CN107832453A - Virtual test paper recommendation method oriented to personalized learning scheme - Google Patents

Virtual test paper recommendation method oriented to personalized learning scheme Download PDF

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CN107832453A
CN107832453A CN201711189590.7A CN201711189590A CN107832453A CN 107832453 A CN107832453 A CN 107832453A CN 201711189590 A CN201711189590 A CN 201711189590A CN 107832453 A CN107832453 A CN 107832453A
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张堃
李太福
辜小花
黄迪
唐海红
黄勇
何光敏
宋健军
胡志轩
何江
刘湘
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Tianbao Experimental School Jiulongpo District Chongqing
Chongqing University of Science and Technology
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Chongqing University of Science and Technology
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Abstract

本发明提供一种面向个性化学习方案的虚拟试卷推荐方法，根据轮盘选择法生成一系列题量百分比围绕主难度系数的左右两边呈均匀梯度下降分布的虚拟试卷，在学生的DNA信息中匹配试卷里试题的知识点的理解程度系数，并结合该道题满分计算出学生的理论得分，将试卷中每道试题的理论得分求和，即可得出学生本套试题的理论得分，以此理论得分为参考目标，针对学生的难点和弱点推荐依次难度的试卷，从而达到逐步提高学生学习成绩的目的。

The present invention provides a method for recommending virtual test papers oriented to individualized learning schemes. According to the roulette selection method, a series of virtual test papers with question volume percentages that are uniformly gradient-decreasing distributed around the left and right sides of the main difficulty coefficient are generated and matched in the students' DNA information. The degree of understanding coefficient of the knowledge points in the test paper, combined with the full score of the question to calculate the theoretical score of the student, and the theoretical score of each test question in the test paper can be summed to obtain the theoretical score of the student's test questions. The theoretical score is used as a reference target, and test papers in order of difficulty are recommended according to students' difficulties and weaknesses, so as to achieve the goal of gradually improving students' academic performance.

