CN107798638B - Course selection recommendation method based on improved radar map - Google Patents
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Abstract
The invention aims to solve the technical problem of providing a course selection recommendation method based on an improved radar chart, which comprises the following steps: s1, establishing/correcting a course selection recommendation evaluation index system; s2, calculating and generating a single student curriculum taken evaluation index radar chart; s3, calculating and generating a guidance index radar chart for guiding teachers to select courses; s4, calculating and generating a course selection student individual demand index radar map; s5: dynamically determining the hierarchical relationship between the course selection individuals and the modified groups; s6: generating an individual student course selection target evaluation improved radar map; s7: calculating and issuing individual student course selection target recommendation values; s8: the analysis system recommends a rate of difference for students with the course selection. The course selection method is changed from a single index to a combined index and from a single radar model to a multi-radar-map superposition model, and establishes a multi-reference-source-based data analysis precaution, thereby avoiding series problems that strict and clear course selection guidance cannot be provided for students, or the intervention of course selection behaviors of the students is too deep and the like.
Description
Technical Field
The invention relates to a course selection method, in particular to a calculation method for carrying out the recommended value of a selected course according to the superposition of group indexes, individual indexes and guide indexes.
Background
Along with the improvement of education and teaching taking course selection and school classification as important contents, the course selection operation is easy to have blindness and followability in the face of dazzling course information especially for university newborns accustomed to the primary and secondary school education mode. The optimized selection of courses not only relates to the ordered arrangement of learning vigor and learning time and influences the personal affairs of awarding funds, graduation, direct research, abroad, employment and the like, but also relates to the reasonable arrangement of teaching resources of schools and is important content in the process of teaching activities.
At present, the course selection system realizes the comprehensive display of the information such as the name, type, credit, teacher, place of class, course arrangement condition and the like of the current course selectable by students, and allows the evaluation of the course in a message leaving mode or the statistics of the historical course selection condition. The technical measures still make the decision of course selection of students more shallow, and in some cases, the probability of blind follow is increased, and even certain misguidance exists.
For a special research analysis of course selection in tens of schools, we recognize that: course selection practices for many years accumulate a large amount of course selection processes and result data in the system, contain rich group experiences, and need to be further scientifically and fully mined; the individual knowledge accumulation, interests and hobbies and learning purposes of students have great influence on course selection and should be respected; meanwhile, as the reasonable regulation and control of teaching resources and the specific realization of personalized culture, schools also play a role in guiding course selection of students. Therefore, the establishment of a digital model of opinions of a single group cannot fully reveal the correspondence between the course attributes and the student attributes. The course selection recommendation method is researched under the technical background of intelligent analysis of personal behaviors, a multi-position integrated mathematical model containing reference group indexes, individual demand indexes and guide regulation and control indexes is established, a single numerical value is obtained according to a specific algorithm through separation and superposition of radar maps, and reference guidance is provided for course selection of students.
The method has certain popularization value under other application scenes.
Disclosure of Invention
The invention aims to solve the technical problem of providing a course selection recommendation method based on an improved radar map, wherein the course selection method is converted from a single index to a combined index and from a single radar model to a multi-radar map superposition model, and establishes data analysis prevention based on multiple reference sources, so that the series problems that the course recommendation index system of the current course selection system is imperfect, the calculation method is unscientific, rigorous and clear course selection guidance cannot be provided for students, or the course selection behavior of the students intervenes deeply are solved.
Therefore, the course selection recommendation method provided by the invention comprises the following steps:
s1, establishing/correcting a course selection recommendation evaluation index system;
s2, calculating and generating a single student curriculum taken evaluation index radar chart;
s3, calculating and generating a guidance index radar chart for guiding teachers to select courses;
s4, calculating and generating a course selection student individual demand index radar map;
s5: dynamically determining the hierarchical relationship between the course selection individuals and the modified groups;
s6: generating an individual student course selection target evaluation improved radar map;
s7: calculating and issuing individual student course selection target recommendation values;
s8: the analysis system recommends a rate of difference for students with the course selection.
