Digitization-based teaching system

Abstract

The invention discloses a digitization-based teaching system, and the system comprises a data processing module which is used for collecting and integrating teaching design related data and student assessment score data, and carrying out the preprocessing of the collected data; and the evaluation analysis module performs multi-aspect evaluation analysis based on the student assessment score data. According to the method, the score rates of the students on different questions are obtained, so that a teacher can know the mastering condition of the students on each knowledge point, personalized evaluation and guidance are carried out, the students are helped to improve academic records in a targeted manner, and comprehensive evaluation indexes corresponding to the achievement degrees of the courses are obtained by analyzing the score rates of the students in the courses. Therefore, the performance of the student on different questions and different courses can be comprehensively considered, the learning condition of the student is more comprehensively evaluated, and misjudgment possibly caused by one-sidedness evaluation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of teaching evaluation, and in particular to a teaching system based on digitization. Background Art

[0002] In today's world, scientific and technological progress is changing with each passing day. Modern information technologies such as the Internet, cloud computing, and big data are profoundly changing the way people think, produce, live, and learn. In response to the development of information technology, promoting educational reform and innovation, building a networked, digital, personalized, and lifelong education system, and building a learning society where "everyone can learn, learn everywhere, and learn at any time" are major issues facing educators. The digitalization of education will deepen the transformation of my country's educational concepts. The digitalization of education is not simply to make traditional education methods online or video-based, but to transform traditional empirical teaching into digital education based on data interaction and information evaluation through the collection, analysis, and application of big data of the entire teaching process, so as to promote education and learning activities everywhere and at all times. Therefore, a teaching evaluation model and system based on digitalization is needed;

[0003] However, the current technology of process evaluation has one-sided data and is difficult to track students' learning trajectory; evaluation analysis is difficult to implement, and paper materials are difficult to compare and analyze; evaluation efficiency is difficult to improve, and conversion into objective indicators and mining valuable information have become bottlenecks. Obviously, this evaluation system has at least the following problems:

[0004] 1. In process evaluation, it is difficult to fully record and reflect the various behaviors and performances of students in the learning process. Therefore, the evaluation is usually based on explicit data such as students' test scores at important learning nodes. Although these data are easy to obtain, they are often one-sided, static, and coarse-grained. These data are not enough to fully, dynamically, and accurately reflect the students' learning growth trajectory, nor can they fully reflect the students' learning process and development. Therefore, it is difficult to track and analyze the evaluation process;

[0005] 2. At present, process evaluation is often carried out in the form of student growth portfolios. This method requires the collection, organization, analysis and evaluation of a large amount of paper materials, which is not only time-consuming and labor-intensive, but also difficult to conduct comparative analysis. This evaluation method has high implementation costs, high analysis difficulty, and is difficult to carry out on a regular basis. Therefore, the implementation of evaluation analysis is difficult, and more effective evaluation models and tools need to be improved and introduced;

[0006] 3. The difficulty in improving evaluation effectiveness is mainly due to the difficulty in converting realistic materials into quantifiable indicators, the challenge of integrating and analyzing different types of data, and the difficulty in extracting valuable information from massive data. Solving these problems requires the comprehensive use of technical means, professional knowledge and innovative thinking to improve the application effect of process evaluation. Summary of the invention

[0007] The purpose of the present invention is to provide a digital-based teaching system to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A digital teaching system, including

[0010] A data processing module, which is used to collect and integrate teaching design related data and student assessment score data, and perform preprocessing operations on the collected data;

[0011] An evaluation and analysis module, which performs multi-faceted evaluation and analysis based on student assessment score data, including individual course goal achievement, course goal achievement, achievement rate analysis, deviation acquisition, and teacher teaching model evaluation analysis, with real-time data transmission between analysis units;

[0012] A machine learning module, wherein the machine learning module uses a machine learning algorithm to learn historical teaching evaluation data, predicts and optimizes current teaching evaluation data based on the learning results, and automatically adjusts data weights;

[0013] A visualization module, which displays teaching evaluation results in the form of intuitive charts and can customize the display content according to user needs;

[0014] The authority management module supports multiple types of user logins and grants corresponding authority according to user levels.

