the student evaluation information; Each student has different learning abilities, knowledge bases, and learning progress. The system can accurately identify students' learning levels by dividing them into different categories through comprehensive scoring. The first type of students usually have a high level of knowledge mastery and learning ability, and the courses pushed by the system will be more challenging and in-depth, which will help them further expand their knowledge boundaries and improve their professional skills; the second type of students are at a medium level, and the system will provide courses with moderate difficulty and focus on consolidation and improvement to help them improve steadily; the third type of students may have relatively weak foundations, and the courses pushed by the system will focus on the explanation and consolidation of basic knowledge, so that they can gradually build a solid knowledge system. This personalized course push method can meet the learning needs of different students to the greatest extent and improve learning outcomes.

[0039] When students receive courses that are suitable for their level, they are more likely to succeed in the learning process. For the first type of students, conquering difficult courses will bring a sense of accomplishment, motivating them to constantly challenge themselves; the second type of students can see their own progress and enhance their confidence in learning by completing courses that suit them; the third type of students will gradually overcome learning difficulties and build up motivation and interest in learning after mastering the basic knowledge. This positive learning experience helps to cultivate students' independent learning ability and enthusiasm for learning, forming a virtuous circle.

[0040] Students do not need to select the courses that suit them from a vast amount of course resources. The system will accurately push them based on the evaluation information, saving students' time and energy. Students can devote more time and energy to learning courses that are truly helpful to their own improvement, avoiding blind and ineffective learning and improving learning efficiency.

[0041] Teachers can understand each student's learning status according to their assessment categories, and adjust teaching strategies and methods. For the first category of students, teachers can provide more open and exploratory learning tasks to encourage them to conduct independent research and innovation; for the second category of students, teachers can strengthen the guidance and consolidation of key knowledge; for the third category of students, teachers can provide more basic guidance and personalized help. This can make teaching more targeted and improve teaching quality.

[0042] Education managers can reasonably allocate teaching resources based on the classification of students. For example, they can provide more advanced course resources and competition opportunities for the first category of students, arrange appropriate extended learning activities for the second category of students, and strengthen the investment in basic teaching facilities and teaching staff for the third category of students. At the same time, by analyzing the number and proportion of students in different categories, managers can promptly identify problems in teaching, adjust teaching plans and course settings, and optimize teaching management.

[0043] Accurate student evaluation and personalized course push can make students feel the professionalism and considerate service of the system, and improve students' recognition and loyalty to the teaching system. Students who have a good learning experience in the system will be more willing to use the system for learning, thereby improving the user activity and reputation of the system and attracting more students to use it.

[0044] The teaching system can reasonably arrange and recommend course resources according to the classification of students to avoid waste and idleness of resources. Pushing suitable courses to suitable students can give full play to the value of teaching resources, improve resource utilization, and achieve optimal allocation of teaching resources.

[0045] The specific process of pushing the computer teaching course of corresponding content is as follows: Classify courses according to the computer knowledge system, and then add labels to each course, including course difficulty, applicable population, knowledge point coverage and course duration information; Extracting student evaluation information from the ecological classroom teaching system, including students’ comprehensive scores and categories, i.e., first-class students, second-class students, and third-class students; At the same time, the student's operation information and questionnaire information are associated; Use data mining technology to analyze student evaluation information, and then develop and push courses after discovering students' learning interests, knowledge gaps, and learning abilities; Course development means selecting the courses that best match the learning interests, knowledge gaps and learning abilities, and developing a timetable based on the students’ learning habits, and then delivering the courses according to the timetable contents; Collect student evaluation information: Extract student evaluation information from the ecological classroom teaching system, including comprehensive scores, categories, and related operation information (such as click events, browsing time, search keywords) and questionnaire information (basic content test scores, computer exposure, etc.).

[0046] Data cleaning and preprocessing: Check the collected data and remove duplicate, erroneous or incomplete data records. Standardize the data, such as unifying different types of score data to the same scale range for subsequent analysis.

