CN111507534A - Student learning data-based predictive analysis algorithm for knowledge point mastering conditions - Google Patents

Student learning data-based predictive analysis algorithm for knowledge point mastering conditions Download PDF

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CN111507534A
CN111507534A CN202010323746.1A CN202010323746A CN111507534A CN 111507534 A CN111507534 A CN 111507534A CN 202010323746 A CN202010323746 A CN 202010323746A CN 111507534 A CN111507534 A CN 111507534A
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吴春来
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Abstract

The invention provides a predictive analysis algorithm for knowledge point mastering conditions based on student learning data, and relates to the technical field of data prediction. The predictive analysis algorithm for knowledge point mastering conditions based on student learning data comprises the following specific steps: s1, collecting learning data about students based on a Caera algorithm based on a search method for shielding irrelevant content; s2, establishing a student learning database, classifying and storing data according to the characteristics, and detecting and updating the classification characteristics; and S3, generating a student simulation question bank based on the database, and testing the simulation question bank. According to the invention, the simulation question bank can be effectively controlled, meanwhile, the student knowledge mastery degree can be quickly and effectively obtained by comprehensively and automatically analyzing the student simulation condition, the student knowledge mastery degree is predicted according to the student knowledge mastery degree, the prediction effectiveness and accuracy are ensured, the interference of repeated data to the prediction result is avoided, and the learning efficiency and the learning effect of the student are greatly improved.

Description

Student learning data-based predictive analysis algorithm for knowledge point mastering conditions
Technical Field
The invention relates to the technical field of data prediction, in particular to a prediction analysis algorithm based on knowledge point mastering conditions of student learning data.
Background
At present, in traditional teaching, a teacher generally checks the learning effect of students in homework and examination, and in homework and examination, the teacher can roughly know the knowledge mastering degree of the students according to the answering conditions of the students, but is difficult to carry out full statistics on the knowledge mastering conditions, and the teacher can detect the learning effect of different students through unified homework and examination generally, and cannot provide personalized detection and evaluation for the students.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a predictive analysis algorithm for knowledge point mastering conditions based on student learning data, and solves the defects in the prior art.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the predictive analysis algorithm for knowledge point mastering conditions based on student learning data comprises the following specific steps:
s1, collecting learning data about students based on a Caera algorithm based on a search method for shielding irrelevant content;
s2, establishing a student learning database, classifying and storing data according to the characteristics, and detecting and updating the classification characteristics;
s3, generating a student simulation question bank based on the database, and testing the simulation question bank;
s4, performing simulation test on the students by using the simulation question bank, and repeating the test for multiple times;
and S5, obtaining the test data of the students, and performing predictive analysis on the learning conditions of the students.
Preferably, in the step 1, the search method based on the shielding irrelevant content collects the learning data about the student based on the Caera algorithm, and the specific content is as follows:
1) firstly, determining each knowledge point learned by students, counting all the knowledge points, converting the knowledge points into keywords, words or phrases, extracting the attribute value of each keyword, word or phrase, and setting the attribute value as a relevance judgment value Q;
2) searching knowledge points in a search engine by using keywords, words or phrases, and collecting all learning data about each knowledge point of a student based on the Caera algorithm;
3) extracting the correlation value of each learning data in each knowledge point, setting the correlation value to be Qi, and simultaneously setting the correlation of the collected knowledge point data to be G, then setting the correlation
Figure BDA0002462418390000021
Wherein, the closer the value of G is to 1, the stronger the relevance of the knowledge point data is, and the relevance must be set to be 0.95 < G ≦ 1.
Preferably, in the step 2, a student learning database is established, data is classified and stored according to characteristics, and the classification characteristics are detected and updated, and the specific contents are as follows:
1) classifying the statistical knowledge points according to the types of the statistical knowledge points, establishing a knowledge point data storage database, and dividing a plurality of storage areas in the database, wherein the data of the storage areas are related to the types of the knowledge point data;
2) classifying all collected data according to different characteristics of the knowledge points, marking each characteristic of the knowledge points as T, and then sequentially setting the T1、T2、T3...TnThen, each type of knowledge point is placed into a corresponding storage area, and the storage area is annotated with related knowledge point information;
3) after all the data knowledge points are classified and stored, the characteristic of each knowledge point in each area is verified, and the characteristic is judged to be in accordance with the set T1、T2、T3.. or TnAnd replacing and updating the knowledge point data in the storage area.