[0015] In the present invention, the data processing module can collect teaching design related data, including training objectives, graduation requirements, curriculum system, support matrix Q of graduation requirements for training objectives, support matrix C of curriculum for graduation requirements and curriculum objectives in the talent training program. It can also collect students' score data on each question in the assessment link, and integrate data from different data sources to ensure data consistency to support subsequent analysis and evaluation processes.

[0016] In the present invention, the evaluation and analysis module includes

[0017] The score acquisition unit uses the student score data to calculate the score rate of each student in each course for each question based on multiple courses, multiple students, and multiple questions.

[0018] The individual course goal achievement analysis unit obtains the student's individual evaluation index by substituting the score rate into the score rate calculation formula, and then compares it with the preset standard to determine whether the student's course goal has been achieved;

[0019] The scoring rate calculation formula is as follows:

[0020]

[0021] Among them, i represents the number corresponding to each student, k represents the number corresponding to each question, j represents the number corresponding to each course, σ represents the correction factor corresponding to the student's question score rate, and y i j Represents a personal assessment indicator.

[0022] In the present invention, the evaluation and analysis module also includes

[0023] The course goal achievement analysis unit obtains the course comprehensive evaluation index by substituting the score rate into the evaluation index calculation formula, and compares it with the preset standard to determine whether the course major has achieved the course goal;

[0024] The achievement rate analysis unit calculates the achievement rate based on the total number of course objectives and the number of achieved objectives, and ranks the achievement rates of each course;

[0025] The evaluation index calculation formula is as follows:

[0026]

[0027] Among them, i represents the number corresponding to each student, k represents the number corresponding to each question, j represents the number corresponding to each course, and y represents the number corresponding to each student. j represents the comprehensive evaluation index, Expressed as the correction factor corresponding to the student's question score rate.

[0028] In the present invention, the evaluation and analysis module further comprises

[0029] The deviation acquisition unit acquires the deviation of each dimension of the teaching mode based on the teacher's course design information, and the dimensions include but are not limited to teaching assessment and teaching time;

[0030] The teacher teaching model evaluation analysis unit substitutes the relevant deviation of each teacher into the deviation calculation formula to obtain the teaching model evaluation index, and then ranks the teaching model evaluation index of each teacher.

[0031] In the present invention, the machine learning module uses a machine learning algorithm to learn historical teaching evaluation data, wherein the machine learning algorithm includes but is not limited to a neural network algorithm and a decision tree algorithm, and predicts and optimizes the current teaching evaluation data based on the learning results, including but not limited to predicting the achievement of course objectives and student performance trends, and then continuously updates the model parameters to improve the accuracy and efficiency of the evaluation.

[0032] In the present invention, the visualization display module includes a student course related display unit, which is used to present graduation requirement indicators, course objectives, assessment links and their weights, scores and information related to the achievement of course objectives, and draw a time series graph of the average assessment scores from the beginning of the course to the current week.

[0033] In the present invention, the visualization display module includes a course statistics and assessment link display unit, which is used to display a time series graph of the mean, variance, skewness, and kurtosis of the course goal achievement, as well as the arrangement week, type, description, and average score distribution information of the course assessment link.

[0034] In the present invention, the visualization display module also includes a teacher teaching mode display unit, which is used to display the relevant statistical values ​​of the teaching time under different course objectives, the proportion pie chart and the time series graph of the cumulative value, as well as the relevant time information of different teaching methods, and the relevant statistical values ​​of the total amount of process assessment under different course objectives, the proportion pie chart and the time series graph of the cumulative value.

[0035] In the present invention, the visualization display module further includes a course goal achievement ranking and teacher teaching mode evaluation display unit, which is used to display the cumulative statistics of the mean achieved under different course goals and its ranking in the teaching group, including the ranking of the overall cumulative mean achieved and the cumulative mean achieved in each assessment link and its trend time series diagram, as well as a trend time series diagram showing the deviation evaluation of the teacher's teaching mode in pre-class design, teaching assessment, teaching time, assessment link assessment amount and assessment difficulty and its ranking;

[0036] The deviation calculation formula is as follows:

[0037]

