Block chain-based after-class service open data platform

Abstract

The invention relates to the technical field of management, and particularly discloses an after-class service open data platform based on a block chain, which is used for solving the problems of information asymmetry, resource waste and increased supervision difficulty caused by difficulty in data sharing and circulation in the existing after-class service industry. According to the method, the after-class service data is collected and uploaded, the first block chain, the second block chain and the third block chain are respectively constructed, the third circulation data in the after-class service data are extracted, the data circulation efficiency analysis model is constructed, and the data circulation efficiency is calculated; a first behavior evaluation model is constructed based on the first learning behavior data in the after-class service data to evaluate the curriculum knowledge absorption rate of the student, and a second learning effect evaluation model is constructed based on the second learning effect data to evaluate the curriculum effect value of the student, so that the data barrier of the after-class service industry can be broken, and the efficiency of the after-class service industry can be improved. The data value is improved, and data interconnection and intercommunication are promoted.

Description

Technical Field

[0001] The present invention relates to the field of management technology, and more specifically, to an after-school service open data platform based on blockchain. Background Art

[0002] In the current after-school service industry, the phenomenon of data silos is prevalent, that is, each participant (such as platforms, course providers, teachers, schools, parents, education bureaus, the Ministry of Education, etc.) each has its own data, but these data are often difficult to share and circulate, resulting in information asymmetry, waste of resources, increased difficulty in supervision and other problems, which seriously hinders the healthy development of the industry.

[0003] Blockchain is a distributed ledger technology, and its core features include decentralization, immutability, traceability and transparency. Blockchain, as a peer-to-peer trusted data network technology for building decentralized distributed storage, provides technical support for building trusted, end-to-end data security sharing. Blockchain enables users to establish end-to-end trusted value transfer between incompletely trusted nodes without relying on third-party trusted nodes, because the data transmitted and transferred by users on the blockchain network are open and transparent. In order to protect the user's sensitive privacy data, the privacy data is often encrypted before transmission. The existing after-school service technology system involves a large amount of personal information and sensitive data, such as students' learning records, teachers' qualification certificates, etc. The security and privacy protection of these data are often difficult to be fully guaranteed, and there is a risk of data leakage and abuse; and due to the lack of an effective verification mechanism, the participants of after-school services may have the problem of falsifying qualifications, which not only affects the quality of the service, but also brings difficulties to the supervision of government regulatory departments. In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an after-school service open data platform based on blockchain, which calculates the data circulation efficiency by extracting the third circulation data in the after-school service data and constructing a data circulation efficiency analysis model, constructs a first behavior evaluation model based on the first learning behavior data in the after-school service data, and evaluates the students' course knowledge absorption rate, and constructs a second learning effect evaluation model based on the second learning effect data, and evaluates the students' course effect value, which helps to break the data barriers in the after-school service industry and enhance the data value, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An after-school service open data platform based on blockchain includes an after-school service sharing terminal, an after-school service access terminal, a blockchain architecture terminal, a data chain management center and an after-school service evaluation center. The after-school service sharing terminal is used to collect and upload after-school service data and manage the authorization request of the after-school service data owner for data sharing; the data chain management center is used to extract the third flow data in the after-school service data to build a data flow efficiency analysis model and calculate the data flow efficiency; the data chain management center includes a data flow efficiency analysis module, which is used to extract the third flow data and import the flow data upload quantity, flow data upload time, flow data access time and flow data throughput quantity in the third flow data into the data flow efficiency analysis model to calculate the data flow efficiency. The formula of the data flow efficiency analysis model is:

[0007]

[0008] Where: x sz For data flow efficiency, is the duration of streaming data upload for the i-th streaming data upload quantity, The duration of uploading the i-1th stream data. is the maximum streaming data upload duration, is the maximum streaming data upload duration, μ is the adjustment parameter to prevent the denominator from being zero, is the access time of the streaming data for the i-th streaming data upload quantity, The access time of the streaming data for the i-1th streaming data upload quantity, is the maximum access time of streaming data, is the minimum flow data access time, t h is the throughput of streaming data, and n is the number of streaming data uploads.