Preferably, in the step 3, a student simulation question bank is generated based on the database, and the simulation question bank is tested, wherein the specific contents are as follows:
1) generating a student simulation question bank, extracting questions in the simulation question bank from a student learning database, setting the number of the questions in the simulation question bank to be 10, 20, 30, 40 and the like by self, recording the number of the simulation questions as W, wherein the set number W cannot exceed the total number of the categories of the knowledge points in the learning database, and recording the total number of the categories of the knowledge points as Z;
2) randomly generating questions in the simulation question bank, and performing repetition rate test and coverage rate test on the simulation question bank;
i) and (3) testing the repetition rate: setting the repetition rate to P1Counting the number of repeated questions in the simulation questions, and marking the number as C to obtain the number of repeated questions in the simulation questions
Figure BDA0002462418390000031
ii) coverage test by setting the coverage to P2Counting the number of the knowledge point types appearing in the simulation question, and marking the number as V, thereby obtaining the number of the knowledge point types
Figure BDA0002462418390000032
3) Adjusting the repetition rate and the coverage rate of the knowledge points, and controlling the repetition rate P1Tending to 0, controlling the number V of the appeared knowledge point categories to simulate the number W of questions, and setting the repetition rate P1And coverage rate P2Is measured.
Preferably, in the step 4, the simulated question bank is used for performing simulation test on the student, and the test is repeated for multiple times, wherein the specific contents are as follows:
1) generating simulation questions by using a simulation question bank to perform simulation test on students, wherein the simulation test times are multiple, and the total knowledge point quantity of the simulation test is at least 50% of the knowledge point quantity in the database;
2) counting the questions simulated by the students, automatically counting the number of knowledge points, the total error rate of answering and the error rate of the same knowledge point, and then automatically generating a statistical form.
Preferably, the student test data obtained in step 5 is used for performing predictive analysis on the learning condition of the student, and the specific contents are as follows:
1) acquiring test data of students on the knowledge points, counting the number of the same knowledge points, and marking as M1Then, the error number of each knowledge point is counted and marked as M2；
2) The student's knowledge mastery degree is set to L
Figure BDA0002462418390000033
Wherein 0 is equal to or less than L is equal to or less than 1, the mastery degree L is defined, when 0.9 is equal to or less than L is equal to or less than 1, the mastery condition of the knowledge of the marking students is excellent, when 0.7 is equal to or less than L and less than 0.9, the mastery condition of the knowledge of the marking students is excellent, when 0.5 is equal to or less than L and less than 0.7, the mastery condition of the knowledge of the marking students is general, when 0.3 is equal to or less than L and less than 0.5, the mastery condition of the knowledge of the marking students is poor, and when 0 is equal to or less than L and less than.
(III) advantageous effects
The invention provides a predictive analysis algorithm for knowledge point mastering conditions based on student learning data. The method has the following beneficial effects:
1. according to the invention, through carrying out the repetition rate test and the coverage rate test, the types of questions in the simulated question bank can be effectively controlled, meanwhile, through carrying out comprehensive automatic analysis on the student simulated condition, the student knowledge mastery degree can be quickly and effectively obtained, the student knowledge mastery condition is predicted according to the student knowledge mastery degree, the effectiveness and the accuracy of prediction are ensured, the interference of repeated data on the prediction result is avoided, and the learning efficiency and the learning effect of students are greatly improved.
2. According to the invention, the search of the knowledge points is carried out in the search engine by utilizing the keywords, the words or the phrases, so that irrelevant search contents are shielded, the collected contents are not required to be screened subsequently by the system, the workload of the collection system is reduced, meanwhile, the collected knowledge point data is ensured to be real and effective, and a good basis is provided for subsequent prediction.
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FIG. 1 is a schematic flow chart of the present invention.
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.
