WO2022068435A1 - Method for measuring knowledge point mastering state - Google Patents

Abstract

Disclosed is a method for measuring the knowledge point mastering state, comprising: acquiring the answering time and the answering results of titles relevant to knowledge points and answered by a student; calculating the knowledge state of the student on the titles according to the answering time and the answering results; and using the average value of the knowledge state of all the titles relevant to the knowledge points and completed answering by the student as the comprehensive knowledge state of the student on the knowledge points for measuring the knowledge point mastering state of the student. In this way, personalized adaptive education can be achieved.

Description

A method for measuring the state of mastery of knowledge points

This application claims the priority of the Chinese patent application with application number 202011059182.1 filed with the China Patent Office on September 30, 2020, the entire contents of which are incorporated into this application by reference.

technical field

The present application relates to the application field of artificial intelligence and adaptive technology in education, for example, a method for measuring the mastery state of knowledge points.

Background technique

The process of students learning knowledge points is actually a process of continuously improving their mastery of knowledge points. In the process of learning, for example, in the process of learning by doing practice questions, students are generally allowed to practice in the order of easy first and then difficult. , with the improvement of the mastery state, the difficulty of the questions is continuously increased, or the corresponding difficulty of the questions is provided according to the students' changing mastery state of knowledge points, so it is necessary to evaluate the students' mastery state of knowledge points.

In the traditional teaching process, the students as a whole are usually pushed to the same topic to all students without considering the different learning situations of each student. This method is bound to appear. For students who have a good grasp of knowledge points, the difficulty of the questions is too low, and simply repeating exercises will result in low learning efficiency and poor effect; for students with poor grasp of knowledge points, Said that the difficulty of the topic is too high, not only does not have the effect of practice, but will dampen the enthusiasm of learning. Moreover, in the traditional teaching process, the order of learning the contents of multiple knowledge points is determined in advance by the teacher or the lesson plan, and the different situations of each student are not considered.

The methods of evaluating students' knowledge point mastery mainly include correct answer rate (that is, correct or incorrect answering questions), ability value (based on Item Response Theory (IRT)) and other indicators to measure students' knowledge point granularity. state of knowledge. IRT, also known as Item Response Theory and Latent Trait Theory, is a general term for a series of psychostatistical models. IRT is a mathematical model used to analyze test scores or questionnaire data. The goal of these models is to determine whether underlying psychological characteristics can be passed through The test questions are reflected, and the interaction between the test questions and the testee.

There are the following problems in the measurement indicators related to students' knowledge point mastery status:

(1) The correct answer rate only considers correct and incorrect situations, and considers too few dimensions and is not comprehensive enough.

(2) Although the ability value takes into account the correctness and difficulty and the difficulty, the obtained value is relatively discrete. There will be a large number of students' ability values concentrated on multiple values that are equal or close to each other, and it is difficult to distinguish the mastery state of different students' knowledge points. , the discrimination is poor.

(3) Although the difficulty of the questions pushed to the students can be adjusted according to the correct answer rate, the initial difficulty of the questions pushed to the students is the same (the questions of the knowledge point have not yet been answered, and the correct answer rate indicator is empty), which is difficult to personalize. Customize the difficulty of the initial push question for students.

(4) When it is necessary to push new knowledge points to students for learning, the above methods are all pushed according to the predetermined order or rules, which are difficult to adapt to the individual learning requirements of students.

The above methods are more concerned with the correct answer rate, that is, the knowledge status of students is considered from the perspective of correctness and error, but it is not considered or difficult to comprehensively consider the difficulty of the question and the time to do the question.

SUMMARY OF THE INVENTION

The present application provides a method for measuring the mastery state of knowledge points, which can comprehensively evaluate students' knowledge point mastery state from multiple perspectives, and can better distinguish students, which is conducive to more accurately locating students' ability levels, In this way, personalized learning paths can be recommended to students in a more targeted manner, which solves the problem of not distinguishing, considering, and locating students' knowledge status and ability level from multiple perspectives.

The present application provides a method for measuring the mastery state of knowledge points, including:

Collect the answering time and answering results of students answering questions related to knowledge points; calculate the students' knowledge status on the questions according to the answering time and answering results; take the average value of the knowledge statuses of all questions related to knowledge points answered by students , as the comprehensive knowledge status of students on knowledge points, in order to measure students' mastery status of knowledge points.

