CN108765224A - Course classification is analyzed with the rate of attendance and device - Google Patents

Abstract

The embodiment of the present invention relates to the technical field of teaching management, and provides a method and device for analyzing course categories and attendance rates. The method includes: obtaining the statistical data of student attendance corresponding to each course category; The sum of the squares of the deviations and the sum of the squares of the deviations between groups; based on the sum of the squares of the deviations within the group and the sum of the squares of the deviations between the groups, the first attendance rate deviation and the second attendance rate deviation are calculated; if the first attendance rate deviation is less than or equal to the second If the second attendance rate deviation is the ratio of the second attendance rate deviation to the first attendance rate deviation, the attendance influence rate is obtained; if the attendance influence rate is greater than the value corresponding to the F distribution table under the preset significant level, then determine the impact of the course category on the student attendance rate influential. The embodiment of the present invention can accurately analyze whether the course category has an impact on the attendance rate, thereby instructing the school teaching management system to reasonably modify the course requirements of different course categories according to the analysis results to improve the teaching quality.

Description

Course category and attendance analysis and installation

technical field

The embodiment of the present invention relates to the technical field of teaching management, and specifically relates to a course category and attendance rate analysis and device.

Background technique

At present, the courses of college students can be divided into public compulsory courses, public elective courses, professional compulsory courses and professional elective courses. Different types of courses have different requirements and difficulties. The attendance rate of some courses is extremely low, which is usually attributed to teacher teaching. The quality is not good, but it may also be due to course requirements that some courses are not valued by students. However, it is currently impossible to accurately analyze whether the student attendance rate will be affected by the course category, making the school teaching management system unable to adjust the attendance rate in time. Low course requirements.

Contents of the invention

The purpose of the embodiment of the present invention is to provide a method and device for analyzing course category and attendance rate to determine whether course category affects student attendance rate.

In order to achieve the above object, the technical solution adopted in the embodiment of the present invention is as follows:

In the first aspect, an embodiment of the present invention provides a method for analyzing course categories and attendance rates. The method includes: obtaining student attendance statistics corresponding to each course category; The sum of the squares of the intragroup deviations of the individual errors in the category, and the sum of the squares of the squares of the intergroup deviations of the degree of difference between different course categories; Attendance rate deviation and second attendance rate deviation; if the first attendance rate deviation is less than or equal to the second attendance rate deviation, the attendance influence rate is obtained according to the ratio of the second attendance rate deviation to the first attendance rate deviation ; If the attendance impact rate is greater than the value corresponding to the F distribution table under the preset significant level, then it is determined that the course category has an impact on the student attendance rate.

In the second aspect, the embodiment of the present invention also provides a course category and attendance rate analysis device, the device includes a data acquisition module, a first calculation module, a second calculation module, a first execution module and a second execution module. Wherein, the data acquisition module is used to obtain the statistical data of student attendance corresponding to each course category; the first calculation module is used to calculate the sum of squared deviations within the group representing individual errors in the same course category according to the student attendance statistical data, And the sum of squares of the difference between different course categories; the second calculation module is used to calculate the first attendance rate deviation and the second deviation according to the square of the deviation within the group and the sum of the squares of the deviation between the groups. Attendance rate deviation; the first execution module is used to obtain the attendance influence rate according to the ratio of the second attendance rate deviation to the first attendance rate deviation if the first attendance rate deviation is less than or equal to the second attendance rate deviation ; The second execution module is used to determine that the course category has an impact on the student attendance rate if the attendance impact rate is greater than the value corresponding to the F distribution table under the preset significance level.

