Intelligent adult self-learning examination content and test question recommendation system and method

Abstract

The invention relates to an intelligent adult self-learning examination content and test question recommendation system and method, and relates to the technical field of data processing. Establishing a first adult self-learning examination knowledge point graph corresponding to different adult self-learning examination majors in each area; based on the basic information, the learning ability feature information and the self-learning records of the plurality of test users, performing classification, determining a plurality of test users corresponding to each first adult self-learning examination knowledge point graph, and generating a corresponding second adult self-learning examination knowledge point graph; based on the basic information, the learning ability feature information and the classification result of the to-be-recommended user, determining a plurality of reference test users and a second adult self-learning examination knowledge point graph, and generating self-learning examination recommendation content and recommendation test questions in combination with the self-learning record of the to-be-recommended user. The method has the advantage of improving the accuracy of adult self-learning examination content and test question recommendation.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an intelligent adult self-study examination content and test question recommendation system and method. Background Art

[0002] Self-taught higher education examinations, referred to as self-taught examinations or self-examinations, are national examinations for higher education that are mainly academic qualifications for participants of self-taught examinations. They are a form of higher education that combines personal self-study, social assistance and national examinations. Participants of self-taught examinations register and take the examinations at the time and place specified by the provincial education examination agency. After obtaining qualified scores in one or more courses (including) a course, the provincial education examination agency will establish an examination management file for them. After participants of self-taught examinations obtain qualified scores in all courses specified in the professional plan, complete graduation assessments or other teaching practice tasks such as thesis defense of the main examination school, and pass ideological and moral appraisals, they can obtain graduation certificates and their academic qualifications are recognized by the state. Graduates of self-taught examinations who meet the conditions for degree awarding will be awarded a bachelor's degree by the main examination school with degree awarding authority in accordance with relevant regulations. The main advantages of adult self-examinations include loose registration conditions, flexible study time, and high academic qualifications recognition. The disadvantage is that the examination is difficult and requires candidates to have strong self-study and time management abilities. In addition, the tuition fees for self-taught undergraduates are relatively low, but candidates need to arrange their own study and examinations.

[0003] Recommending exam content and test questions to students can effectively improve students' learning effect when the number of questions they have to answer is determined. In the traditional way, this part is generally completed by the instructor. The instructor selects appropriate exam content and test questions for students to practice based on their understanding of the students and the questions in their impression. First of all, considering the number of questions students have to answer, it is difficult for teachers to directly recommend a new question after each question is completed. Secondly, considering that teachers have limited memory, it is difficult to remember the abilities of each student in various knowledge aspects, and it is also difficult to remember all the exam content and test questions. Therefore, this recommendation method can only ensure that the students' most important needs match the exam content and test questions that teachers want to recommend the most, and it is difficult to ensure that they are still matched after long-term use. Common recommendation algorithms are generally based on the two basic assumptions that "users like similar things" and "similar users like similar content", thereby recommending content similar to the content that users like, and content that similar users like. However, this solution is not feasible in the recommendation of questions. In the process of adult self-study, because most valuable exam content and test questions are difficult, students often do not like those exam content and test questions that are really helpful for learning. Therefore, the assumption based on liking cannot be applied to the recommendation of exam content and test questions, and the reliability of such recommendation algorithms is naturally out of the question. General automated exam content and test question recommendations are based on the classification and / or difficulty of exam content and test questions. This method can ensure that the recommended exam content and test questions meet the requirements of students from the general direction of exam content and test questions, but in fact the recommendation accuracy is still very poor. First of all, the classification-based solution is not completely reasonable. Questions often involve knowledge from multiple aspects. Forcibly classifying them into a certain category itself loses the accuracy of recommendation. At the same time, since the difficulty is often marked for the questions, the matching of student abilities can only be done at the question level, and the mutual influence of the difficulty between the various parts of the exam content and test questions is often ignored. Due to the above two reasons, the data accuracy of its recommendation algorithm is poor. At the same time, most traditional recommendation algorithms determine the exam content and test questions based entirely on the size of indicators, which makes it easier for students at the same level to see the same exam content and test questions, and repeatedly studying the same exam content or test questions cannot effectively improve the students' real level. Therefore, a completely determined algorithm cannot meet the needs of students' self-study.

[0004] Therefore, it is necessary to provide an intelligent adult self-study examination content and test question recommendation system and method to improve the accuracy of adult self-study examination content and test question recommendations. Summary of the invention

[0005] The present invention provides an intelligent method for recommending content and test questions of adult self-study examinations, comprising: obtaining test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; establishing a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region based on the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; obtaining basic information, learning ability characteristic information and self-study records of multiple test users; classifying the multiple test users based on the basic information, learning ability characteristic information and self-study records of the multiple test users, and generating classification results; determining multiple test users corresponding to each of the first adult self-study examination knowledge point maps based on the classification results; and for each of the first adult self-study examinations, A self-study examination knowledge point map, based on multiple test users corresponding to the first adult self-study examination knowledge point map, generates a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map; obtains basic information and learning ability characteristic information of the user to be recommended; based on the basic information and learning ability characteristic information of the user to be recommended and the classification result, determines multiple reference test users and the second adult self-study examination knowledge point map corresponding to the user to be recommended from the multiple test users; obtains the self-study record of the user to be recommended; based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map, generates recommended self-study examination content and recommended test questions corresponding to the user to be recommended.

