CN110704746A - Method and system for recommending test questions according to strong and weak knowledge point analysis results - Google Patents
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Abstract
The application discloses a method and a system for recommending test questions according to strong and weak knowledge point analysis results, wherein the method comprises the following steps: acquiring data of each examination participated by all students, obtaining knowledge point mastery according to the acquired data of each examination participated by the students, and creating a recommendation question set according to original wrong questions of weak knowledge points, wherein the similarity between the recommendation question set and original wrong question types of the weak knowledge points and the weak knowledge points is not more than 67%; selecting the number of test questions from the recommended question set for pushing, wherein the pushed test questions comprise weak knowledge points; and recommending the test questions selected from the recommendation set to the students. The method and the system for recommending the test questions according to the analysis results of the weak and strong knowledge points, provided by the invention, create the recommendation question set according to the weak knowledge points, set the priorities of different recommendation questions according to students in different stages, and more accurately provide wrong questions and related high-quality recommendation questions of the weak knowledge points for the students in the current stage.
Description
Technical Field
The application relates to the field of network teaching, in particular to a method and a system for recommending test questions according to strong and weak knowledge point analysis results.
Background
With the development of the internet industry, the education mode changes from the world to the earth, and meanwhile, a large amount of educational situation data are accumulated, on the basis, the efficient utilization of big data resources and the realization of personalized recommendation become important means for assisting students to learn.
The wrong question plays an important role in rapidly mastering weak knowledge points, optimizing learning paths and reviewing for students. In the prior art, an important problem is ignored aiming at the related design of wrong questions, no personalized wrong questions and high-quality matched exercise questions exist, the mastering condition of students on knowledge points is ignored, and the fact that the knowledge points contained in recommended test questions are really needed to be exercised by the students is difficult to ensure. In addition, students in each learning stage are not suitable for adopting a thought of recommending matching exercises by wrong exercises, and the students in different stages can adopt different priorities to select the matching exercises, so that the students in the current stage can be further matched with learning of the students.
Disclosure of Invention
The application discloses a method for recommending test questions according to strong and weak knowledge point analysis results, which comprises the following steps:
data is collected for each examination in which all students participate, including: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the score of the test question full score, the knowledge points included by the test questions and the original error questions;
acquiring the mastery degree of the knowledge point according to the data of each examination of student participation,
the knowledge point mastery degree is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge points, g is the importance degree of the knowledge points, a1, a2 and a3..
The knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, and j is the ranking gear of the knowledge point in the ascending examination of the score in a certain time;
arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds and the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points;
the wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a timestamp of the test time;
the test question discrimination c is obtained by the following method:
c＝(v1-v2)/b，
wherein v1 is the average score of the test results 27% before the result ranking, and v2 is the average score of the test results 27% after the result ranking;
creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points is not more than 67%;
selecting the number of test questions from the recommended question set for pushing, wherein the pushed test questions comprise weak knowledge points; recommending the test questions selected from the recommendation set to students.
Preferably, the recommended question set comprises a related question subset, a simulation subset, a true question subset, a monthly exam subset, a unit test subset, an interim test question subset and an interim end test question subset;
and setting a relevance threshold value in the relevant question subset, wherein the recommendation question set comprises the condition that the similarity between the knowledge point and the weak knowledge point in the relevant question subset is higher than the relevance threshold value.
Preferably, the number of the test questions is selected from the recommended question set to be pushed, the pushed test questions comprise weak knowledge points, further,
the method comprises the steps that a recommended question set is set with priorities, the priorities comprise a first priority and a second priority, the students taking the ascending examination in the current year select test questions from the recommended question set according to the first priority, and the students not taking the ascending examination in the current year select the test questions from the recommended question set according to the second priority.
Preferably, the first priority is sequentially ordered according to the related questions subset, the real questions subset, the simulation subset, the interim test question subset, the end-of-term test question subset, the monthly test subset and the unit test subset;
the second priority is sequentially ordered according to the related question subset, the unit test subset, the monthly test subset, the interim test question subset, the end-of-term test question subset, the simulation subset and the true question subset.
Preferably, the dominant knowledge point includes the related original error problem.