[0009] Preferably, the after-school service access end is used to provide authorized users with the function of accessing and querying after-school service data; the blockchain architecture end is used to construct the first blockchain, the second blockchain and the third blockchain respectively; the after-school service evaluation center is used to extract the first learning behavior data and the second learning effect data in the after-school service data respectively, construct a first behavior evaluation model based on the first learning behavior data, and evaluate the students' course knowledge absorption rate; and construct a second learning effect evaluation model based on the second learning effect data, and evaluate the students' course effect value.

[0010] Preferably, the blockchain architecture end includes a first blockchain architecture module, a second blockchain architecture module and a third blockchain architecture module; the after-school service evaluation center includes a first behavior evaluation module and a second effect evaluation module.

[0011] Preferably, the after-school service data include first learning behavior data, second learning effect data and third flow data; the first learning behavior data include students' single learning time, learning frequency and single course participation; the second learning effect data include students' historical grades, course assignment completion rate and course attendance rate; the third flow data include the number of flow data uploads, flow data upload time, flow data access time and flow data throughput.

[0012] Preferably, the first blockchain architecture module is used to obtain the first learning behavior data, and summarize the student's single learning time, learning frequency and single course participation in the first learning behavior data into the first block, the second block and the third block in the first blockchain respectively;

[0013] The second blockchain architecture module is used to obtain the second learning effect data, and summarize the student's historical grades, coursework completion rate, and course attendance rate in the second learning effect data into the first block, the second block, and the third block in the second blockchain respectively;

[0014] The third blockchain architecture module is used to obtain the third circulation data, and summarize the circulation data upload quantity, circulation data upload time, circulation data access time and circulation data throughput quantity in the third circulation data into the first block, the second block and the third block in the third blockchain.

[0015] Preferably, the first behavior evaluation module is used to extract the first learning behavior data, import the student's single learning time, learning frequency and single course participation in the first learning behavior data into the first behavior evaluation model, and evaluate the student's course knowledge absorption rate. The formula of the first behavior evaluation model is:

[0016]

[0017] Where: x sa is the student's course knowledge absorption rate, A is the total number of studies, and f a For learning frequency, is the maximum length of a single learning session, is the minimum value of a single learning time. is the duration of the a-th study, is the a-1th learning duration, μ is the adjustment parameter to prevent the denominator from being zero, is the participation rate of the ath course, is the participation rate of the a-1 course, is the maximum value of course participation, is the minimum value for course participation.

[0018] Preferably, the second effect evaluation module is used to extract the second learning effect data, import the student's historical grades, coursework completion rate and course attendance rate in the second learning effect data into the second learning effect evaluation model, and evaluate the student's course effect value. The formula of the second learning effect evaluation model is:

[0019]

[0020] Where: x sj is the course effect value for students, is the historical score of the student in the jth test, is the student's history score for the J-1 exam, is the historical average score, m is the number of exams, and w is the k is the course completion rate, w c For course attendance.

[0021] The technical effects and advantages of an after-school service open data platform based on blockchain in the present invention: the present invention collects and uploads after-school service data, constructs a first blockchain, a second blockchain and a third blockchain respectively, extracts the third flow data in the after-school service data and constructs a data flow efficiency analysis model, calculates the data flow efficiency, constructs a first behavior evaluation model based on the first learning behavior data in the after-school service data, evaluates the students' course knowledge absorption rate, constructs a second learning effect evaluation model based on the second learning effect data, and evaluates the students' course effect value, which helps to break the data barriers in the after-school service industry, enhances the data value, promotes data interconnection and interoperability, and promotes the healthy and rapid development of the industry; can prevent the data and qualifications of the participants in the after-school service from being falsified, and is convenient for management and supervision by government regulatory departments; helps to break the data islands, connect the upstream and downstream of the industry, realize information interaction and trust consensus, and improve data flow efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the structure of an after-school service open data platform based on blockchain provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0024] Figure 1 The following is a schematic diagram showing the structure of an after-school service open data platform based on blockchain provided by the first embodiment of the present invention. Figure 1 As shown, in this embodiment, an after-school service open data platform based on blockchain includes an after-school service sharing terminal, an after-school service access terminal, a blockchain architecture terminal, a data chain management center and an after-school service evaluation center; the after-school service sharing terminal is connected to the after-school service access terminal, the blockchain architecture terminal, the data chain management center and the after-school service evaluation center respectively, the after-school service access terminal is connected to the after-school service sharing terminal, the data chain management center and the after-school service evaluation center respectively, and the blockchain architecture terminal is connected to the data chain management center and the after-school service evaluation center respectively.