[0006] Furthermore, based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions, a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors is established for each region, including: for each region, based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in the region, determining the examination knowledge points corresponding to the different adult self-study examination majors in the region and the examination features corresponding to each examination knowledge point; for each adult self-study examination major, based on the examination knowledge points of the adult self-study examination major corresponding to each region and the examination features corresponding to each examination knowledge point, clustering the multiple regions to determine multiple regional clusters corresponding to the adult self-study examination major, and based on the examination knowledge points of the adult self-study examination major corresponding to the multiple regions included in each regional cluster and the examination features corresponding to each examination knowledge point, establishing a first adult self-study examination knowledge point map corresponding to the adult self-study examination major in the regional cluster.

[0007] Furthermore, based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in the area, the examination knowledge points corresponding to the different adult self-study examination majors in the area and the examination features corresponding to each examination knowledge point are determined, including: for each of the adult self-study examination majors, a knowledge point library corresponding to the adult self-study examination major is constructed; for each adult self-study examination paper, the adult self-study examination paper is preprocessed to generate a preprocessed adult self-study examination paper, and according to the knowledge point library corresponding to the adult self-study examination major related to the adult self-study examination paper, knowledge points and knowledge point features are extracted from the preprocessed self-study examination paper, wherein the knowledge point features include at least the proportion of test questions, test question difficulty distribution and question type distribution for each knowledge point; for each of the adult self-study examination majors, based on the knowledge points and knowledge point features of the adult self-study examination papers of previous years corresponding to the adult self-study examination major in the area, the examination knowledge points corresponding to the adult self-study examination major in the area and the examination features corresponding to each examination knowledge point are determined.

[0008] Furthermore, based on the examination knowledge points of the adult self-study examination major corresponding to each of the regions and the examination features corresponding to each examination knowledge point, the multiple regions are clustered to determine multiple regional clusters corresponding to the adult self-study examination major; based on the examination knowledge points of the adult self-study examination major corresponding to each of the regions, the overlap of the examination knowledge points of the adult self-study examination majors corresponding to any two of the regions is calculated; based on the examination features corresponding to each examination knowledge point of the adult self-study examination major corresponding to each of the regions, the similarity of the examination features of the adult self-study examination majors corresponding to any two of the regions is calculated; based on the overlap of the examination knowledge points of the adult self-study examination majors corresponding to any two of the regions and the similarity of the examination features of the adult self-study examination majors corresponding to any two of the regions, the regional similarity of the adult self-study examination majors corresponding to any two of the regions is calculated; based on the regional similarity of any two of the regions corresponding to the adult self-study examination majors corresponding to the adult self-study examination majors, the multiple regions are clustered to determine multiple regional clusters corresponding to the adult self-study examination majors.

[0009] Furthermore, the learning ability characteristic information of the test user is obtained, including: determining multiple learning ability factors; obtaining ability test data of multiple test users; for each of the adult self-study examination majors, determining the correlation coefficient between each learning ability factor and the adult self-study examination major based on the ability test data of the multiple test users, and determining the target learning ability factor corresponding to the adult self-study examination major from the multiple learning ability factors based on the correlation coefficient between each learning ability factor and the adult self-study examination major; for each of the test users, obtaining the learning ability characteristic information of the test user based on the target learning ability factor corresponding to the adult self-study examination major to which the test user belongs.

[0010] Furthermore, based on the basic information, learning ability characteristic information and self-study records of the multiple test users, the multiple test users are classified to generate a classification result, including: based on the adult self-study examination major to which each of the test users belongs, the multiple test users are grouped to generate a plurality of user units; for each of the user units, based on the region to which the test users included in the user unit belong and the plurality of regional clusters corresponding to each of the adult self-study examination majors, the test users included in the user unit are grouped to generate a plurality of user groups included in the user unit; for each of the user groups, based on the learning ability characteristic information and self-study records of the multiple test users included in the user group, the user similarity of any two test users included in any two of the user groups is calculated; according to the user similarity of any two test users included in any two of the user groups, the multiple test users included in the user group are clustered to determine the plurality of user clusters included in the user group, wherein the classification result includes the plurality of user clusters included in each of the user groups.

[0011] Furthermore, based on the classification result, a plurality of test users corresponding to each of the first adult self-study examination knowledge point maps are determined, including: for each of the first adult self-study examination knowledge point maps, based on the adult self-study examination major corresponding to the first adult self-study examination knowledge point map, a first target user unit is determined from the plurality of user units, based on the regional cluster corresponding to the first adult self-study examination knowledge point map, a first target user group is determined from the plurality of user groups included in the first target user unit, and the test users included in the first target user group are used as the plurality of test users corresponding to the first adult self-study examination knowledge point map; based on the first Multiple test users corresponding to the adult self-study examination knowledge point map generate a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map, including: determining the association parameters between any two adult self-study examination knowledge points included in the first adult self-study examination knowledge point map based on the self-study records of the multiple test users corresponding to the first adult self-study examination knowledge point map; generating a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map based on the association parameters between any two adult self-study examination knowledge points included in the first adult self-study examination knowledge point map and the first adult self-study examination knowledge point map.

