CN114936809A - Examination score data processing system and terminal based on big data - Google Patents
Examination score data processing system and terminal based on big data Download PDFInfo
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Abstract
The invention provides an examination result data processing system and a terminal based on big data, relating to the technical field of student situation analysis.A result general analysis module is used for acquiring the results of all examinees in an examination area and carrying out total score ranking and branch ranking; filling the obtained examinee scores into the set examination subjects, and calculating the percentage ranking of each subject in the examination area; the score comparison module is used for calling the obtained scores, obtaining the highest score and the lowest score of each department in the examination area through comparison and calculating the average score of each department; the weak department score analysis module is used for analyzing scores of all departments of the examinees, extracting the score loss rate of each question in the examination paper, and predicting the learning content capable of improving the score within the preset period of the students by combining the score change trend of the exams of the examinees in the past examinations. The system carries out all-round analysis to each subject and reads, for the teaching of school provides digital support, and all-round understanding student learns the feelings, and teacher's teaching feelings finally realizes the accurate teaching of high quality.
Description
Technical Field
The invention relates to the technical field of emotion analysis, in particular to an examination score data processing system and a terminal based on big data.
Background
The study condition analysis is the analysis of the learning process and the learning scores of the students in the teaching process. The learning condition refers to the learning state of a student in a certain unit time or a certain learning activity, and the learning condition comprises learning characteristics, learning interest, learning habits, learning modes and the like.
The study condition analysis is to analyze the examination scores of the students to obtain the corresponding study conditions. At present, the study analysis means in the campus is to count the examination scores of students according to the scores of each department and list the students in the campus. And then counting the total scores of the student examinations and naming the students side by side. But teachers and students cannot know weak knowledge points of students and cannot know the tendency of the test results of the previous times. When the examination in the study area is not ranked, teachers and students cannot know the pre-estimated positions of the scores and the total scores of all the disciplines in the examination area, and when the students carry out the study promotion and fill in the volunteers, the schools matched with the students cannot be pre-estimated, so that the study promotion rate of the students is influenced. And after daily examinations, only the general scores are summarized by a formula, so that students cannot learn pointedly, the learning scores of the students cannot be effectively improved, and the subsequent examination scores of the students are influenced.
Disclosure of Invention
The invention provides an examination result data processing system based on big data, which can lead students to carry out targeted learning and improve the learning results.
The system comprises: the system comprises a score profile analysis module, a subject score analysis module, a score comparison module, a historical exam score trend analysis module and a Weak subject score analysis module;
the score profile analysis module is used for acquiring scores of all examinees in the examination area and performing total score ranking and branch ranking on the scores of the examinees;
the subject score analysis module is used for providing an examination subject setting interface for a user to set examination subjects; filling the obtained examinee scores into the set examination subjects, and calculating the percentage ranking of each subject in the examination area;
the score comparison module is used for calling the obtained scores, obtaining the highest score and the lowest score of each department in the examination area through comparison and calculating the average score of each department;
the system comprises a historical examination score trend analysis module, a score change trend chart, a score analysis module and a score analysis module, wherein the historical examination score trend analysis module is used for configuring a score change trend chart for the total score and each section score of an examinee's historical examination;
the weak department score analysis module is used for analyzing scores of all departments of the examinees, extracting the score loss rate of each question in the examination paper, and predicting the learning content capable of improving the score within the preset period of the students by combining the score change trend of the exams of the examinees in the past examinations.
It is further to be noted that it is,
the historical examination score trend analysis module is also used for calling the previous examination state information and calculating the examination rank X of the examination in the following way 1 ；
P0 is the average score of the test taker's past exam;
q0 is the average difficulty coefficient of previous examinations;
X 0 the examination name of the previous examination of the examinee is given;
p1 is the total score of the test;
q1 is the difficulty factor of the test.
The subject score analysis module is further used for sequencing the original scores of the students in the examination area from high to low, and determining the proportion of the number of the students according to the statistical distribution of the volume scores of the groups of the students; according to the number proportion of the volume-face scores, dividing the examinees into examinee groups with preset number, giving a grade to each group examinee, converting the volume-face scores of the examinee groups into the corresponding grade, and determining the grade of each examinee according to the ranking of each examinee group in the corresponding examinee group.