[0047] Keyword analysis: frequency statistics and semantic analysis of students’ search keywords. Frequently appearing keywords often reflect students’ interests. For example, if a student frequently searches for keywords such as “artificial intelligence algorithm” and “machine learning model”, it indicates that he or she has a strong interest in the fields of artificial intelligence and machine learning.

[0048] Click behavior analysis: Analyze the students' click page location information and click times. The page types and content that students click more often are usually what they are interested in. For example, students who frequently click on pages related to programming competitions may be interested in programming competitions.

[0049] Sequential pattern mining: By analyzing the operation sequence of students, we can find the behavior patterns of students in the learning process. For example, after learning the basic programming language course, students click on the database-related course page, which may indicate that students are interested in the knowledge of combining programming language with database.

[0050] Test score analysis: Based on the students’ basic content test scores and course test scores, find out the students’ weaknesses in various knowledge points. For example, if a student scores low in the function part of the math foundation test, it means that he or she has a weakness in function knowledge.

[0051] Learning progress analysis: Compare students’ learning progress and completion status in different course modules. If a student’s learning progress in a particular course module is significantly behind that in other modules, it may mean that the knowledge corresponding to this module is their weak point.

[0052] Error analysis: Collect students’ wrong answers in homework and tests, and analyze the error types and distribution. Frequently occurring error types often correspond to knowledge points that are students’ knowledge weaknesses.

[0053] Learning speed analysis: Assess students’ learning speed based on how long and how much progress they take to complete the course. Students who learn faster are likely to have stronger learning and comprehension abilities.

[0054] Problem-solving ability analysis: Analyze students' performance in problem solving, such as the ability to debug code in programming practice, the ability to propose solutions in case analysis, etc. Students who can solve problems quickly and effectively usually have strong learning abilities.

[0055] Knowledge transfer ability analysis: observe the students' ability to apply the knowledge they have learned to new situations. For example, after learning a programming algorithm, can they apply it to similar problems? Students with strong knowledge transfer ability also have relatively high learning ability.

[0056] Personalized course recommendations: Filter relevant courses from the course library based on students’ learning interests. For example, for students interested in artificial intelligence, we recommend courses such as deep learning and natural language processing.

[0057] Course combination design: Combine related courses to form a personalized learning path. For example, for students interested in game development, a learning package including programming language basics, game engine usage, game planning, etc. can be designed.

[0058] Targeted supplementary courses: Choose courses that can make up for students' knowledge deficiencies. If students have deficiencies in mathematical functions, we recommend courses related to function foundations and function applications.

[0059] Step-by-step learning plan: Develop a step-by-step learning plan for students, gradually improving from basic knowledge to advanced knowledge. For example, first learn the basic concepts and properties of functions, and then learn the application and extension of functions.

[0060] Difficulty Adapted Courses: Recommend courses of appropriate difficulty to students based on their learning ability. Students with stronger learning ability can choose advanced courses, while students with weaker learning ability can start with basic courses.

[0061] Extension courses: For students with strong learning ability, we provide some extension courses and projects, such as scientific research projects, innovative practices, etc., to further enhance their abilities; By adding detailed labels such as course difficulty, applicable population, knowledge point coverage and course duration to the courses, and combining student evaluation information, it can accurately match students' learning levels and needs. For example, for learners with high comprehensive scores and rated as Category 1 students, the system can push courses with advanced difficulty, in-depth knowledge points and covering cutting-edge technologies, such as artificial intelligence algorithm optimization, advanced blockchain development, etc., to meet their needs to further explore the boundaries of knowledge; and for Category 3 students, the system will provide basic introductory courses, such as basic computer operations, programming language basics, etc., to help them lay a solid foundation.