Compared with the prior art, the embodiment of the present invention provides a course category and attendance rate analysis and device. First, according to the student attendance statistics data corresponding to each course category, the intra-group deviation square that characterizes the individual error in the same course category is calculated. and, as well as the sum of squares of deviations between groups of differences between different course categories; then, according to the sum of squares of deviations within a group and the sum of squares of deviations between groups, the deviation of the first attendance rate and the deviation of the second attendance rate are calculated, if the second When the first attendance rate deviation is less than or equal to the second attendance rate deviation, the attendance influence rate is obtained according to the ratio of the second attendance rate deviation to the first attendance rate deviation; finally, if the attendance influence rate is greater than the value corresponding to the F distribution table under the preset significant level , then it is determined that the course category has an impact on student attendance. The embodiment of the present invention can accurately analyze whether the course category has an impact on the attendance rate, thereby instructing the school teaching management system to reasonably modify the course requirements of different course categories according to the analysis results to improve the teaching quality.

In order to make the above-mentioned objects, features and advantages of the present invention more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and thus It should be regarded as a limitation on the scope, and those skilled in the art can also obtain other related drawings based on these drawings without creative work.

Fig. 1 shows a schematic block diagram of an electronic device provided by an embodiment of the present invention.

FIG. 2 shows a flowchart of a method for analyzing course categories and attendance rates provided by an embodiment of the present invention.

Fig. 3 shows a schematic block diagram of a device for analyzing course categories and attendance rates provided by an embodiment of the present invention.

Icons: 100-electronic equipment; 101-memory; 102-storage controller; 103-processor; 200-course category and attendance rate analysis device; 201-data acquisition module; 202-first calculation module; 203-second calculation module; 204-the first execution module; 205-the second execution module.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts belong to the protection scope of the present invention.

It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

Please refer to FIG. 1 , which shows a schematic block diagram of an electronic device 100 provided by an embodiment of the present invention. The electronic device 100 may be, but is not limited to, a portable computer, a notebook computer, a desktop computer, and the like. The electronic device 100 includes a course type and attendance analysis device 200 , a memory 101 , a storage controller 102 and a processor 103 .

The components of the memory 101 , the memory controller 102 and the processor 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines. The course category and attendance rate analysis device 200 includes at least one software function that can be stored in the memory 101 in the form of software or firmware (firmware) or solidified in the operating system (operating system, OS) of the electronic device 100 module. The processor 103 is configured to execute executable modules stored in the memory 101 , such as software function modules or computer programs included in the course type and attendance rate analysis apparatus 200 .

Wherein, memory 101 can be, but not limited to, random access memory (Random Access Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-OnlyMemory, PROM), erasable In addition to read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

The processor 103 may be an integrated circuit chip with signal processing capability. Above-mentioned processor 103 can be general-purpose processor, comprises central processing unit (Central Processing Unit, CPU), network processor (NetworkProcessor, NP), speech processor and video processor etc.; Integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. Various methods, steps and logic block diagrams disclosed in the embodiments of the present invention may be implemented or executed. The general-purpose processor may be a microprocessor, or the processor 103 may be any conventional processor or the like.

first embodiment

Please refer to FIG. 2 , which shows a flowchart of a method for analyzing course categories and attendance rates provided by an embodiment of the present invention. The course category and attendance rate analysis method includes the following steps:

Step S101, obtaining the attendance statistical data of students corresponding to each course category.

In the embodiment of the present invention, the course categories include public compulsory courses, public elective courses, professional compulsory courses and professional elective courses. First, the student attendance data of each course is obtained, and the student attendance data of each course are stored in the school's teaching management system; then, the student attendance data of all courses are grouped according to the preset multiple course categories, and the obtained The statistical data of student attendance corresponding to each course category, that is to say, according to the four course categories of public compulsory courses, public elective courses, professional compulsory courses and professional elective courses, the student attendance data of all courses are divided into four groups, and each Student attendance statistics for each course category.

There are differences in the attendance rate of different course categories, but this difference is not necessarily caused by the course category, and may also be caused by the students themselves. Therefore, the attendance rate deviation of different course categories is divided into two categories, including the same course category The deviation of attendance rate between different courses, and the deviation between the overall attendance rate of different course categories, the attendance rate deviation of different courses between the same course category is caused by the individual error in the same course category (including individual differences between students and individual courses) The deviation between the overall attendance rates of different course categories is caused by the attendance rates of different course categories.