It should be further noted that, according to the equal proportion conversion mode, the subject score analysis module calculates the rank of each examinee in the following way:
wherein the content of the first and second substances,
S 1 ，S 2 respectively representing the lower limit and the upper limit of the volume surface partition corresponding to a certain grade;
T 1 ，T 2 respectively representing the lower limit and the upper limit between the grading assigned areas of the corresponding grades;
S 0 representing the volume score of a test taker in the corresponding level;
T 0 indicating the assigned score of a test taker in the corresponding grade.
It is further noted that the subject achievement analysis module also provides the score percentage ranking calculation information;
calculating the percentage ranking of the fraction F in a certain fraction sample;
F= [（B+0.5E）/n]×100%
b is the number of fractions less than F, E is the frequency of occurrence of F in the sample, and n is the total number of fractional samples.
It is further noted that the trend analysis module for the examination results of the previous times also calculates a trend graph of the change of the super average rate of the previous examinations of the examinees;
the super-average rate calculation mode is as follows: (total fraction-average fraction)/100%;
and a score change trend chart of the total scores and the scores of all departments of the examination of the past and a super-average rate change trend chart are displayed on the terminal for teachers and students to check.
Further, it should be noted that the method further includes: a ranking analysis module;
the ranking analysis module is used for providing a ranking analysis page for the student end, and the ranking analysis page shows the total number of the examinees, the highest score, the lowest score, the average score, the median, the mode and the ranking condition of the examinees;
and (3) configuring a column distribution diagram of the scores and the number of the students in each examination subject, grouping the scores, finding out the number of the students under each score, indicating the number of the students according to the height of the column, and marking the positions of the students.
It should be further noted that the ranking analysis module is further configured to analyze a standard deviation of the performance, where the standard deviation is calculated as:
the score of each examinee is subtracted from the average score to obtain a score difference CA, and the score difference CA is subjected to square calculation to obtain a square result value CB of each examinee score;
adding the square result values CB of each examinee score to obtain a total square result value CD;
and dividing the total square result value CD by the total number CY of examinees to obtain CZ, and squaring the CZ to obtain the standard deviation of the score.
Further, it should be noted that the method further includes: a scoring policy module;
the scoring strategy module is used for scoring the knowledge points of the test into parent knowledge points and child knowledge points of the next level of the parent knowledge points;
extracting the wrong questions of the father-level knowledge points, calling a plurality of corresponding child-level knowledge points from the father-level knowledge points, and displaying the child-level knowledge points to students for learning;
configuring the parent-level knowledge points and the associated multiple child-level knowledge points into a scoring strategy page;
and acquiring the accuracy of the test questions corresponding to the secondary knowledge points of the students, and acquiring the mastery level of the secondary knowledge points.
The invention also provides a terminal for realizing the examination result data processing system based on big data, which comprises:
the memory is used for storing a computer program and an examination result data processing system based on big data;
the processor is used for executing the computer program and the examination result data processing system based on the big data so as to realize the examination result data processing system based on the big data;
the display screen is used for displaying data information in the examination result data processing system based on the big data;
and the communication module is used for uploading data information in the examination result data processing system based on the big data to the cloud server and sending the data information in the examination result data processing system based on the big data to the student client and the teacher client.
According to the technical scheme, the invention has the following advantages:
the examination result data processing system based on the big data provided by the invention realizes multi-angle and multi-direction situation analysis through the result general analysis module, the subject result analysis module, the result comparison module, the historical examination result trend analysis module and the Weak subject result analysis module, provides a scoring strategy, analyzes scoring directions of different knowledge points of students, makes teaching more pertinent and improves the results of the students.
The system can analyze the tendency of the scores of the examinations of the past, so that students, parents and teachers can know the change of the scores of the examinations of each time, the students are helped to improve the scores, and the students can be assisted to find the direction for enhancing learning.
The system can provide total point data, average points, a total point change trend graph of examinations of all times, total points of the examination areas, schools and classes and the highest points and the lowest points of the total points for each student; the teacher, the parents and the students can clearly know the academic state of the students. The student learning rate is improved, so that students can learn in a targeted manner, the learning score is effectively improved, and the preparation for subsequent examinations is purposefully carried out.
The examination result data processing system based on big data provided by the invention can also analyze the existing academic level of the students and the academic level to which the students possibly develop, thereby realizing the rapid promotion of the students' academic.