Example (b):
as shown in fig. 1, an embodiment of the present invention provides a predictive analysis algorithm for knowledge point grasping based on student learning data, where the predictive analysis algorithm includes the following specific steps:
s1, collecting learning data about students based on a Caera algorithm based on a search method for shielding irrelevant content;
s2, establishing a student learning database, classifying and storing data according to the characteristics, and detecting and updating the classification characteristics;
s3, generating a student simulation question bank based on the database, and testing the simulation question bank;
s4, performing simulation test on the students by using the simulation question bank, and repeating the test for multiple times;
and S5, obtaining the test data of the students, and performing predictive analysis on the learning conditions of the students.
According to the invention, the search of the knowledge points is carried out in the search engine by utilizing the keywords, the words or the phrases, so that irrelevant search contents are shielded, the collected contents are not required to be screened subsequently by the system, the workload of the collection system is reduced, meanwhile, the collected knowledge point data is ensured to be real and effective, and a good basis is provided for subsequent prediction.
In step 1, a search method based on shielding irrelevant content collects learning data about students based on the Caera algorithm, and the specific content is as follows:
1) firstly, determining each knowledge point learned by students, counting all the knowledge points, converting the knowledge points into keywords, words or phrases, extracting the attribute value of each keyword, word or phrase, and setting the attribute value as a relevance judgment value Q;
2) searching knowledge points in a search engine by using keywords, words or phrases, and collecting all learning data about each knowledge point of a student based on the Caera algorithm;
3) extracting the correlation value of each learning data in each knowledge point, setting the correlation value to be Qi, and simultaneously setting the correlation of the collected knowledge point data to be G, then setting the correlation
Figure BDA0002462418390000051
Wherein, the closer the value of G is to 1, the stronger the relevance of the knowledge point data is, and the relevance must be set to be 0.95 < G ≦ 1.
Establishing a student learning database in the step 2, classifying and storing the data according to the characteristics, detecting and updating the classification characteristics, and specifically providing the following contents:
1) classifying the statistical knowledge points according to the types of the statistical knowledge points, establishing a knowledge point data storage database, and dividing a plurality of storage areas in the database, wherein the data of the storage areas are related to the types of the knowledge point data;
2) classifying all collected data according to different characteristics of the knowledge points, marking each characteristic of the knowledge points as T, and then sequentially setting the T1、T2、T3...TnThen, each type of knowledge point is placed into a corresponding storage area, and the storage area is annotated with related knowledge point information;
3) after all the data knowledge points are classified and stored, the characteristic of each knowledge point in each area is verified, and the characteristic is judged to be in accordance with the set T1、T2、T3.. or TnAnd replacing and updating the knowledge point data in the storage area.
And 3, generating a student simulation question bank based on the database, testing the simulation question bank, and specifically providing the following contents:
1) generating a student simulation question bank, extracting questions in the simulation question bank from a student learning database, setting the number of the questions in the simulation question bank to be 10, 20, 30, 40 and the like by self, recording the number of the simulation questions as W, wherein the set number W cannot exceed the total number of the categories of the knowledge points in the learning database, and recording the total number of the categories of the knowledge points as Z;
2) randomly generating questions in the simulation question bank, and performing repetition rate test and coverage rate test on the simulation question bank;
i) and (3) testing the repetition rate: setting the repetition rate to P1Counting the number of repeated questions in the simulation questions, and marking the number as C to obtain the number of repeated questions in the simulation questions
Figure BDA0002462418390000061
ii) coverage test by setting the coverage to P2Counting the number of the knowledge point types appearing in the simulation question, and marking the number as V, thereby obtaining the number of the knowledge point types
Figure BDA0002462418390000062
3) Adjusting the repetition rate and the coverage rate of the knowledge points, and controlling the repetition rate P1Tending to 0, controlling the number V of the appeared knowledge point categories to simulate the number W of questions, and setting the repetition rate P1And coverage rate P2Is measured.