Further, step S2 includes the following steps:
s21: determining a student group set meeting the sampling requirement according to a preset acquisition rule;
s22: distributing questionnaires to the sampling students through the system to obtain the evaluation values of the students on the repaired course evaluation index items;
s23: generating a Radar graph set Radar _ mapS for sampling personal evaluation indexes of taken courses of studentsim. Wherein i and m represent variable identifications of the student and the lesson, respectively, and a collection of assessment scores of the student for a plurality of assessment items of the specified lesson is stored in the form of a radar map.
Further, step S3 includes the following steps:
s31: respectively determining the corresponding tutors of each course according to a preset association rule to form a group set;
s32: a corresponding course selection guide scoring table is issued to a teacher for guidance through the system, and the teacher scores course index items which may influence course selection behaviors of students to form course selection suggestion quantitative data;
s33, calculating the combined score value T of the teacher group course guide index itemm。
Further, step S4 includes the following steps:
s41: currently, students set individual course selection requirements through a system and set the score of course selection target index items;
s42: calculating the score value U of the current student index itemmIf there are 6 index items, then calculate U separately1、U2、U3……U6To generate an individual demand index Radar map Radar _ mapU.
Further, step S5 includes the following steps:
s51: drawing a corresponding scatter diagram according to a preset student group subdivision rule, and determining a step relation between the individual curriculum selection students and the revised student group aiming at a specific curriculum;
s52: based on a scatter point model, taking the value of the current student as a base number, and according to a constant volume difference rate (adjustable), evaluating an index Radar graph set Radar _ mapS from individualsimScreening out student population index values which are converged with the background and the target/result of the student population as reference samples to form a reference population index Radar map set Radar _ mapSCnmThe index item score value calculation formula is as follows:
further, step S6 is detailed as follows: :
and generating a Radar _ mapT, a Radar _ mapU and a Radar _ map superposed by the indexes.
Further, step S7 includes the following steps:
s71: calculating the area value ST of the superposition area of the Radar map Radar _ mapT and Radar _ mapU, and the area value SU of the superposition area of the Radar map Radar _ mapS and Radar _ mapU;
s72: calculating the area difference SCT of the Radar map areas of the Radar _ mapT Radar and the Radar _ mapU Radar, and the area difference SCU of the Radar map areas of the Radar _ mapS Radar and the Radar _ mapU Radar;
s73: calculating a final course recommended value S according to a preset weight by taking the intersection ST and SU values of the areas of the radar map as reference values and the SCT and SCU values of the areas of the radar map as correction values;
further, step S8 is detailed as follows:
calculating the difference rate between the obtained course recommendation value and the final course selection condition of the student, providing the difference rate to a manager as a reference basis for revising index items and weight ratios so as to continuously improve the accuracy of course recommendation, integrating all course recommendation values of the students facing to individual students, and positioning the minimum recommendation value SminAnd a maximum recommended value SmaxThe interval is divided into five score sections, and the adoption rate of each section is calculated by combining the course finally selected by the student and the section where the course is located in the recommended value.
Compared with the prior art, the course selection recommendation method based on the improved radar chart has the following advantages:
1. the existing informatization application construction results of schools are fully utilized, and normalization, matching and accuracy of sample extraction are improved through big data analysis and utilization;
2. through reasonable induction and analysis of opinions and suggestions of different groups on the same affair by a mathematical model, and reference weight is distributed by combining individual tendency, so that the final result presents mutual coordination and respect of group opinions and individual opinions;
3. the introduction of the course selection recommendation value mode ensures that students in different levels can obtain referential quantitative guidance in different learning stages and different course selection purposes, avoids the neglect of a single evaluation result on the individual requirements of the students, and avoids the actual intervention and intervention on business behaviors.
4. The application of the radar map mode not only enables the evaluation process to be visualized from the aspect of display, but also is different from the conventional method that the adopted person is only affected by the final result value, and the adopted person can only focus on the result value of a single index item or a plurality of index items. Meet the requirements of different individuals for cutting the body.
5. The application of the multi-radar map superposition mode enables data processing to be smoother, the calculation method to be simpler, a foreground user can conveniently and visually know the data analysis process, and further comparison and balance between comprehensive indexes and individual indexes are achieved.