[0025] The after-school service sharing terminal is used to collect and upload after-school service data, and manage the authorization requests for data sharing from after-school service data owners.

[0026] The after-school service access terminal is used to provide authorized users with the function of accessing and querying after-school service data.

[0027] The blockchain architecture end is used to construct the first blockchain, the second blockchain and the third blockchain respectively.

[0028] The data chain management center is used to extract the third-party circulation data from the after-school service data to build a data circulation efficiency analysis model and calculate the data circulation efficiency.

[0029] The after-class service evaluation center is used to extract the first learning behavior data and the second learning effect data from the after-class service data respectively, construct a first behavior evaluation model based on the first learning behavior data to evaluate the students' course knowledge absorption rate, and construct a second learning effect evaluation model based on the second learning effect data to evaluate the students' course effect value.

[0030] Among them, the after-school service sharing end includes data upload module, data authorization management module and API interface.

[0031] The blockchain architecture end includes a first blockchain architecture module, a second blockchain architecture module and a third blockchain architecture module.

[0032] The data chain management center includes a data encryption management module and a data flow efficiency analysis module.

[0033] The after-school service evaluation center includes a first behavior evaluation module and a second effect evaluation module.

[0034] Among them, the data upload module is used to collect and upload after-school service data.

[0035] The data authorization management module is used to manage the authorization requests for data sharing from after-school service data owners, ensuring that data can only be accessed after authorization.

[0036] The API interface is used to provide a standardized API interface to facilitate participants to upload and share after-school service data.

[0037] The after-school service data includes first learning behavior data, second learning effect data and third flow data.

[0038] The first learning behavior data includes the student’s single learning duration, learning frequency, and single course participation.

[0039] The second learning effect data includes students’ historical grades, course completion rate, and course attendance rate.

[0040] The third flow data includes the flow data upload quantity, the flow data upload duration, the flow data access duration and the flow data throughput quantity.

[0041] The first learning behavior data includes the student's single learning time, learning frequency and single course participation; among them, the student's single learning time is calculated by recording the start time when the student logs into the course and the end time when the student exits the course; the learning frequency is obtained by counting the number of times the student studies within a fixed time period; the single course participation is obtained by recording the student's interactive behavior in the course, such as the number of questions asked, the number of questions answered, and the number of times participated in discussions.

[0042] The second learning effect data includes students' historical grades, course assignment completion rate and course attendance rate; among them, students' historical grades are students' test scores within a fixed time period; course assignment completion rate is obtained by recording the submission of each assignment by students, calculating the number of submitted assignments and the total number of assignments, and obtaining the assignment completion rate; course attendance rate is calculated through attendance records.

[0043] The third circulation data includes the number of circulation data uploads, the circulation data upload time, the circulation data access time and the circulation data throughput; among them, the circulation data upload time is the time required for the data to be successfully uploaded to the third blockchain architecture module; the circulation data access time is the time from the user making a data access request to obtaining the data; the circulation data throughput is the amount of data that the blockchain network can process per unit time, including data upload, storage, sharing and access.