[0062] By associating student operation information with questionnaire information and using data mining technology to explore students' learning interests, the system can push courses that match their interests to students. For example, if students frequently search for game development-related content in their operation information and the questionnaire also shows that they are interested in game programming, the system will push game development-related courses to improve students' learning enthusiasm and involvement.

[0063] By analyzing students' assessment information to identify knowledge gaps, the system can push targeted courses to make up for the gaps. For example, if a student is weak in the algorithm part of programming language, the system will push courses related to algorithm design and analysis to help students strengthen their knowledge in this area and promote all-round development.

[0064] Precisely matched courses prevent students from blindly selecting from a large number of courses, saving time and energy. Students can focus on courses that suit them, making the learning process smoother and easier to master knowledge and skills, thereby improving learning efficiency and effectiveness.

[0065] The system automatically pushes courses based on student characteristics. Teachers do not need to spend a lot of time selecting suitable courses for each student individually. They can devote more energy to the design of teaching content and innovation of teaching methods to improve teaching quality.

[0066] Students’ course learning status and feedback can provide teachers with teaching references. Teachers can understand students’ knowledge mastery and learning needs based on their learning performance in the pushed courses, and adjust teaching strategies and methods in a timely manner to make teaching more targeted.

[0067] Personalized course push can give students a good learning experience and improve their satisfaction and loyalty to the teaching system and educational institutions. Satisfied students are more likely to continue using the system to learn and recommend it to others, which helps expand the influence of the system and institutions.

[0068] By analyzing student needs and course usage, teaching systems and educational institutions can understand which courses are popular and which courses need to be improved or updated, thereby optimizing the configuration of course resources and improving resource utilization efficiency.

[0069] The specific process of course push is as follows: according to the student's study time habits, set the course push rules, that is, extract the study history within the previous x days, record the time point with the most occurrences, and set the push time point five minutes before the time point for course push, x≥7; After the course is pushed, learning feedback information is collected, including manual feedback and automatic feedback; Manual feedback is feedback on learning difficulty, including difficult, normal and easy; When the manual feedback is that the course is difficult or easy, a course with a higher or lower difficulty level will be pushed to the student again; Automatic feedback is the duration of the learning course tracked by the system. When the duration of the learning course is less than the preset value, a prompt message is generated; When a learning course is not opened within the preset time after the push time point, a reminder message is generated; Based on the student's learning history in the previous x days, find the time point with the most occurrences and push the course five minutes in advance, which can match the student's study time habits. When students receive course push at the time they are used to studying, it will be easier for them to enter the learning state and improve their initiative and enthusiasm in learning. Because at this time, their biological clock and psychological state are more inclined to study, and their acceptance of courses is also higher.

[0070] By collecting learning feedback information, including manual feedback and automatic feedback, the system can dynamically adjust the course difficulty according to the students' actual learning situation. When students give feedback that the course is difficult or easy, the system will promptly push courses of appropriate difficulty to ensure that students are always in the "zone of proximal development" for learning, which can not only avoid frustration caused by courses that are too difficult, but also prevent wasting time due to courses that are too easy, thus ensuring learning results.

[0071] The course duration monitoring and course unopening reminder in automatic feedback can remind students to complete their learning tasks in time. When the course duration is less than the preset value or the course is not opened after the preset time after being pushed, the system generates a reminder message, which helps to enhance students' learning self-discipline, help them develop good learning habits, and ensure the continuity and systematicness of learning.

[0072] The process of obtaining the teacher management information is as follows: Extract the collected teacher-related information, including teacher teaching effect information and teacher teaching time information; Extracting teaching effect information, the teaching effect information is the test score of the teacher teaching the computer course; Continuously collect u test scores, the test score is the average test score of all students, analyze the u test scores, take the first test score as the benchmark, perform trend analysis on subsequent test scores, and obtain the test score trend. When the test score trend is in a continuous decline, extract the teacher's teaching time information. When the teacher's teaching time information is less than the preset value, generate teacher management information, u≥10; By analyzing the trend of continuously collected test scores, teachers can timely understand the changes in their teaching effectiveness. When the test score trend is in a continuous decline, it means that there may be some problems in the teaching process, such as inappropriate teaching methods and unreasonable arrangement of teaching content. Teachers can use this as an opportunity to reflect on and adjust their teaching, thereby improving teaching strategies and improving teaching levels.