6. The method integrates the statistical principle, the big data analysis technology and the graph calculation technology, has clear venation, reasonable formula and proper scheduling, and can be realized easily.
7. The calculation method is separated from the service method to the maximum extent, and the flexible definition and development of the evaluation index are supported, so that the applicable range of the method is expanded.
Drawings
FIG. 1 shows the main steps of the process according to the invention.
FIG. 2 is a schematic diagram of a relationship model between course selection individuals and modified groups.
Fig. 3 is a schematic diagram of a radar map overlay model.
FIG. 4 is a schematic diagram of an unbalanced included angle radar map model.
FIG. 5 is a schematic diagram of the intersection of triangles formed in the area of adjacent indicator lines.
FIG. 6 is a schematic diagram of intersection of quadrangles formed in the areas of adjacent indicator lines.
Fig. 7 is an example of a radar map set of personal evaluation indices.
FIG. 8 is an example of a course A guidance index radar chart.
FIG. 9 is an example course B guidance index radar chart.
Fig. 10 is an example of a radar chart of individual demand indicators of course selection students 1.
Fig. 11 is an example of a group index radar chart for course a of the course selection student 1.
Fig. 12 is an example of a group index radar chart for course B of course selection student 1.
Fig. 13 is an example of a superimposed radar chart for course a of the lecturer 1.
Fig. 14 is an example of a superimposed radar chart for course B of the course selection student 1.
Detailed Description
The invention provides a course selection recommendation method based on an improved radar chart, which is further described in detail below for making the purpose, technical scheme and effect of the invention clearer and clearer, and it should be understood that the specific embodiments described herein are only used for explaining the invention and are not used for limiting the invention.
The first embodiment is as follows:
the invention provides a course selection recommendation method based on an improved radar map, which is improved in that single indexes are changed to combined indexes, a single radar map model is changed to a multi-radar map superposition model, and a data analysis method based on multiple reference sources is established according to the single radar map model, so that series problems that a course recommendation index system of a current course selection system is incomplete, a calculation method is not scientific, rigorous and clear course selection guidance cannot be provided for students, or the course selection behavior of the students intervenes deeply are solved.
A course selection recommendation method based on an improved radar map comprises the following steps:
s1: establishing/correcting a course selection recommendation evaluation index system;
the course selection recommendation evaluation index system comprises the contents of evaluation index item definition, student group subdivision method definition, teacher association method definition guidance, data acquisition rule definition, evaluation index management method definition and the like. The quantity and the content of the index items keep due elasticity. And the quantity of the evaluation index items of the recommended course is controlled within the range of 4-10 items from the viewpoint of convenient scoring operation.
S2: calculating to generate a single student taken course evaluation index radar chart;
further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S2 includes:
s21: determining a student group set meeting the sampling requirement according to a preset acquisition rule;
s22: distributing questionnaires to the sampling students through the system to obtain the evaluation values of the students on the repaired course evaluation index items;
s23, generating a Radar map set Radar _ mapS for sampling personal evaluation indexes of the curriculum taken by the studentim. Wherein i and m represent variable identifications of the student and the lesson, respectively, and a collection of assessment scores of the student for a plurality of assessment items of the specified lesson is stored in the form of a radar map.
S3: calculating and generating a radar chart for guiding teachers to select courses;
further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S3 includes:
s31: respectively determining the corresponding tutors of each course according to a preset association rule to form a group set;
s32: a corresponding course selection guide scoring table is issued to a teacher for guidance through the system, and the teacher scores course index items which may influence course selection behaviors of students to form course selection suggestion quantitative data;
s33, calculating the combined score value T of the teacher group course guide index itemm. If there are 6 index items, then respectively calculating T1、T2、T3……T6Forming the course guidance regulation index Radar graph Radar _ mapT.
The index item scoring value calculation formula is as follows:
s4: calculating and generating course selection student individual demand index radar map
Further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S4 includes:
s41: currently, students set individual course selection requirements through a system and set the score of course selection target index items;
s42: calculating the score value U of the current student index itemmIf there are 6 index items, then calculate U separately1、U2、U3……U6To generate an individual demand index Radar map Radar _ mapU.