Description

Virtual paper towards individualized learning scheme recommends method
Technical field
The present invention relates to artificial intelligence and big data technical field, more specifically, is related to a kind of towards individualized learning The virtual paper of scheme recommends method.
Background technology
With the popularization of Internet technology, the arriving in big data epoch, Web education has obtained rapid development, in face of mesh The TB of preceding accumulation even the mass digital educational resource of PB levels, the similar big data information processing technology demand of data mining is increasingly Urgently.Educational resource push is the production of educational pattern and method reform in teaching that Modern Education Technology is guided using network by carrier A kind of thing, it is intended to explore Student oriented and teacher, there is provided the service mode of high-quality information resources.And personalized recommendation is this Popular domain in research, personalized recommendation is combined with educational resource, learner is extracted with related big data digging technology Learning behavior feature, be each user it is customized rationally effective Learning Scheme.
In modern education, because of technology and the missing of resource integrated method, student can not often find rapidly from a large number of homework exercises for primary and middle school students The method for lifting oneself achievement；Teachers still remain the method that Traditional Man makes the test when end of term group is rolled up.In fact, pass through Count substantial amounts of student and do the common problem inscribed and recorded with regard to students can be excavated, the achievement with regard to student can be greatly improved.
The content of the invention
In view of the above problems, it is an object of the invention to provide a kind of virtual paper recommendation side towards individualized learning scheme Method, it is difficult to study condition for single student to solve traditional paper, the problem of recommending optimal personalized paper.
Virtual paper provided by the invention towards individualized learning scheme recommends method, including：
Step S1：Preset main degree-of-difficulty factor；Wherein, main degree-of-difficulty factor is higher than the learning adjustment difficulty of student at this stage；
Step S2：A set of volume percentage is generated according to wheel disc back-and-forth method gradient decline distribution is presented around main degree-of-difficulty factor Virtual paper；Wherein, step S2 includes：
Step S21：The volume for 10 degree-of-difficulty factors for including main degree-of-difficulty factor area percentage shared in wheel disc is set Than；Wherein, the selected probability P (i) of the volume of each degree-of-difficulty factor surrounds left and right two of the main degree-of-difficulty factor to main degree-of-difficulty factor Side declines distribution in uniform gradient；
Step S22：The volume that the examination question of 10 degree-of-difficulty factors in virtual paper is produced by wheel disc back-and-forth method is distributed；
Step S23：Volume according to the examination question of 10 degree-of-difficulty factors caused by step S22, which is distributed from test item bank, extracts examination Topic forms virtual paper；
Step S3：Virtual paper is combined with the DNA information of student to obtain theoretical score；
Wherein, DNA information is the three-dimensional tensor being made up of knowledge point, degree of understanding coefficient and time, and knowledge point is to institute The high level overview of category section purpose core content；Degree of understanding coefficient refers to after student completes the exercise of certain knowledge point, and this is known Know the description of the overall grasp situation of point；Time refer to student to the degree of understanding coefficient of knowledge point from a upper grade rise to The time span of next grade；And
Degree of understanding coefficient of the student to the knowledge point is obtained by the knowledge point of per pass examination question in virtual paper, under utilization State formula and obtain theoretical score of the student to virtual paper：
In above formula, Score is theoretical score of the student to the virtual paper；STgFor the full marks of g problems, wherein g =1 ..., n；UDiKnowledge point i degree of understanding coefficient is inscribed to g for the student；
Step S4：Virtual paper of all theoretical scores in 60 to 80 points is saved as virtual Pipers database；
Step S5：Virtual paper is extracted from virtual Pipers database and recommends student.
Compared with prior art, the virtual paper provided by the invention towards individualized learning scheme recommends method, passes through Wheel disc back-and-forth method produces virtual paper, and look-ahead goes out the theoretical score of student's paper under certain difficulty, when with this theoretical score For reference target, when difficult point and weakness for student recommend the paper of difficulty successively, just can rationally be adjusted by its performance Whole study plan, reach the purpose for stepping up Students ' Learning achievement.
Brief description of the drawings
By reference to the explanation below in conjunction with accompanying drawing, and with the present invention is more fully understood, of the invention is other Purpose and result will be more apparent and should be readily appreciated that.In the accompanying drawings：
Fig. 1 is to set figure according to the area percentage of the virtual paper of the embodiment of the present invention.
Embodiment
Virtual paper provided by the invention towards individualized learning scheme recommends method, comprises the following steps：
Step S1：Preset main degree-of-difficulty factor.
Default main degree-of-difficulty factor should be higher than that the learning adjustment difficulty of student at this stage.
Step S2：A set of volume percentage is generated according to wheel disc back-and-forth method gradient decline distribution is presented around main degree-of-difficulty factor Virtual paper.
Detailed process is as follows：
Step S21：The volume for 10 degree-of-difficulty factors for including main degree-of-difficulty factor area percentage shared in wheel disc is set Than；Wherein, the selected probability P (i) of the volume of each degree-of-difficulty factor surrounds left and right two of the main degree-of-difficulty factor to main degree-of-difficulty factor Side declines distribution in uniform gradient.