Description of drawings

Fig. 1 is a function curve diagram of a sigmoid (β) provided by an embodiment of the present application;

2 is a function curve diagram of a knowledge state (Knowledge State, KS) value provided by an embodiment of the present application;

FIG. 3 is a schematic flowchart of a method for measuring a knowledge point mastery state provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below. The present application can be embodied in many different forms of embodiments, and the protection scope of the present application is not limited to the embodiments mentioned herein.

As used in this application, the singular forms "a", "the" and "the" are intended to include both the singular and the plural unless the context dictates otherwise. "Plurality" or "plurality" and the like generally include at least two or at least two. The term "and/or" used in this application is only an association relationship to describe associated objects, indicating that there may be three kinds of relationships, for example, A and/or B, which may indicate: the existence of A alone, the existence of A and B at the same time, There are three cases of B alone. In addition, the character "/" in this application generally indicates that the related objects are an "or" relationship.

The terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device comprising a list of elements includes not only those elements, but also other elements not listed , or elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

In the technical solution of this application, the concept of KS is proposed to measure and distinguish students' mastery of knowledge points.

The present application provides a method for measuring the mastery state of knowledge points, which may include the following steps:

Step 1: Collect the answering time and answering results of students answering questions related to knowledge points. In some embodiments, the answer result may include two states: correct and incorrect, with correct being 1 and being incorrect being 0. In other embodiments, the answer result may also be represented by a score rate, which is the ratio of the actual score to the total score of the question.

Step 2: Calculate the student's KS on the question according to the answering time and the answering result.

Each question corresponds to a question difficulty, and the knowledge status can be calculated according to the answering time, answering result, and question difficulty. The question difficulty is calculated according to the distribution of the answering results of all the students who answered the question. The greater the difficulty of the question, the greater the value of the knowledge state. In some embodiments, the value range of the item difficulty is [0, 1], and the higher the value, the more difficult the item is.

In some embodiments, if the answer result is wrong, the knowledge state is calculated only according to the difficulty of the question; if the answer result is correct, the knowledge state is calculated according to the answering time and the difficulty of the question. The longer the answering time, the smaller the value of the knowledge state.

In some embodiments, the answering time may also be processed, so that as the answering time increases, the change trend of the value of the knowledge state is more gradual, that is, when the answering time is too long, the change of the answering time will affect the value of the knowledge state. The impact will gradually decrease.

In some embodiments, the knowledge state is calculated according to the multiple of the answering time relative to the estimated question-making time of the question and the answering result, wherein the estimated question-making time is the median answering time of all students who answered the question. When the actual answering time is close to the range of the estimated test time, the value of the knowledge state varies greatly and the degree of discrimination is high; and when the actual answer time is far beyond the range of the estimated test time, the value of the knowledge state changes are small.

In some embodiments, the knowledge state may also be calculated according to the reference state value, the answering time, and the answering result, wherein the reference state value represents the knowledge when a question with the lowest difficulty is answered correctly and the answering time for answering the question meets the preset duration condition The value of the state. The reference state value can be a preset fixed value, or a value that is calculated and updated and adjusted according to historical data. When the answer result is correct or incorrect, the reference state value is introduced, so that the calculated KS can take all the values in the range of [0, 1] without faults.

In the case of wrong answers, the reasons affecting the answering time are complex and usually do not affect the final analysis and judgment. Therefore, in some embodiments, when the answering result is wrong, the impact of answering time on KS may not be considered.

In some embodiments, even if the answer result is wrong, a knowledge state greater than 0 can still be set for the student, because in the adaptive education system, the student can complete the question of higher difficulty, which means that the student is considered to have this problem based on historical performance. The ability to do a question of this difficulty, so even if the student makes a mistake on the question, a knowledge state greater than 0 will be set for the student.

In some embodiments, if a student does a question of lower difficulty, it is likely that the system considers the student's ability to be suitable for a more basic question, so even if the student does the question correctly, the answering time is very fast, and the value of the knowledge status It will also be limited to a certain extent, not very high.

Step 3: Take the average value of the KS of all the questions related to the knowledge point answered by the student as the student's comprehensive knowledge status (comprehensive KS) on the knowledge point, so as to measure the student's mastery of the knowledge point.