S5: dynamically establishing the hierarchical relationship between the course selection individuals and the modified groups
Further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S5 includes:
s51: drawing a corresponding scatter diagram according to a preset student group subdivision rule, and determining the rank relation between the individual course selection students and the corrected student group aiming at the specific course.
An example is shown in figure 2. Fig. 2 is a schematic diagram of a relationship model between course selection individuals and corrected groups, in which the middle solid black point represents the current course selection students and the other gray points represent the corrected corresponding course students. The background value is an x axis, and the values obtained by learning ability association evaluation items (such as a preorder course, learning performance points of an associated course and the like) of each student before learning the current course are marked; the target value/outcome value is the y-axis, representing the expected achievement score for the course selection student and the actual achievement score for the lesson-modified student.
S52: based on a scatter point model, taking the value of the current student as a base number, and according to a constant volume difference rate (adjustable), evaluating an index Radar graph set Radar _ mapS from individualsimIn the method, the student group which is converged with the background and the target/result is screened outThe standard value is used as a reference sample to form a reference population index Radar map set Radar _ mapSCnm。
S53 calculating a group index Radar map set Radar _ mapSCnmThe combined score value S of each index itemm. If there are 6 index items, S is calculated respectively1、S2、S3……S6Forming the course population index single Radar map Radar _ mapS.
The index item scoring value calculation formula is as follows:
s6: generating an improved radar map for individual student course selection target assessment
Further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S6 includes:
generating a Radar _ mapT, Radar _ mapS and Radar _ mapU corresponding to the indexes, as shown in fig. 3, where values of an angle a between adjacent index items in the Radar map may adopt an average separation method according to actual requirements of business rules, that is, a is 360 ° ÷ n, where n represents the number of index items; or manually setting the line included angle values of different index items, strengthening the proportion relation among the index items, and enlarging the influence of the value of the specified index item on the evaluation result, wherein the formed radar map model is shown in fig. 4, and fig. 4 is a schematic diagram of the unbalanced included angle radar map model.
S7: calculating and issuing individual student course selection target recommendation value
Further, according to the course selection recommendation method based on the improved radar chart of the present invention, the step S7 includes:
s71: and calculating the area value ST of the Radar _ mapT and Radar map overlapping area, and the area value SU of the Radar _ mapS and Radar map overlapping area.
The n index items divide the whole radar map into n blocks of areas, the intersection area areas of the radar map in the two index item lines are calculated respectively, and the total area of the corresponding intersection area is formed through accumulation.
We fit two index terms to the in-line radar chartThe intersection area is always placed in the first quadrant for calculation with SnShowing the single region area. In the face of possible appearance of the intersection region, a corresponding calculation mode is provided, and the calculation mode is effective for both ST and SU evaluation.
(1) In the two adjacent index term line regions, if the index values corresponding to the same system are all the lowest values, the intersection graph of the regions is a triangle, as shown in fig. 5 OP1P2Region, FIG. 5 is a schematic diagram of the intersection of triangles formed in the neighboring index term line regions, OP1P2Area calculation method:
the area calculation mode is as follows:
a＝|OP1|
b＝|OP2|
(2) if the corresponding index values of any two systems are respectively the lowest values, the intersection graph of the area is a quadrangle. OP as shown in FIG. 61MP4Region, FIG. 6 is a schematic diagram of the intersection of quadrangles formed in the adjacent index item line regions
Designating a block OP1MP4The area of the region is calculated as follows:
known | OP1|，|OP2|,|OP3|,|OP4And |, applying a trigonometric function to obtain the coordinates of corresponding points:
P1(x1,y1),P2(x2,y2),P3(x3,y3),P4(x4,y4)
consists of:
l:y＝k1x+b1
m:y＝k2x+b2
wherein:
b1＝y1-k1·x1
b2＝y3-k2·x3
and (3) solving corresponding coordinates of the M points:
y5＝x5·k1+b1
from the equation for the distance between two points:
and (3) pushing out:
order:
a＝|OP1|,b＝|P1M|,c＝|P4M|,d＝|OP4|
calculating area value S of single intersection region by applying Helen formulan：
(3) The ST and SU value calculation is completed according to the method. The calculation formula is as follows, wherein n represents the number of index items:
s72: and calculating the area difference SCT of the Radar regions of the Radar _ mapT Radar map and the Radar _ mapU Radar map, and the area difference SCU of the Radar regions of the Radar _ mapS Radar map and the Radar _ mapU Radar map.
