CN108765224A - Course classification is analyzed with the rate of attendance and device - Google Patents
Course classification is analyzed with the rate of attendance and device Download PDFInfo
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Abstract
The present embodiments relate to teaching management technical field, a kind of course classification and rate of attendance analysis method and device are provided, the method includes：Obtain the corresponding student attendance statistical data of each course classification；According to student attendance statistical data, the interior sum of squares of deviations between sum of squares of deviations and group of group is calculated；According to sum of squares of deviations between sum of squares of deviations group in group, the first rate of attendance deviation and the second rate of attendance deviation are calculated；If the first rate of attendance deviation is less than or equal to the second rate of attendance deviation, the ratio according to the second rate of attendance deviation and the first rate of attendance deviation is worth to contributive rate of turning out for work；If 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 has an impact student attendance rate.The embodiment of the present invention can accurately analyze whether course classification has an impact the rate of attendance, improve the quality of teaching to instruct the school's teaching management system according to analysis result rationally to change the curriculum requirements of different course classifications.
Description
Technical field
The present embodiments relate to teaching management technical fields, in particular to a kind of course classification and the rate of attendance point
Analysis and device.
Background technology
It is taken as an elective course currently, the course of university student can be divided into public required course, Public optional subjects, specialized compulsory class and profession
Class, different classes of curriculum requirements and difficulty difference, the rate of attendance of some courses are extremely low, it will usually which its reason is attributed to teacher
Quality of instruction is bad, but it could also be possible that since partial category course caused by curriculum requirements problem is not paid attention to by student, still
It can not accurately analyze whether student attendance rate can be influenced by course classification at present so that school's teaching management system can not
The requirement of the adjustment extremely low course of the rate of attendance in time.
Invention content
The embodiment of the present invention is designed to provide a kind of course classification and rate of attendance analysis method and device, to determination
Whether course classification influences student attendance rate.
To achieve the goals above, technical solution used in the embodiment of the present invention is as follows：
In a first aspect, an embodiment of the present invention provides a kind of course classification and rate of attendance analysis method, the method includes：
Obtain the corresponding student attendance statistical data of each course classification；According to the student attendance statistical data, it is same to calculate characterization
Deviation is flat between the group of difference degree between sum of squares of deviations and different course classifications in the group of individual error in one course classification
Fang He；According to sum of squares of deviations between group described in sum of squares of deviations in described group, calculates the first rate of attendance deviation and second and turn out for work
Rate 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 the ratio of 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 F under the default level of signifiance
The corresponding value of distribution table, it is determined that course classification has an impact student attendance rate.
Second aspect, the embodiment of the present invention additionally provide a kind of course classification and rate of attendance analytical equipment, described device packet
Include data obtaining module, the first computing module, the second computing module, the first execution module and the second execution module.Wherein, data
Module is obtained for obtaining the corresponding student attendance statistical data of each course classification；First computing module is used for according to
Diligent statistical data is born, calculates and characterizes in same course classification sum of squares of deviations and different courses in the group of individual error
Sum of squares of deviations between the group of difference degree between classification；Second computing module is used for according to group described in sum of squares of deviations in described group
Between sum of squares of deviations, calculate the first rate of attendance deviation and the second rate of attendance deviation；If the first execution module is used for described first
Rate of attendance deviation is less than or equal to second rate of attendance deviation, then inclined according to the second rate of attendance deviation and first rate of attendance
The ratio of difference is worth to contributive rate of turning out for work；If the second execution module is more than under the default level of signifiance F points for the contributive rate of turning out for work
The corresponding value of cloth table, it is determined that course classification has an impact student attendance rate.