[0012] Furthermore, based on the basic information and learning ability characteristic information of the user to be recommended and the classification result, multiple reference test users and a second adult self-study examination knowledge point map corresponding to the user to be recommended are determined from the multiple test users, including: based on the adult self-study examination major corresponding to the user to be recommended, a second target user unit is determined from the multiple user units; based on the region to which the user to be recommended belongs, a second target user group is determined from the multiple user groups included in the second target user unit; based on the basic information and learning ability characteristic information of the user to be recommended, a target user cluster is determined from the user cluster included in the second target user group; based on the basic information and learning ability characteristic information of the user to be recommended, multiple reference test users corresponding to the user to be recommended are determined from the target user cluster; based on the adult self-study examination major and the region to which the user to be recommended corresponds, a second adult self-study examination knowledge point map corresponding to the user to be recommended is determined.

[0013] Furthermore, based on the self-study records of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map, the recommended self-study examination content and recommended test questions corresponding to the user to be recommended are generated, including: through a recommendation prediction model, based on the self-study records of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map, the recommended self-study examination content and recommended test questions corresponding to the user to be recommended are generated.

[0014] The present invention provides an intelligent adult self-study examination content and test question recommendation system, which is used to execute the above-mentioned intelligent adult self-study examination content and test question recommendation method, including: a test paper acquisition module, which is used to acquire the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; a map establishment module, which is used to establish a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region based on the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; a user testing module, which is used to acquire basic information, learning ability characteristic information and self-study records of multiple test users; a user classification module, which is used to classify the multiple test users based on the basic information, learning ability characteristic information and self-study records of the multiple test users, and generate classification results; the map establishment module is also used to determine the multiple test users corresponding to each of the first adult self-study examination knowledge point maps based on the classification results. user; the map establishment module is also used to generate a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map for each of the first adult self-study examination knowledge point maps based on multiple test users corresponding to the first adult self-study examination knowledge point map; the information acquisition module is used to obtain the basic information and learning ability characteristic information of the user to be recommended; the self-study examination recommendation module is used to determine the multiple reference test users and the second adult self-study examination knowledge point map corresponding to the user to be recommended from the multiple test users based on the basic information and learning ability characteristic information of the user to be recommended and the classification result; the information acquisition module is also used to obtain the self-study record of the user to be recommended; the self-study examination recommendation module is also used to generate the self-study examination recommended content and recommended test questions corresponding to the user to be recommended based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map.

[0015] Compared with the prior art, the intelligent adult self-study examination content and test question recommendation system and method provided in this specification has at least the following beneficial effects:

[0016] 1. By analyzing the basic information, learning ability characteristics and self-study records of the test users, personalized learning recommendations can be generated. This helps learners choose the most suitable learning resources according to their actual situation and improve their learning efficiency. Based on the user's learning progress and knowledge point mastery, corresponding test questions are recommended to help learners consolidate their knowledge and fill in the gaps. Through the analysis of past test papers, a detailed knowledge point map can be constructed to reveal the associations and dependencies between knowledge points. This helps educators better understand the course structure, optimize teaching design, and ensure the consistency and systematicness of teaching content. Personalized learning recommendations and targeted test questions can help candidates better master knowledge points, improve problem-solving ability and test-taking skills, and thus increase the chances of passing the exam. Through precise learning resource recommendations, candidates can avoid blindly searching in massive learning materials and save valuable study time;

[0017] 2. By determining multiple learning ability factors and calculating their correlation coefficients with different adult self-study examination majors, it is possible to achieve an accurate assessment of the learning ability of each test user. This assessment not only takes into account the user's overall learning ability, but also focuses on the ability factors closely related to specific majors. Based on the target learning ability factors, learning resources and strategies that better meet the user's personalized needs can be recommended to the user, thereby improving learning effects and satisfaction. More reasonable adult self-study examination content and test questions can be recommended based on the user's learning ability characteristic information;

[0018] 3. Through in-depth analysis of the adult self-study examination papers of previous years, we can comprehensively cover the examination knowledge points of each region and each major to ensure the integrity and accuracy of the knowledge system. Not only extracting knowledge points, but also extracting the characteristics of the proportion of questions, the distribution of question difficulty and the distribution of question types for each knowledge point will help to deeply understand the examination requirements and trends. By calculating the overlap of examination knowledge points and the similarity of examination characteristics between regions, we can accurately evaluate the similarity between regions and provide a basis for clustering. For adult self-study examination majors in different regional clusters, we can build a more targeted learning content and question recommendation system to meet the needs of learners in different regions and majors. Through personalized recommendations, learners can find learning resources and paths that suit them more quickly and improve learning efficiency and effectiveness. By constructing a knowledge point map and performing regional clustering, we have accumulated a large amount of valuable data resources, which provides a scientific basis for the recommendation of adult self-study examination content and questions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0020] Figure 1It is a flow chart of an intelligent adult self-study examination content and test question recommendation method shown in one embodiment of the present application;

[0021] Figure 2 It is a module diagram of an intelligent adult self-study examination content and test question recommendation system shown in one embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly introduces the drawings required for describing the embodiments.

[0023] Figure 1 1 is a flowchart of an intelligent method for recommending content and test questions for adult self-study examinations shown in one embodiment of the present application. Figure 1 As shown, an intelligent method for recommending content and test questions for adult self-study examinations may include the following steps.

[0024] Step 111, obtaining the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions.

[0025] Specifically, crawler technology can be used to obtain adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions from the official websites, databases or public information of provincial education examination institutes, self-study examination offices or relevant educational institutions.

[0026] Step 112, based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions, establish a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region.

