CN105844562A - Learner learning performance analysis method based on curriculum teaching model - Google Patents
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Abstract
The invention belongs to the data analysis and application field and provides a learner learning performance analysis method based on a curriculum teaching model. The method comprises the following steps: 1) constructing the curriculum teaching model; 2) establishing a learner learning effect evaluation calculation formula; and 3) carrying out learner learning performance analysis: analyzing fitting polynomial functions of all learners and describing learning performance of the learners according to function change information. The method is simple and feasible, and can solve the problems of low efficiency and not high analysis precision and the like of a conventional method.
Description
Technical field
The invention belongs to data analysis and application, be specifically related to the study of a kind of learner based on course teaching model
Performance point analysis method.
Background technology
Data analysis refers to be analyzed, with suitable statistical analysis technique, the mass data gathered, and extracts useful letter
Cease and form conclusion, i.e. to data in addition research and the process of summary in detail.In actual applications, data analysis can be helped
Help others judge, in order to take appropriate action.The technology that data in education sector carry out data analysis is referred to as learning
Practise analytical technology.
Data analysis mainly includes the collection of data, processes, processes and the step such as analysis.Learner data analysis can be helped
Helping it to efficiently accomplish school work, its data being available for analyzing are made up of data under data on line and line.Wherein, under line, data refer to class
The data collected in hall teaching process, these data acquisitions need artificial commander and intervention, but under line, data can
Reflecting the various subjective situation of learner, it analyzes the most meaningful with process.
Under line, teachers ' teaching activity is carried out and is played important guide effect by the research of teaching quality evaluation, is to promote
Patterns in Teaching reform, the important means of raising classroom instruction effectiveness.
The major defect that under existing line, teaching quality evaluation exists has: first, and evaluation content is substantially not related to teaching and lives
Data during Dong, the data type collected is fairly simple, is mainly the examination data gathering learner and becomes at ordinary times
Achievement data.Second, processing method is single, and mode is traditional, content is plain, it is little to act on, and main process is by simply adding up
And analysis, as calculated average achievement, asking best result and minimum point, the achievement distribution etc. of statistical correlation mark section.
Above-mentioned 2 to cause precision in existing learner learning performance analytical technology the highest, the problem of inefficiency.
Summary of the invention
For the above-mentioned deficiency of prior art, the present invention proposes a kind of learner learning table based on course teaching model
Now analyzing method, the method is simple and easy to do, can effectively solve the highest problem with inefficiency of existing methods analyst precision.
Specifically, a kind of learner learning performance based on course teaching model of the present invention analyzes method, including following
Step:
(1) Give lecture teaching mode
Course teaching model is made up of course and two submodels of education activities.Wherein, course submodel is by this course set point
Collection be combined into；Education activities submodel is made up of education activities within the class period and extracurricular education activities, and imparts knowledge to students within the class period and mainly include
Paying attention to the class activity within the class period, answer a question and work attendance activity, extracurricular teaching mainly includes the activity of doing one's assignment, does experimental activity and review pre-
Habit activity.As such, it is possible to design course teaching model as shown in Figure 1.
(2) learner the Assessment of Learning Effect computing formula is set up
First, gather teaching data within the class period, and according to needing these data are carried out quantification treatment.Wherein classroom is answered a question into
AchievementWith work attendance activity achievementCan directly obtain；Evaluating data is paid attention to the class in classroomTable obtains by inquiry.
Second, gather extracurricular teaching data, and according to needing these data are carried out quantification treatment.Wherein operation achievementAnd experiment gradeCan directly obtain；Review previews evaluating data
Table obtains by inquiry.Wherein operation and experiment grade can be averaged.