The present invention sets altogether the volume of 10 degree-of-difficulty factors, and 1 in 10 degree-of-difficulty factors be main degree-of-difficulty factor, residue 9 be other degree-of-difficulty factors, the probability that the volumes of 9 other degree-of-difficulty factors is selected in virtual paper, be designated as P (i), its In, i=1 ..., 10, here obvious Sum (P)=1, P (i) around the right and left from main degree-of-difficulty factor to main degree-of-difficulty factor in equal Even gradient declines distribution.
One degree-of-difficulty factor represents the difficulty of a grade.
Such as area percentage can be set to as shown in figure 1, each difficulty system by the virtual paper that main degree-of-difficulty factor is 5 Several probability distribution are：
Step S22：The volume that the examination question of 10 degree-of-difficulty factors in virtual paper is produced by wheel disc back-and-forth method is distributed.
Total volume of virtual paper can be with self-defined.
Such as：Total volume of virtual paper is 25 problems, toward the wheel disc for divided sector in throw away dice, throw away 25 every time, Wheel disc is motionless, and the region that dice is fallen is selection result.Experiment 1000 times is repeated, draws end product.Obviously, sector is bigger, The probability chosen by dice is bigger.That is, the examination question amount of difficulty 5 is maximum in virtual paper under difficulty 5, next to that difficult 5 neighbouring difficulty 4 and 6 are spent, difficulty 10 volume farthest from difficulty 5 is minimum, i.e., the examination question of difficulty 10 farthest from difficulty 5 is to learning The raw help learnt at present is minimum.
Step S23：Volume according to the examination question of 10 degree-of-difficulty factors caused by step S22, which is distributed from test item bank, extracts examination Topic forms virtual paper.
When extracting examination question, the examination question under same difficulty is randomly selected, and can be attempted all with the method traversal of permutation and combination The possibility of examination question combination.
Step S3：Virtual paper is combined with the DNA information of student to obtain theoretical score.
The DNA information of student is the three-dimensional tensor being made up of knowledge point, degree of understanding coefficient and time, and knowledge point is subordinated to Subject, it is, and high level overview to section purpose core content subdivided to section's purpose；
Degree of understanding coefficient refers to after student completes the exercise of certain knowledge point, to the overall grasp situation of this knowledge point Description, degree of understanding coefficient utilize big data analytical technology, pass through COMPREHENSIVE CALCULATING knowledge point exercise accuracy and deadline The ratio for accounting for the stipulated time is drawn；Time refers to that student is risen to next to the degree of understanding coefficient of knowledge point from a upper grade The time span of individual grade.
Per pass examination question in virtual paper forms examination question DNA, examination question DNA and student by numbering, knowledge point and degree-of-difficulty factor DNA information be combined and obtain theoretical score.Specifically, student is obtained to this by the knowledge point of per pass examination question in virtual paper The degree of understanding coefficient of knowledge point, theoretical score of the student to virtual paper is obtained using following formula：
In above formula, Score is theoretical score of the student to the virtual paper；STgFor the full marks of g problems, wherein g =1 ..., n；UDiThe degree of understanding coefficient of knowledge point is inscribed to g for the student, i is the knowledge point of g topics.
The degree of understanding coefficient of corresponding knowledge point in the DNA information of student is looked for by the knowledge point of examination question, is inscribed with this Full marks score be multiplied by theoretical score of the student to i.e. this subject of degree of understanding coefficient of the knowledge point, then the reason of n roads examination question By the theoretical score of the cumulative i.e. virtual paper of this set of score.
Step S4：Virtual paper of all theoretical scores in 60 to 80 points is saved as virtual Pipers database.
Wherein, paper of the theoretical score below 60 points should be that difficulty is higher for the student, and theoretical score is relatively low；Together Sample, paper of the theoretical score more than 80 points is easier to student's difficulty, comparatively the paper in the two sections does not possess Improve the effect of student performance.Therefore, virtual paper of the theoretical score in 60 to 80 points can weigh student to knowledge point Grasping level.
By virtual paper of the theoretical score in 60 to 80 points be integrated into just can be faster more accurate in a Pipers database realization Personalized recommendation.
Such as：Zhang San is as follows for the degree of understanding of a set of virtual paper including five problems：
Then Zhang San is scored to the theory of the virtual paper of the set：
The full marks of the virtual paper of the set are 45 points, then it is 30.6/45=68% that Zhang San's score, which accounts for total score ratio,
Then this set examination question belongs to the medium paper of difficulty to Zhang San classmate, is included in the virtual Pipers database of Zhang San.
Step S5：Virtual paper is extracted from virtual Pipers database and recommends student.
When extracting virtual paper, it is contemplated that the factor such as the degree-of-difficulty factor of paper, theoretical score, Distribution of knowledge gists, also may be used Rational personalized recommendation is carried out automatically according to the weak knowledge point of student.
Step S6：Student performs study according to the virtual paper of recommendation.
Step S7：Update the DNA information of student.
Student accounts for the stipulated time after paper is completed, according to the time that student completes the accuracy of certain topic and completes the topic Ratio renewal knowledge point degree of understanding coefficient UDi, and record degree of understanding coefficient UD of the student to the topic knowledge pointiRise to Preceding time and the time after rising to, so as to update the DNA information of student.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any Those familiar with the art the invention discloses technical scope in, change or replacement can be readily occurred in, should all be contained Cover within protection scope of the present invention.Therefore, protection scope of the present invention described should be defined by scope of the claims.