[0027] Specifically include:

[0028] For each region, based on the past adult self-study examination papers of different adult self-study examination majors corresponding to the region, determine the examination knowledge points corresponding to different adult self-study examination majors in the region and the examination features corresponding to each examination knowledge point;

[0029] For each adult self-study examination major, multiple regions are clustered based on the examination knowledge points of the adult self-study examination major corresponding to each region and the examination characteristics corresponding to each examination knowledge point, and multiple regional clusters corresponding to the adult self-study examination major are determined. Based on the examination knowledge points of the adult self-study examination major corresponding to the multiple regions included in each regional cluster and the examination characteristics corresponding to each examination knowledge point, a first adult self-study examination knowledge point map corresponding to the adult self-study examination major of the regional cluster is established.

[0030] As a preferred method, based on the test papers of previous years' adult self-study examinations corresponding to different adult self-study examination majors in the region, the test knowledge points corresponding to different adult self-study examination majors in the region and the test features corresponding to each test knowledge point are determined, including:

[0031] For each adult self-study examination major, a knowledge point database corresponding to the adult self-study examination major is constructed. For example, a knowledge point database corresponding to the adult self-study examination major can be constructed based on the examination syllabus, textbooks or expert opinions;

[0032] For each adult self-study examination paper, preprocess the adult self-study examination paper (for example, removing irrelevant information (such as question number, examinee name, etc.), formatting text (such as separating questions and answers), segmenting words, using optical character recognition technology to convert non-text data into text data, etc.), generate a preprocessed adult self-study examination paper, and extract knowledge points and knowledge point features from the preprocessed self-study examination paper according to a knowledge point library corresponding to an adult self-study examination major related to the adult self-study examination paper, wherein the knowledge point features at least include a question ratio, question difficulty distribution, and question type distribution for each knowledge point, the question ratio of the knowledge point can represent the ratio of the number of questions for the knowledge point included in the adult self-study examination paper to the total number of questions, the question difficulty distribution can represent the number of questions corresponding to different difficulties of the knowledge points included in the adult self-study examination paper, and the question type distribution can represent the number of questions corresponding to different question types (such as fill-in-the-blank questions, multiple-choice questions, short-answer questions, essay questions, etc.) of the knowledge points included in the adult self-study examination paper;

[0033] For each adult self-study examination major, based on the knowledge points and knowledge point characteristics of the adult self-study examination papers of the corresponding adult self-study examination major in the region in previous years, the examination knowledge points of the corresponding adult self-study examination major in the region and the examination characteristics corresponding to each examination knowledge point are determined.

[0034] Specifically, the knowledge points and knowledge point features of the adult self-study examination papers of the adult self-study examination majors corresponding to the region are weightedly fused to obtain the examination knowledge points of the adult self-study examination majors corresponding to the region and the examination features corresponding to each examination knowledge point, wherein the examination features include the knowledge points of the adult self-study examination majors corresponding to the region and the proportion of test questions, the distribution of test question difficulty and the distribution of question types for each knowledge point.

[0035] Preferably, based on the examination knowledge points of the adult self-study examination major corresponding to each region and the examination features corresponding to each examination knowledge point, multiple regions are clustered to determine multiple regional clusters corresponding to the adult self-study examination major;

[0036] Based on the examination knowledge points of the adult self-study examination major corresponding to each region, calculate the overlap of the examination knowledge points of the adult self-study examination major corresponding to any two regions;

[0037] Based on the examination features corresponding to each examination knowledge point of the adult self-study examination major in each region, calculate the similarity of the examination features of the adult self-study examination majors in any two regions;

[0038] Based on the overlap of test knowledge points of any two regions corresponding to the adult self-study examination majors and the similarity of test features of any two regions corresponding to the adult self-study examination majors, the regional similarity of any two regions corresponding to the adult self-study examination majors is calculated;

[0039] Based on the regional similarity of adult self-study examination majors corresponding to any two regions, multiple regions are clustered to determine multiple regional clusters corresponding to adult self-study examination majors.

[0040] Specifically, the similarity of the examination characteristics of the adult self-study examination majors in two regions can be calculated according to the following formula:

[0041] S ((i，j)，k，1) =a 11 ×cos(F ((i，k，1) , F ((j，k，1) )+a 12 ×cos(F ((i，k，2) , F ((j，k，2) )+a 13 ×cos(F ((i，k，3) , F ((j，k，3) )

[0042] Among them, S ((i，j)，k，1) is the similarity of the examination characteristics of the kth adult self-study examination major in the i-th region and the j-th region, a 11 、a 12 and a 13 is the preset weight, a 11 、a 12 and a 13 Greater than 0, a 11 +a 12 +a 13 =1, cos(F ((i，k，1) , F ((j，k，1) ) is the cosine similarity between the proportion of questions for each knowledge point of the k-th adult self-study examination major in the ith region and the proportion of questions for each knowledge point of the k-th adult self-study examination major in the j-th region, cos(F ((i，k，2) , F ((j，k，2) ) is the cosine similarity between the distribution of test difficulty of each knowledge point of the k-th adult self-study examination major in the ith region and the distribution of test difficulty of each knowledge point of the k-th adult self-study examination major in the j-th region, cos(F ((i，k，3) , F ((j，k，3) ) is the cosine similarity between the question type distribution of each knowledge point of the k-th adult self-study examination major in the ith area and the question type distribution of each knowledge point of the k-th adult self-study examination major in the j-th area.