In some embodiments, the average value of the KS of all the questions related to the knowledge point completed and answered by the student is a weighted average, wherein each question corresponds to a weight. Questions whose completion date is closer to the current date have greater weights, which strengthens the impact of recent question performance and weakens the impact of long-term historical question performance, so that it can more accurately reflect the latest state and situation of students.

In some embodiments, a weighting sequence may be set according to the number of questions, and each weight in the sequence may be assigned to each question in a front-to-back order according to the order of answering dates from farthest to nearer. As an embodiment, the weight sequence may be an arithmetic sequence whose sum is 1, and the value in the front is small and the value in the back is large. In some other embodiments, the weight sequence may also be a sequence of increasing difference, and the difference between the later two values is larger, so that the weight and influence of the recent topic can be strengthened.

In some embodiments, the average value of the students' comprehensive knowledge states on all knowledge points may be used as the student's overall knowledge state (Student Knowledge State, SKS), and the initial difficulty of pushing a question to the student is determined according to the SKS. A student's SKS reflects the student's own ability level. When learning a new knowledge point, students can be pushed to the students with questions that match the SKS according to the difficulty of the question.

In some embodiments, the average value of the comprehensive knowledge state of all students under the knowledge point may be taken as the average knowledge state (Knowledge Point Knowledge State, KPKS) of the knowledge point, and the push to the student is determined according to the KPKS of the knowledge point and the SKS of the student order of knowledge points. When there are multiple related knowledge points that students need to learn, they can determine the learning order of knowledge points according to KPKS and SKS, and learn the knowledge points closest to SKS first. The granularity of the knowledge points here can be very finely divided, each knowledge point can represent a small range of knowledge content, in a large learning content, there can be multiple such refined knowledge points, and learning these The order of refining knowledge points may not be fixed. By determining different learning orders of knowledge points according to different knowledge states of different students, learning efficiency and learning effect can be effectively improved, and self-adaptive personalized learning can be realized.

According to the above ideas and methods, different calculation methods can be designed to calculate KS. In some embodiments, the KS of a student on a question can be calculated according to the difficulty of the question, the answering time, the answering result, and the reference status value. The calculation formula is as follows:

Among them, α represents the difficulty of the question, and the value range is [0,1]. The larger the α, the more difficult the question is. In some embodiments, the difficulty of a question may be calculated according to the distribution of correct and incorrect answers under a question.

β represents the relative time, that is, the ratio of the actual answering time (real_time) of the student to the estimated answering time (estimates_time) of the question. The smaller the β, the faster the student will answer the question. In some embodiments, the estimated time for answering the question may be calculated according to the median duration or the average duration of the answering time of the question in the question-making history data.

σ is correct or incorrect, 1 is correct and 0 is incorrect.

θ is the reference state value, which indicates the KS value when the question with the lowest difficulty is done correctly and the time spent is long enough. When σ=1 or σ=0, the θ correlation function is taken, so that KS can take all the values between [0, 1], and no fault will appear.

The reason why the student made a wrong question and the student's KS value is still greater than 0 is because in the adaptive system, the student can complete the question of higher difficulty. Even if the student gets the question wrong, a knowledge status greater than 0 will be set for the student.

Because of the complicated reasons that affect the answering time when the question is wrong, KS is not affected by the answering time when the question is wrong. If the student does a question of lower difficulty, it is likely that the system believes that the student's ability is suitable for doing the more basic question. Therefore, even if the student does the question correctly and the answering time is fast, the KS value will be limited to a certain extent. It can be seen that this method is directly related to adaptive learning.

For questions that are done right, their scores will be adjusted upwards according to the relative time of answering questions using the sigmoid function as an index. The value of sigmoid(β) (exponent) in the (0,+∞) interval is (0.5,1), and the base (θ+(1-θ)*α) is between (0,1), so the exponent The smaller the sigmoid(β), the larger the KS value. When the students do the right questions and the relative answering time β is also small, the value of sigmoid(β) is close to 0.5, and the KS value increases greatly; when the relative answering time β is large, the value of sigmoid(β) is close to 1, The increase is smaller.