3rd, teacher the weight of course teaching model is set:
The weight of education activities submodel is set.Wherein, education activities weight within the class period be, such as 0.6；Extracurricular religion
Activity weight is, such as 0.4.Require:。
The weight of each subactivity in education activities within the class period is set.Wherein, paying attention to the class activity achievement weight is,
Such as 0.5；Activity achievement weight of answering a question is, such as 0.3；Work attendance activity achievement weight is, such as 0.2.Require:。
The weight of each subactivity in extracurricular education activities is set.Wherein, operation achievement weight is, as
0.4；Experiment grade weight is, such as 0.4；Review previews achievement weight, as
0.2.Require:。
The weight of a certain course i knowledge point is set, if the knowledge of this course is counted as n.Require:。
4th, set up for the Assessment of Learning Effect computing formula of each learner, wherein the Assessment of Learning Effect value
Span is the real number between 0-100.Specific formula for calculation is as follows.
A () calculates the Assessment of Learning Effect value of a knowledge point of any one learner
The Assessment of Learning Effect value=education activities the weight within the class period of the jth knowledge point of any one learner k × (pay attention to the class movable one-tenth
Achievement weight × pay attention to the class evaluating data+activity achievement weight of answering a question × achievement of answering a question+work attendance activity achievement weight × examine
Diligent activity achievement)+extracurricular education activities weight × (operation achievement weight × operation achievement+experiment grade weight × experiment grade+
Review previews achievement weight × review and previews evaluating data), as follows referring specifically to formula
Wherein, j ∈ [1..n], i.e. this course have n knowledge point；
B () calculates the average the Assessment of Learning Effect value of the knowledge point j of class's learner
Average the Assessment of Learning Effect value=∑ (study effect of this knowledge point of kth learner of the knowledge point j of class's learner
Really evaluation of estimate) ÷ learner number m, wherein, as follows referring specifically to formula
C () calculates the average the Assessment of Learning Effect value of the course i of any one learner k
The average the Assessment of Learning Effect value of any one learner k course i=［ ∑ (the Assessment of Learning Effect value of learner k knowledge point j
The weight of × jth knowledge point) ］ the number n of this course set point of ÷, wherein j ∈ [1..n], i.e. this course have n knowledge
Point, as follows referring specifically to formula
D () calculates the average the Assessment of Learning Effect value of class learner course i
The average the Assessment of Learning Effect value=∑ of the course of class's learner (comment by the average learning effect of kth learner course i
It is worth) ÷ learner number m, wherein, as follows referring specifically to formula
All result of calculations in (a)-(d) are stored, to facilitate follow-up learning data analysis.
(3) learner learning performance is analyzed
This step is respectively adopted linear fit method and uses the learning performance of the polynomial fitting method person that comes analytic learning, and
Both analytical effects are contrasted on the basis of this.
(3-1) the learner learning performance using linear fit method is analyzed.Analytical procedure is as follows:
(3-1-1) in the data of knowledge point j, learner can be carried out according to the knowledge point the Assessment of Learning Effect value of learner
Ranking, can obtain each learner ranking in each knowledge point, then to some learner in all knowledge points
The situation of change of ranking is studied.As a example by learner k, obtain this learner unit at knowledge point j according to the method described above
Marks sequencing；
(3-1-2) with learning knowledge point as X-axis, the ranking of this knowledge point the Assessment of Learning Effect value is Y-axis, in rectangular coordinate system
Drawing the scatterplot of the study ranking of all of knowledge point of certain learner, Y value the least expression ranking is the best, and achievement is the most outstanding.