[0043] According to the following formula, the regional similarity of any two regions corresponding to adult self-study examination majors is:

[0044] S ((i，j)，k，2) =b 11 ×C ((i，j)，k) +b 12 ×S ((i，j)，k，1)

[0045] Among them, S ((i，j)，k，2) is the regional similarity between the i-th region and the j-th region corresponding to the k-th adult self-study examination major, b 11 and b 12 is the preset weight, b 11 and b 12 Greater than 0, b 11 +b 12 =1, C ((i，j)，k) It is the overlap of the examination knowledge points of the kth adult self-study examination major in the ith area and the jth area.

[0046] The K-means clustering algorithm is used to cluster multiple regions based on the regional similarity of adult self-study examination majors corresponding to any two regions, and multiple regional clusters corresponding to adult self-study examination majors are determined.

[0047] The first adult self-study examination knowledge point map may include nodes representing multiple knowledge points of different adult self-study examination majors corresponding to the region.

[0048] Step 113, obtaining basic information, learning ability characteristic information and self-study records of multiple test users.

[0049] As an example only, the basic information of the test user may include at least age, occupation, education, and adult self-study goals (e.g., region, adult self-study examination major, target college, etc.). The learning ability characteristic information may include at least cognitive style, learning preference, concentration, etc. The self-study record may include the knowledge points, learning progress, exercise completion, and learning time of the test user at multiple self-study time points.

[0050] Step 114, based on the basic information, learning ability characteristic information and self-study records of the multiple test users, the multiple test users are classified to generate classification results.

[0051] Specifically include:

[0052] Based on the adult self-study examination major to which each test user belongs, multiple test users are grouped to generate multiple user units, wherein one adult self-study examination major corresponds to one user unit;

[0053] For each user unit, based on the region to which the test user included in the user unit belongs and the multiple regional clusters corresponding to each adult self-study examination major, the test users included in the user unit are grouped to generate multiple user groups included in the user unit, wherein one user group included in the user unit corresponds to one regional cluster corresponding to the adult self-study examination major to which the user unit belongs;

[0054] For each user group, based on the learning ability feature information and self-study records of the multiple test users included in the user group, the user similarity between any two test users included in any two user groups is calculated, and according to the user similarity between any two test users included in any two user groups, the multiple test users included in the user group are clustered to determine the multiple user clusters included in the user group, wherein the classification result includes the multiple user clusters included in each user group.

[0055] Specifically, the user similarity of any two test users included in any two user groups can be calculated based on the learning ability feature information and self-study records of the multiple test users included in the user group through a similarity determination model, wherein the similarity determination model can be a convolutional neural network model. The multiple test users included in the user group are clustered according to the user similarity of any two test users included in any two user groups through a K-means clustering algorithm to determine multiple user clusters included in the user group.

[0056] Step 115, based on the classification result, determine a plurality of test users corresponding to each first adult self-study examination knowledge point map.

[0057] Specifically include:

[0058] For each first adult self-study examination knowledge point map, based on the adult self-study examination major corresponding to the first adult self-study examination knowledge point map, a first target user unit is determined from multiple user units, based on the regional cluster corresponding to the first adult self-study examination knowledge point map, a first target user group is determined from multiple user groups included in the first target user unit, and the test users included in the first target user group are used as multiple test users corresponding to the first adult self-study examination knowledge point map.

[0059] For example, the first adult self-study examination knowledge point map corresponds to the adult self-study examination major A, then the user unit corresponding to the adult self-study examination major A is taken as the first target user unit, the first target unit includes user group B1, user group B2, and user group B3, user group B1 corresponds to regional cluster C1, user group B2 corresponds to regional cluster C2, and user group B3 corresponds to regional cluster C3, and the first adult self-study examination knowledge point map corresponds to regional cluster C1, then user group B1 is taken as the first target user group.

[0060] Step 116: for each first adult self-study examination knowledge point map, based on a plurality of test users corresponding to the first adult self-study examination knowledge point map, generate a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map.

[0061] Specifically include:

[0062] Determine, based on the self-study records of multiple test users corresponding to the first adult self-study exam knowledge point map, a correlation parameter between any two adult self-study exam knowledge points included in the first adult self-study exam knowledge point map;

[0063] Based on the association parameters between any two adult self-study examination knowledge points included in the first adult self-study examination knowledge point map and the first adult self-study examination knowledge point map, a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map is generated.

[0064] Specifically, for any two adult self-study examination knowledge points (for example, adult self-study examination knowledge point e and adult self-study examination knowledge point f), the multiple test users corresponding to the first adult self-study examination knowledge point map can be divided into three categories according to the self-study records of the multiple test users corresponding to the first adult self-study examination knowledge point map, where the first category is test users who have only studied adult self-study examination knowledge point e, the second category is test users who have only studied adult self-study examination knowledge point f and test users who have completed the study of adult self-study examination knowledge point e and adult self-study examination knowledge point f, and the association parameters of the two adult self-study examination knowledge points are calculated according to the test scores of the first category of test users corresponding to adult self-study examination knowledge point e, the test scores of the second category of test users corresponding to adult self-study examination knowledge point f, and the test scores of the third category of test users corresponding to adult self-study examination knowledge point e and adult self-study examination knowledge point f.