The examination result data processing system based on the big data provided by the invention can be used for collecting, deeply mining and analyzing the learning data of students, monitoring the learning condition of the students from digitalization to datamation, carrying out all-dimensional analysis and interpretation aiming at each subject, analyzing knowledge holes in a targeted manner, focusing weak points, providing a special promotion scheme, pushing personalized learning resources, strengthening weak knowledge points of the students and enabling the learning and promotion of the students to be more targeted. The digital support is provided for the teaching of the school, students can learn the situation in all directions, teachers can teach the situation, and high-quality accurate teaching is finally realized.
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In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of a big data based test result data processing system.
Fig. 2 is a schematic diagram of a terminal implementing a big data based examination score data processing system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a terminal machine for realizing an examination result data processing system based on big data, as shown in figure 1, comprising: memory, processor, display screen, communication module, printing module, user input module, interface module, and power module, among others. It is to be understood that not all illustrated components are required to be implemented. More or fewer components may alternatively be implemented.
Specifically, the memory is used for storing a computer program and an examination result data processing system based on big data; the processor is used for executing the computer program and the examination result data processing system based on the big data so as to realize the examination result data processing system based on the big data; the display screen is used for displaying data information in the examination result data processing system based on the big data; and the communication module is used for uploading data information in the examination result data processing system based on the big data to the cloud server and sending the data information in the examination result data processing system based on the big data to the student client and the teacher client.
The terminal may include a mobile terminal such as a mobile phone, a smart phone, a notebook computer, a Personal Digital Assistant (PDA), a PAD computer (PAD), etc., and a fixed terminal such as a Digital TV, a desktop computer, etc. In the following, it is assumed that the terminal is a mobile terminal. However, it will be understood by those skilled in the art that the configuration according to the embodiment of the present invention can be applied to a fixed type terminal in addition to elements particularly used for moving purposes.
The terminal can obtain the examination scores of students each time, summarize the examination scores of the students, calculate to obtain total scores and automatically rank the scores. The terminal machine can store the examination scores and examination data of each time for teachers and students to look up.
As shown in fig. 2, the examination result data processing system based on big data according to the present invention includes: the system comprises a score profile analysis module, a subject score analysis module, a score comparison module, a historical exam score trend analysis module and a Weak subject score analysis module;
the score profile analysis module is used for acquiring scores of all examinees in the examination area and performing total score ranking and branch ranking on the scores of the examinees;
the examination area related to the invention can be a school, or an administrative district of a city, or a city, etc. A class ranking, school ranking, and city ranking of the total score, among others, may be performed. The score difference between the ranks can be listed, the change condition of each rank can be displayed, and different colors can be displayed when the change condition is displayed, so that students and teachers can conveniently look up the change condition.
Since the examination has a plurality of subjects, the invention can also display and rank the scores of the various subjects. Such as mathematics, languages and foreign languages. Then the three departments can be ranked separately or displayed with a total score ranking. The invention can display the achievements in various forms according to the teaching requirement.
The subject score analysis module is used for providing an examination subject setting interface for a user to set examination subjects; according to the needs of the examination area, teachers and schools can set systematic examination subjects, and the requirements of statistical analysis of examination scores of students are met. The system can provide a corresponding test subject setting interface for the user to set the test subjects; for example, the school sets the subjects of Chinese, mathematics, biology and English, so that the subjects of examination set the interface of Chinese, mathematics, biology and English, and the scores of the students after examination are filled into the corresponding positions. The filling mode can be realized by the system through a crawler tool to acquire the examination scores of the students, and the system can also automatically acquire the examination scores of the students each time through other modes for analysis and use.
The system reports the obtained examinee scores to the set examination subjects, and calculates the percentage ranking of each subject in the examination area;
in the present invention,
the historical examination score trend analysis module is also used for calling the previous examination state information and calculating the examination rank X of the examination in the following way 1 ；
P0 is the average score of the test taker's past exam;
q0 is the average difficulty coefficient of previous examinations;
X 0 the examination name of the previous examination of the examinee is given;
p1 is the total score of the test;
q1 is the difficulty factor of the test.
Through the calculation mode, the examination name can be pre-judged in advance, and then the name can be pre-judged for filling and reporting the wish, or the self learning condition and the name are pre-judged.