Compared with the prior art, a kind of course classification provided in an embodiment of the present invention and rate of attendance analysis and device, first, according to
Calculated according to the corresponding student attendance statistical data of each course classification characterize in same course classification in the group of individual error from
Sum of squares of deviations between the group of difference degree between poor quadratic sum and different course classifications；Then, according to sum of squares of deviations in group
Sum of squares of deviations between group calculates the first rate of attendance deviation and the second rate of attendance deviation, if the first rate of attendance deviation is less than or waits
Ratio according to the second rate of attendance deviation and the first rate of attendance deviation when the second rate of attendance deviation is worth to contributive rate of turning out for work；Most
Afterwards, if 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 has student attendance rate
It influences.The embodiment of the present invention can accurately analyze whether course classification has an impact the rate of attendance, to instruct school instruction
Management system rationally changes the curriculum requirements of different course classifications according to analysis result to improve the quality of teaching.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment cited below particularly, and coordinate
Appended attached drawing, is described in detail below.
Description of the drawings
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 shows the block diagram of electronic equipment provided in an embodiment of the present invention.
Fig. 2 shows course classifications provided in an embodiment of the present invention and rate of attendance analysis method flow chart.
Fig. 3 shows the block diagram of course classification and rate of attendance analytical equipment provided in an embodiment of the present invention.
Icon：100- electronic equipments；101- memories；102- storage controls；103- processors；200- courses classification with
Rate of attendance analytical equipment；201- data obtaining modules；The first computing modules of 202-；The second computing modules of 203-；204- first is held
Row module；The second execution modules of 205-.
Specific implementation mode
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, the detailed description of the embodiment of the present invention to providing in the accompanying drawings is not intended to limit claimed invention below
Range, but it is merely representative of the selected embodiment of the present invention.Based on the embodiment of the present invention, those skilled in the art are not doing
The every other embodiment obtained under the premise of going out creative work, shall fall within the protection scope of the present invention.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined, then it further need not be defined and explained in subsequent attached drawing in a attached drawing.Meanwhile the present invention's
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
Fig. 1 is please referred to, Fig. 1 shows the block diagram of electronic equipment 100 provided in an embodiment of the present invention.Electronic equipment
100 may be, but not limited to, pocket computer, laptop, desktop computer etc..The electronic equipment 100 includes course class
Not with rate of attendance analytical equipment 200, memory 101, storage control 102 and processor 103.
The memory 101, storage control 102 and 103 each element of processor are directly or indirectly electrical between each other
Connection, to realize the transmission or interaction of data.For example, these elements can pass through one or more communication bus or letter between each other
Number line, which is realized, to be electrically connected.The course classification and rate of attendance analytical equipment 200 include it is at least one can be with software or firmware
(firmware) form is stored in the memory 101 or is solidificated in the operating system of the electronic equipment 100
Software function module in (operating system, OS).The processor 103 is used to execute to store in memory 101
Module is can perform, such as the software function module or computer program that the course classification includes with rate of attendance analytical equipment 200.
Wherein, memory 101 may 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-Only
Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only Memory, EPROM),
Electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only Memory, EEPROM) etc..
Processor 103 can be a kind of IC chip, have signal handling capacity.Above-mentioned processor 103 can be with
It is general processor, including central processing unit (Central Processing Unit, CPU), network processing unit (Network
Processor, NP), speech processor and video processor etc.；Can also be digital signal processor, application-specific integrated circuit,
Field programmable gate array either other programmable logic device, discrete gate or transistor logic, discrete hardware components.
It may be implemented or execute disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor can be
Microprocessor or the processor 103 can also be any conventional processor etc..
First embodiment
Fig. 2 is please referred to, Fig. 2 shows course classifications provided in an embodiment of the present invention and rate of attendance analysis method flow chart.
Course classification includes the following steps with rate of attendance analysis method：
Step S101 obtains the corresponding student attendance statistical data of each course classification.
In embodiments of the present invention, course classification includes public required course, Public optional subjects, specialized compulsory class and profession choosing
Repair class.First, the student attendance data per subject are obtained, the student attendance data per subject are stored in the teaching of school
In management system；Then, according to preset multiple course classifications, the student attendance data of all courses is grouped, are obtained
The corresponding student attendance statistical data of each course classification, that is to say, that must according to public required course, Public optional subjects, profession
It repaiies class and elective special course this four course classifications and the student attendance data of all courses is divided into four groups, obtain each course class
Not corresponding student attendance statistical data.