[0065] The correlation parameters of two adult self-study examination knowledge points can be calculated according to the following formula:

[0066] C (e，f)) =c 11 ×C (e，f)，1) +c 12 ×C (e，f)，2)

[0067]

[0068]

[0069] Among them, C (e，f)) is the correlation parameter between the e-th adult self-study examination knowledge point and the f-th adult self-study examination knowledge point, c 11 and c 12 is the preset weight, c11 and c 12 Greater than 0, c 11 +c 12 =1, C (e，f)，1) is the first single point association parameter between the e-th adult self-study examination knowledge point and the f-th adult self-study examination knowledge point, C (e，f)，2) is the second single point association parameter between the e-th adult self-study examination knowledge point and the f-th adult self-study examination knowledge point, is the test score of the m1th test user in the first category corresponding to the adult self-study examination knowledge point e, M1 is the total number of test users in the first category, is the test score of the m3th test user in the third category corresponding to the adult self-study examination knowledge point e, M3 is the total number of test users in the third category, M2 is the total number of test users in the second category, is the test score of the m2-th test user in the second category corresponding to the adult self-study examination knowledge point f, is the test score of the m3th test user in the third category corresponding to the adult self-study examination knowledge point f.

[0070] The second adult self-study examination knowledge point map may include nodes representing multiple knowledge points corresponding to different adult self-study examination majors in the representation area and edges connecting the nodes corresponding to the two adult self-study examination knowledge points for representing the association parameters between two adult self-study examination knowledge points. The shorter the edge, the larger the association parameter between the two adult self-study examination knowledge points.

[0071] Step 117, obtaining basic information and learning ability characteristic information of the user to be recommended.

[0072] Specifically include:

[0073] Identify multiple learning ability factors;

[0074] Acquire ability test data of multiple test users, wherein the ability test data of the test users may include scores of the test users in each learning ability factor;

[0075] For each adult self-study examination major, based on the ability test data of multiple test users, determine the correlation coefficient between each learning ability factor and the adult self-study examination major, and based on the correlation coefficient between each learning ability factor and the adult self-study examination major, determine the target learning ability factor corresponding to the adult self-study examination major from multiple learning ability factors;

[0076] For each test user, the learning ability characteristic information of the test user is obtained based on the target learning ability factor corresponding to the adult self-study examination major to which the test user belongs.

[0077] Specifically, for each learning ability factor, the correlation coefficient between the learning ability factor and the adult self-study examination major can be calculated based on the self-study efficiency of multiple test users belonging to the adult self-study examination major and the scores of the learning ability factor.

[0078] For example, the correlation coefficient between the learning ability factor and the adult self-study examination major can be calculated according to the following formula:

[0079]

[0080] Among them, r (P，Q) is the correlation coefficient between the Pth learning ability factor and the Qth adult self-study examination major, M4 is the total number of test users belonging to the adult self-study examination major, is the score of the m4th test user who majors in adult self-study examination on the Pth learning ability factor, It is the self-study efficiency of the m4th test user belonging to the adult self-study examination major in the Qth adult self-study examination major.

[0081] Step 118, based on the basic information, learning ability characteristic information and classification results of the user to be recommended, determine multiple reference test users and the second adult self-study examination knowledge point map corresponding to the user to be recommended from multiple test users.

[0082] Specifically include:

[0083] Determine a second target user unit from a plurality of user units based on the adult self-study examination major corresponding to the user to be recommended;

[0084] Based on the area to which the to-be-recommended user belongs, determining a second target user group from a plurality of user groups included in the second target user unit;

[0085] Based on the basic information and learning ability characteristic information of the user to be recommended, determining a target user cluster from the user clusters included in the second target user group;

[0086] Based on the basic information and learning ability characteristic information of the user to be recommended, determine multiple reference test users corresponding to the user to be recommended from the target user cluster;

[0087] Based on the adult self-study examination major and the region to which the user to be recommended belongs, a second adult self-study examination knowledge point map corresponding to the user to be recommended is determined.

[0088] Step 119, obtaining the self-study record of the user to be recommended.

[0089] Step 120, based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map, generate the self-study examination recommended content and recommended test questions corresponding to the user to be recommended.

[0090] Specifically include:

[0091] Through the recommendation prediction model, based on the self-study records of the user to be recommended, the corresponding self-study records of multiple reference test users and the second adult self-study examination knowledge point map, the self-study examination recommended content and recommended test questions corresponding to the user to be recommended are generated, wherein the recommendation prediction model can be a convolutional neural network model.

[0092] Figure 2 is a module diagram of an intelligent adult self-study examination content and test question recommendation system shown in an embodiment of the present application, such as Figure 2 As shown, an intelligent adult self-study examination content and test question recommendation system may include a test paper acquisition module, a map establishment module, a user testing module, a user classification module, an information acquisition module and a self-study examination recommendation module.

[0093] The test paper acquisition module can be used to obtain the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions;

[0094] The map establishment module can be used to establish the first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions;

[0095] The user testing module can be used to obtain basic information, learning ability characteristics and self-study records of multiple test users;

[0096] The user classification module can be used to classify multiple test users based on their basic information, learning ability characteristic information and self-study records, and generate classification results;

[0097] The graph establishment module is also used to determine multiple test users corresponding to each first adult self-study examination knowledge point graph based on the classification results;

[0098] The graph establishment module is also used to generate, for each first adult self-study examination knowledge point graph, a second adult self-study examination knowledge point graph corresponding to the first adult self-study examination knowledge point graph based on a plurality of test users corresponding to the first adult self-study examination knowledge point graph;

[0099] The information acquisition module can be used to obtain basic information and learning ability characteristic information of the user to be recommended;

[0100] The self-study exam recommendation module can be used to determine multiple reference test users and a second adult self-study exam knowledge point map corresponding to the user to be recommended from multiple test users based on the basic information and learning ability characteristic information of the user to be recommended and the classification results;

[0101] The information acquisition module is also used to obtain the self-study records of the user to be recommended;

[0102] The self-study exam recommendation module is also used to generate self-study exam recommendation content and recommended test questions corresponding to the user to be recommended based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study exam knowledge point map.