For example, the difficulty factor of the test at this time is Q1 for a city or an administrative area, which may be set by the test administration organization prior to the test. P1 is the total score of the test, which is the total score of the current test, which has also been determined before the test. X 0 The test rank of the previous test of an examinee is determined based on the average rank of the previous test of the examinee or the average rank of the previous test under the condition that the total score is the same as or different from the total score of the current test, and Q0 is the average difficulty coefficient of the previous test and can beThe difficulty coefficient may be averaged by the previous difficulty coefficients, or may be only the difficulty coefficient of a certain test.
If the difficulty coefficients are different, the average difficulty coefficient of the Q0 previous test is calculated. P0 corresponds to the average score of the previous examination of the examinee. The examination name of the examinee can be obtained through the parameter calculation.
Furthermore, the subject score analysis module is also used for sequencing the original scores of the students in the examination area from high to low and determining the proportion of the number of the students according to the statistical distribution of the volume scores of the groups of the students; according to the number proportion of the volume-face scores, dividing the examinees into examinee groups with preset number, giving a grade to each group examinee, converting the volume-face scores of the examinee groups into the corresponding grade, and determining the grade of each examinee according to the ranking of each examinee group in the corresponding examinee group.
The subject score analysis module calculates the grade of each examinee according to the equal proportion conversion mode in the following calculation mode:
wherein the content of the first and second substances,
S 1 ，S 2 respectively representing the lower limit and the upper limit of the volume surface partition corresponding to a certain grade;
T 1 ，T 2 respectively representing the lower limit and the upper limit between the grading assigned areas of the corresponding grades;
S 0 representing the volume score of a test taker in the corresponding grade;
T 0 indicating the assigned score of a test taker in the corresponding class.
For example, as shown in Table 1 below
The original scores of the students are sequentially arranged from high to low, and the number of people is determined according to the statistical distribution of the volume scores of the examinee groups and the scores of the examinee groups and is divided according to the proportion. And dividing the selected subject test taker group into 5 groups, wherein each group test taker is endowed with 1 grade, the volume-plane score of the whole test taker group is converted into 5 grades from high to low of A, B, C, D, E, and the grade of each test taker is determined by the ranking of the selected subject group.
Suppose that the roll surface of a certain student ideological and political subject is divided into 75 grades, the grade determined according to the roll surface grading ranking of the subject examinee group is B grade, and the section of the roll surface corresponding to the subject B grade in the examination is 80-61. The table 1 shows that the grade of the B grade is assigned to the interval of 82-71. Then the rank of the classmatic ideological and political subject can be calculated according to equation (1):
（80-75）/（75-61）=（82-T 0 ）/（T 0 -71）
get T after decomposition 0 =79。
The average score of the whole school of the examination can be as follows: the score of students in the whole school/the number of students in the whole school.
The subject score analysis module can also analyze the difficulty level of the subject in each subject. And the number of questions, the fraction of each question and the answering condition of students are also analyzed and compared with the answering accuracy of the last test.
Wherein the examination subject difficulty can be divided into extreme difficulty, generality, easiness and extreme easiness. The score of the difficulty in the scroll can be calculated by the full score of the subject and/or the full score of the test paper.
The accuracy can be determined by the number of questions/the number of total questions in the difficulty level.
The accuracy is as follows: the correct rate of the questions with the same difficulty in the previous examination-the correct rate of the questions with the same difficulty in the current examination; rises above 0 and falls below 0.
Further, the subject score analysis module also provides score percentage ranking calculation information;
that is, the score percentage ranking information is calculated by the ratio of the test takers with a certain score in the test area.
Specifically, calculating the percentage ranking of the fraction F in a certain fraction sample;
F= [（B+0.5E）/n]×100%
b is the number of fractions less than F, E is the frequency of occurrence of F in the sample, and n is the total number of fractional samples.
Therefore, the proportion ranking of the examination scores of each student in the examination area can be obtained, the students can be helped to analyze the ranking and ranking, the students can be helped to fill in the volunteers, and the self ranking of the students can be known.
The invention also relates to a score comparison module which is used for calling the obtained scores, obtaining the highest score and the lowest score of each department in the examination area through comparison and calculating the average score of the examinations of each department; the highest score, the lowest score and the average score of each department in the examination area can be displayed through the display screen for reference of teachers and schools.