The application discloses system for recommending test questions according to strong and weak knowledge point analysis results comprises: the recommendation system comprises an acquisition module, a data processing module, a recommendation item integrating module and a recommendation item selecting module;
the acquisition module is coupled with the data processing module and used for acquiring data of each examination participated by all students and sending the data to the data processing module;
data for each examination in which all students participate, including: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the score of the test question full score, the knowledge points included by the test questions and the original error questions;
the data processing module is respectively coupled with the acquisition module and the recommendation question aggregation module, and is used for receiving the data of each examination participated by all students and sent by the acquisition module, calculating and generating weak knowledge points, and sending the weak knowledge points to the recommendation question aggregation module;
the mastery degree of the knowledge point is obtained according to the data of each test of student participation,
the knowledge point mastery degree is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge points, g is the importance degree of the knowledge points, a1, a2 and a3..
The knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, and j is the ranking gear of the knowledge point in the ascending examination of the score in a certain time;
arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds, the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points;
the wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a timestamp of the test time;
the test question discrimination c is obtained by the following method:
c＝(v1-v2)/b，
wherein v1 is the average score of the test results 27% before the result ranking, and v2 is the average score of the test results 27% after the result ranking;
the recommendation question set module is respectively coupled with the data processing module and the recommendation question selecting module, and is used for receiving the weak knowledge points sent by the data processing module, creating a recommendation question set and sending the recommendation question set to the recommendation question selecting module;
creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points is not more than 67%;
the selected recommendation question module is coupled with the recommendation question aggregation module and used for receiving the recommendation question aggregation, pushing the number of the test questions selected from the recommendation question aggregation module, wherein the pushed test questions comprise weak knowledge points, and recommending the test questions selected from the recommendation aggregation to students.
Preferably, the recommendation item integration module includes: the test system comprises a correlation problem subset unit, a simulation subset unit, a true problem subset unit, a monthly test subset unit, a unit test subset unit, an interim test problem subset unit and an end test problem subset unit;
the related question subset unit, the simulation subset unit, the real question subset unit, the monthly test subset unit, the unit test subset unit, the interim test question subset unit and the end test question subset unit are arranged in parallel, and are respectively coupled with the data processing module and the selected recommendation question module, and are used for receiving the weak knowledge points sent by the data processing module, creating a recommendation question set and sending the recommendation question set to the selected recommendation question module;
the relevance problem subset unit in the recommendation problem set module sets a relevance threshold, and the recommendation problem set module comprises that the similarity between the knowledge point and the weak knowledge point in the relevance problem subset unit is higher than the relevance threshold.
Preferably, the recommendation question selecting module selects the number of the test questions from the recommendation question integrating module to push, the pushed test questions include weak knowledge points, further,
the recommendation question selecting module is used for setting priorities and recommending the test questions selected from the recommendation question set to students according to the priorities;
the priorities comprise a first priority and a second priority, the students taking the ascending examination in the current year select the test questions from the recommended question set according to the first priority, and the students not taking the ascending examination in the current year select the test questions from the recommended question set according to the second priority.
Preferably, the recommendation question selecting module is provided with a first priority unit and a second priority unit, the first priority unit is coupled to the recommendation question integrating module and is configured to receive the recommendation question set, set a first priority and recommend a test question selected from the recommendation question set to a student according to the first priority, and the second priority unit is coupled to the recommendation question integrating module and is configured to receive the recommendation question set, set a second priority and recommend the test question selected from the recommendation question set to the student according to the second priority;
the first priority unit is sequentially ordered according to the related question subset unit, the real question subset unit, the simulation subset unit, the interim test question subset unit, the end test question subset unit, the monthly test subset unit and the unit test subset unit;
the second priority unit is sequentially ordered according to the related question subset unit, the unit test subset unit, the monthly test subset unit, the interim test question subset unit, the end test question subset unit, the simulation subset unit and the true question subset unit.
Preferably, the dominant knowledge point includes the related original error problem.
Compared with the prior art, the method for recommending test questions according to the analysis result of the strong and weak knowledge points provided by the invention has the following beneficial effects:
according to the method and the system for recommending the test questions according to the analysis results of the strong and weak knowledge points, weak knowledge points can be calculated and summarized through the collection of examination data of students, the recommendation question set is created according to the weak knowledge points, different recommendation question priorities are set according to the students in different stages, and wrong questions of the weak knowledge points and related high-quality recommendation questions can be accurately provided for the students in the current stage.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flowchart of a method for recommending test questions according to the analysis result of the strong and weak knowledge points;
FIG. 2 is a block diagram of a system for recommending test questions according to the analysis result of the strong and weak knowledge points.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It should be noted that the described embodiments are merely some embodiments, rather than all embodiments, of the invention and are merely illustrative in nature and in no way intended to limit the invention, its application, or uses. The protection scope of the present application shall be subject to the definitions of the appended claims.