[0038] Among them, the deviation of teaching mode, the deviation of teaching assessment and the deviation of teaching time are respectively denoted as q′ z ′、e′ z ′ and w′ z ′, z represents the number corresponding to each teacher, It represents the teaching mode evaluation index, Δq is the deviation from the set permitted teaching mode, Δe is the deviation from the set permitted teaching assessment, and Δw is the deviation from the set permitted teaching duration.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. By obtaining the scoring rates of students on different questions, the present invention allows teachers to understand students’ mastery of various knowledge points, conduct personalized evaluation and guidance, and help students improve their academic performance in a targeted manner. By analyzing the scoring rates of each student in each course, a comprehensive evaluation index corresponding to the achievement of each course can be obtained. In this way, students’ performance on different questions and courses can be comprehensively considered, and students’ learning situation can be more comprehensively evaluated to reduce the misjudgment that may be caused by one-sided evaluation. Through comprehensive evaluation indicators, it can be judged whether each course major has achieved the course objectives. This is helpful for the teaching management and improvement of the course, and can timely discover the shortcomings of the course objectives, and make corresponding optimizations and adjustments to improve the teaching effect. It can provide more objective and accurate evaluation results, provide valuable feedback to teachers, schools and students, and promote the personalization of teaching and the scientific management of teaching.

[0041] 2. The teaching model evaluation indicators of the present invention can be provided to teachers for reference, so that they can understand how their own teaching models perform in the overall situation. Teachers can understand whether their own teaching models need to be adjusted and improved based on the results of the evaluation indicators to improve the teaching effect and students' learning outcomes. By sorting the teaching model evaluation indicators, it can be found out which teachers' teaching models perform better. These excellent teaching models can be used as objects of reference and promotion to help other teachers improve their teaching methods and models and improve the overall teaching quality. By sorting the teaching model evaluation indicators, the advantages and disadvantages of the teaching models and the room for improvement can be found, which is helpful to improve the evaluation benefits, promote educational decision-making and improve practice. The teaching model can be adjusted and improved based on the evaluation results to improve the teaching effect and students' learning outcomes. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of module connection of a digital teaching system according to the present invention;

[0043] Figure 2 This is a schematic diagram of an evaluation and analysis module of a digital teaching system according to the present invention;

[0044] Figure 3 The figure is a schematic diagram of a visualization display module of a digital-based teaching system of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1-3 As shown, the present invention provides a technical solution:

[0047] A digital teaching system, including

[0048] A data processing module, which is used to collect and integrate teaching design related data and student assessment score data, and perform preprocessing operations on the collected data;

[0049] In this embodiment, the data processing module can collect teaching design related data, including training objectives, graduation requirements, curriculum system, support matrix Q of graduation requirements for training objectives, support matrix C of courses for graduation requirements and curriculum objectives in the talent training program. It can also collect students' score data on each question in the assessment phase and integrate data from different data sources to ensure data consistency to support subsequent analysis and evaluation processes.

[0050] An evaluation and analysis module, which performs multi-faceted evaluation and analysis based on student assessment score data, including individual course goal achievement, course goal achievement, achievement rate analysis, deviation acquisition, and teacher teaching model evaluation analysis, with real-time data transmission between analysis units;

[0051] The evaluation and analysis module includes

[0052] The score rate acquisition unit calculates the score rate of each student in each course for each question using the student score data based on multiple courses, multiple students and multiple questions;

[0053] Specifically, collect the score data of each student in each course. These data can come from the students' test or quiz scores, or other forms of assessment and testing. Organize the collected data into a table or data set, with each row representing a student and each column representing a question. In the table, fill in the student's score on each question. For each question, calculate the proportion or percentage of the student's score. The following formula can be used to calculate the score rate: score rate = (student score / full score of the question) * 100%. For each course and question, calculate the average score rate of all students on the question. The average value or other statistical methods can be used for calculation.

[0054] The individual course goal achievement analysis unit obtains the student's individual evaluation index by substituting the score rate into the score rate calculation formula, and then compares it with the preset standard to determine whether the student's course goal has been achieved;

[0055] The scoring rate calculation formula is as follows:

[0056] The corresponding score rate of each student in each course on each question is recorded as Among them, i represents the number corresponding to each student, i=1,2......n, k represents the number corresponding to each question, k=1,2......m, j represents the number corresponding to each course, j=1,2......u, substitute into the calculation formula In the above example, we can obtain the personal evaluation index y corresponding to the goal achievement of each student in each course. i j , where σ represents the correction factor corresponding to the student’s question score rate.