[0103] An intelligent adult self-study examination content and test question recommendation system can be used to execute an intelligent adult self-study examination content and test question recommendation method. For more descriptions of an intelligent adult self-study examination content and test question recommendation system, please refer to the relevant descriptions of an intelligent adult self-study examination content and test question recommendation method, which will not be repeated here.

[0104] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. An intelligent method for recommending content and test questions for adult self-study examinations, characterized in that: include: Obtain the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; Based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions, a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region is established; Obtain basic information, learning ability characteristics and self-study records of multiple test users; Based on the basic information, learning ability characteristic information and self-study records of the multiple test users, the multiple test users are classified to generate classification results; Based on the classification result, determining a plurality of test users corresponding to each of the first adult self-study examination knowledge point graphs; For each of the first adult self-study examination knowledge point maps, based on a plurality of test users corresponding to the first adult self-study examination knowledge point map, generate a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map; Obtain basic information and learning ability characteristic information of the user to be recommended; Based on the basic information and learning ability characteristic information of the user to be recommended and the classification result, determining a plurality of reference test users and a second adult self-study examination knowledge point map corresponding to the user to be recommended from the plurality of test users; Obtaining the self-study record of the user to be recommended; Based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study examination knowledge point map, the self-study examination recommended content and recommended test questions corresponding to the user to be recommended are generated.

2. The intelligent method for recommending content and test questions for adult self-study examinations according to claim 1, characterized in that: Based on the examination papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions, a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region is established, including: For each region, based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in the region, determine the examination knowledge points corresponding to different adult self-study examination majors in the region and the examination features corresponding to each examination knowledge point; For each adult self-study examination major, based on the examination knowledge points corresponding to the adult self-study examination major in each area and the examination features corresponding to each examination knowledge point, the multiple areas are clustered to determine a plurality of area clusters corresponding to the adult self-study examination major, and based on the examination knowledge points corresponding to the adult self-study examination major in the multiple areas included in each area cluster and the examination features corresponding to each examination knowledge point, a first adult self-study examination knowledge point map corresponding to the adult self-study examination major is established for the area cluster.

3. The intelligent method for recommending content and test questions for adult self-study examinations according to claim 2, characterized in that: Based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in the region, the examination knowledge points corresponding to different adult self-study examination majors in the region and the examination features corresponding to each examination knowledge point are determined, including: For each of the adult self-study examination majors, a knowledge point library corresponding to the adult self-study examination major is constructed; For each adult self-study examination paper, pre-process the adult self-study examination paper to generate a pre-processed adult self-study examination paper, and extract knowledge points and knowledge point features from the pre-processed self-study examination paper according to a knowledge point library corresponding to an adult self-study examination major related to the adult self-study examination paper, wherein the knowledge point features at least include a question ratio, question difficulty distribution, and question type distribution for each knowledge point; For each of the adult self-study examination majors, based on the knowledge points and knowledge point features of the adult self-study examination papers of the adult self-study examination majors in the area corresponding to the adult self-study examination majors in previous years, the examination knowledge points corresponding to the adult self-study examination majors in the area and the examination features corresponding to each examination knowledge point are determined.

4. The intelligent method for recommending content and test questions for adult self-study examinations according to claim 2, characterized in that: Based on the examination knowledge points corresponding to the adult self-study examination major of each of the regions and the examination features corresponding to each of the examination knowledge points, the multiple regions are clustered to determine multiple regional clusters corresponding to the adult self-study examination major; Based on the examination knowledge points of the adult self-study examination major corresponding to each of the areas, calculating the overlap of the examination knowledge points of the adult self-study examination major corresponding to any two of the areas; Based on the examination features corresponding to each examination knowledge point of the adult self-study examination major corresponding to each of the regions, the similarity of the examination features corresponding to the adult self-study examination majors of any two regions is calculated; Based on the overlap of the examination knowledge points of any two of the regions corresponding to the adult self-study examination majors and the similarity of the examination features of any two of the regions corresponding to the adult self-study examination majors, the regional similarity of any two of the regions corresponding to the adult self-study examination majors is calculated; Based on the regional similarity between any two of the regions corresponding to the adult self-study examination majors, the multiple regions are clustered to determine multiple regional clusters corresponding to the adult self-study examination majors.

5. An intelligent method for recommending content and test questions for adult self-study examinations according to any one of claims 1 to 4, characterized in that: Obtain the learning ability characteristic information of the test user, including: Identify multiple learning ability factors; Obtain capability test data of multiple test users; For each of the adult self-study examination majors, based on the ability test data of the multiple test users, determine the correlation coefficient between each learning ability factor and the adult self-study examination major, and based on the correlation coefficient between each learning ability factor and the adult self-study examination major, determine the target learning ability factor corresponding to the adult self-study examination major from the multiple learning ability factors; For each of the test users, the learning ability characteristic information of the test user is obtained based on the target learning ability factor corresponding to the adult self-study examination major to which the test user belongs.