[0044] By analyzing students' single learning time, learning frequency and course participation, it is helpful to understand each student's learning habits and behavior patterns, so as to customize personalized teaching plans for them and improve learning effects. The teaching content can be adjusted according to students' interactive behaviors in the course (such as the number of questions asked, the number of questions answered, etc.) to ensure that the course content is more in line with students' needs and interests, and to improve the attractiveness and participation of the course; data such as historical grades, course completion rate and course attendance rate can help educational institutions evaluate the quality of teaching, find deficiencies in teaching, and thus continuously improve teaching methods and strategies. Data such as historical grades, course completion rate and course attendance rate can help educational institutions evaluate the quality of teaching, find deficiencies in teaching, and thus continuously improve teaching methods and strategies; data such as historical grades, course completion rate and course attendance rate can help educational institutions evaluate the quality of teaching, find deficiencies in teaching, and thus continuously improve teaching methods and strategies, optimize the process of data uploading and access, reduce the time of data circulation, improve the efficiency of data processing, ensure that educational data can be transmitted and used in a timely manner, and provide support for educational decision-making.

[0045] The first blockchain architecture module is used to obtain the first learning behavior data, and summarize the student's single learning time, learning frequency and single course participation in the first learning behavior data into the first block, the second block and the third block in the first blockchain.

[0046] The second blockchain architecture module is used to obtain the second learning effect data, and summarize the students' historical grades, course assignment completion rate and course attendance rate in the second learning effect data into the first block, the second block and the third block in the second blockchain respectively.

[0047] The third blockchain architecture module is used to obtain the third circulation data, and summarize the circulation data upload quantity, circulation data upload time, circulation data access time and circulation data throughput quantity in the third circulation data into the first block, the second block and the third block in the third blockchain.

[0048] The distribution of various types of data in different blockchain modules helps to improve data security. Even if one module is attacked, the data in other modules can still remain safe, and the privacy of sensitive data can also be better protected. Blockchain technology itself has the characteristics of decentralization and immutability, ensuring the integrity and reliability of data. Storing different types of data in different blocks can effectively prevent data from being tampered with and improve data credibility. Different types of data are distributed in different blocks and can be optimized and managed according to their respective characteristics. For example, learning behavior data, learning effect data and flow data each have different processing and analysis requirements. Block management can improve the efficiency and effectiveness of data processing. Storing learning behavior data, learning effect data and flow data separately can perform more detailed analysis on different types of data. For example, students' learning habits can be analyzed specifically for learning behavior data, grades and homework completion can be evaluated for learning effect data, and blockchain network performance can be optimized for flow data. By analyzing data in different blocks, more accurate and detailed information support can be provided for educational decision-making. For example, analyzing learning behavior data can optimize teaching strategies, analyzing learning effect data can improve the evaluation system, and analyzing flow data can improve the performance and reliability of blockchain systems.

[0049] The data encryption management module is used to implement data encryption and privacy protection measures to ensure data security.

[0050] The data flow efficiency analysis module is used to extract the third flow data, and import the flow data upload quantity, flow data upload duration, flow data access duration and flow data throughput quantity in the third flow data into the data flow efficiency analysis model to calculate the data flow efficiency. The formula of the data flow efficiency analysis model is:

[0051]

[0052] Where: x sz For data flow efficiency, is the duration of streaming data upload for the i-th streaming data upload quantity, The duration of uploading the i-1th stream data. is the maximum streaming data upload duration, is the maximum streaming data upload duration, μ is the adjustment parameter to prevent the denominator from being zero, is the access time of the streaming data for the i-th streaming data upload quantity, The access time of the streaming data for the i-1th streaming data upload quantity, is the maximum access time of streaming data, is the minimum flow data access time, t his the throughput of streaming data, and n is the number of streaming data uploads.

[0053] The data encryption management module ensures that data is protected during storage and transmission to prevent unauthorized access and data leakage. Even if the data is intercepted, it cannot be interpreted, thereby protecting the security of sensitive information. Through encryption and signature technology, the integrity of data during transmission can be ensured to prevent data from being tampered with or damaged, thereby ensuring the reliability of data. The data flow efficiency analysis module can discover and solve bottlenecks in the system through analysis of data flow efficiency, improve the efficiency of data upload and access, and optimize system performance. Analysis of data flow efficiency can help manage and allocate system resources, ensure efficient operation of the blockchain network, and reduce resource waste. The data encryption management module ensures that data is protected during storage and transmission to prevent unauthorized access and data leakage. Even if the data is intercepted, it cannot be interpreted, thereby protecting the security of sensitive information.