The invention also relates to a score trend analysis module for configuring score change trend graphs for the total scores and the scores of each department of the examination of the previous times;
the historical examination score trend analysis module also calculates a mean-square rate change trend graph of the examinee's historical examinations; the super-average rate calculation mode is as follows: (total fraction-average fraction)/100%; and displaying the score change trend graph of the total score and each section score of the previous examination and the score change trend graph of each section score and the super-average rate change trend graph on the terminal for the teacher and the students to view. The student, the parents and the teacher can know the change of the score of each examination, the student is helped to improve the score, and the student can be assisted to find the direction for enhancing learning.
The invention can also analyze the weak family and show the weak family to students and teachers. Specifically, the weak subject score analysis module is used for analyzing the scores of all subjects of the examinees, extracting the score loss rate of each subject in the examination paper, and predicting the learning content capable of improving the score in the preset period of the student according to the score change trend of the examinee in the previous examination.
For example: the original score, the assigned score, the class ranking, the school ranking, the grade and the subject grade of the weak subject are obtained from the ranking percentage interval. As shown in table 2 below:
in the present invention, the system further comprises: a ranking analysis module;
the ranking analysis module is used for providing a ranking analysis page for the student end, and the ranking analysis page shows the total number of the examinees, the highest score, the lowest score, the average score, the median, the mode and the ranking condition of the examinees;
and (3) configuring a bar distribution diagram of the scores and the number of people in each examination subject, grouping the scores, finding out the number of people under each score, representing the number of people according to the height of the bar, and marking the positions of students. The students can visually know the examination ranking condition of the students.
The ranking analysis module is also used for analyzing the standard deviation of the achievement, and the standard deviation calculation mode is as follows: the score of each examinee is subtracted from the average score to obtain a score difference CA, and the score difference CA is subjected to square calculation to obtain a square result value CB of each examinee score; adding the squared result values CB of each examinee score to obtain a total squared result value CD; and dividing the total square result value CD by the total number CY of examinees to obtain CZ, and squaring the CZ to obtain the standard deviation of the score.
The standard deviation can enable schools and teachers to master the overall situation of each examination, the overall academic industry of students can be known, targeted teaching is achieved, the teaching quality is improved, and the score is improved.
By the mode, students and teachers can know learning states and examination conditions, and the system related by the invention further comprises: a scoring policy module;
the scoring strategy module is used for scoring the knowledge points of the test into parent knowledge points and child knowledge points of the next level of the parent knowledge points;
for example, some examination areas divide the discipline into science and literature. Then the science is the parent knowledge point. The sub-levels are mathematical, physical, etc.
For another example, mathematics is the parent level, and the child level is each section in the mathematical subject.
Extracting the wrong questions of the father-level knowledge points, calling a plurality of corresponding child-level knowledge points from the father-level knowledge points, and displaying the child-level knowledge points to students for learning;
and configuring the parent knowledge points and the associated multiple child knowledge points into a scoring strategy page. Analyzing the plurality of sub-level knowledge points into weak knowledge points, and constructing scoring directions of students; and acquiring the accuracy of the test questions corresponding to the secondary knowledge points of the students, and acquiring the mastery level of the secondary knowledge points.
For the scoring strategy, the system can provide a knowledge point grasping level lifting diagram, and can display the grasping conditions of father and son levels through a histogram and a dot diagram, so that the student lifting effect is ensured.
The scoring strategy module can also provide a scoring page, which is a test paper analysis page of students, displays the general view of the subject test paper, and evaluates the comparison condition of the ratio of the knowledge point scores and the ratio of the knowledge point scores of the subject.
And providing a scoring strategy page for the student, analyzing weak knowledge points according to the evaluation result, and analyzing scoring directions of different knowledge points of the student.
Based on the system, the invention also provides a terminal for realizing the examination result data processing system based on the big data, which comprises:
the memory is used for storing a computer program and an examination result data processing system based on big data;
the processor is used for executing the computer program and the examination result data processing system based on the big data so as to realize the examination result data processing system based on the big data;
the display screen is used for displaying data information in the examination result data processing system based on the big data;
and the communication module is used for uploading data information in the examination result data processing system based on the big data to the cloud server and sending the data information in the examination result data processing system based on the big data to the student client and the teacher client.