[0073] Paying attention to the information of teachers' teaching time and combining it with teaching results can help teachers realize the impact of teaching time on teaching results. If the teaching time is less than the preset value and the teaching effect is not good, the teacher will realize that it may be because the teaching time invested is insufficient, so he will arrange the time reasonably in the subsequent teaching to ensure sufficient teaching time to improve the teaching quality. This helps teachers form a scientific teaching time management concept and optimize the arrangement of teaching work.

[0074] When the system finds that the test score trend is declining and the teaching time is insufficient, it generates teacher management information. The school and teachers can jointly analyze the problem and formulate targeted improvement measures. For example, the school can organize teachers to participate in teaching training to help teachers master more effective teaching methods; teachers can also adjust teaching content and progress based on student feedback and test results. This data-based teaching improvement method can effectively improve teaching quality and enable students to achieve better learning results.

[0075] The specific process of obtaining the classroom management information is as follows: Extracting classroom related information, the classroom related information includes first classroom information and second classroom information; The first classroom information is the classroom information when there is no one teaching, including the duration of the course interface and the volume of classroom voices; When the duration of the course interface stays less than the preset value or the volume of classroom voices exceeds the preset value, classroom management information is generated; The second classroom information is classroom information when someone is teaching, including teacher's speaking speed information and teacher's voice clarity; When the teacher's speaking speed information exceeds the preset range or the teacher's voice clarity is less than the preset value, classroom management information is generated.

[0076] Manage unattended classes by monitoring the duration of time spent on the course interface. When the duration is less than the preset value, it means that students may not be studying seriously on the learning interface, and may be distracted or doing things unrelated to learning. The system generates classroom management information to promptly remind relevant personnel to intervene, ensuring that students can focus on learning content and maintain a good learning order even when there is no one teaching.

[0077] If the volume of classroom voices exceeds the preset value, it indicates that the classroom environment is noisy and may interfere with other students' learning. Timely generation of management information can prompt administrators to manage the classroom, reduce the noise level, create a quiet and orderly learning environment, and enable students to learn more effectively.

[0078] When managing a classroom with teachers, if the teacher's speech speed exceeds the preset range, whether it is too fast or too slow, it may affect students' acceptance and understanding of knowledge. The system generates management information to remind teachers to adjust their speech speed to make the teaching rhythm moderate so that students can keep up with the teaching progress and ensure the smooth progress of teaching activities.

[0079] When the teacher's voice clarity is lower than the preset value, students may not be able to hear the teaching content clearly, which will seriously affect the teaching effect. Timely generation of management information can make teachers aware of the problem and take measures to improve voice clarity, such as adjusting the microphone, changing the pronunciation method, etc., to ensure that students can accurately receive knowledge.

[0080] Whether it is an abnormal classroom information when there is no one teaching or when there is someone teaching, the generated classroom management information can provide data support for teaching improvement. For example, if the course interface stays too short for many times, it may be necessary to reflect on the attractiveness of the learning content or whether the arrangement of learning tasks is reasonable; if the teacher's speaking speed often exceeds the preset range, the teacher can conduct targeted speaking speed training to improve teaching ability.

[0081] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0082] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0083] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An ecological classroom teaching system based on intelligent learning, characterized in that: It includes student information collection module, classroom information collection module, teacher information collection module, data processing module and information sending module; The student information collection module is used to collect student related information; The teacher information collection module is used to collect teacher related information; The classroom information collection module is used to collect classroom related information; The data processing module is used to process student related information and obtain student evaluation information; Process teacher-related information and obtain teacher management information; Process classroom-related information to obtain classroom management information; The information sending module is used to send the student evaluation information, teacher management information and classroom management information to a preset receiving terminal after the above information is generated.