Step S102 , according to the statistical data of student attendance, calculate the sum of squares of deviation within a group representing the individual error in the same course category, and the sum of squares of deviation between groups representing the degree of difference between different course categories.

In the embodiment of the present invention, after obtaining the student attendance statistical data corresponding to each course category, first, according to the student attendance statistical data corresponding to each course category, calculate the first average attendance rate of each course category, and the first average attendance rate of all courses. The second average attendance rate, the first average attendance rate is represented by Xi, _i =1, 2, 3, 4, which refers to the average attendance rate of each course category; the second average attendance rate is represented by X, which refers to the average of all courses attendance.

Then, based on the student attendance data of each course and the second average attendance rate, the total deviation sum of squares representing the dispersion degree of all course attendance rates is calculated, and the total deviation square sum is the student attendance number of each course and the second average attendance rate The sum of squared deviations of , is an indicator describing the degree of dispersion of attendance in all courses. Specifically, based on the student attendance data of each course and the second average attendance rate, according to the first formula Calculate the total deviation sum of squares that characterizes the degree of dispersion of all course attendance rates, wherein S ² is the total deviation sum of squares, Xi is the student _attendance data of each course, is the second average attendance rate, and n is the number of courses.

Next, based on the student attendance data of each course and the first average attendance rate of each course category, the sum of squared deviations within a group representing individual errors in the same course category is calculated, and the sum of squared deviations within a group reflects the Individual error within a category, i.e. the effect of individual student and error in a course itself. Specifically, based on the student attendance data of each course and the first average attendance rate of each course category, according to the second formula Calculate the sum of squares of the individual errors in the same course category, wherein, _{within the SS group, it} is the sum of squares of the deviations in the group, _Xij is the student attendance data of the jth course under the ith course category, is the first average attendance rate of each course category, k is the number of course categories, and n is the number of courses.

Finally, according to the first average attendance rate and the second average attendance rate corresponding to each course category, the sum of squared deviations between groups representing the degree of difference between different course categories is calculated. The sum of squared deviations between groups reflects the degree of difference between each course category. The degree of variance between average attendance rates. Specifically, according to the first average attendance rate and the second average attendance rate corresponding to each course category, according to the third formula Calculate the sum of squares of variance between groups that characterizes the degree of difference between different course categories, where SS _between groups is the sum of squares of variance between groups, is the first average attendance rate for each course category, is the second average attendance rate, k is the number of course categories, and n is the number of courses.

Step S103 , calculating the first attendance rate deviation and the second attendance rate deviation according to the square of the deviation within the group and the sum of the squares of the deviation between the groups.

In the embodiment of the present invention, the first attendance rate deviation represents the average attendance rate deviation caused by individual differences of students and individual courses, and the second attendance rate deviation represents the average attendance rate deviation caused by different course categories. The calculation method of the first attendance rate deviation and the second attendance rate deviation may include:

First, according to the sum of squares of variance within a group and the sum of squares of variance between groups, according to the fourth formula Calculate the deviation of the first attendance rate, where the MS _{group is} the first attendance rate deviation, the SS _{group is the sum of squared deviations within} the group, k is the number of course categories, and n is the number of courses;

According to the square of the deviation within the group and the sum of the square of the deviation between the groups, according to the fifth formula The second attendance rate deviation is calculated, wherein, the MS _group is the second attendance rate deviation, and the SS _{group is the sum of squared deviations between} groups.

Step S104, if the first attendance rate deviation is less than or equal to the second attendance rate deviation, the attendance influence rate is obtained according to the ratio of the second attendance rate deviation to the first attendance rate deviation.

In the embodiment of the present invention, if the first attendance rate deviation MS _{group is} greater than the second attendance rate deviation MS _group , then the individual differences in the course category (including student individual differences and course individual differences) have a greater impact on the attendance rate. Large, the impact of course categories on attendance is negligible. If the first attendance rate deviation within the MS _{group is} less than or equal to the second attendance rate deviation MS _{between groups} , it means that the course category has a great influence on the attendance rate, and it is necessary to further calculate the attendance influence rate of the course category on the attendance rate.