FIG. 1 is a function curve diagram of a sigmoid (β) provided by an embodiment of the present application. As shown in Figure 1, the abscissa represents β (that is, the relative answering time, Time Ratio), and the ordinate represents the value of sigmoid (β). As can be seen from the figure, when β is 0, the initial value of sigmoid(β) is 0.5; as β increases, that is, with the increase of answering time, the value of sigmoid(β) gradually increases, but the growth rate increases. slowed down and gradually approached 1.

FIG. 2 is a function curve diagram of a KS value provided by an embodiment of the present application. As shown in Figure 2, the abscissa represents β (that is, the relative answering time, Time Ratio), and the ordinate represents the value of KS. It can be seen from the figure that when β is small, that is, the answering time is short, and the KS value is large; with the increase of β, that is, as the answering time increases, the KS value gradually decreases, but the deceleration slows down. For example: under normal circumstances, most students get a question right within 10 minutes, then within the range of β, due to the larger slope of the function curve, the degree of discrimination is better, even if the answering time of different students is relatively close , can also effectively distinguish different students; and for the time range beyond 10 minutes, such as 30 minutes or even 60 minutes, even if they can finally get the question right, it means that the students' mastery state is not very good, so in this β Within the range of , there is no need to make a more detailed distinction, the slope of the function curve is small, and the change range tends to be gentle. Processing by sigmoid(β) can not only effectively distinguish most students, but also deal with the answering time approaching positive infinity.

On the basis of obtaining the student's KS on a topic (that is, the knowledge status of the person + knowledge point granularity), the student's comprehensive knowledge status (comprehensive KS) on a knowledge point can be calculated to measure the student's knowledge point. Master the state. In some embodiments, the comprehensive KS of the knowledge point may be obtained by assigning a weight to each question under the knowledge point, and then weighted average of the KS values of each question under the knowledge point of the student.

In some embodiments, a neural network may be used to iteratively converge through back-propagation and gradient descent methods to finally obtain the weights. In other embodiments, an arithmetic progression with a sum of 1 can also be used as the weight of the topic under the knowledge point, and the calculation method is as follows:

a _k =a ₁ +(k-1)*d

Among them, S _k is the sum of the first k items of the arithmetic sequence, k is a natural number of [1,n], n is the number of questions answered under the knowledge point (that is, how many questions need to be weighted), d is the arithmetic sequence The difference between two adjacent values in , a ₁ is the first item of the arithmetic sequence (that is, the weight of the first question), and a _k is the kth item of the arithmetic sequence (that is, the kth item of the question). Weights).

Taking _Sn = 1 (the sum of the arithmetic sequence is 1, that is, the sum of the weights of all questions is 1) and a ₁ =d (for the convenience of calculation, and the weight of the newly answered question is large enough), then

For example: n=8 (that is, the student answered 8 questions under this knowledge point), then a ₁ =0.0278, and the weights of the 8 questions from far to near are [0.0278, 0.0556, 0.0833, 0.1111, 0.1389, 0.1667, 0.1944, 0.2222]. If the KS of the 8 questions that the student does on the knowledge point is [0.1, 0.88, 0.57, 0.64, 0.12, 0.74, 0.1, 0.55], then the student's comprehensive KS on the knowledge point is the sum of the two arrays Dot multiply and add to get a combined KS value of 0.45.

The weights are assigned by the above arithmetic sequence, and the weight of each question can be directly obtained only by knowing the number of questions n. This method also strengthens the influence of the performance of recent items (the date of answering the item is more recent), while weakening the weight of the performance of older historical items (the date of answering the item is smaller, the weight is smaller), which can effectively reflect the students The latest performance and the latest knowledge status. In addition, this method is simple and convenient, as long as the relevant parameters of the sequence that need to be weighted are input, the required weight sequence can be returned immediately, and this method also naturally strengthens the weight of the latest answers, which is in line with the actual learning situation of the students. .

The above method of assigning weights through arithmetic progression can also be used in the scheme of assigning the weights of students' multiple unit test scores, that is, according to this method, the weights of students' multiple unit test scores are calculated chronologically, and then A weighted average of multiple unit test scores is calculated to obtain a composite score that reflects a student's recent performance.

FIG. 3 is a schematic flowchart of a method for measuring a knowledge point mastery state provided by an embodiment of the present application, and an example is used for description.