Claims (2)

1.一种面向个性化学习方案的虚拟试卷推荐方法，包括：1. A method for recommending virtual test papers for individualized learning programs, comprising: 步骤S1：预设主难度系数；其中，所述主难度系数高于学生现阶段的学习适应难度；Step S1: preset the main difficulty coefficient; wherein, the main difficulty coefficient is higher than the learning adaptation difficulty of the students at the current stage; 步骤S2：根据轮盘选择法生成一套题量百分比围绕所述主难度系数呈现梯度下降分布的虚拟试卷；其中，所述步骤S2包括：Step S2: According to the roulette selection method, generate a set of virtual test papers in which the percentage of questions presents a gradient descent distribution around the main difficulty coefficient; wherein, the step S2 includes: 步骤S21：设置包括所述主难度系数的10个难度系数的题量在轮盘中所占的面积百分比；其中，每个难度系数的题量被选中的概率P(i)围绕所述主难度系数向所述主难度系数的左右两边呈均匀梯度下降分布；Step S21: Set the percentage of the area occupied by the questions of the 10 difficulty coefficients including the main difficulty coefficient in the roulette; wherein, the probability P(i) of each difficulty coefficient being selected is around the main difficulty The coefficients are distributed in a uniform gradient descent to the left and right sides of the main difficulty coefficient; 步骤S22：通过所述轮盘选择法产生虚拟试卷中10个难度系数的试题的题量分布；Step S22: Generate the question volume distribution of the 10 difficulty coefficient test questions in the virtual test paper through the roulette selection method; 步骤S23：按照所述步骤S22产生的10个难度系数的试题的题量分布从试题库中抽取试题形成所述虚拟试卷；Step S23: extract test questions from the test question bank to form the virtual test paper according to the question volume distribution of the 10 difficulty coefficient test questions generated in the step S22; 步骤S3：将所述虚拟试卷与学生的DNA信息结合得到理论得分；Step S3: combining the virtual test paper with the student's DNA information to obtain a theoretical score; 其中，所述DNA信息是由知识点、理解程度系数和时间构成的三维张量，所述知识点是对所属科目的核心内容的高度概括；所述理解程度系数是指在学生完成某知识点的习题后，对此知识点的总体掌握情况的描述；所述时间是指学生对知识点的理解程度系数从上一个等级跃升到下一个等级的时间长度；以及，Wherein, the DNA information is a three-dimensional tensor composed of knowledge points, understanding degree coefficients and time. The knowledge points are a high-level summary of the core content of the subject; the understanding degree coefficient refers to the time when students complete a knowledge point After the exercise, a description of the overall mastery of the knowledge point; the time refers to the length of time for the coefficient of the student's understanding of the knowledge point to jump from the previous level to the next level; and, 通过所述虚拟试卷中每道试题的知识点获取学生对该知识点的理解程度系数，利用下述公式获得学生对所述虚拟试卷的理论得分：Obtain the student's understanding degree coefficient of this knowledge point through the knowledge point of each test question in the virtual test paper, and use the following formula to obtain the theoretical score of the student on the virtual test paper: <mrow> <mi>S</mi> <mi>c</mi> <mi>o</mi> <mi>r</mi> <mi>e</mi> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mn>1</mn> <mi>n</mi> </munderover> <msub> <mi>ST</mi> <mi>g</mi> </msub> <mo>&amp;times;</mo> <msub> <mi>UD</mi> <mi>i</mi> </msub> </mrow> <mrow><mi>S</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi><mo>=</mo><munderover><mo>&amp;Sigma;</mo><mn>1</mn><mi>n</mi></munderover><msub><mi>ST</mi><mi>g</mi></msub><mo>&amp;times;</mo><msub><mi>UD</mi><mi>i</mi></msub></mrow> 上式中，Score为该学生对所述虚拟试卷的理论得分；STg为第g道题的满分，其中g＝1,...,n；UDi为该学生对第g题知识点i的理解程度系数；In the above formula, Score is the theoretical score of the student on the virtual test paper; ST g is the full score of the gth question, where g=1,...,n; UD i is the student's knowledge point i of the gth question coefficient of understanding degree; 步骤S4：将所有理论得分在60到80分内的虚拟试卷存成虚拟试卷库；Step S4: saving all virtual test papers with theoretical scores within 60 to 80 points into a virtual test paper library; 步骤S5：从所述虚拟试卷库中抽取虚拟试卷推荐给学生。Step S5: Extract virtual test papers from the virtual test paper library and recommend them to students. 2.根据权利要求1所述的面向个性化学习方案的虚拟试卷推荐方法，在从所述虚拟试卷库中抽取虚拟试卷推荐给学生之后，还包括：2. according to the virtual examination paper recommending method of personalized study scheme according to claim 1, after extracting virtual examination paper from described virtual examination paper storehouse and recommending to students, also comprising: 根据学生完成第i题的正确率及完成时间占规定时间的比例更新学生对第i题知识点的理解程度系数UDi，并且记录学生对第i题知识点的理解程度系数UDi跃升前的时间和跃升后的时间，更新学生的DNA信息。Update the student's understanding degree coefficient UD i of the i-th question knowledge point according to the correct rate of the student's completion of the i-th question and the proportion of the completion time in the specified time, and record the student's understanding degree coefficient UD i of the i -th question knowledge point before the jump Time and time after the jump, update the student's DNA information.
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Publication number Priority date Publication date Assignee Title
CN110334204A (en) * 2019-05-27 2019-10-15 湖南大学 A recommendation method for calculating the similarity of exercise problems based on user records
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CN112235333A (en) * 2019-07-15 2021-01-15 北京字节跳动网络技术有限公司 Function package management method, device, equipment and storage medium
CN115905576A (en) * 2023-01-09 2023-04-04 北京布局未来教育科技有限公司 Test paper generation method and device, electronic equipment and medium
CN118690078A (en) * 2024-06-07 2024-09-24 广东开放大学（广东理工职业学院） Learning resource recommendation method and system based on big data
CN118690078B (en) * 2024-06-07 2025-01-14 广东开放大学（广东理工职业学院） Learning resource recommendation method and system based on big data

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