The attendance influence rate F can be the ratio _{between the second attendance rate deviation MS group} and the first attendance rate deviation MS _group , that is The attendance influence rate F represents the influence degree of the course category on the attendance rate, and the greater the influence degree of the course category on the attendance rate, the greater the attendance influence rate F.

Step S105, if the attendance influence rate is greater than the value corresponding to the F distribution table at the preset significance level, it is determined that the course category has an influence on the student attendance rate.

In the embodiment of the present invention, there are certain low-probability events in practical applications, that is, after analyzing the currently obtained student attendance rate, the course category and attendance rate are highly correlated, so it is necessary to ensure that the probability of occurrence of low-probability events is lower than 100%. First, the calculated attendance influence rate F needs to be greater than the value corresponding to the F distribution table under the preset significant level (for example, 0.01), at this time it can be determined that the course category has a significant impact on the student attendance rate.

The embodiment of the present invention can analyze whether the course category has an impact on the attendance rate according to the obtained student attendance data of each course. If the analysis shows that the course category has a significant impact on the attendance rate, it can be judged whether there is a course requirement problem in the school. Classes of courses are not valued by students. Schools can reasonably modify their course requirements according to the analysis results to improve student attendance and further improve teaching quality.

second embodiment

Please refer to FIG. 3 , which shows a schematic block diagram of a course category and attendance rate analysis device 200 provided by an embodiment of the present invention. The apparatus 200 for analyzing course category and attendance rate includes a data acquisition module 201 , a first calculation module 202 , a second calculation module 203 , a first execution module 204 and a second execution module 205 .

The data obtaining module 201 is used to obtain the statistical data of student attendance corresponding to each course category.

In the embodiment of the present invention, the data obtaining module 201 is specifically used to obtain the student attendance data of each course; according to the preset multiple course categories, the student attendance data of all courses are grouped to obtain the student attendance data corresponding to each course category. Student attendance statistics.

The first calculation module 202 is configured to calculate the sum of squares of intragroup deviations representing individual errors in the same course category and the sum of squares of intergroup deviations representing the degree of difference between different course categories according to the statistical data of student attendance.

In the embodiment of the present invention, the first calculation module 202 is specifically configured to calculate the first average attendance rate of each course category and the second average attendance rate of all courses according to the student attendance statistics data corresponding to each course category; Based on the student attendance data of each course and the second average attendance rate, calculate the total deviation sum of squares that characterizes the degree of dispersion of the attendance rates of all courses; based on the student attendance data of each course and the first average attendance rate of each course category, Calculate the sum of squared deviations within the group that characterizes individual errors in the same course category; calculate the group that characterizes the degree of difference between different course categories based on the first average attendance rate and the second average attendance rate corresponding to each course category The sum of squared differences.

The second calculation module 203 is configured to calculate the first attendance rate deviation and the second attendance rate deviation according to the square of the intra-group deviation and the sum of the squares of the inter-group deviation.

In the embodiment of the present invention, the second calculation module 203 is specifically configured to use the fourth formula Calculate the deviation of the first attendance rate, among them, the MS _{group is} the first attendance rate deviation, the SS _{group is the sum of square deviations within} the group, k is the number of course categories, and n is the number of courses; according to the sum of square deviations within the group The sum of squares of the deviations between them, according to the fifth formula The second attendance rate deviation is calculated, wherein, the MS _group is the second attendance rate deviation, and the SS _{group is the sum of squared deviations between} groups.

The first execution module 204 is configured to obtain the attendance influence rate according to the ratio of the second attendance rate deviation to the first attendance rate deviation if the first attendance rate deviation is less than or equal to the second attendance rate deviation.

The second execution module 205 is configured to determine that the course category has an impact on the student attendance rate if the attendance impact rate is greater than the value corresponding to the F distribution table at a preset significance level.