Example 1:
an embodiment of a method for recommending test questions according to the analysis results of the strong and weak knowledge points in the present invention is shown in fig. 1, where fig. 1 is a flowchart of a method for recommending test questions according to the analysis results of the strong and weak knowledge points in the present invention, and the method for recommending test questions according to the analysis results of the strong and weak knowledge points in fig. 1 includes the steps of:
step 101, collecting data of each examination in which all students participate, including: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the test question full score value and the knowledge points included by the test questions;
102, acquiring the mastery degree of a knowledge point according to the data of each examination of student participation,
the knowledge point mastery degree f in the step 102 is obtained through a step 103;
step 103, obtaining the knowledge point mastery degree f by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge points, g is the importance degree of the knowledge points, a1, a2 and a3..
The knowledge point importance g in step 103 is obtained by step 104,
step 104, the knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge points required by the students, and j is the ranking gear of the knowledge points in the high-level entrance examination in a certain time; the conditions i of the students for mastering the knowledge points comprise: the requirement for grasping i 1-1, the requirement for understanding i 2-0.7, and the requirement for understanding i 3-0.4; the step j of the knowledge point in the college entrance examination for the score-to-rank ratio comprises the following steps: j1 ═ 1, j2 ═ 0.7, j3 ═ 0.4; the importance of knowledge points is divided into g including: g1 is more than 0.5, g2 is more than 0.25 and less than or equal to 0.5, and g3 is less than 0.25.
Arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds and the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points; preferably, the number of knowledge points ranked before the weak knowledge point threshold is 10, and the number of knowledge points ranked after the dominant knowledge point threshold is 3; the weak knowledge point threshold and the dominant knowledge point threshold are set according to actual requirements and are not specifically limited.
The wrong-question score value a in step 103 is obtained through step 105;
step 105, obtaining the wrong-question score value a by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is a wrong test question score value, b is a test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is a time factor, and m% is a time factor and is the weight; the time factor is a timestamp of the test time;
the larger the value of the wrong-question score value a is, the higher the test question value is, the more worthy the test question is to be made, that is, the higher the value of the wrong-question score value a is, the higher the test question can be quickly increased in score, and the smaller the corresponding value of the wrong-question score value a is, the lower the test question value is, the less worthy the test question is to be made.
The test question discrimination c in step 105 is obtained through step 106,
step 106, obtaining the test question discrimination c by the following method:
c＝(v1-v2)/b，
wherein v1 is the average score of the examination results 27% before the ranking of the results, and v2 is the average score of the examination results 27% after the ranking of the results;
step 107, creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points are not more than 67%;
in this step, optionally, the recommended question set includes a related question subset, a simulation subset, a true question subset, a monthly exam subset, a unit test subset, an interim test question subset, and an interim end test question subset. And setting a correlation threshold, wherein the recommended question set comprises the condition that the similarity between the knowledge points and the weak knowledge points in the related question subset is higher than the correlation threshold.
Step 108, selecting the number of test questions from the recommended question set to push, wherein the pushed test questions comprise weak knowledge points;
in this step, optionally, the recommendation question set sets priorities, where the priorities include a first priority and a second priority, and the first priority is sequentially sorted according to the related question subset, the true question subset, the simulation subset, the interim test question subset, the end-of-term test question subset, the monthly exam subset, and the unit test subset;
the second priority is ordered according to the related question subset, the unit test subset, the monthly test subset, the interim test question subset, the simulation subset and the real question subset.
Selecting test questions from the recommended question set according to a first priority by students taking ascending examinations in the current year, and selecting test questions from the recommended question set according to a second priority by students not taking ascending examinations in the current year;
step 109, recommending the test questions selected from the recommendation set to the students.
The method for recommending test questions according to the analysis results of the weak and strong knowledge points can calculate and summarize weak knowledge points through the collection of examination data of students, create recommendation question sets according to the weak knowledge points, set different recommendation question priorities according to the students in different stages, and can accurately provide wrong questions of the weak knowledge points and related high-quality recommendation questions for the students in the current stage.