6. An intelligent method for recommending content and test questions for adult self-study examinations according to any one of claims 2 to 4, characterized in that: Based on the basic information, learning ability characteristic information and self-study records of the multiple test users, the multiple test users are classified to generate classification results, including: Based on the adult self-study examination major to which each of the test users belongs, the multiple test users are grouped to generate multiple user units; For each of the user units, based on the area to which the test users included in the user unit belong and the multiple area clusters corresponding to each of the adult self-study examination majors, the test users included in the user unit are grouped to generate multiple user groups included in the user unit; For each of the user groups, based on the learning ability feature information and self-study records of the multiple test users included in the user group, the user similarity between any two test users included in any two of the user groups is calculated; according to the user similarity between any two test users included in any two of the user groups, the multiple test users included in the user group are clustered to determine the multiple user clusters included in the user group, wherein the classification result includes the multiple user clusters included in each of the user groups.

7. The intelligent method for recommending content and test questions for adult self-study examinations according to claim 6, characterized in that: Based on the classification result, determining a plurality of test users corresponding to each of the first adult self-study examination knowledge point graphs includes: For each of the first adult self-study exam knowledge point graphs, based on the adult self-study exam major corresponding to the first adult self-study exam knowledge point graph, determine a first target user unit from the multiple user units, based on the regional cluster corresponding to the first adult self-study exam knowledge point graph, determine a first target user group from the multiple user groups included in the first target user unit, and use the test users included in the first target user group as the multiple test users corresponding to the first adult self-study exam knowledge point graph; Based on a plurality of test users corresponding to the first adult self-study examination knowledge point map, generating a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map, including: Determine, based on the self-study records of a plurality of test users corresponding to the first adult self-study exam knowledge point map, a correlation parameter between any two adult self-study exam knowledge points included in the first adult self-study exam knowledge point map; Based on the association parameters between any two adult self-study examination knowledge points included in the first adult self-study examination knowledge point map and the first adult self-study examination knowledge point map, a second adult self-study examination knowledge point map corresponding to the first adult self-study examination knowledge point map is generated.

8. The intelligent method for recommending content and test questions for adult self-study examinations according to claim 7, characterized in that: Based on the basic information and learning ability characteristic information of the user to be recommended and the classification result, a plurality of reference test users and a second adult self-study examination knowledge point map corresponding to the user to be recommended are determined from the plurality of test users, including: Determine a second target user unit from the plurality of user units based on the adult self-study examination major corresponding to the user to be recommended; Based on the area to which the to-be-recommended user belongs, determining a second target user group from a plurality of user groups included in the second target user unit; Based on the basic information and learning ability characteristic information of the to-be-recommended user, determining a target user cluster from the user clusters included in the second target user group; Based on the basic information and learning ability characteristic information of the user to be recommended, determining a plurality of reference test users corresponding to the user to be recommended from the target user cluster; Based on the adult self-study examination major and the region to which the to-be-recommended user corresponds, a second adult self-study examination knowledge point map corresponding to the to-be-recommended user is determined.

9. An intelligent method for recommending content and test questions for self-study adult examinations according to any one of claims 1 to 4, characterized in that: Based on the self-study record of the user to be recommended, the corresponding reference test users and the second adult self-study examination knowledge point map, the self-study examination recommended content and recommended test questions corresponding to the user to be recommended are generated, including: Through the recommendation prediction model, based on the self-study record of the user to be recommended, the corresponding self-study records of multiple reference test users and the second adult self-study examination knowledge point map, the self-study examination recommended content and recommended test questions corresponding to the user to be recommended are generated.

10. An intelligent adult self-study examination content and test question recommendation system, characterized in that: A method for recommending contents and test questions of an intelligent self-study adult examination as claimed in any one of claims 1 to 9, comprising: The test paper acquisition module is used to obtain the test papers of adult self-study examinations in previous years corresponding to different adult self-study examination majors in multiple regions; A map establishment module is used to establish a first adult self-study examination knowledge point map corresponding to different adult self-study examination majors in each region based on the adult self-study examination papers of previous years corresponding to different adult self-study examination majors in multiple regions; User testing module, used to obtain basic information, learning ability characteristics and self-study records of multiple test users; A user classification module, used to classify the multiple test users based on their basic information, learning ability characteristic information and self-study records, and generate classification results; The graph establishment module is also used to determine a plurality of test users corresponding to each of the first adult self-study examination knowledge point graphs based on the classification results; The graph establishment module is further used to generate, for each of the first adult self-study examination knowledge point graphs, a second adult self-study examination knowledge point graph corresponding to the first adult self-study examination knowledge point graph based on a plurality of test users corresponding to the first adult self-study examination knowledge point graph; The information acquisition module is used to obtain the basic information and learning ability characteristic information of the user to be recommended; A self-study examination recommendation module, for determining a plurality of reference test users and a second adult self-study examination knowledge point map corresponding to the user to be recommended from the plurality of test users based on the basic information and learning ability characteristic information of the user to be recommended and the classification result; The information acquisition module is also used to obtain the self-study record of the user to be recommended; The self-study exam recommendation module is also used to generate self-study exam recommendation content and recommended test questions corresponding to the user to be recommended based on the self-study record of the user to be recommended, the corresponding multiple reference test users and the second adult self-study exam knowledge point map.

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