The big data based test achievement data processing system is the exemplary elements and algorithm steps described in connection with the embodiments disclosed herein, and can be implemented in electronic hardware, computer software, or combinations of both, the exemplary elements and steps having been generally described in the foregoing description by function for clarity of explanation of the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A big-data-based examination result data processing system, comprising: the system comprises a score profile analysis module, a subject score analysis module, a score comparison module, a historical exam score trend analysis module and a Weak subject score analysis module;
the score profile analysis module is used for acquiring scores of all examinees in the examination area and performing total score ranking and branch ranking on the scores of the examinees;
the subject score analysis module is used for providing an examination subject setting interface for a user to set examination subjects; filling the obtained examinee scores into the set examination subjects, and calculating the percentage ranking of each subject in the examination area;
the score comparison module is used for calling the obtained scores, obtaining the highest score and the lowest score of each department in the examination area through comparison and calculating the average score of each department;
the system comprises a historical examination score trend analysis module, a score change trend chart, a score analysis module and a score analysis module, wherein the historical examination score trend analysis module is used for configuring a score change trend chart for the total score and each section score of an examinee's historical examination;
the weak department score analysis module is used for analyzing scores of all departments of the examinees, extracting the score loss rate of each question in the examination paper, and predicting the learning content capable of improving the score within the preset period of the students by combining the score change trend of the exams of the examinees in the past examinations.
2. The big-data-based examination result data processing system of claim 1, wherein the historical examination result trend analysis module is further configured to retrieve previous examination status information and calculate the ranking X of the examination by the following method 1 ；
P0 is the average score of the test of the examinee in the past;
q0 is the average difficulty coefficient of previous examinations;
X 0 the examination name of the previous examination of the examinee is given;
p1 is the total score of the test;
q1 is the difficulty factor of the test.
3. The big-data-based examination result data processing system of claim 1,
the subject score analysis module is also used for sequencing the original scores of the students in the examination area from high to low and determining the proportion of the number of the students according to the statistical distribution of the volume score of the examinee group; according to the number proportion of the volume-face scores, dividing the examinees into examinee groups with preset number, giving a grade to each group examinee, converting the volume-face scores of the examinee groups into the corresponding grade, and determining the grade of each examinee according to the ranking of each examinee group in the corresponding examinee group.
4. The big-data based test achievement data processing system of claim 3,
the subject score analysis module calculates the grade of each examinee according to an equal proportion conversion mode in the following calculation mode:
wherein the content of the first and second substances,
S 1 ，S 2 respectively representing the lower limit and the upper limit of the volume surface partition corresponding to a certain grade;
T 1 ，T 2 respectively representing the lower limit and the upper limit between the grading assigned areas of the corresponding grades;
S 0 representing the volume score of a test taker in the corresponding level;
T 0 indicating the assigned score of a test taker in the corresponding class.
5. The big-data based test achievement data processing system of claim 1,
the subject score analysis module also provides score percentage ranking information;
calculating the percentage ranking of the fraction F in a certain fraction sample;
F= [（B+0.5E）/n]×100%
b is the number of fractions less than F, E is the frequency of occurrence of F in the sample, and n is the total number of fractional samples.
6. The big-data based test achievement data processing system of claim 1,
the historical examination score trend analysis module also calculates a mean-square rate change trend graph of the examinee's historical examinations;
the super-average rate calculation mode is as follows: (total fraction-average fraction)/100%;
and the score change trend graph of the total scores and each department scores of the examinee's routine examinations and the score change trend graph of the scores and the super-average rate change trend graph are displayed on the terminal for the teacher and the students to view.
7. The big-data based test achievement data processing system of claim 1, further comprising: a ranking analysis module;
the ranking analysis module is used for providing a ranking analysis page for the student end, and the ranking analysis page shows the total number of the examinees, the highest score, the lowest score, the average score, the median, the mode and the ranking condition of the examinees;
and (3) configuring a bar distribution diagram of the scores and the number of people in each examination subject, grouping the scores, finding out the number of people under each score, representing the number of people according to the height of the bar, and marking the positions of students.
8. The big-data based test achievement data processing system of claim 7,
the ranking analysis module is also used for analyzing the standard deviation of the achievement, and the standard deviation calculation mode is as follows:
the score of each examinee is subtracted from the average score to obtain a score difference CA, and the score difference CA is subjected to square calculation to obtain a square result value CB of each examinee score;
adding the square result values CB of each examinee score to obtain a total square result value CD;
and dividing the total square result value CD by the total number CY of examinees to obtain CZ, and squaring the CZ to obtain the standard deviation of the score.