Similarly, the whole Radar map is divided into n blocks of areas by the n index items, the areas of the Radar map in the two index item lines are respectively calculated, and the areas are accumulated to form the total areas of the Radar _ mapT, Radar _ mapS and Radar _ mapU areas. The radar map areas in the two index terms are always placed in the first quadrant for calculation. The foregoing calculation method is effective for evaluating SCT and SCU, and the calculation formula is as follows:
s73: and calculating a final course recommended value S according to a preset weight by taking the area intersection ST and SU values of the radar map as reference values and the area difference SCT and SCU values of the radar map as correction values. The calculation formula is as follows:
S＝ST·R1+SU·(1-R1)-|SCT|·R2-|SCU|·(1-R2)
wherein R is1Representing the weight relationship between the 'coincidence degree of the guide regulation value and the individual demand value' and the 'coincidence degree of the group index value and the individual demand value' in result calculation; r2Representing the weight relationship between the difference between the guide regulation value and the individual demand value and the difference between the group index value and the individual demand value in result calculation.
The weight ratio is set by a manager according to specific conditions, and the value interval is 1% -100%. The introduction of the method further strengthens the influence of the specified index on the final result value.
And the final recommended value S is opened to the student who requests to initiate, and provides reference for course selection behaviors. The recommended value of the same course may be different for different students, thereby forming personalized course selection recommendation service
S8: analysis system recommends and adopts difference rate with lecture selection student
And calculating the difference rate between the obtained course recommendation value and the final course selection condition of the student, and providing the difference rate for a manager as a reference basis for revising the index items and the weight ratio so as to continuously improve the course recommendation accuracy.
Aiming at individual students, integrating all course recommended values and positioning minimum recommended value SminAnd a maximum recommended value SmaxThe interval is divided into five score sections, and the adoption rate of each section is calculated by combining the course finally selected by the student and the section where the course is located in the recommended value. Theoretically, the high-level recommended value adoption rate should be the highest, otherwise, the manager needs to further analyze specific reasons affecting the adoption rate, including: and reasonableness of setting factors such as index items and weight ratios.
By further applying the achievement of the invention, the system can reasonably give consideration to the combined course selection target requirements of students through a rotation training calculation mode, and recommend course selection courses in batches, thereby improving the quality of course selection service of students.
Example two:
the application of the principles of the present invention will be further described with reference to the accompanying drawings.
(1) Establishing/correcting course selection recommendation evaluation system
This step is the aforementioned step S1.
The course selection recommendation evaluation system is defined by combining with the practical situation of the school, and is adjusted along with the yearly accumulation of the service data and the evaluation of the practical use situation of the method.
The index items are flexibly set by combining the specific conditions of schools and the evaluation requirements, and multi-level index division can be performed. The value trend is the high score which is most suitable for personal expectation under the general condition, otherwise, the low score is the reference example of the evaluation index item in the following table 1:
part of index item scoring values can be extracted and analyzed through the existing data of campus informatization application, for example, the teacher teaching condition evaluation data can be from a teaching quality monitoring and evaluation system of a school, and a targeted questionnaire survey is carried out, so that the objectivity of an evaluation result is improved.
(2) Calculating and generating a radar chart of the evaluation indexes of the taken courses of a single student
This step is the aforementioned step S2.
A group evaluation value list of the sampling students for the code a course is calculated by taking the following table 2 as an example:
a list of group assessment values of the sampling students for the code B course is calculated by taking the following table 3 as an example:
from the list values, two courses of A, B student pairs were created, respectively, and a total of 18 individual evaluation index Radar charts Radar _ mapS were createdimAs shown in fig. 7.
(3) Calculating and generating a radar chart for guiding teachers to select courses
This step is the aforementioned step S3.