The rate of attendance of different course classifications has differences, but this species diversity is not necessarily all caused by course classification,
It could also be possible that therefore the rate of attendance deviation of different course classifications is divided into two classes, including identical course by the reason of student itself
Deviation between classification between the rate of attendance deviation of different courses and the different course classification entirety rate of attendance, identical course class
The rate of attendance deviation of different courses is by individual error (including student's individual differences and the course in same course classification between not
Body difference) cause, the deviation between the different course classification entirety rate of attendance is caused by the rate of attendance size of different course classifications.
Step S102 is calculated and is characterized in same course classification in the group of individual error according to student attendance statistical data
Sum of squares of deviations between the group of difference degree between sum of squares of deviations and different course classifications.
In embodiments of the present invention, after obtaining the corresponding student attendance statistical data of each course classification, first, foundation
Each corresponding student attendance statistical data of course classification calculates the first average rate of attendance of each course classification and owns
The average rate of attendance of the second of course, the first average rate of attendance Xi, i=1,2,3,4 indicate, refer to averaging out for each course classification
Diligent rate；The second average rate of attendance is indicated with X, refers to the average rate of attendance of all courses.
Then, according to the student attendance data and the second average rate of attendance per subject, all courses of characterization is calculated and are gone out
The square sum of total departure of diligent rate dispersion degree, square sum of total departure are the student attendance number and the second average rate of attendance per subject
Sum of squares of deviations, be an index for describing all course rate of attendance dispersion degrees.Specifically, according to per subject
Raw attendance data and the second average rate of attendance, according to the first formulaAll courses of characterization are calculated to turn out for work
The square sum of total departure of rate dispersion degree, wherein S2For the square sum of total departure, XiFor the student attendance data of every subject,For the described second average rate of attendance, n is course number.
Next, according to the student attendance data of every subject and the first average rate of attendance of each course classification, calculate
Go out to characterize in same course classification sum of squares of deviations in the group of individual error, organize in sum of squares of deviations reflect a course classification
In individual error, i.e. the influence of student individual and certain subject itself error.Specifically, go out according to the student per subject
The first average rate of attendance of diligent data and each course classification, according to the second formulaCalculate table
Levy in same course classification sum of squares of deviations in the group of individual error, wherein SSIn groupFor sum of squares of deviations in described group, XijIt is
The student attendance data of j-th of course under i course classification,For the first average rate of attendance of each course classification, k is course
Classification number, n are course number.
Finally, according to the corresponding first average rate of attendance of each course classification and the second average rate of attendance, characterization is calculated
Sum of squares of deviations between the group of difference degree between different course classifications, sum of squares of deviations reflects each course classification and averages out between group
Difference degree between diligent rate.Specifically, it is averaged out according to the corresponding first average rate of attendance of each course classification and second
Diligent rate, according to third formulaCalculate between characterizing different course classifications between the group of difference degree from
Poor quadratic sum, wherein SSBetween groupThe sum of squares of deviations between group,For the first average rate of attendance of each course classification,It is second
The average rate of attendance, k are course classification number, and n is course number.
Step S103 calculates the first rate of attendance deviation and second according to sum of squares of deviations between sum of squares of deviations group in group
Rate of attendance deviation.
In embodiments of the present invention, the first rate of attendance deviation represents goes out caused by student's individual differences and course individual difference
Diligent rate average deviation, the second rate of attendance deviation represent the average deviation for the rate of attendance that different course classifications are brought.First rate of attendance
The computational methods of deviation and the second rate of attendance deviation may include：
First, according to sum of squares of deviations between group described in sum of squares of deviations in group, according to the 4th formulaIt calculates
Go out the first rate of attendance deviation, wherein MSIn groupFor the first rate of attendance deviation, SSIn groupFor sum of squares of deviations in group, k is course classification
Number, n are course number；
According to sum of squares of deviations between sum of squares of deviations group in group, according to the 5th formulaSecond is calculated to go out
Diligent rate deviation, wherein MSBetween groupFor the second rate of attendance deviation, SSBetween groupThe sum of squares of deviations between group.