9. The big-data based test achievement data processing system of claim 1, further comprising: a scoring policy module;
the scoring strategy module is used for scoring the knowledge points of the test into parent knowledge points and child knowledge points of the next level of the parent knowledge points;
extracting the wrong questions of the father-level knowledge points, calling a plurality of corresponding child-level knowledge points from the father-level knowledge points, and displaying the child-level knowledge points to students for learning;
configuring a parent knowledge point and a plurality of associated child knowledge points into a scoring strategy page;
and acquiring the accuracy of the test questions corresponding to each sublevel knowledge point of the students, and acquiring the mastering level of the sublevel knowledge points.
10. A terminal for implementing a big data-based examination score data processing system, comprising:
the memory is used for storing a computer program and an examination result data processing system based on big data;
a processor for executing the computer program and big data based test achievement data processing system to realize the big data based test achievement data processing system according to any one of claims 1 to 9;
the display screen is used for displaying data information in the examination result data processing system based on the big data;
and the communication module is used for uploading data information in the test result data processing system based on the big data to the cloud server and sending the data information in the test result data processing system based on the big data to the student client and the teacher client.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1547129A (en) * | 2003-11-28 | 2004-11-17 | 王功学 | Method for dynamic omnidistance score tracking and processing of the entire personnel based on exam place |
| CN105869085A (en) * | 2016-03-29 | 2016-08-17 | 河北师范大学 | Transcript inputting system and method for processing images |
| CN107038497A (en) * | 2017-03-31 | 2017-08-11 | 珠海知未科技有限公司 | A kind of student performance forecasting system and method |
| CN107610011A (en) * | 2017-09-13 | 2018-01-19 | 云峰数巨（北京）科技有限公司 | A kind of total marks of the examination statistical analysis system |
| CN110807173A (en) * | 2019-10-15 | 2020-02-18 | 广州摩翼信息科技有限公司 | Studying situation analysis method and device, computer equipment and storage medium |
| CN111160743A (en) * | 2019-12-19 | 2020-05-15 | 广东德诚大数据科技有限公司 | Data processing system for contrastively analyzing scores of examinations |
| CN112132717A (en) * | 2020-09-29 | 2020-12-25 | 上海松鼠课堂人工智能科技有限公司 | Intelligent shift distributing method and system based on big data |
| CN114707757A (en) * | 2022-03-31 | 2022-07-05 | 北京和气聚力教育科技有限公司 | Computer-implemented score analysis method, storage medium and electronic device |
-
2022
- 2022-07-22 CN CN202210859647.4A patent/CN114936809A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1547129A (en) * | 2003-11-28 | 2004-11-17 | 王功学 | Method for dynamic omnidistance score tracking and processing of the entire personnel based on exam place |
| CN105869085A (en) * | 2016-03-29 | 2016-08-17 | 河北师范大学 | Transcript inputting system and method for processing images |
| CN107038497A (en) * | 2017-03-31 | 2017-08-11 | 珠海知未科技有限公司 | A kind of student performance forecasting system and method |
| CN107610011A (en) * | 2017-09-13 | 2018-01-19 | 云峰数巨（北京）科技有限公司 | A kind of total marks of the examination statistical analysis system |
| CN110807173A (en) * | 2019-10-15 | 2020-02-18 | 广州摩翼信息科技有限公司 | Studying situation analysis method and device, computer equipment and storage medium |
| CN111160743A (en) * | 2019-12-19 | 2020-05-15 | 广东德诚大数据科技有限公司 | Data processing system for contrastively analyzing scores of examinations |
| CN112132717A (en) * | 2020-09-29 | 2020-12-25 | 上海松鼠课堂人工智能科技有限公司 | Intelligent shift distributing method and system based on big data |
| CN114707757A (en) * | 2022-03-31 | 2022-07-05 | 北京和气聚力教育科技有限公司 | Computer-implemented score analysis method, storage medium and electronic device |
Non-Patent Citations (1)
| Title |
|---|
| 周宏锐等: "《新时代 新高考》", 30 April 2020, 中国海洋大学出版社 * |
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