And 4, performing simulation test on the students by using the simulation question bank, and repeating the test for many times, wherein the specific contents are as follows:
1) generating simulation questions by using a simulation question bank to perform simulation test on students, wherein the simulation test times are multiple, and the total knowledge point quantity of the simulation test is at least 50% of the knowledge point quantity in the database, and the simulation test can be in an on-machine simulation mode;
2) counting the questions simulated by the students, automatically counting the number of knowledge points, the total error rate of answering and the error rate of the same knowledge point, and then automatically generating a statistical form.
And 5, acquiring student test data, and performing predictive analysis on the learning condition of the students, wherein the specific contents are as follows:
1) acquiring test data of students on the knowledge points, counting the number of the same knowledge points, and marking as M1Then, the error number of each knowledge point is counted and marked as M2；
2) The student's knowledge mastery degree is set to L
Figure BDA0002462418390000071
Wherein 0 is equal to or less than L is equal to or less than 1, the mastery degree L is defined, when 0.9 is equal to or less than L is equal to or less than 1, the mastery condition of the knowledge of the marking students is excellent, when 0.7 is equal to or less than L and less than 0.9, the mastery condition of the knowledge of the marking students is excellent, when 0.5 is equal to or less than L and less than 0.7, the mastery condition of the knowledge of the marking students is general, when 0.3 is equal to or less than L and less than 0.5, the mastery condition of the knowledge of the marking students is poor, and when 0 is equal to or less than L and less than.
According to the invention, through carrying out the repetition rate test and the coverage rate test, the types of questions in the simulated question bank can be effectively controlled, meanwhile, through carrying out comprehensive automatic analysis on the student simulated condition, the student knowledge mastery degree can be quickly and effectively obtained, the student knowledge mastery condition is predicted according to the student knowledge mastery degree, the effectiveness and the accuracy of prediction are ensured, the interference of repeated data on the prediction result is avoided, and the learning efficiency and the learning effect of students are greatly improved.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. The predictive analysis algorithm of knowledge point mastering conditions based on student learning data is characterized in that: the predictive analysis algorithm comprises the following specific steps:
s1, collecting learning data about students based on a Caera algorithm based on a search method for shielding irrelevant content;
s2, establishing a student learning database, classifying and storing data according to the characteristics, and detecting and updating the classification characteristics;
s3, generating a student simulation question bank based on the database, and testing the simulation question bank;
s4, performing simulation test on the students by using the simulation question bank, and repeating the test for multiple times;
and S5, obtaining the test data of the students, and performing predictive analysis on the learning conditions of the students.
2. The predictive analysis algorithm for knowledge point grasp situation based on student learning data according to claim 1, characterized in that: in the step 1, the search method based on the shielding irrelevant content collects the learning data about the students based on the Caera algorithm, and the specific content is as follows:
1) firstly, determining each knowledge point learned by students, counting all the knowledge points, converting the knowledge points into keywords, words or phrases, extracting the attribute value of each keyword, word or phrase, and setting the attribute value as a relevance judgment value Q;
2) searching knowledge points in a search engine by using keywords, words or phrases, and collecting all learning data about each knowledge point of a student based on the Caera algorithm;
3) extracting the correlation value of each learning data in each knowledge point, setting the correlation value to be Qi, and simultaneously setting the correlation of the collected knowledge point data to be G, then setting the correlation
Figure FDA0002462418380000011
Wherein, the closer the value of G is to 1, the stronger the relevance of the knowledge point data is, and the relevance must be set to be 0.95 < G ≦ 1.
3. The predictive analysis algorithm for knowledge point grasp situation based on student learning data according to claim 1, characterized in that: establishing a student learning database in the step 2, classifying and storing the data according to the characteristics, and detecting and updating the classification characteristics, wherein the specific contents are as follows:
1) classifying the statistical knowledge points according to the types of the statistical knowledge points, establishing a knowledge point data storage database, and dividing a plurality of storage areas in the database, wherein the data of the storage areas are related to the types of the knowledge point data;
2) classifying all collected data according to different characteristics of the knowledge points, marking each characteristic of the knowledge points as T, and then sequentially setting the T1、T2、T3...TnThen, each type of knowledge point is placed into a corresponding storage area, and the storage area is annotated with related knowledge point information;
3) after all the data knowledge points are classified and stored, the characteristic of each knowledge point in each area is verified, and the characteristic is judged to be in accordance with the set T1、T2、T3.. or TnAnd replacing and updating the knowledge point data in the storage area.