[0057] Determine whether the student's course objectives have been achieved. The specific determination process is as follows:

[0058] The personal assessment index corresponding to the goal achievement of each student in each course is compared with the personal assessment index corresponding to the preset standard student goal achievement. If the personal assessment index corresponding to the goal achievement of a student in a course is less than the personal assessment index corresponding to the preset standard student goal achievement, it is judged that the course goal of the student in the course has not been achieved. If the personal assessment index corresponding to the goal achievement of a student in a course is greater than or equal to the personal assessment index corresponding to the preset standard student goal achievement, it is judged that the course goal of the student in the course has been achieved.

[0059] By obtaining the score rates of students on different questions, teachers can understand the students' mastery of various knowledge points, conduct personalized evaluation and guidance, and help students improve their academic performance in a targeted manner. By analyzing the score rates of each student in each course, the comprehensive evaluation indicators corresponding to the achievement of each course can be obtained. In this way, the performance of students on different questions and different courses can be comprehensively considered, and the students' learning situation can be more comprehensively evaluated, which can reduce the misjudgment that may be caused by one-sided evaluation. Through comprehensive evaluation indicators, it can be judged whether each course major has achieved the course objectives. This is helpful for the teaching management and improvement of the course, and can timely discover the shortcomings of the course objectives, and make corresponding optimization and adjustments to improve the teaching effect. It can provide more objective and accurate evaluation results, provide valuable feedback for teachers, schools and students, and promote the personalization of teaching and the scientific management of teaching.

[0060] The course goal achievement analysis unit obtains the course comprehensive evaluation index by substituting the score rate into the evaluation index calculation formula, and compares it with the preset standard to determine whether the course major has achieved the course goal;

[0061] The calculation formula of the evaluation index is as follows:

[0062] The corresponding score rate of each student in each course on each question is recorded as Among them, i represents the number corresponding to each student, i=1,2......n, k represents the number corresponding to each question, k=1,2......m, j represents the number corresponding to each course, j=1,2......u, substitute into the calculation formula The comprehensive evaluation index y corresponding to the achievement of each course is obtained j ,in, Expressed as the correction factor corresponding to the student's question score rate.

[0063] Determine whether each course major has achieved the course objectives. The specific determination process is as follows:

[0064] The comprehensive evaluation indicators corresponding to the achievement of each course are compared with the comprehensive evaluation indicators corresponding to the preset standard course achievement. If the comprehensive evaluation indicator corresponding to the achievement of a course is less than the comprehensive evaluation indicator corresponding to the preset standard course achievement, it is determined that the course major has not achieved the course objectives. If the comprehensive evaluation indicator corresponding to the achievement of a course is greater than or equal to the comprehensive evaluation indicator corresponding to the preset standard course achievement, it is determined that the course major has achieved the course objectives. In this way, it is determined whether each course major has achieved the course objectives.

[0065] The achievement rate analysis unit calculates the achievement rate based on the total number of course objectives and the number of achieved objectives, and ranks the achievement rates of each course;

[0066] Analyze the achievement rate of each course. The specific analysis process is as follows:

[0067] Count the total number of course objectives corresponding to each course major, recorded as h j According to the achievement of course objectives of each course major, the number of course objectives achieved by each course major is counted, recorded as s j , substitute into the calculation formula The achievement rate of each course is obtained

[0068] The achievement rate of each course is ranked in ascending order.

[0069] It should be noted that the achievement rate of each course is ranked in ascending order and displayed on the existing teaching resource platform.

[0070] The deviation acquisition unit acquires the deviation of each dimension of the teaching mode based on the teacher's course design information, and the dimensions include but are not limited to teaching assessment and teaching time;

[0071] Method 1: First, compare the teacher's teaching model with the set standard. For example, the standard requires the use of an interactive teaching model, but the teacher uses a traditional lecture model. Then, based on the comparison results, a binary variable (0 or 1) can be used to indicate whether it deviates from the standard, for example, 1 indicates deviation and 0 indicates compliance with the standard. Finally, calculate the deviation of the teaching model. Deviation indicators such as average deviation, proportional deviation, etc. can be used.

[0072] Method 2: First, compare the teacher's teaching assessment plan with the set standards. For example, the standard requires multiple assessment methods, but the teacher only uses one assessment method. Then, based on the comparison results, a binary variable (0 or 1) can be used to indicate whether it deviates from the standard, for example, 1 indicates deviation and 0 indicates compliance with the standard. Finally, calculate the deviation of the teaching assessment. Deviation indicators such as average deviation, proportional deviation, etc. can be used.