To sum up, the embodiment of the present invention provides a course category and attendance rate analysis method and device. The method includes: obtaining the student attendance statistical data corresponding to each course category; The sum of the squares of the intragroup deviation of the individual error in the course category, and the sum of the squares of the intergroup deviation of the degree of difference between different course categories; calculate the first attendance rate deviation based on the square of the intragroup deviation and the sum of the squares of the intergroup deviation and the second attendance rate deviation; if the first attendance rate deviation is less than or equal to the second attendance rate deviation, the attendance influence rate is obtained based on the ratio of the second attendance rate deviation to the first attendance rate deviation; if the attendance influence rate is greater than the preset significant The value corresponding to the F distribution table under the horizontal level determines that the course category has an impact on student attendance. The embodiment of the present invention can accurately analyze whether the course category has an impact on the attendance rate, thereby instructing the school teaching management system to reasonably modify the course requirements of different course categories according to the analysis results to improve the teaching quality.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functions and possible implementations of devices, methods and computer program products according to multiple embodiments of the present invention. operate. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.

In addition, each functional module in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. . It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. any such actual relationship or order exists between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention. It should be noted that like numerals and letters denote similar items in the following figures, therefore, once an item is defined in one figure, it does not require further definition and explanation in subsequent figures.

Claims (10)

1. a kind of course classification and rate of attendance analysis method, which is characterized in that the method includes：

Obtain the corresponding student attendance statistical data of each course classification；

According to the student attendance statistical data, calculates and characterize in same course classification deviation square in the group of individual error And and different course classification between difference degree group between sum of squares of deviations；

According to sum of squares of deviations between group described in sum of squares of deviations in described group, the first rate of attendance deviation and second rate of attendance are calculated Deviation；

If first rate of attendance deviation is less than or equal to second rate of attendance deviation, according to the second rate of attendance deviation and institute The ratio for stating the first rate of attendance deviation is worth to contributive rate of turning out for work；

If the contributive rate of turning out for work is more than the corresponding value of F distribution tables under the default level of signifiance, it is determined that course classification goes out student Diligent rate has an impact.

2. the method as described in claim 1, which is characterized in that it is described according to the student attendance statistical data, calculate table Levy in same course classification in the group of individual error between sum of squares of deviations and different course classifications between the group of difference degree from The step of poor quadratic sum, including：

According to the corresponding student attendance statistical data of each course classification, calculate each course classification the first average rate of attendance, And the second average rate of attendance of all courses；

According to per subject student attendance data and the second average rate of attendance, calculate all course rate of attendance of characterization from The square sum of total departure for the degree of dissipating；

The first average rate of attendance according to the student attendance data and each course classification per subject, calculates the same class of characterization Sum of squares of deviations in the group of individual error in journey classification；

According to the corresponding first average rate of attendance of each course classification and the second average rate of attendance, the different classes of characterization are calculated Sum of squares of deviations between the group of difference degree between journey classification.

3. method as claimed in claim 2, which is characterized in that student attendance data and described the of the foundation per subject The two average rate of attendance, the step of calculating the square sum of total departure for characterizing all course rate of attendance dispersion degrees, including：

Student attendance data according to every subject and the second average rate of attendance, according to the first formula Calculate the square sum of total departure for characterizing all course rate of attendance dispersion degrees, wherein S²For the square sum of total departure, X_iFor Student attendance data per subject,For the described second average rate of attendance, n is course number.

4. method as claimed in claim 2, which is characterized in that student attendance data and each class of the foundation per subject Journey class other first is averaged the rate of attendance, calculates the step for characterizing sum of squares of deviations in the group of individual error in same course classification Suddenly, including：

The first average rate of attendance according to the student attendance data and each course classification per subject, according to the second formulaIt calculates and characterizes in same course classification sum of squares of deviations in the group of individual error, wherein SS_{In group}For sum of squares of deviations in described group, X_ijFor the student attendance data of j-th of course under i-th of course classification,It is each The average rate of attendance of the first of course classification, k are course classification number, and n is course number.