Step 1, as shown in Table 1, pull the correctness (is_right), difficulty (difficulty), estimated time (estimates_time), actual error (is_right), difficulty (difficulty), estimated time (estimates_time), actual Time (cost_time), start time (create_time) and other fields, and the start time (the date of the start of the topic + time) is sorted in ascending order (order by) as a standard to ensure that the topic records are displayed from old to new , and then export to csv file.

Table 1

Step 2, as shown in Table 2, import the above csv file using pandas' read csv file function (read_csv) in Python, and obtain the data frame (DataFrame) format in pandas as shown in Table 2. Add a new field knowledge_state to it, and then use the mapping function (map) in Python to map the is_right, difficulties, estimates_time, and cost_time of each row according to the formula described in the technical solution, and obtain the knowledge_state value of each row.

Table 2

user_id	tag_code	question_id	is_right	difficulty	estimates_time	cost_time	create_time		knowledge_state
33261	3093	26375	0	0.5	160	39	2019/10/26	13:53:45	0.1000
33261	3093	27171	1	0.8	180	158	2019/10/27	9:56:49	0.8841
33261	3093	21342	1	0.2	30	6	2019/11/1	13:12:43	0.5702
33261	3093	21793	1	0.3	120	twenty four	2019/11/2	16:26:13	0.6367
33261	3093	17221	0	0.6	120	112	2019/11/9	12:20:42	0.1200
33261	3093	15157	1	0.5	120	34	2019/12/1	11:03:06	0.7472
33261	3093	19738	0	0.5	160	28	2019/12/6	19:48:10	0.1000
33261	3093	20306	1	0.2	90	30	2019/12/7	16:25:51	0.5515
47162	7884	18358	1	0.4	150	54	2019/12/14	11:03:37	0.6803
47162	7884	26698	0	0.2	30	3	2019/12/15	9:58:03	0.0400
47162	7884	16941	0	0.8	180	17	2019/12/19	19:54:12	0.1600
47162	7884	20663	1	0.2	100	75	2019/12/21	8:46:40	0.4996
47162	7884	17081	0	0.5	180	135	2019/12/21	9:46:56	0.1000
47162	7884	11571	0	0.5	180	26	2019/12/21	14:23:34	0.1000
47162	7884	28182	0	0.8	210	58	2019/12/21	14:30:00	0.1600
47162	7884	10074	1	0.8	240	315	2019/12/21	21:59:32	0.8716
47162	7884	11903	0	0.5	120	27	2019/12/27	20:46:37	0.1000
47162	7884	27758	1	0.8	120	87	2019/12/28	9:54:24	0.8892
40586	6521	20483	0	0.8	240	18	2019/12/29	9:56:44	0.1600
40586	6521	17294	1	0.2	90	32	2020/1/4	11:14:19	0.5484
40586	6521	25358	1	0.5	130	43	2020/1/4	11:18:14	0.7428

Step 3, as shown in Table 3, use pandas' grouping statistical function (groupby) to group the data table by student account number (user_id) and knowledge point number (tag_code), and use the aggregation function (agg) to group people + knowledge point granularity. The following series of KS values are combined into the form of a list (list), and then use the reset index function (reset_index) to reset the fields and index of the table.

table 3

user_id	tag_code	knowledge_state_list
34716	7012	[0.34, 0.78, 0.12, 0.26, 0.53]
34955	6706	[0.26, 0.83, 0.1, 0.23, 0.34]
34969	3538	[0.83, 0.78, 0.53, 0.87, 0.12, 0.34]
35308	3631	[0.15, 0.84, 0.29, 0.39, 0.88, 0.47]
35586	7794	[0.52, 0.7, 0.84, 0.18, 0.3, 0.51]
35789	4745	[0.27, 0.12, 0.79, 0.17, 0.67, 0.28]
36291	3038	[0.44, 0.65, 0.06, 0.61, 0.37, 0.61, 0.29]
36420	4256	[0.64, 0.82, 0.33, 0.29, 0.03, 0.53, 0.15]
36620	5285	[0.73, 0.28, 0.82, 0.25, 0.24, 0.74, 0.17]
36750	7185	[0.84, 0.66, 0.04, 0.8, 0.1, 0.58]