[0073] Method 3: First, compare the teacher's teaching time with the set standard. For example, the standard requires that each class should be 50 minutes long, but the teacher's teaching time is 60 minutes. Then, calculate the deviation. The difference between the actual teaching time and the standard teaching time can be used as the deviation indicator.

[0074] The teacher teaching model evaluation analysis unit substitutes the relevant deviation of each teacher into the deviation calculation formula to obtain the teaching model evaluation index, and then ranks the teaching model evaluation index of each teacher;

[0075] The teaching mode information corresponding to each teacher is analyzed. The specific analysis process is as follows:

[0076] The deviation of the teaching mode, the deviation of the teaching assessment and the deviation of the teaching time corresponding to each teacher's course combination are recorded as q′ z ′、e′ z ′ and w′ z ′, where z represents the number of each teacher, z=1,2......c, and substitute it into the deviation calculation formula The teaching mode evaluation index corresponding to the teaching mode information after combining each teacher's courses is obtained. Among them, q, e, and w represent the deviation of the standard teaching mode, the deviation of the standard teaching assessment, and the deviation of the standard teaching duration after the teacher's course combination, respectively. Δq is the deviation of the set permitted teaching mode, Δe is the deviation of the set permitted teaching assessment, and Δw is the deviation of the set permitted teaching duration.

[0077] The teaching model evaluation indicators corresponding to each teacher’s teaching model information are sorted in ascending order.

[0078] The teaching model evaluation indicators can be provided to teachers for reference to understand how their teaching model performs overall. Teachers can understand whether their teaching model needs to be adjusted and improved based on the results of the evaluation indicators to improve teaching effectiveness and student learning outcomes. By ranking the teaching model evaluation indicators, it can be found out which teachers' teaching models perform better. These excellent teaching models can be used as objects of reference and promotion to help other teachers improve their teaching methods and models and improve the overall teaching quality. By ranking the teaching model evaluation indicators, the advantages and disadvantages of the teaching model and the room for improvement can be discovered, which helps to improve the evaluation efficiency, promote educational decision-making and improve practice. The teaching model can be adjusted and improved based on the evaluation results to improve teaching effectiveness and student learning outcomes.

[0079] A machine learning module, wherein the machine learning module uses a machine learning algorithm to learn historical teaching evaluation data, predicts and optimizes current teaching evaluation data based on the learning results, and automatically adjusts data weights;

[0080] In this embodiment, the machine learning module uses a machine learning algorithm to learn historical teaching evaluation data, wherein the machine learning algorithm includes but is not limited to a neural network algorithm and a decision tree algorithm, and predicts and optimizes the current teaching evaluation data based on the learning results, including but not limited to predicting the achievement of course objectives and student performance trends, and then continuously updates the model parameters to improve the accuracy and efficiency of the evaluation.

[0081] A visualization module, which displays teaching evaluation results in the form of intuitive charts and can customize the display content according to user needs;

[0082] In this embodiment, the visual display module includes

[0083] The student course related display unit is used to present graduation requirement indicators, course objectives, assessment links and their weights, scores and information related to the achievement of course objectives, and draw a time series graph of the average assessment scores from the start of the course to the current week.

[0084] In this embodiment, firstly, data related to the achievement of course objectives are obtained from the data processing module, including graduation requirement index points, course objectives, assessment link weights, link total scores, actual average scores, etc., as well as process assessment average score data from the beginning of the course to the current week. Then these data are sorted, and the course objective related data are classified according to the course objectives, and the process assessment scores are arranged in the order of the teaching week. For the achievement of course objectives, a bar graph is used to show the achievement of different course objectives, and a Sankey diagram is used to show the relationship between the course objectives and the graduation requirement index points (such as normalized contribution value). For the process assessment scores, a line graph is used to show its changing trend with the teaching week, with the horizontal axis being the teaching week and the vertical axis being the average score.

[0085] The course statistics and assessment link display unit is used to display the time series graph of the mean, variance, skewness, and kurtosis of the course goal achievement, as well as the arrangement week, type, description, and average score distribution information of the course assessment link.