Example 2:
referring to fig. 2, fig. 2 is a block diagram of a system for recommending test questions according to the analysis result of the strong and weak knowledge points; the system for recommending test questions according to the analysis result of the strong and weak knowledge points in fig. 2 includes: the system comprises an acquisition module 1, a data processing module 2, a recommendation question integrating module 3 and a recommendation question selecting module 4;
the acquisition module 1 is coupled with the data processing module 2, and is used for acquiring data of each examination participated by all students and sending the data to the data processing module 2;
the data for each test the student takes part in comprises: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the test question full score value and the knowledge points included by the test questions;
the data processing module 2 is coupled to the acquisition module 1 and the report generating module 3, and configured to receive data of each examination in which all students participate, which is sent by the acquisition module 1, calculate and generate weak knowledge points, and send the generated weak knowledge points to the report generating module 3;
the mastery degree of the knowledge point is obtained according to the data of each test of student participation,
the knowledge point mastery degree f is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge points, g is the importance degree of the knowledge points, a1, a2 and a3..
The knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, and j is the ranking gear of the knowledge point in the college entrance examination in the score-to-score ratio within a certain time; the conditions i of the students' mastery of the knowledge points are required to include: the requirement for grasping i 1-1, the requirement for understanding i 2-0.7, and the requirement for understanding i 3-0.4; the step j of the knowledge point in the college entrance examination, which is the score-to-rank ratio, comprises the following steps: j1 ═ 1, j2 ═ 0.7, j3 ═ 0.4; the importance degree of the knowledge points is divided into g and comprises the following steps: g1 is more than 0.5, g2 is more than 0.25 and less than or equal to 0.5, and g3 is less than 0.25;
arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds and the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points; the dominant knowledge points comprise related original fault problems, preferably, the number of the knowledge points sequenced before the weak knowledge point threshold is 10, and the number of the knowledge points sequenced after the dominant knowledge point threshold is 3; the weak knowledge point threshold and the dominant knowledge point threshold are set according to actual requirements and are not specifically limited. The wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a timestamp of the test time;
the test question discrimination c is obtained by the following method:
c＝(v1-v2)/b，
wherein v1 is the average score of the test results 27% before the result ranking, and v2 is the average score of the test results 27% after the result ranking.
The recommendation question integrating module 3 is respectively coupled to the data processing module 2 and the recommendation question selecting module 4, and is configured to receive the weak knowledge points sent by the data processing module 2, create a recommendation question set, and send the recommendation question set to the recommendation question selecting module 4;
the recommendation-question integrating module 3 includes: a correlation question subset unit 31, a simulation subset unit 32, a true question subset unit 33, a monthly test subset unit 34, a unit test subset unit 35, an interim test question subset unit 36 and an interim test question subset unit 37;
the relevance problem subset unit 31 in the recommendation problem aggregation module 3 sets a relevance threshold, and the recommendation problem aggregation module 3 includes that the similarity between the knowledge point and the weak knowledge point in the relevance problem subset unit 31 is higher than the relevance threshold.
The related question subset unit 31, the simulation subset unit 32, the real question subset unit 33, the monthly test subset unit 34, the unit test subset unit 35, the interim test question subset unit 36 and the end test question subset unit 37 are arranged in parallel, and are respectively coupled with the data processing module 2 and the selected recommendation question module 4, and are used for receiving the weak knowledge points sent by the data processing module 2, creating a recommendation question set and sending the recommendation question set to the selected recommendation question module 4.
Creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points is not more than 67%;
the selected recommendation question module 4 is coupled to the recommendation question aggregation module 3, and configured to receive the recommendation question set, push the number of test questions selected from the recommendation question aggregation module 3, set a priority level when the pushed test questions include weak knowledge points, and recommend the test questions selected from the recommendation question set to students according to the priority level;
the priorities include a first priority and a second priority,
the recommendation question selecting module 4 is configured with a first priority unit 41 and a second priority unit 42, the first priority unit 41 is coupled to the recommendation question aggregation module 3, and is configured to receive the recommendation question aggregation, set a first priority, and recommend a test question selected from the recommendation question aggregation to a student according to the first priority, and the second priority unit 42 is coupled to the recommendation question aggregation module 3, and is configured to receive the recommendation question aggregation, set a second priority, and recommend a test question selected from the recommendation question aggregation to a student according to the second priority;
the first priority unit 41 sequentially sorts the relevant questions subset unit 31, the true questions subset unit 33, the simulation subset unit 32, the interim test questions subset unit 36, the interim test questions subset unit 37, the monthly test subset unit 34, and the unit test subset unit 35;
the second priority unit 42 is arranged in sequence according to the related questions subset unit 31, the unit test subset unit 35, the monthly test subset unit 34, the interim test question subset unit 36, the interim test question subset unit 37, the simulation subset unit 32, and the true question subset unit 33.