4. The predictive analysis algorithm for knowledge point grasp situation based on student learning data according to claim 1, characterized in that: in the step 3, a student simulation question bank is generated based on the database, and the simulation question bank is tested, wherein the specific contents are as follows:
1) generating a student simulation question bank, extracting questions in the simulation question bank from a student learning database, setting the number of the questions in the simulation question bank to be 10, 20, 30, 40 and the like by self, recording the number of the simulation questions as W, wherein the set number W cannot exceed the total number of the categories of the knowledge points in the learning database, and recording the total number of the categories of the knowledge points as Z;
2) randomly generating questions in the simulation question bank, and performing repetition rate test and coverage rate test on the simulation question bank;
i) and (3) testing the repetition rate: setting the repetition rate to P1Counting the number of repeated questions in the simulation questions, and marking the number as C to obtain the number of repeated questions in the simulation questions
Figure FDA0002462418380000021
ii) coverage test by setting the coverage to P2Counting the number of the knowledge point types appearing in the simulation question, and marking the number as V, thereby obtaining the number of the knowledge point types
Figure FDA0002462418380000022
3) Adjusting the repetition rate and the coverage rate of the knowledge points, and controlling the repetition rate P1The trend is toward a value of 0 (m),controlling the number V of the appeared knowledge point categories to tend to simulate the number W of questions, and setting the repetition rate P1And coverage rate P2Is measured.
5. The predictive analysis algorithm for knowledge point grasp situation based on student learning data according to claim 1, characterized in that: in the step 4, the simulation test is carried out on the students by using the simulation question bank, and the test is repeated for many times, wherein the specific contents are as follows:
1) generating simulation questions by using a simulation question bank to perform simulation test on students, wherein the simulation test times are multiple, and the total knowledge point quantity of the simulation test is at least 50% of the knowledge point quantity in the database;
2) counting the questions simulated by the students, automatically counting the number of knowledge points, the total error rate of answering and the error rate of the same knowledge point, and then automatically generating a statistical form.
6. The predictive analysis algorithm for knowledge point grasp situation based on student learning data according to claim 1, characterized in that: in the step 5, the student test data is obtained, and the learning condition of the student is subjected to predictive analysis, wherein the specific contents are as follows:
1) acquiring test data of students on the knowledge points, counting the number of the same knowledge points, and marking as M1Then, the error number of each knowledge point is counted and marked as M2；
2) The student's knowledge mastery degree is set to L
Figure FDA0002462418380000031
Wherein 0 is equal to or less than L is equal to or less than 1, the mastery degree L is defined, when 0.9 is equal to or less than L is equal to or less than 1, the mastery condition of the knowledge of the marking students is excellent, when 0.7 is equal to or less than L and less than 0.9, the mastery condition of the knowledge of the marking students is excellent, when 0.5 is equal to or less than L and less than 0.7, the mastery condition of the knowledge of the marking students is general, when 0.3 is equal to or less than L and less than 0.5, the mastery condition of the knowledge of the marking students is poor, and when 0 is equal to or less than L and less than.
CN202010323746.1A 2020-04-22 2020-04-22 Student learning data-based predictive analysis algorithm for knowledge point mastering conditions Pending CN111507534A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112446558A (en) * 2021-01-29 2021-03-05 北京世纪好未来教育科技有限公司 Model training method, learning result acquisition method, device, equipment and medium
CN116739438A (en) * 2023-08-10 2023-09-12 南通四建集团有限公司 Safety education learning result testing method and system for virtual reality

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112446558A (en) * 2021-01-29 2021-03-05 北京世纪好未来教育科技有限公司 Model training method, learning result acquisition method, device, equipment and medium
CN116739438A (en) * 2023-08-10 2023-09-12 南通四建集团有限公司 Safety education learning result testing method and system for virtual reality
CN116739438B (en) * 2023-08-10 2023-11-17 南通四建集团有限公司 Safety education learning result testing method and system for virtual reality

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