[0086] In this embodiment, the statistical indicator data such as the mean, variance, skewness, and kurtosis of the course goal achievement are obtained from the evaluation and analysis module, and the data such as the arrangement week, assessment type, assessment link description, and assessment average score distribution of the course assessment link are obtained from the data processing module. The statistical indicator data are sorted according to the order of the teaching week, and the assessment link data are integrated to combine the relevant data of the same assessment link. For statistical indicators, a line graph is used to show the changing trend of the mean, variance, skewness, and kurtosis with the teaching week. Different color lines represent different indicators. The horizontal axis is the teaching week, and the vertical axis is the corresponding indicator value. For the introduction of the assessment link, a table is used to show the information such as the arrangement week, assessment type, and assessment link description, and a histogram is used to show the distribution of the average assessment score. The horizontal axis is the score interval, and the vertical axis is the number or proportion of students.

[0087] The teacher teaching mode display unit is used to display the relevant statistical values ​​of teaching time under different course objectives, the pie chart of proportion and the time series graph of the cumulative value, as well as the relevant time information of different teaching methods, and the relevant statistical values ​​of the total amount of process assessment under different course objectives, the pie chart of proportion and the time series graph of the cumulative value.

[0088] In this embodiment, the statistical value, proportion, and cumulative value of the teaching time under different course objectives, the duration-related data of different teaching methods, and the statistical value, proportion, and cumulative value of the total process assessment under different course objectives are obtained from the data processing module. These data are classified and stored according to the course objectives. For the proportion of teaching time and the proportion of the total process assessment, a pie chart is used to show the proportional relationship under different course objectives. For the cumulative value of teaching time and the cumulative value of the total process assessment, a line graph is used to show the changing trend with the teaching week, with the horizontal axis being the teaching week and the vertical axis being the corresponding cumulative value. For the duration-related data of different teaching methods, a pie chart is also used to show the proportion, and a line graph is used to show the change of the cumulative value with the teaching week

[0089] The course goal achievement ranking and teacher teaching model evaluation display unit is used to display the cumulative statistics of the average achievement under different course goals and its ranking in the teaching group, including the ranking of the overall cumulative average achievement and the cumulative average achievement of each assessment link and their trend time series graphs, as well as a trend time series graph showing the deviation evaluation of the teacher's teaching model in pre-class design, teaching assessment, teaching time, assessment link assessment quantity and assessment difficulty and its ranking.

[0090] In this embodiment, the cumulative statistical data and ranking data of the mean values ​​achieved under different course objectives are obtained from the evaluation and analysis module, as well as the deviation evaluation and ranking data of the teacher's teaching mode in terms of pre-class design, teaching assessment, teaching time, assessment link assessment amount and assessment difficulty. These data are sorted according to the teaching week. The authority management module supports multiple types of users to log in and grants corresponding permissions according to the user level. For the course objectives to achieve the cumulative mean and ranking, a bar chart is used to show the situation under different course objectives, the column height indicates the cumulative mean, and the column is marked with a ranking. For each assessment link to achieve the cumulative mean and ranking trend, a line chart is used to show the changes with the teaching week, and different color lines represent different assessment links. For the deviation and ranking data of the teacher's teaching model, a line chart is used to show the changing trend with the teaching week, and different color lines represent different aspects (such as pre-class design deviation, teaching assessment deviation, etc.), the horizontal axis is the teaching week, and the vertical axis is the value of the deviation or ranking.

[0091] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0092] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital teaching system, characterized by: include A data processing module, which is used to collect and integrate teaching design related data and student assessment score data, and perform preprocessing operations on the collected data; An evaluation and analysis module, which performs multi-faceted evaluation and analysis based on student assessment score data, including individual course goal achievement, course goal achievement, achievement rate analysis, deviation acquisition, and teacher teaching model evaluation analysis, with real-time data transmission between analysis units; A machine learning module, wherein the machine learning module uses a machine learning algorithm to learn historical teaching evaluation data, predicts and optimizes current teaching evaluation data based on the learning results, and automatically adjusts data weights; A visualization module, which displays teaching evaluation results in the form of intuitive charts and can customize the display content according to user needs; The authority management module supports multiple types of user logins and grants corresponding authority according to user levels.

2. A digital-based teaching system according to claim 1, characterized in that: The data processing module can collect teaching design related data, including training objectives, graduation requirements, curriculum system, support matrix Q of graduation requirements for training objectives, support matrix C of curriculum for graduation requirements and curriculum objectives in the talent training program. It can also collect students' score data on each question in the assessment phase and integrate data from different data sources to ensure data consistency to support subsequent analysis and evaluation processes.