The scatterplot of each learner can be obtained in this approach；
(3-1-3) scatterplot in each scatterplot is fitted, obtains fitted linear regression line equation, utilize linear returning
The characteristic returning straight line draws the integral elevating situation of scatterplot in each scatterplot, wherein equation of linear regression formula, referring specifically to
Formula is as follows
WhereinRepresent jth knowledge point (j=1,2 ..., n), n represents knowledge point number,Represent corresponding the knowing of learner
Know some j ranking (),ForMeansigma methods,ForMeansigma methods；
(3-1-4) calculating the degree of fitting of each equation of linear regression, wherein r represents the correlation coefficient of x, y, and linearly dependent coefficient is
In order to reflect the statistical indicator of two variable linearly dependency relations, as follows referring specifically to formula
WhereinRepresent jth knowledge point (j=1,2 ..., n),Represent knowledge point j corresponding to learner ranking (),ForMeansigma methods,ForMeansigma methods；
(3-1-5) combineSlope with regression straight lineJudging the learning performance of the whole course of learner, it judges rule
Then design is as follows:
Rule 1, if< 0.5, the fluctuation of person's school grade is relatively big to illustrate relational learning, unstable；
Rule 2, if> 0.5, person learns situation stably to illustrate relational learning, can sentence according to the slope b of regression straight line
Disconnected along with the propelling of study course, the situation of change of learner marks sequencing；
Rule 3, if, illustrate that this learner presents progressive situation in whole course learning, andThe biggest, progressive
Situation is the most obvious；
Rule 4, if, illustrate that this learner presents the situation of room for manoeuvre in whole course learning, andThe biggest, room for manoeuvre
Situation is the most obvious；
In sum, it is judged that the rule of learner learning performance can be summarized as: slope is for bearing then for progressive, and the least progress of slope
The biggest；Slope be canonical be room for manoeuvre, and slope is the biggest, regresses the biggest.
(3-2) the learner learning performance using polynomial fitting method is analyzed, and circular is as follows:
(3-2-1) for learner k, foundationValue obtain this learner and exist
Ranking on the j of knowledge point；
(3-2-2) in rectangular coordinate system, draw the scatterplot of the study ranking of all knowledge points of learner k.Abscissa X generation
Table knowledge point sequence number, vertical coordinate Y represents this learner ranking in corresponding knowledge point, and Y value the least expression ranking is the most forward, achievement
The best, the scatterplot of the person that simultaneously can also obtain Ensemble learning；
(3-2-3) calculate the equation of polynomial fit function corresponding to each scatterplot, polynomial fit function be one with big
The curve of most scatterplot phase matchings, utilizes the characteristic of polynomial fit function, we can see that scatterplot in each scatterplot
Overall variation situation, wherein polynomial fit function formula equation below
Wherein,Represent jth knowledge point t power (j=1,2 ..., n),Represent the one-tenth of knowledge point j corresponding for learner k
Achievement ranking (),It it is the coefficient matrix of polynomial fit function；
(3-2-4) each fitting of a polynomial degree is calculated, it is as follows that specific formula for calculation sees formula
Wherein,Represent jth knowledge point (j=1,2 ..., n),Represent knowledge point j corresponding for learner k marks sequencing (),It it is the coefficient matrix of polynomial fit function；
(3-2-5) foundationPass judgment on the fitting degree of polynomial fit function, curve degree of fittingIt is that each point arrives this curve
Distance sum, the fit solution of numerical value the least explanation function is the best；
(3-2-6) fit polynomial function of all Ensemble learning persons is analyzed, describes according to the situation of change of function and learn
The learning performance of habit person.
The present invention compared with prior art has the advantage that
First, Give lecture teaching mode, these models can realize data in advance and process, and preserve results of intermediate calculations, for rear
The learner learning performance analysis that continues directly provides support, to improve the efficiency that learner learning performance is analyzed；
Second, use fitting of a polynomial to improve linear fit and analyze method, to disclose learner trend change in learning activity
Change details, improve analysis precision.
Accompanying drawing explanation
Fig. 1 is course teaching model schematic in the present invention.
Fig. 2 is the course teaching model schematic of the embodiment of the present invention " High-level Language Programming ".
Fig. 3 is the learning performance schematic diagram of learner K1.
Fig. 4 is the learning performance schematic diagram of learner K3.
Fig. 5 is the learning performance schematic diagram of learner K5.
Fig. 6 is the learning performance schematic diagram of learner K7.
Fig. 7 is the learning performance schematic diagram of learner K9.
Detailed description of the invention
Below in conjunction with the accompanying drawings and embodiment the invention will be further described.With certain a branch of instruction in school, such as high-level language journey
As a example by sequence design, concrete learner learning performance based on course teaching model is analyzed the step of method and is described as follows:
The first step, Give lecture teaching mode.