2. According to claim 1, an ecological classroom teaching system based on smart learning is characterized by: The specific process of the student information collection module collecting student related information is as follows: A logging script is embedded in the computers in the school's teaching computer room to capture students' operation information in the computer room. The operation information includes real-time capture of learners' click event information, browsing time and search keywords. At the same time, questionnaires are sent to users who use the school's teaching computer room to obtain questionnaire scores; The survey questions include a user information filling form and a basic content test form; The basic content test form is issued after the user completes the user information form; The content of the user information filling form and the score of the basic content test constitute the questionnaire information; Operation information and questionnaire information constitute student-related information.

3. According to claim 2, an ecological classroom teaching system based on smart learning is characterized by: The process of obtaining the student evaluation information is as follows: Extracting operation information, extracting click events of learners in real time from the operation information, the click events include click page location information, click page location and click times; Then extract the browsing time, which is the browsing time of the clicked page; Processing the clicked page location information and the browsing time of the clicked page to obtain browsing determination information, analyzing the browsing determination information to obtain a first parameter; Then extract the clicked page position and the number of clicks, process the clicked page position and the number of clicks, and obtain the second parameter; Finally, the search keyword is extracted, the search keyword is processed, and the third parameter is obtained; Calculate the first parameter, the second parameter and the third parameter to obtain the passive basic score; Then extract the questionnaire information, and obtain the content of the user information filling form and the score of the basic content test; The contents of the user information form include those who have been exposed to computers and those who have not been exposed to computers; When the user information form contains information about having been exposed to computers, extract the scores of the basic content test, import them into the preset score mapping set, extract the scores of the corresponding basic content test, and obtain the active basic score. When the score of the basic content test is lower than the preset standard score a1, it means that the student's information is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to not having been exposed to computers is sent to the user for testing; When the user information form contains content that the user has never been exposed to computers, the score of the basic content test is extracted and imported into the preset score mapping set, from which the corresponding basic content test score is extracted to obtain the active basic score. When the score of the basic content test is higher than the preset standard score a2, it means that the student's filling is abnormal, and the user needs to be reclassified, and the basic content test form corresponding to the user who has been exposed to computers is sent to the user for testing; The passive basic scores and active basic scores are processed to obtain a comprehensive score, and student evaluation information is generated based on the comprehensive score.

4. The ecological classroom teaching system based on intelligent learning according to claim 3 is characterized by: The process of obtaining the first parameter is as follows: Extract the clicked page location information and the browsing time of the clicked page, process the clicked page location information, analyze whether the clicked page location is computer learning content, and if the clicked page location information is computer learning content, extract the browsing time of the clicked page. When the browsing time of the clicked page is within a preset time range, it is recorded as valid browsing. When the browsing time of the clicked page exceeds the preset time range, it is recorded as invalid browsing. Extracting all clicked page location information as the number of valid views and the number of invalid views in the computer learning content, and calculating the ratio of the number of valid views to the number of invalid views, that is, obtaining the first parameter; The process of obtaining the second parameter is as follows: Extract the clicked page position, import the clicked page position into the database, analyze whether the clicked page position is a computer learning content position, then extract the number of clicks when the clicked page position is a computer learning content position and the number of clicks when the clicked page position is a non-computer learning content position, calculate the ratio of the number of clicks when the clicked page position is a computer learning content position to the number of clicks when the clicked page position is a non-computer learning content position, and obtain the second parameter; Extracting search keywords, recording the number of search keywords for computer learning content and the number of search keywords for non-computer learning content, and calculating the ratio of the number of search keywords for computer learning content to the number of search keywords for non-computer learning content, to obtain a third parameter; The first parameter is recorded as Z1, the second parameter is recorded as Z2, and the third parameter is recorded as Z3; Assign weight Q1 to the first parameter Z1, weight Q2 to the second parameter Z2, and weight Q3 to the third parameter Z3, Q1+Q2+Q3=1, Q1>Q2>Q3; Through the formula Z1*Q1+Z2*Q2+Z3*Q3=Zz, the passive basic score Zz is obtained; Then extract the active basic score, mark it as Yy, assign the passive basic score Zz correction value P1, the active basic score Yy correction value P2, P2>P1, P1+P2=1; The comprehensive score can be obtained through the formula Zz*P1+Yy*P2=Zy.