[0054] The first behavior evaluation module is used to extract the first learning behavior data, import the student's single learning time, learning frequency and single course participation in the first learning behavior data into the first behavior evaluation model, and evaluate the student's course knowledge absorption rate. The formula of the first behavior evaluation model is:

[0055]

[0056] Where: x sa is the student's course knowledge absorption rate, A is the total number of studies, and f a For learning frequency, is the maximum length of a single learning session, is the minimum value of a single learning time. is the duration of the a-th study, is the a-1th learning duration, μ is the adjustment parameter to prevent the denominator from being zero, is the participation rate of the ath course, is the participation rate of the a-1 course, is the maximum value of course participation, is the minimum value for course participation.

[0057] The first behavior assessment model takes into account data from multiple dimensions such as learning time, learning frequency, and course participation. By comprehensively analyzing these data, students' learning behavior and knowledge absorption can be comprehensively assessed. By analyzing students' course knowledge absorption rate, problems in the learning process can be accurately identified, such as insufficient learning frequency, insufficient learning time, or low participation, so that targeted improvement measures can be taken. By introducing the first behavior assessment module and using the assessment model, a comprehensive assessment of students' course knowledge absorption rate can be achieved, teaching strategies can be optimized, learning efficiency can be improved, and a scientific basis can be provided for educational decision-making, ultimately improving teaching quality and students' learning outcomes.

[0058] The second effect evaluation module is used to extract the second learning effect data, import the student's historical grades, coursework completion rate and course attendance rate in the second learning effect data into the second learning effect evaluation model, and evaluate the student's course effect value. The formula of the second learning effect evaluation model is:

[0059]

[0060] Where: x sj is the course effect value for students, is the historical score of the student in the jth test, is the student's history score for the J-1 exam, is the historical average score, m is the number of exams, and w is the k is the course completion rate, w c For course attendance.

[0061] By considering students' historical grades, course completion rates and course attendance rates, a comprehensive evaluation of students' learning outcomes is provided, fully reflecting students' performance in the course; by analyzing the dynamic changes in students' test scores, it is possible to effectively track students' learning progress, identify progress or regression in the learning process, and provide a basis for timely adjustment of teaching strategies; the evaluation model takes course completion rates and course attendance rates into consideration, which can motivate students to actively participate in the course, improve homework completion rates and attendance rates, and thus improve the overall learning outcomes; analyzing students' attendance rates and homework completion rates can help teachers and parents identify problems with students' learning attitudes, provide corresponding guidance and support, and promote students' all-round development; by introducing the second effect evaluation module and using the evaluation model, a comprehensive and dynamic evaluation of students' course outcomes can be achieved, teaching strategies can be optimized, students' participation and learning outcomes can be improved, a scientific basis can be provided for educational decision-making, and ultimately the overall teaching quality and students' learning outcomes can be improved.

[0062] The embodiment of the present invention collects and uploads after-school service data, constructs a first blockchain, a second blockchain and a third blockchain respectively, extracts the third flow data in the after-school service data and constructs a data flow efficiency analysis model, calculates the data flow efficiency, constructs a first behavior evaluation model based on the first learning behavior data in the after-school service data, evaluates the students' course knowledge absorption rate, constructs a second learning effect evaluation model based on the second learning effect data, and evaluates the students' course effect value. This helps to break the data barriers in the after-school service industry, enhance the value of data, promote data interconnection, and promote the healthy and rapid development of the industry; it can prevent the data and qualifications of the participants in the after-school service from being falsified, and facilitates the management and supervision of government regulatory departments; it helps to break the data islands, connect the upstream and downstream of the industry, realize information interaction and trust consensus, and improve data flow efficiency.