The number of the test questions selected by the students from the recommendation set according to the priority is matched with the number of the weak knowledge points, the students taking the ascending examination in the current year select the test questions from the recommendation set according to the first priority, and the students not taking the ascending examination in the current year select the test questions from the recommendation set according to the second priority.
According to the invention, the individual strong and weak knowledge points of the students are analyzed and calculated, so that the original wrong questions which are most easily scored by the students are screened out, and the related questions of the original wrong questions are recommended according to the original wrong questions, so that the students can recommend the test questions through practice, the grasping condition of the weak knowledge points is improved, and the scores of the students are improved.
Example 3:
in another embodiment of the method for recommending test questions according to the analysis result of the strong and weak knowledge points, a method for recommending test questions according to the analysis result of the strong and weak knowledge points is provided, which includes the steps of:
step 301, collecting examination data of students:
collecting examination data of students including examination time, examination questions, examination papers, examination scores, etc
Step 302, extracting examination data of students, examination question data and examination paper data;
extracting test paper data: examination belonging to examination paper ID, examination number of examination paper and examination paper name
Extracting test question data: the test paper comprises the ID of the test questions, the full score of the test questions, the differentiation degree of the test questions and the score rate of the test questions, wherein the differentiation degree c of the test questions is obtained by the following method:
c＝(v1-v2)/b，
wherein v1 is the average score of the test results 27% before the result ranking, and v2 is the average score of the test results 27% after the result ranking;
step 303, student data extraction: student ID, student examination time, student examination score, number of examination taking, and examination taking name;
step 304, knowledge point mastery, wrong topic score value calculation and knowledge point importance:
the knowledge point mastery degree f is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge points, g is the importance degree of the knowledge points, a1, a2 and a3..
The knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, j is the ranking gear of the knowledge point in the college entrance examination in the score-divided ratio within a certain time, and the mastery condition i of the knowledge point required by the student comprises the following steps: request to master i1Claim 1 for understanding i2When 0.7, require knowledge of i30.4; the step j of the knowledge point in the college entrance examination, which is the score-to-rank ratio, comprises the following steps: j is a function of1＝1、j2＝0.7、j30.4; the importance degree of the knowledge points is divided into g and comprises the following steps: g1＞0.5、0.25＜g2≤0.5、g3＜0.25；
Arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds, the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points; the dominant knowledge points comprise related original fault problems, preferably, the number of the knowledge points sequenced before the weak knowledge point threshold is 10, and the number of the knowledge points sequenced after the dominant knowledge point threshold is 3; the weak knowledge point threshold and the dominant knowledge point threshold are set according to actual requirements and are not specifically limited.
The wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a time stamp of the test time.
Step 305, creating a recommendation question recalling source, namely a recommendation question set:
and dividing the recommended questions into 7 recalling sources according to related questions, simulation questions, true questions, monthly examinations, unit tests, interim test questions and end test questions, and recommending the test questions according to the priorities of the recalling sources when recommending the test questions.
Correlation problems: the test questions have the same knowledge points and the same question types as the original wrong questions and the question stem similarity reaches a certain threshold value;
simulation: a college entrance examination simulation question and a middle entrance examination simulation question;
true question: the college entrance examination question and the middle entrance examination question;
and (3) a monthly test: monthly exam questions;
unit testing: follow unit progress tests and examinations;
interim test questions: interim examination questions;
end-of-term test: examination questions of an end-of-term examination;
step 305, screening recommendation questions:
according to the similarity of the original and wrong questions, calculating the same knowledge points and the same question types as the original and wrong questions, and taking the test questions with the similarity not more than 0.67 as similar questions as one of recall sources
Selecting questions from the recalling sources according to the priorities of the recalling sources, wherein the priorities of the recalling sources in different grades are different
For example: the first year and the second year are higher, and the recall source priority is related questions, unit exercises, monthly exam questions, interim exam questions, end-of-term exam questions, college entrance examination simulation and college entrance examination true questions;
the recall source priority of the third highest grade is related questions, college entrance examination true questions, college entrance examination simulation, interim test questions, end-of-term test questions, monthly examination questions and unit exercises.