Analysis demand according to particular problem, it is assumed that education activities within the class period mainly pay attention to the class activity within the class period, answer a question and
Work attendance activity forms, and imparts knowledge to students after class and mainly include the activity of doing one's assignment, do experimental activity and review previews activity.So, set
The concrete course that obtains of meter, as " High-level Language Programming " course teaching model as shown in Figure 2.
Second step, sets up learner the Assessment of Learning Effect computing formula.
As a example by " High-level Language Programming " course, by 2014 grades of undergraduates of certain school School of Computer Science 100
The study situation of learner (2 classes) early stage carries out statistical analysis, obtains the learner school grade ratio distribution of this grade
For: the learner of 30% is in good standing (achievement is distributed in 90-100), and the learner achievement of 40% is general, and (achievement is distributed in 70-
89), the learner low academic (achievement is distributed in 60-69) of 20%, the learner achievement of 10% is excessively poor, and (achievement is distributed in 0-
59).To coming from the learning data of these 100 learners, clear up according to verity, integrity and ethical rule and sieve
After choosing, the learner of the most same learning hierarchy (outstanding, general, poor, very poor) select 10 learners altogether in this course
Learning data, the existing analytical mathematics that this method is described as example.
Concrete sub-step includes:
First, obtain teaching data within the class period, and according to needing these data are carried out quantification treatment；
Second, obtain extracurricular teaching data, and according to needing these data are carried out quantification treatment；
3rd, teacher the weight of course teaching model is set, wherein the span of the Assessment of Learning Effect value is between 0-100
Real number, in this step reality arrange weight situation be: each blocks of knowledge is devised respective weights, weight design result
For: programming basis 10%, array 15%, structure 10%, function 15%, file 25%, pointer 25%.Set with extracurricular study impact within the class period
Putting weight, weight design result is: learn 60% within the class period, Outside Class Studying 40%.To within the class period/extracurricular activity arranges weight, weight
It is set to: classroom pays attention to the class 50%, answer a question 30%, work attendance 20%, operation 40%, computer experiment 20%, review one's lessons by oneself 20%.
4th, set up the Assessment of Learning Effect computing formula concrete for the design of each learner.
3rd step, learner learning performance is analyzed.
This step uses the learner learning performance of linear fit method to analyze and uses the relevant of polynomial fitting method
Analyzing two steps, the purpose of do so is to contrast analytical effect.
As a example by student K1, use linear fit method and polynomial fitting method, available corresponding learning table successively
Existing, wherein, use the learning performance obtained by linear fit method, see the straight line in Fig. 3；Use polynomial fitting method institute
The learning performance obtained, sees the curve in Fig. 3.
As it is shown on figure 3, first, according to the result of calculation of linear fit function, we can obtain the overall of learner K1
Learning performance, this situation shows that K1 is in constantly progress in whole course learning, but this result of calculation is not
Depict the details in the learning process of K1.
Secondly, according to the result of calculation of polynomial fit function, we can depict the learning process details of this life, as
According to the achievement of this raw 1st blocks of knowledge, this life compares (the 8th) to the rear in whole group ranking.The 2nd knowledge
After modular learning, the ranking of this life has promoted；Then, in the learning process of the 3rd and the 4th blocks of knowledge, this raw one-tenth
Achievement ranking is more stable；And in the study of the 5th blocks of knowledge, the achievement of this life has had lifting by a relatively large margin；But arrive
Some declines the achievement that in the study of the 6th blocks of knowledge, this is raw again.For Zong He, the whole learning process performance of this life is ripple
Disorder of internal organs progress.
4th step, result presents.
According to said method, we can also obtain the study trend with 4 classmates representing meaning, referring specifically to figure
4-7。
According to above-mentioned empirical result, we have summarized the learning performance of K1, K3, K5, K7, K9, and concrete conclusion sees table 1
Shown in.