[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An after-school service open data platform based on blockchain, including an after-school service sharing terminal, an after-school service access terminal, a blockchain architecture terminal, a data chain management center and an after-school service evaluation center, characterized in that: The after-school service sharing terminal is used to collect and upload after-school service data, and manage the authorization request of the owner of after-school service data for data sharing; the data chain management center is used to extract the third flow data in the after-school service data to construct a data flow efficiency analysis model, and calculate the data flow efficiency; the data chain management center includes a data flow efficiency analysis module, and the data flow efficiency analysis module is used to extract the third flow data, and import the flow data upload quantity, flow data upload time, flow data access time and flow data throughput quantity in the third flow data into the data flow efficiency analysis model to calculate the data flow efficiency. The formula of the data flow efficiency analysis model is: Where: x sz For data flow efficiency, is the duration of streaming data upload for the i-th streaming data upload quantity, The duration of uploading the i-1th stream data. is the maximum streaming data upload duration, is the maximum streaming data upload duration, μ is the adjustment parameter to prevent the denominator from being zero, is the access time of the streaming data for the i-th streaming data upload quantity, The access time of the streaming data for the i-1th streaming data upload quantity, is the maximum flow data access duration, is the minimum flow data access time, t h is the throughput of streaming data, and n is the number of streaming data uploads.

2. According to the blockchain-based after-school service open data platform of claim 1, it is characterized in that: The after-school service access terminal is used to provide authorized users with the function of accessing and querying after-school service data; the blockchain architecture terminal is used to construct a first blockchain, a second blockchain and a third blockchain respectively; the after-school service evaluation center is used to extract first learning behavior data and second learning effect data from the after-school service data respectively, and construct a first behavior evaluation model based on the first learning behavior data to evaluate the students' course knowledge absorption rate; and construct a second learning effect evaluation model based on the second learning effect data to evaluate the students' course effect value.

3. According to the blockchain-based after-school service open data platform of claim 2, it is characterized in that: The blockchain architecture end includes a first blockchain architecture module, a second blockchain architecture module and a third blockchain architecture module; the after-school service evaluation center includes a first behavior evaluation module and a second effect evaluation module.

4. According to the blockchain-based after-school service open data platform of claim 1, it is characterized in that: The after-school service data includes first learning behavior data, second learning effect data and the third flow data; the first learning behavior data includes the student's single learning time, learning frequency and single course participation; the second learning effect data includes the student's historical grades, course assignment completion rate and course attendance rate; the third flow data includes the number of flow data uploads, flow data upload time, flow data access time and flow data throughput.

5. According to the blockchain-based after-school service open data platform of claim 3, it is characterized in that: The first blockchain architecture module is used to obtain first learning behavior data, and summarize the student's single learning time, learning frequency, and single course participation in the first learning behavior data into the first block, the second block, and the third block in the first blockchain; The second blockchain architecture module is used to obtain second learning effect data, and to summarize the student's historical grades, coursework completion rate, and course attendance rate in the second learning effect data into the first block, the second block, and the third block in the second blockchain respectively; The third blockchain architecture module is used to obtain the third circulation data, and respectively summarize the circulation data upload quantity, circulation data upload duration, circulation data access duration and circulation data throughput quantity in the third circulation data into the first block, the second block and the third block in the third blockchain.

6. According to claim 3, the after-school service open data platform based on blockchain is characterized in that: The first behavior evaluation module is used to extract the first learning behavior data, import the student's single learning time, learning frequency and single course participation in the first learning behavior data into the first behavior evaluation model, and evaluate the student's course knowledge absorption rate. The formula of the first behavior evaluation model is: Where: x sa is the student's course knowledge absorption rate, A is the total number of studies, and f a For learning frequency, is the maximum length of a single learning session, is the minimum value of a single learning time. is the duration of the a-th study, is the a-1th learning duration, μ is the adjustment parameter to prevent the denominator from being zero, is the participation rate of the ath course, is the participation rate of the a-1 course, is the maximum value of course participation, is the minimum value for course participation.

7. According to claim 3, the after-school service open data platform based on blockchain is characterized in that: The second effect evaluation module is used to extract the second learning effect data, import the student's historical grades, course homework completion rate and course attendance rate in the second learning effect data into the second learning effect evaluation model, and evaluate the student's course effect value. The formula of the second learning effect evaluation model is: Where: is the course effect value for students, is the historical score of the student in the jth test, is the student's history score for the J-1 exam, is the historical average score, is the number of examinations, is the course completion rate, For course attendance.

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