5. method as claimed in claim 2, which is characterized in that each course classification of the foundation corresponding first is averagely turned out for work Rate and the second average rate of attendance, calculate the step of sum of squares of deviations between the group of difference degree between characterizing different course classifications Suddenly, including：

According to the corresponding first average rate of attendance of each course classification and the second average rate of attendance, according to third formulaCalculate between characterizing different course classifications sum of squares of deviations between the group of difference degree, wherein SS_{Between group}Sum of squares of deviations between being described group,For the first average rate of attendance of each course classification,It is averaged out for described second Diligent rate, k are course classification number, and n is course number.

6. the method as described in claim 1, which is characterized in that described according to deviation between group described in sum of squares of deviations in described group Quadratic sum, the step of calculating the first rate of attendance deviation and the second rate of attendance deviation, including：

According to sum of squares of deviations between group described in sum of squares of deviations in described group, according to the 4th formulaCalculate institute State the first rate of attendance deviation, wherein MS_{In group}For first rate of attendance deviation, SS_{In group}For sum of squares of deviations in described group, k is class Journey classification number, n are course number；

According to sum of squares of deviations between group described in sum of squares of deviations in described group, according to the 5th formulaCalculate institute State the second rate of attendance deviation, wherein MS_{Between group}For second rate of attendance deviation, SS_{Between group}Sum of squares of deviations between being described group.

7. the method as described in claim 1, which is characterized in that described to obtain the corresponding student attendance statistics of each course classification The step of data, including：

Obtain the student attendance data per subject；

According to preset multiple course classifications, the student attendance data of all courses are grouped, obtain each course classification Corresponding student attendance statistical data.

8. a kind of course classification and rate of attendance analytical equipment, which is characterized in that described device includes：

Data obtaining module, for obtaining the corresponding student attendance statistical data of each course classification；

First computing module is missed for according to the student attendance statistical data, calculating individual in the same course classification of characterization Sum of squares of deviations between the group of difference degree between sum of squares of deviations and different course classification in the group of difference；

Second computing module, for according to sum of squares of deviations between group described in sum of squares of deviations in described group, calculating first and turning out for work Rate deviation and the second rate of attendance deviation；

First execution module, if being less than or equal to second rate of attendance deviation, foundation for first rate of attendance deviation The ratio of second rate of attendance deviation and first rate of attendance deviation is worth to contributive rate of turning out for work；

Second execution module, if being more than the corresponding value of F distribution tables under the default level of signifiance for the contributive rate of turning out for work, it is determined that Course classification has an impact student attendance rate.

9. device as claimed in claim 8, which is characterized in that first computing module is specifically used for：

According to the corresponding student attendance statistical data of each course classification, calculate each course classification the first average rate of attendance, And the second average rate of attendance of all courses；

According to per subject student attendance data and the second average rate of attendance, calculate all course rate of attendance of characterization from The square sum of total departure for the degree of dissipating；

The first average rate of attendance according to the student attendance data and each course classification per subject, calculates the same class of characterization Sum of squares of deviations in the group of individual error in journey classification；

According to the corresponding first average rate of attendance of each course classification and the second average rate of attendance, the different classes of characterization are calculated Sum of squares of deviations between the group of difference degree between journey classification.

10. device as claimed in claim 8, which is characterized in that second computing module is specifically used for：

According to sum of squares of deviations between group described in sum of squares of deviations in described group, according to the 4th formulaCalculate institute State the first rate of attendance deviation, wherein MS_{In group}For first rate of attendance deviation, SS_{In group}For sum of squares of deviations in described group, k is class Journey classification number, n are course number；

According to sum of squares of deviations between group described in sum of squares of deviations in described group, according to the 5th formulaCalculate institute State the second rate of attendance deviation, wherein MS_{Between group}For second rate of attendance deviation, SS_{Between group}Sum of squares of deviations between being described group.