Step S104, it is inclined according to second rate of attendance if the first rate of attendance deviation is less than or equal to the second rate of attendance deviation
The ratio of poor and described first rate of attendance deviation is worth to contributive rate of turning out for work.
In embodiments of the present invention, if the first rate of attendance deviation MSIn groupMore than the second rate of attendance deviation MSBetween group, then saying
Individual difference (including student's individual differences and course individual difference) in bright course classification is affected to the rate of attendance, course
Influence very little of the classification to the rate of attendance is negligible.If the first rate of attendance deviation MSIn groupLess than or equal to the second rate of attendance deviation
MSBetween group, then it is big to illustrate that course classification influences the rate of attendance, needs to further calculate course classification at this time and turn out for work to the rate of attendance
Contributive rate.
The contributive rate F that turns out for work can be the second rate of attendance deviation MSBetween groupAnd the first rate of attendance deviation MSIn groupRatio, i.e.,The contributive rate F that turns out for work represents influence degree of the course classification to the rate of attendance, influence journey of the course classification to the rate of attendance
The degree the big, and the contributive rate F that turns out for work is bigger.
Step S105, if 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
Have an impact to student attendance rate.
In embodiments of the present invention, there are certain small probability event in practical application, i.e., the student attendance that currently obtains
Course classification is obtained after rate analysis and the rate of attendance is highly relevant, therefore is necessary to ensure that the probability that small probability event occurs will be less than percentage
One of, that is, the contributive rate F that turns out for work calculated needs to be more than the corresponding value of F distribution tables under the default level of signifiance (for example, 0.01),
It can determine that course classification has a significant impact student attendance rate at this time.
Whether the embodiment of the present invention can analyze course classification according to the student attendance data of every subject of acquisition right
There is influence in the rate of attendance, had a significant impact to the rate of attendance if analyzing course classification, can go to judge that school whether there is
Curriculum requirements problem causes the course of partial category not paid attention to by student, and school can rationally change oneself according to analysis result
Curriculum requirements improve the rate of attendance of student, further increase quality of instruction.
Second embodiment
Fig. 3 is please referred to, Fig. 3 shows the side of course classification and rate of attendance analytical equipment 200 provided in an embodiment of the present invention
Frame schematic diagram.Course classification includes data obtaining module 201, the first computing module 202, second with rate of attendance analytical equipment 200
Computing module 203, the first execution module 204 and the second execution module 205.
Data obtaining module 201, for obtaining the corresponding student attendance statistical data of each course classification.
In embodiments of the present invention, data obtaining module 201 are specifically used for obtaining the student attendance data per subject；
According to preset multiple course classifications, the student attendance data of all courses are grouped, each course classification is obtained and corresponds to
Student attendance statistical data.
First computing module 202, for according to 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 error.
In embodiments of the present invention, the first computing module 202, specifically for going out according to the corresponding student of each course classification
Diligent statistical data calculates the first average rate of attendance of each course classification and the second average rate of attendance of all courses；Foundation
The average rate of attendance of student attendance data and second per subject, calculates the total inclined of all course rate of attendance dispersion degrees of characterization
Poor quadratic sum；The first average rate of attendance according to the student attendance data and each course classification per subject, calculates characterization
Sum of squares of deviations in the group of individual error in same course classification；According to the corresponding first average rate of attendance of each course classification and
The described second average rate of attendance calculates between characterizing different course classifications sum of squares of deviations between the group of difference degree.
Second computing module 203, for according to sum of squares of deviations between sum of squares of deviations group in group, calculating first rate of attendance
Deviation and the second rate of attendance deviation.
In embodiments of the present invention, the second computing module 203 is specifically used for flat according to deviation between sum of squares of deviations group in group
Fang He, according to the 4th formulaCalculate the first rate of attendance deviation, wherein MSIn groupFor the first rate of attendance deviation,
SSIn groupFor sum of squares of deviations in group, k is course classification number, and n is course number；According to deviation square between sum of squares of deviations group in group
With according to the 5th formulaCalculate the second rate of attendance deviation, wherein MSBetween groupFor the second rate of attendance deviation,
SSBetween groupThe sum of squares of deviations between group.