Step 4, as shown in Table 4, use a self-defined weighted knowledge state function (weighted_knowledge_state) to weight and aggregate KS at the granularity of people + knowledge points according to the arithmetic sequence. The custom function only takes the sequence of the KS list form on the topic shown in step 3, such as [0.83, 0.72, 0.69, 0.23, 0.8] as the input parameter, to obtain the n value of the arithmetic sequence, thereby generating the arithmetic difference The weight of the sequence, and dot multiplication with the input parameters, weighted summation (the summation function (sum) in numpy is used in the middle), and the final KS value is obtained as the output parameter, which is convenient and fast. The KS derived from this step is updated every 24 hours to capture the student's latest ability level.

Table 4

Step 5: Take the mean value of the KS of all knowledge points of each student as the student's SKS. As described in step 4, update the SKS every 24 hours, remove the old data to obtain new data, and obtain the student's latest ability level, thereby correspondingly. Update the initial difficulty value of the student's subsequent questions. For example, the updated overall SKS values of the three students are 0.82, 0.57, and 0.35, respectively, then the initial difficulty of pushing the questions to the students on the knowledge points on the second day will be different according to a certain mapping method (such as 0.8, 0.5, 0.2) to adapt to The level of different students reflects the adaptability of the system (while the initial difficulty in the traditional method is the same, such as 0.5).

Step 6: Take the mean value of students' KS under each knowledge point as the KPKS of the knowledge point, update the KPKS every 24 hours, and get the latest KPKS of the knowledge point. When a student faces N knowledge points to learn, the knowledge points are pushed from small to large according to the absolute value of the difference between KPKS and SKS, that is, those with similar KS values are pushed first. For example: a student whose SKS is 0.67 has four knowledge points A, B, C, and D with KPKS of 0.82, 0.7, 0.45, and 0.58 to learn, then the learning order of knowledge points is B, D, A, C ( The traditional method is to push according to certain rules or order, which cannot reflect the adaptability).

In this embodiment, Python is used as a tool, and the Python libraries used are: Pandas, Numpy. Numpy is a library that provides a variety of complex mathematical operations in Python. Pandas is a library for data analysis and processing through data frames. The data is pulled from the PostgreSQL database. As an example, part of the code is as follows:

import pandas as pd

import numpy as np

from sklearn.metrics.pairwise import cosine_similarity

The method for measuring the state of mastery of knowledge points provided by the present application has at least the following advantages over related technologies:

(1) By comprehensively considering the state of students' knowledge points through correctness, time, and difficulty, it is possible to better distinguish the level of different students, and get rid of the knowledge state of students only from correctness and difficulty and difficulty, which leads to too many identical values, which cannot be distinguished. Questions for students of different levels.

(2) The sigmoid function is a nonlinear function, and is placed in the exponential position rather than simply multiplied by (θ+(1-θ)*α). Using this feature, the value of KS can not always be greatly reduced due to the increase of relative time, but can be gradually reduced gradually. When the relative time is large to a certain extent, the impact on KS is also very small. The sigmoid function can effectively control the value range of the relative time between (0.5, 1), and adjust KS appropriately. That is, if the students do the questions fast, they can raise the KS value by taking the base value to the power of 0.5; if the students do the questions slowly, the base value is basically not raised.

(3) Use the map function in Python to generate the knowledge_state value, and use the groupby, agg, reset_index functions to generate the KS sequence in the form of a list, which is simple and fast, and consumes less time.

(4) The KS is obtained by weighting the arithmetic sequence weight. The weighted_knowledge_state function is simple, convenient and quick to input parameters, which can play a role in strengthening the recent performance of students. Other sequences can also be used, and the weight sequence is a sequence in which the sum is equal to 1, the weight value in the front of the two adjacent weights is small, and the weight value in the back is large, and the difference between the two adjacent weights is gradually increasing or equal.

(5) By locating students' KS at the granularity of topics, knowledge points, etc., this indicator can be used to deduce questions and help students find knowledge loopholes and plan learning paths in a targeted manner. If the average KS of the students on the knowledge points is very high (eg 0.8), it means that the students have done quickly and accurately on the knowledge points, especially the last few questions, and the ability is very strong. Then the difficulty of the subsequent push questions will become larger to adapt to the latest ability level of students. Subsequent push knowledge points will also give priority to knowledge points with similar KS values.