The course learning performance of table 1. student
Claims (3)
1. learner learning performance based on course teaching model analyzes method, it is characterised in that the method comprises the following steps:
(1) Give lecture teaching mode
Course teaching model is made up of course and two submodels of education activities, and wherein, course submodel is by this course set point
Collection be combined into；Education activities submodel is made up of education activities within the class period and extracurricular education activities, and teaching within the class period includes listening within the class period
Saying activity, answer a question and work attendance activity, extracurricular teaching includes the activity of doing one's assignment, do experimental activity and review previews activity；
(2) learner the Assessment of Learning Effect computing formula is set up
(2-1) gather teaching data, extracurricular teaching data within the class period, these data are carried out quantification treatment, teacher course is set
The weight of teaching mode；
(2-2) the Assessment of Learning Effect computing formula for each learner, the wherein value of the Assessment of Learning Effect value are set up
Scope is the real number between 0-100, and specific formula for calculation is as follows
(2-2-1) the Assessment of Learning Effect value of a knowledge point of any one learner is calculated
The Assessment of Learning Effect value=education activities the weight within the class period of the jth knowledge point of any one learner k × (pay attention to the class movable one-tenth
Achievement weight × pay attention to the class evaluating data+activity achievement weight of answering a question × achievement of answering a question+work attendance activity achievement weight × examine
Diligent activity achievement)+extracurricular education activities weight × (operation achievement weight × operation achievement+experiment grade weight × experiment grade+
Review previews achievement weight × review and previews evaluating data), as follows referring specifically to formula
Wherein, j ∈ [1..n], i.e. this course have n knowledge point；
(2-2-2) the average the Assessment of Learning Effect value of the knowledge point j of class's learner is calculated
Average the Assessment of Learning Effect value=∑ (study effect of this knowledge point of kth learner of the knowledge point j of class's learner
Really evaluation of estimate) ÷ learner number m, wherein, as follows referring specifically to formula
(2-2-3) the average the Assessment of Learning Effect value of the course i of any one learner k is calculated
The average the Assessment of Learning Effect value of any one learner k course i=［ ∑ (the Assessment of Learning Effect value of learner k knowledge point j
The weight of × jth knowledge point) ］ the number n of this course set point of ÷, wherein j ∈ [1..n], i.e. this course have n knowledge
Point, as follows referring specifically to formula
(2-2-4) the average the Assessment of Learning Effect value of class learner course i is calculated
The average the Assessment of Learning Effect value=∑ of the course of class's learner (comment by the average learning effect of kth learner course i
It is worth) ÷ learner number m, wherein, as follows referring specifically to formula
(2-2-5) all result of calculations in (2-2-1)-(2-2-4) are stored, to facilitate follow-up learning data analysis；
(3) learner learning performance is analyzed
It is respectively adopted linear fit method and uses the learning performance of the polynomial fitting method person that comes analytic learning；
(3-1) the learner learning performance using linear fit method is analyzed, and analytical procedure is as follows:
(3-1-1) in the data of knowledge point j, according to the knowledge point the Assessment of Learning Effect value of learner, learner is arranged
Name, obtains each learner ranking in each knowledge point, then to some learner ranking in all knowledge points
Situation of change is studied, and as a example by learner k, obtains this learner according to the method described above and arranges in the unit achievement of knowledge point j
Name；
(3-1-2) with learning knowledge point as X-axis, the ranking of this knowledge point the Assessment of Learning Effect value is Y-axis, in rectangular coordinate system
Drawing the scatterplot of the study ranking of all of knowledge point of certain learner, Y value the least expression ranking is the best, and achievement is the most outstanding,
Obtain the scatterplot of each learner in this approach；
(3-1-3) scatterplot in each scatterplot is fitted, obtains fitted linear regression line equation, utilize linear returning
The characteristic returning straight line draws the integral elevating situation of scatterplot in each scatterplot, wherein equation of linear regression formula, referring specifically to
Formula is as follows
WhereinRepresent jth knowledge point (j=1,2 ..., n), n represents knowledge point number,Represent the knowledge that learner is corresponding
Point j ranking (),ForMeansigma methods,ForMeansigma methods；