First execution module 204, if being less than or equal to the second rate of attendance deviation for the first rate of attendance deviation, according to the
The ratio of two rate of attendance deviations and first rate of attendance deviation is worth to contributive rate of turning out for work.
Second execution module 205, if being more than the corresponding value of F distribution tables under the default level of signifiance for contributive rate of turning out for work,
Determine that course classification has an impact student attendance rate.
In conclusion a kind of course classification provided in an embodiment of the present invention and rate of attendance analysis method and device, the side
Method includes：Obtain the corresponding student attendance statistical data of each course classification；According to student attendance statistical data, characterization is calculated
Deviation between the group of difference degree between sum of squares of deviations and different course classification in the group of individual error in same course classification
Quadratic sum；According to sum of squares of deviations between sum of squares of deviations group in group, the first rate of attendance deviation and the second rate of attendance deviation are calculated；
If the first rate of attendance deviation is less than or equal to the second rate of attendance deviation, according to the second rate of attendance deviation and the first rate of attendance deviation
Ratio be worth to contributive rate of turning out for work；If 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 has an impact student attendance rate.The embodiment of the present invention can accurately analyze whether course classification generates shadow to the rate of attendance
It rings, is taught to instruct school's teaching management system rationally to change the curriculum requirements of different course classifications according to analysis result to improve
Learn quality.
In several embodiments provided herein, it should be understood that disclosed device and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, the flow chart in attached drawing and block diagram
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this regard, each box in flowchart or block diagram can represent the one of a module, section or code
Part, a part for the module, section or code, which includes that one or more is for implementing the specified logical function, to be held
Row instruction.It should also be noted that at some as in the realization method replaced, the function of being marked in box can also be to be different from
The sequence marked in attached drawing occurs.For example, two continuous boxes can essentially be basically executed in parallel, they are sometimes
It can execute in the opposite order, this is depended on the functions involved.It is also noted that every in block diagram and or flow chart
The combination of box in a box and block diagram and or flow chart can use function or the dedicated base of action as defined in executing
It realizes, or can be realized using a combination of dedicated hardware and computer instructions in the system of hardware.
In addition, each function module in each embodiment of the present invention can integrate to form an independent portion
Point, can also be modules individualism, can also two or more modules be integrated to form an independent part.
It, can be with if the function is realized and when sold or used as an independent product in the form of software function module
It is stored in a computer read/write memory medium.Based on this understanding, technical scheme of the present invention is substantially in other words
The part of the part that contributes to existing technology or the technical solution can be expressed in the form of software products, the meter
Calculation machine software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be
People's computer, server or network equipment etc.) it performs all or part of the steps of the method described in the various embodiments of the present invention.
And storage medium above-mentioned includes：USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
The various media that can store program code such as reservoir (RAM, Random Access Memory), magnetic disc or CD.It needs
Illustrate, herein, relational terms such as first and second and the like be used merely to by an entity or operation with
Another entity or operation distinguish, and without necessarily requiring or implying between these entities or operation, there are any this realities
The relationship or sequence on border.Moreover, the terms "include", "comprise" or its any other variant are intended to the packet of nonexcludability
Contain, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes
Other elements that are not explicitly listed, or further include for elements inherent to such a process, method, article, or device.
In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including the element
Process, method, article or equipment in there is also other identical elements.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, any made by repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.It should be noted that：Similar label and letter exist
Similar terms are indicated in following attached drawing, therefore, once being defined in a certain Xiang Yi attached drawing, are then not required in subsequent attached drawing
It is further defined and is explained.
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, XiFor
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
SSIn groupFor sum of squares of deviations in described group, XijFor 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
SSBetween groupSum 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 MSIn groupFor first rate of attendance deviation, SSIn groupFor 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 MSBetween groupFor second rate of attendance deviation, SSBetween groupSum 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 MSIn groupFor first rate of attendance deviation, SSIn groupFor 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 MSBetween groupFor second rate of attendance deviation, SSBetween groupSum of squares of deviations between being described group.
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