The technical solutions provided in this application may be systems, methods, apparatuses and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement aspects of the present application.

In some embodiments, the present application also provides a computer apparatus, device or terminal. The computer device, device or terminal includes a processor, memory, network interface, display screen and input device connected by a system bus. The processor is configured to provide computing and control capabilities, and the memory includes a non-volatile storage medium and an internal memory. The nonvolatile storage medium stores an operating system and a computer program. Internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium. The network interface is configured to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements various methods, processes, and steps disclosed in the present application, or when the processor executes the computer program, it implements the functions of multiple modules or units in the embodiments disclosed in the present application. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device can be a touch layer covered on the display screen, a button, a trackball or a touchpad set on the casing, or an external keyboard, touch board or mouse, etc.

Exemplarily, a computer program can be divided into one or more modules or units, and these modules or units are stored in a memory and executed by a processor to implement the technical solutions of the present application. These modules or units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer program in an apparatus, device or terminal.

The above-mentioned apparatus, device or terminal may be a desktop computer, a notebook, a mobile electronic device, a palmtop computer, a cloud server and other computing devices. The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the device, equipment or terminal to which the solution of the present application is applied. shown in more or less components, or in combination of some components, or with different component arrangements.

The processor can be a central processing unit (CPU), or other general-purpose or special-purpose processors, microprocessors, digital signal processors (DSPs), application specific integrated circuits (Application Specific Integrated Circuits) , ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the control center of the above-mentioned apparatus, equipment or terminal, and uses various interfaces and lines to connect various parts of the apparatus, equipment or terminal.

The memory can be configured to store computer programs, modules and data, and the processor implements various functions of the apparatus, device or terminal by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; Created various types of data (such as multimedia data, documents, operation history, etc.) and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card , Flash Card, magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The present application also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented. All or part of the process in the method of the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium, and the computer program is stored in a non-volatile computer-readable storage medium. When executed, it may include the flow of the embodiment of the above-mentioned method. Wherein, any reference to memory, storage, database or other medium used in the embodiments provided in this application may include non-volatile and/or volatile memory. Non-volatile memory may include read-only memory (Read-Only Memory, ROM), programmable ROM (Programmable ROM, PROM), electrically programmable ROM (Electrical PROM, EPROM), electrically erasable programmable ROM (Electrically Erasable ROM) PROM, EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (Double Data) Rate SDRAM, DDR SDRAM), enhanced SDRAM (Enhanced SDRAM, ESDRAM), synchronous link DRAM (Synchlink DRAM, SLDRAM), memory bus direct RAM (Rambus Direct RAM, RDRAM), direct memory bus dynamic RAM (Direct Rambus DRAM, DRDRAM) ), and memory bus dynamic RAM (Rambus DRAM, RDRAM), etc.

If the above-mentioned integrated modules and units of the apparatus or terminal equipment are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the implementation of all or part of the processes in the various methods disclosed in the present application can also be completed by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program is in When executed by a processor, the steps of the various methods described above may be implemented. The computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate forms, and the like. Computer readable media may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, removable hard disks, magnetic disks, optical discs, computer memory, ROM, RAM, electrical carrier signals, telecommunication signals, and software distribution media, etc. . Computer readable media may contain suitable additions or deletions as required by legislation and patent practice in jurisdictions.

In some embodiments, the various methods, processes, modules, apparatus, devices, or systems disclosed herein may be implemented in one or more processing devices (eg, digital processors, analog processors, devices designed and arranged to process information) implemented or implemented in digital circuits, analog circuits designed to process information, state machines, computing devices, computers, and/or other mechanisms configured to process information electronically). The one or more processing devices may include one or more devices that perform some or all of the operations of the method in response to instructions electronically stored on an electronic storage medium. The one or more processing means may comprise one or more means specially designed for carrying out one or more operations of the method configured by hardware, firmware and/or software.