Step 306, recommending test questions:
and determining the number of recommended test questions according to the number of wrong questions, and selecting the recommended questions from the recall source.
The method for recommending test questions according to the analysis results of the weak and strong knowledge points can calculate and summarize weak knowledge points through the collection of examination data of students, create recommendation question sets according to the weak knowledge points, set different recommendation question priorities according to the students in different stages, and can accurately provide wrong questions of the weak knowledge points and related high-quality recommendation questions for the students in the current stage.
According to the embodiments, the application has the following beneficial effects:
the method and the system for recommending the test questions according to the analysis results of the strong and weak knowledge points can calculate and summarize weak knowledge points through the collection of examination data of students, create recommendation question sets according to the weak knowledge points, set different recommendation question priorities according to the students in different stages, and can accurately provide wrong questions of the weak knowledge points and related high-quality recommendation questions for the students in the current stage.
While the present invention has been described in detail with reference to the drawings and examples, it is to be understood that the foregoing examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. A method for recommending test questions according to analysis results of strong and weak knowledge points is characterized by comprising the following steps:
data is collected for each examination in which all students participate, including: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the score of the test question full score, the knowledge points included by the test questions and the original error questions;
acquiring the mastery degree of the knowledge point according to the data of each examination of student participation,
the knowledge point mastery degree is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge point, g is the importance degree of the knowledge point, a1、a2、a3...anThe number of the wrong topic score values is 1 st, 2 nd or 3 rd, wherein n is the number of the knowledge points;
the knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, and j is the ranking gear of the knowledge point in the ascending examination of the score in a certain time;
arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds and the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points;
the wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a timestamp of the test time;
the test question discrimination c is obtained by the following method:
c＝(v1-v2)/b，
wherein v is1Is the average score, v, of the examination score 27% of the top score ranking2Is the average score of the examination scores 27% after the score ranking;
creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points is not more than 67%;
selecting the number of test questions from the recommended question set for pushing, wherein the pushed test questions comprise weak knowledge points;
recommending the test questions selected from the recommendation set to students.
2. The method according to claim 1, wherein the set of recommended questions includes a subset of related questions, a simulation subset, a subset of true questions, a subset of monthly examinations, a unit testing subset, a subset of interim questions, and a subset of end-of-term questions;
and setting a relevance threshold value in the relevant question subset, wherein the recommendation question set comprises the condition that the similarity between the knowledge point and the weak knowledge point in the relevant question subset is higher than the relevance threshold value.
3. The method of claim 2, wherein the number of test questions selected from the set of recommended questions is pushed, the pushed test questions include weak knowledge points, and further,
the method comprises the steps that a recommended question set is set with priorities, the priorities comprise a first priority and a second priority, the students taking the ascending examination in the current year select test questions from the recommended question set according to the first priority, and the students not taking the ascending examination in the current year select the test questions from the recommended question set according to the second priority.
4. The method for recommending test questions according to the analysis result of strong and weak knowledge points as claimed in claim 3, wherein said first priority is sequentially ranked according to said subset of related questions, said subset of true questions, said subset of simulation, said subset of interim test questions, said subset of end-of-term test questions, said subset of monthly examinations, and said subset of unit tests;
the second priority is sequentially ordered according to the related question subset, the unit test subset, the monthly test subset, the interim test question subset, the end-of-term test question subset, the simulation subset and the true question subset.