5. The ecological classroom teaching system based on intelligent learning according to claim 4 is characterized by: The specific process of generating student evaluation information based on comprehensive scores is as follows: The comprehensive score is extracted. When the comprehensive score is greater than the preset value f1, the student evaluation information is generated, and the student is rated as a Class I student; When the comprehensive score is between the preset values ​​f1 and f2, the student evaluation information is generated, and the student is rated as a second-class student; When the comprehensive score is less than the preset value f2, the student evaluation information is generated, and the student is rated as a third-category student; When the student evaluation information is generated, the ecological classroom teaching system pushes the computer teaching courses with corresponding contents according to the content of the student evaluation information.

6. The ecological classroom teaching system based on smart learning according to claim 5 is characterized by: The specific process of pushing the computer teaching course of corresponding content is as follows: Classify the courses according to the computer knowledge system, and then add labels to each course; Extracting student evaluation information from the ecological classroom teaching system, including students’ comprehensive scores and categories, i.e., first-class students, second-class students, and third-class students; At the same time, the student's operation information and questionnaire information are associated; Use data mining technology to analyze student evaluation information, discover students' learning interests, knowledge gaps and learning abilities, and then formulate and push courses.

7. The ecological classroom teaching system based on smart learning according to claim 6 is characterized by: The specific process of course push is as follows: according to the student's study time habits, set the course push rules, that is, extract the study history within the previous x days, record the time point with the most occurrences, and set the push time point five minutes before the time point for course push; After the course is pushed, learning feedback information is collected, including manual feedback and automatic feedback; Manual feedback is feedback on learning difficulty, including difficult, normal and easy; When the manual feedback is that the course is difficult or easy, a course with a higher or lower difficulty level will be pushed to the student again; Automatic feedback is the duration of the learning course tracked by the system. When the duration of the learning course is less than the preset value, a prompt message is generated; When a learning course has not been opened within the preset time after the push time point, a reminder message is generated.

8. The ecological classroom teaching system based on smart learning according to claim 1 is characterized by: The process of obtaining the teacher management information is as follows: Extract the collected teacher-related information, including teacher teaching effect information and teacher teaching time information; Extracting teaching effect information, the teaching effect information is the test score of the teacher teaching the computer course; Continuously collect u test scores, the test score is the average test score of all students, analyze the u test scores, take the first test score as the benchmark, perform trend analysis on subsequent test scores, obtain the test score trend, when the test score trend is in a continuous decline, extract the teacher's teaching time information, when the teacher's teaching time information is less than the preset value, generate teacher management information.

9. The ecological classroom teaching system based on smart learning according to claim 1 is characterized by: The specific process of obtaining the classroom management information is as follows: Extracting classroom related information, the classroom related information includes first classroom information and second classroom information; The first classroom information is the classroom information when there is no one teaching, including the duration of the course interface and the volume of classroom voices; When the duration of the course interface stays less than the preset value or the volume of classroom voices exceeds the preset value, classroom management information is generated; The second classroom information is classroom information when someone is teaching, including teacher's speaking speed information and teacher's voice clarity; When the teacher's speaking speed information exceeds the preset range or the teacher's voice clarity is less than the preset value, classroom management information is generated.

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