Embodiments of the present application may be implemented in hardware, firmware, software, or various combinations thereof, and may also be implemented as instructions stored on a machine-readable medium that may be read and executed using one or more processing devices. In some implementations, a machine-readable medium can include various mechanisms for storing and/or transmitting information in a form readable by a machine (eg, a computing device). For example, machine-readable storage media may include read-only memory, random-access memory, magnetic disk storage media, optical storage media, flash memory devices, and other media configured to store information, and machine-readable transmission media may include a variety of forms Propagated signals (including carrier waves, infrared signals, digital signals) and other media configured to transmit information. Although firmware, software, routines, or instructions may be described in the above disclosure in terms of certain exemplary aspects and implementations that perform some actions, it will be apparent that such descriptions are for convenience only and that such actions are practical generated by a machine device, computing device, processing device, processor, controller, or other device or machine executing firmware, software, routines or instructions.

As used herein, a module for performing a specified function, or a module described using a functional feature, is intended to encompass any means capable of performing the function, such as a combination of circuit elements that perform the function, a module used to perform or implement the function Software, hardware, and a combination of software and hardware, or any form of software, firmware, code, and their combination with suitable circuitry or other means. The functions provided by the various modules are combined together in the manner claimed herein, and it is therefore intended that any module, component, element that provides the functions is equivalent or equivalent to the modules defined in the claims. According to the principle of the equivalent transformation of the circuit, the circuit structure of some embodiments in this application can also be changed or modified, for example, the current source is transformed into a voltage source, the series structure is transformed into a parallel structure, etc., so as to obtain more diversified Examples, but these changes and modifications belong to the scope of the disclosure of the present application.

The application is disclosed herein using examples, one or more of which are described or illustrated in the specification and its drawings. Each example is provided to explain the application, not to limit the application.

Claims (10)

1. A method for measuring the mastery state of knowledge points, including:

   Collect the answering time and answering results of students answering questions related to knowledge points;

   Calculate the knowledge status of the student on the topic according to the answering time and the answering result;

   The average value of the knowledge status of all the questions related to the knowledge point answered by the student is taken as the comprehensive knowledge status of the student on the knowledge point, so as to measure the student's understanding of the knowledge point. Master the state.
2. The method according to claim 1, wherein each question corresponds to a question difficulty;

   The calculating the knowledge status of the student on the topic according to the answering time and the answering result, including:

   Calculate the knowledge state according to the answering time, the answering result and the question difficulty, wherein the question difficulty is calculated according to the distribution of answering results of all students who answered the question;

   Wherein, the greater the difficulty of the question, the greater the value of the knowledge state.
3. The method according to claim 2, wherein calculating the knowledge state according to the answering time, the answering result and the question difficulty comprises:

   In the case that the answer result is wrong, the knowledge state is only calculated according to the difficulty of the question;

   When the answering result is correct, the knowledge state is calculated according to the answering time and the difficulty of the question.
4. The method of claim 3, wherein,

   The longer the answering time, the smaller the value of the knowledge state; and as the answering time increases, the more gentle the change trend of the value of the knowledge state.
5. The method according to claim 1, wherein calculating the knowledge status of the student on the topic according to the answering time and the answering result, comprising:

   Calculate the knowledge state according to the multiple of the answering time relative to the estimated answering time of the question and the answering result;

   Wherein, the estimated time for answering the question is the median of the answering time of all the students who answered the question.
6. The method of claim 1, wherein,

   The average value of the knowledge status of all the questions related to the knowledge point completed by the student is a weighted average, wherein each question corresponds to a weight.
7. The method of claim 6, wherein,

   Questions whose answer date is closer to the current date are given greater weight.
8. The method according to claim 1, wherein calculating the knowledge status of the student on the topic according to the answering time and the answering result, comprising:

   The knowledge state is calculated according to the reference state value, the answering time, and the answering result, wherein the reference state value indicates that the answering time for answering a question with the lowest difficulty meets a predetermined requirement. The value of the knowledge state when the duration condition is set.
9. The method of claim 1, further comprising:

   The average value of the comprehensive knowledge state of the student on all knowledge points is taken as the overall knowledge state of the student, and the initial difficulty of pushing a question to the student is determined according to the overall knowledge state.
10. The method of claim 9, further comprising:

    The average value of the comprehensive knowledge state of all students under the knowledge point is taken as the average knowledge state of the knowledge point, and the direction to all students is determined according to the average knowledge state of the knowledge point and the overall knowledge state of the students. Describe the order in which students push knowledge points.

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