3. A digital-based teaching system according to claim 1, characterized in that: The evaluation and analysis module includes The score rate acquisition unit calculates the score rate of each student in each course for each question using the student score data based on multiple courses, multiple students and multiple questions; The individual course goal achievement analysis unit obtains the student's individual evaluation index by substituting the score rate into the score rate calculation formula, and then compares it with the preset standard to determine whether the student's course goal has been achieved; The scoring rate calculation formula is as follows: Among them, i represents the number corresponding to each student, k represents the number corresponding to each question, j represents the number corresponding to each course, σ represents the correction factor corresponding to the student's question score rate, and y i j Represents a personal assessment indicator.

4. A digital-based teaching system according to claim 1, characterized in that: The evaluation and analysis module also includes The course goal achievement analysis unit obtains the course comprehensive evaluation index by substituting the score rate into the evaluation index calculation formula, and compares it with the preset standard to determine whether the course major has achieved the course goal; The achievement rate analysis unit calculates the achievement rate based on the total number of course objectives and the number of achieved objectives, and ranks the achievement rates of each course; The evaluation index calculation formula is as follows: Among them, i represents the number corresponding to each student, k represents the number corresponding to each question, j represents the number corresponding to each course, and y represents the number corresponding to each student. j represents the comprehensive evaluation index, It is expressed as the correction factor corresponding to the student's question score rate.

5. A digital-based teaching system according to claim 1, characterized in that: The evaluation and analysis module further includes The deviation acquisition unit acquires the deviation of each dimension of the teaching mode based on the teacher's course design information, and the dimensions include but are not limited to teaching assessment and teaching time; The teacher teaching model evaluation analysis unit substitutes the relevant deviation of each teacher into the deviation calculation formula to obtain the teaching model evaluation index, and then ranks the teaching model evaluation index of each teacher.

6. A digital-based teaching system according to claim 1, characterized in that: The machine learning module uses machine learning algorithms to learn historical teaching evaluation data, where the machine learning algorithms include but are not limited to neural network algorithms and decision tree algorithms, and predicts and optimizes current teaching evaluation data based on the learning results, including but not limited to predicting the achievement of course goals and student performance trends, and then continuously updates model parameters to improve the accuracy and efficiency of the evaluation.

7. A digital-based teaching system according to claim 1, characterized in that: The visualization display module includes a student course related display unit, which is used to present graduation requirement indicators, course objectives, assessment links and their weights, scores and information related to the achievement of course objectives, and draw a time series graph of the average assessment scores from the beginning of the course to the current week.

8. A digital-based teaching system according to claim 1, characterized in that: The visualization display module includes a course statistics and assessment link display unit, which is used to display a time series graph of the mean, variance, skewness, and kurtosis of the course goal achievement, as well as the arrangement week, type, description, and average score distribution information of the course assessment link.

9. A digital-based teaching system according to claim 1, characterized in that: The visualization display module also includes a teacher teaching mode display unit, which is used to display the relevant statistical values ​​of teaching time under different course objectives, proportion pie charts and time series graphs of cumulative values, as well as relevant duration information of different teaching methods, and display the relevant statistical values ​​of the total amount of process assessment under different course objectives, proportion pie charts and time series graphs of cumulative values.

10. A digital-based teaching system according to claim 1, characterized in that: The visualization display module further includes a course goal achievement ranking and teacher teaching mode evaluation display unit, which is used to display the cumulative statistics of the mean achieved under different course goals and their rankings in the teaching group, including the rankings of the overall cumulative mean achieved and the cumulative mean achieved in each assessment link and their trend time series graphs, as well as a trend time series graph showing the deviation evaluation of the teacher's teaching mode in terms of pre-class design, teaching assessment, teaching time, assessment link assessment quantity and assessment difficulty and its ranking; The deviation calculation formula is as follows: Among them, the deviation of teaching mode, the deviation of teaching assessment and the deviation of teaching time are respectively denoted as q′ z ′、e′ z ′ and w′ z ′, z represents the number corresponding to each teacher, It represents the teaching mode evaluation index, Δq is the deviation from the set permitted teaching mode, Δe is the deviation from the set permitted teaching assessment, and Δw is the deviation from the set permitted teaching duration.

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