(3-1-4) calculating the degree of fitting of each equation of linear regression, wherein r represents the correlation coefficient of x, y, and linearly dependent coefficient is
In order to reflect the statistical indicator of two variable linearly dependency relations, as follows referring specifically to formula
WhereinRepresent jth knowledge point (j=1,2 ..., n), n represents knowledge point number,Represent the knowledge that learner is corresponding
Point j ranking (),ForMeansigma methods,ForMeansigma methods；
(3-1-5) combineSlope with regression straight lineJudge the learning performance of the whole course of learner, its decision rule
Design as follows:
Rule 1, if< 0.5, the fluctuation of person's school grade is relatively big to illustrate relational learning, unstable；
Rule 2, if> 0.5, person learns situation stably to illustrate relational learning, can judge according to the slope b of regression straight line
Along with the propelling of study course, the situation of change of learner marks sequencing；
Rule 3, if, illustrate that this learner presents progressive situation in whole course learning, andThe biggest, progressive
Situation is the most obvious；
Rule 4, if, illustrate that this learner presents the situation of room for manoeuvre in whole course learning, andThe biggest, room for manoeuvre
Situation is the most obvious；
(3-2) the learner learning performance using polynomial fitting method is analyzed, and analytical procedure is as follows:
(3-2-1) for learner k, foundationValue obtain this learner and exist
Ranking on the j of knowledge point；
(3-2-2) in rectangular coordinate system, draw the scatterplot of the study ranking of all knowledge points of learner k, abscissa X generation
Table knowledge point sequence number, vertical coordinate Y represents this learner ranking in corresponding knowledge point, and Y value the least expression ranking is the most forward, achievement
The best, the scatterplot of the person that simultaneously obtains Ensemble learning；
(3-2-3) calculate the equation of polynomial fit function corresponding to each scatterplot, polynomial fit function be one with big
The curve of most scatterplot phase matchings, utilizes the characteristic of polynomial fit function, we can see that scatterplot in each scatterplot
Overall variation situation, wherein polynomial fit function formula equation below
Wherein,Represent jth knowledge point t power (j=1,2 ..., n),Represent the one-tenth of knowledge point j corresponding for learner k
Achievement ranking (),It it is the coefficient matrix of polynomial fit function；
(3-2-4) each fitting of a polynomial degree is calculated, it is as follows that specific formula for calculation sees formula
Wherein,Represent jth knowledge point (j=1,2 ..., n),Represent knowledge point j corresponding for learner k marks sequencing (),It it is the coefficient matrix of polynomial fit function；
(3-2-5) foundationPass judgment on the fitting degree of polynomial fit function, curve degree of fittingIt is that each point arrives matched curve
Distance sum, the fit solution of numerical value the least explanation function is the best；
(3-2-6) fit polynomial function of all Ensemble learning persons is analyzed, describes according to the situation of change of function and learn
The learning performance of habit person.
Learner learning performance based on course teaching model the most according to claim 1 analyzes method, it is characterised in that: step
(2-1) gather teaching data within the class period in, answer a question achievement including classroom, work attendance activity achievementEvaluating data is paid attention to the class with classroom, wherein answer a question achievement in classroom
With work attendance activity achievementDirectly obtaining, evaluating data is paid attention to the class in classroomBy inquiry
Table obtains；
Gather extracurricular teaching data, including operation achievement, experiment gradePreview with review and comment
Valence mumber evidence, wherein operation achievementAnd experiment gradeDirectly
Obtain, review and preview evaluating dataTable obtains by inquiry.
Learner learning performance based on course teaching model the most according to claim 1 analyzes method, it is characterised in that
Step (2-1) arranges weight include:
Arranging the weight of education activities submodel, wherein, education activities weight within the class period is, extracurricular education activities are weighed
It is heavily, it is desirable to:；
Arranging the weight of each subactivity in education activities within the class period, wherein, paying attention to the class activity achievement weight is, answer
Problem activity achievement weight is, work attendance activity achievement weight is, it is desirable to:；
Arranging the weight of each subactivity in extracurricular education activities, wherein, operation achievement weight is, experiment grade
Weight is, review previews achievement weight and is, it is desirable to:；
The weight of a certain course i knowledge point is set, if the number of this course set point is n, it is desirable to:。
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