The guidance evaluation value for the tutor for the a course was calculated by taking table 4 below as an example.
Drawing a Radar map Radar _ mapT guiding course selection index for guiding teacher A course according to the result average valueaAs shown in fig. 8.
The guidance evaluation value of the tutor for the B course is calculated by taking table 5 below as an example.
Drawing a Radar map Radar _ mapT guiding course selection index for guiding the teacher B course according to the result average valuebAs shown in fig. 9.
(4) Calculating and generating course selection student individual demand index radar map
This step is the aforementioned step S4.
Obtaining the requirement score of the course selection student index item, which is represented as the following example:
respectively drawing a Radar map Radar _ mapU of course selection requirement indexes of student 1 and student 2 according to the result values1、Radar_mapU2As shown in fig. 10.
(5) Dynamically establishing the hierarchical relationship between the course selection individuals and the modified groups
According to the preset rule, the student 1 selecting courses facing the course A forms sample correspondence with the sampling students 1, 2, 3, 4, 5 and 8.
Calculating a reference population index value:
generating a group index Radar map Radar _ mapS facing course A of course selection student 1 according to the average value1aAs shown in fig. 11.
The course selection student 1 facing the course B forms sample correspondence with the sampling student 2, the sampling student 3, the sampling student 5, the sampling student 6, the sampling student 7, and the sampling student 9.
Calculating a reference population index value:
generating a group index Radar map Radar _ mapS facing course B of course selection student 1 according to the average value1bAs shown in fig. 12.
(6) Generating an improved radar map for individual student course selection target assessment
This step is the aforementioned step S6.
For the course selection student 1, summarizing various index values for the course A:
the corresponding superimposed radar map is generated as shown in fig. 13.
For the course selection student 1, summarizing various index values for the course B:
the corresponding superimposed radar map is generated as shown in fig. 14.
(7) Calculating and issuing individual student course selection target recommendation value
This step is the aforementioned step S7.
According to the above calculation formula, the course selection recommended value for course a of course selection student 1:
ST＝St1ot2+St2ot3+St3ou4+Su4ou5+Su5ot6+St6ot1
＝3.9+3.9+3.9+3.9+3.9+3.9
＝23.4
SU＝Su1ou2+Su2ou3+Su3ou4+Su4os5m1+Ss5ou6m2+Su6ou1
＝3.9+3.9+3.9+3.4+3.5+3.9
＝22.5
|SCT|＝|St1t2t3t4t5t6-Su1u2u3u4u5u6|
＝|32.0-23.4|
＝8.6
|SCU|＝|Ss1s2s3s4s5s6-Su1u2u3u4u5u6|
＝|32.4-23.4|
＝9
R1＝0.5
R2＝0.5
S1a＝ST·R1+SU·(1-R1)-|SCT|·R2-|SCU|·(1-R2)
＝23.4·0.5+22.5·0.5-8.6·0.5-9·0.5
＝14.2
according to the above calculation formula, the course selection recommended value for course B of course selection student 1:
ST＝Su1u2u3u4u5u6
＝23.4
SU＝Su1u2u3u4u5u6
＝23.4
|SCT|＝|St1t2t3t4t5t6-Su1u2u3u4u5u6|
＝|40.3-23.4|
＝16.9
|SCU|＝|Ss1s2s3s4s5s6-Su1u2u3u4u5u6|
＝|35.7-23.4|
＝12.3
R1＝0.5
R2＝0.5
S1b＝ST·R1+SU·(1-R1)-|SCT|·R2-|SCU|·(1-R2)
＝23.4·0.5+23.4·0.5-16.9·0.5-12.3·0.5
＝8.8
in comparison, it is very obvious that course a is more compliant than course B with respect to the course selection requirements of student 1.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (3)
1. The course selection recommendation method based on the improved radar map is characterized by comprising the following steps of:
s1, establishing/correcting a course selection recommendation evaluation index system;
s2, calculating and generating a single student curriculum taken evaluation index radar chart;
s3, calculating and generating a guidance index radar chart for guiding teachers to select lessons, wherein the step S3 includes:
s31: respectively determining the corresponding tutors of each course according to a preset association rule to form a group set;
s32: a corresponding course selection guide scoring table is issued to a teacher for guidance through the system, and the teacher scores course index items which may influence course selection behaviors of students to form course selection suggestion quantitative data;
s33, calculating the combined score value T of the teacher group course guide index itemmIf there are 6 index items, then calculate T separately1、T2、T3……T6Forming a course guidance regulation index Radar chart Radar _ mapT;
s4, calculating and generating a course selection student individual demand index radar map, wherein the step S4 comprises the following steps:
s41: currently, students set individual course selection requirements through a system and set the score of course selection target index items;
s42: calculating the score value U of the current student index itemmIf there are 6 index items, then calculate U separately1、U2、U3……U6Generating an individual demand index Radar map Radar _ mapU;
s5: dynamically determining the hierarchical relationship between the course selection individuals and the modified groups;
s6: generating an individual student course selection target evaluation improved radar map;
s7: calculating and issuing individual student course selection target recommendation values;
s8: the analysis system recommends a difference rate for students with course selection;
step S5 includes the following steps:
s51: drawing a corresponding scatter diagram according to a preset student group subdivision rule, and determining a step relation between the individual curriculum selection students and the revised student group aiming at a specific curriculum;
s52: based on a scatter point model, taking the value of the current student as a base number, and according to a certain volume difference rate, evaluating an index Radar graph set Radar _ mapS from individualsimScreening out student population index values which are converged with the background and the target/result of the student population as reference samples to form a reference population index Radar map set Radar _ mapSCnmThe index item score value calculation formula is as follows:
the value of the angle a between adjacent index item lines in the radar chart may be an average separation method according to actual requirements of business rules, where a is 360 ° ÷ n, where n represents the number of index items, and n is the number of index items that the radar chart is equally separated, and S is the number of index items that the radar chart is equally separatednM is variable identification of the course taken by the student, SmFor reference purposesMerging the score values of all index items in the population index radar image set;
s53 calculating a group index Radar map set Radar _ mapSCnmThe combined score value S of each index itemmIf there are 6 index items, S is calculated respectively1、S2、S3……S6Forming a course group index single Radar map Radar _ mapS;
step S6 is detailed as:
generating a Radar _ mapT, a Radar _ mapU and a Radar _ mapU superposed Radar graph corresponding to the indexes;
step S7 includes the following steps:
s71: calculating the area value ST of the superposition area of the Radar map Radar _ mapT and Radar _ mapU, and the area value SU of the superposition area of the Radar map Radar _ mapS and Radar _ mapU;
s72: calculating the area difference SCT of the Radar map areas of the Radar _ mapT Radar and the Radar _ mapU Radar, and the area difference SCU of the Radar map areas of the Radar _ mapS Radar and the Radar _ mapU Radar;
s73: and calculating a final course recommended value S according to a preset weight by taking the area intersection ST and SU values of the radar map as reference values and the area difference SCT and SCU values of the radar map as correction values.
2. The course selection recommendation method based on the improved radar chart as claimed in claim 1, wherein the step S2 comprises the steps of:
s21: determining a student group set meeting the sampling requirement according to a preset acquisition rule;
s22: distributing questionnaires to the sampling students through the system to obtain the evaluation values of the students on the repaired course evaluation index items;
s23: generating a Radar graph set Radar _ mapS for sampling personal evaluation indexes of taken courses of studentsimWherein i and m represent variable identifications of the student and the lesson, respectively, and storing a collection of assessment scores of the student for a plurality of assessment items of the given lesson in the form of a radar map.
3. The improved radar chart-based course selection recommendation method according to claim 1, wherein step S8 is detailed as follows:
calculating the difference rate between the obtained course recommendation value and the final course selection condition of the student, providing the difference rate to a manager as a reference basis for revising index items and weight ratios so as to continuously improve the accuracy of course recommendation, integrating all course recommendation values of the students facing to individual students, and positioning the minimum recommendation value SminAnd a maximum recommended value SmaxThe interval is divided into five score sections, and the adoption rate of each section is calculated by combining the course finally selected by the student and the section where the course is located in the recommended value.
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