5. The method of claim 1, wherein the dominant knowledge points comprise related original error problems.
6. A system for recommending test questions according to strong and weak knowledge point analysis results is characterized by comprising: the recommendation system comprises an acquisition module, a data processing module, a recommendation item integrating module and a recommendation item selecting module;
the acquisition module is coupled with the data processing module and used for acquiring data of each examination participated by all students and sending the data to the data processing module;
data for each examination in which all students participate, including: examination time, examination questions, examination papers and examination scores, wherein the examination papers comprise: the test paper name, the score of the test question full score, the knowledge points included by the test questions and the original error questions;
the data processing module is respectively coupled with the acquisition module and the recommendation question aggregation module, and is used for receiving the data of each examination participated by all students and sent by the acquisition module, calculating and generating weak knowledge points, and sending the weak knowledge points to the recommendation question aggregation module;
the mastery degree of the knowledge point is obtained according to the data of each test of student participation,
the knowledge point mastery degree is obtained by the following method:
f＝[(a1+a2+a3+...+an)÷n]×g，
wherein f is the mastery degree of the knowledge point, g is the importance degree of the knowledge point, a1、a2、a3...anThe number of the wrong topic score values is 1 st, 2 nd or 3 rd, wherein n is the number of the knowledge points;
the knowledge point importance g is obtained by the following method:
g＝70％i+30％j，
wherein i is the mastery condition of the knowledge point required by the student, and j is the ranking gear of the knowledge point in the ascending examination of the score in a certain time;
arranging the knowledge points in sequence according to the knowledge point mastery degrees from low to high, and setting weak knowledge point thresholds and dominant knowledge point thresholds, wherein the knowledge point mastery degrees are sequenced before the weak knowledge point thresholds, the knowledge points with related original wrong questions are weak knowledge points, and the knowledge points with the knowledge point mastery degrees sequenced after the dominant knowledge point thresholds are dominant knowledge points;
the wrong question score value a is obtained by the following method:
a＝x％b×y％(c+1)×z％(d+1)×m％e，
wherein, a is the wrong question score value, b is the test question full score value, x% is the weight of the test question full score value, c is the test question discrimination, y% is the weight of the test question discrimination, d is the difference value of the personal score rate and the class score rate of the knowledge point, z% is the weight of the score rate difference value, e is the time factor, and m% is the time factor and is the weight; the time factor is a timestamp of the test time;
the test question discrimination c is obtained by the following method:
c＝(v1-v2)/b，
wherein v is1Is the average score, v, of the examination score 27% of the top score ranking2Is the average score of the examination scores 27% after the score ranking;
the recommendation question set module is respectively coupled with the data processing module and the recommendation question selecting module, and is used for receiving the weak knowledge points sent by the data processing module, creating a recommendation question set and sending the recommendation question set to the recommendation question selecting module;
creating a recommendation question set according to the original wrong questions of the weak knowledge points, wherein the similarity between the recommendation question set and the original wrong question types of the weak knowledge points is not more than 67%;
the selected recommendation question module is coupled with the recommendation question aggregation module and used for receiving the recommendation question aggregation, pushing the number of the test questions selected from the recommendation question aggregation module, wherein the pushed test questions comprise weak knowledge points, and recommending the test questions selected from the recommendation aggregation to students.
7. The system of claim 6, wherein the recommendation test topic collection module comprises: the test system comprises a correlation problem subset unit, a simulation subset unit, a true problem subset unit, a monthly test subset unit, a unit test subset unit, an interim test problem subset unit and an end test problem subset unit;
the related question subset unit, the simulation subset unit, the real question subset unit, the monthly test subset unit, the unit test subset unit, the interim test question subset unit and the end test question subset unit are arranged in parallel, and are respectively coupled with the data processing module and the selected recommendation question module, and are used for receiving the weak knowledge points sent by the data processing module, creating a recommendation question set and sending the recommendation question set to the selected recommendation question module;
the relevance problem subset unit in the recommendation problem set module sets a relevance threshold, and the recommendation problem set module comprises that the similarity between the knowledge point and the weak knowledge point in the relevance problem subset unit is higher than the relevance threshold.
8. The system of claim 7, wherein the test questions selecting module selects and pushes the number of test questions from the test question integrating module, the pushed test questions include weak knowledge points, and further,
the recommendation question selecting module sets priority and recommends test questions selected from the recommendation question set to students according to the priority;
the priorities comprise a first priority and a second priority, the students taking the ascending examination in the current year select the test questions from the recommended question set according to the first priority, and the students not taking the ascending examination in the current year select the test questions from the recommended question set according to the second priority.
9. The system according to claim 8, wherein the test questions selecting module is configured to set a first priority unit and a second priority unit, the first priority unit is coupled to the test questions recommending module and configured to receive the set of test questions, set a first priority, and recommend test questions selected from the set of test questions to students according to the first priority, the second priority unit is coupled to the test questions recommending module and configured to receive the set of test questions, set a second priority, and recommend test questions selected from the set of test questions to students according to the second priority;
the first priority unit is sequentially ordered according to the related question subset unit, the real question subset unit, the simulation subset unit, the interim test question subset unit, the end test question subset unit, the monthly test subset unit and the unit test subset unit;
the second priority unit is sequentially ordered according to the related question subset unit, the unit test subset unit, the monthly test subset unit, the interim test question subset unit, the end test question subset unit, the simulation subset unit and the true question subset unit.
10. The system of claim 6, wherein the dominant knowledge points comprise related original wrong questions.
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